detection debugging going poorly
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__all__: list[str] = []
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# Enumerations
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StsOk: int
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STS_OK: int
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StsBackTrace: int
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STS_BACK_TRACE: int
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StsError: int
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STS_ERROR: int
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StsInternal: int
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STS_INTERNAL: int
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StsNoMem: int
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STS_NO_MEM: int
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StsBadArg: int
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STS_BAD_ARG: int
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StsBadFunc: int
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STS_BAD_FUNC: int
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StsNoConv: int
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STS_NO_CONV: int
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StsAutoTrace: int
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STS_AUTO_TRACE: int
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HeaderIsNull: int
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HEADER_IS_NULL: int
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BadImageSize: int
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BAD_IMAGE_SIZE: int
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BadOffset: int
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BAD_OFFSET: int
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BadDataPtr: int
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BAD_DATA_PTR: int
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BadStep: int
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BAD_STEP: int
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BadModelOrChSeq: int
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BAD_MODEL_OR_CH_SEQ: int
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BadNumChannels: int
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BAD_NUM_CHANNELS: int
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BadNumChannel1U: int
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BAD_NUM_CHANNEL1U: int
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BadDepth: int
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BAD_DEPTH: int
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BadAlphaChannel: int
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BAD_ALPHA_CHANNEL: int
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BadOrder: int
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BAD_ORDER: int
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BadOrigin: int
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BAD_ORIGIN: int
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BadAlign: int
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BAD_ALIGN: int
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BadCallBack: int
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BAD_CALL_BACK: int
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BadTileSize: int
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BAD_TILE_SIZE: int
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BadCOI: int
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BAD_COI: int
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BadROISize: int
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BAD_ROISIZE: int
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MaskIsTiled: int
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MASK_IS_TILED: int
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StsNullPtr: int
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STS_NULL_PTR: int
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StsVecLengthErr: int
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STS_VEC_LENGTH_ERR: int
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StsFilterStructContentErr: int
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STS_FILTER_STRUCT_CONTENT_ERR: int
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StsKernelStructContentErr: int
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STS_KERNEL_STRUCT_CONTENT_ERR: int
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StsFilterOffsetErr: int
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STS_FILTER_OFFSET_ERR: int
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StsBadSize: int
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STS_BAD_SIZE: int
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StsDivByZero: int
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STS_DIV_BY_ZERO: int
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StsInplaceNotSupported: int
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STS_INPLACE_NOT_SUPPORTED: int
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StsObjectNotFound: int
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STS_OBJECT_NOT_FOUND: int
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StsUnmatchedFormats: int
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STS_UNMATCHED_FORMATS: int
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StsBadFlag: int
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STS_BAD_FLAG: int
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StsBadPoint: int
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STS_BAD_POINT: int
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StsBadMask: int
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STS_BAD_MASK: int
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StsUnmatchedSizes: int
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STS_UNMATCHED_SIZES: int
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StsUnsupportedFormat: int
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STS_UNSUPPORTED_FORMAT: int
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StsOutOfRange: int
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STS_OUT_OF_RANGE: int
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StsParseError: int
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STS_PARSE_ERROR: int
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StsNotImplemented: int
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STS_NOT_IMPLEMENTED: int
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StsBadMemBlock: int
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STS_BAD_MEM_BLOCK: int
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StsAssert: int
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STS_ASSERT: int
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GpuNotSupported: int
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GPU_NOT_SUPPORTED: int
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GpuApiCallError: int
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GPU_API_CALL_ERROR: int
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OpenGlNotSupported: int
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OPEN_GL_NOT_SUPPORTED: int
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OpenGlApiCallError: int
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OPEN_GL_API_CALL_ERROR: int
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OpenCLApiCallError: int
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OPEN_CLAPI_CALL_ERROR: int
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OpenCLDoubleNotSupported: int
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OPEN_CLDOUBLE_NOT_SUPPORTED: int
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OpenCLInitError: int
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OPEN_CLINIT_ERROR: int
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OpenCLNoAMDBlasFft: int
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OPEN_CLNO_AMDBLAS_FFT: int
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Code = int
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"""One of [StsOk, STS_OK, StsBackTrace, STS_BACK_TRACE, StsError, STS_ERROR, StsInternal, STS_INTERNAL, StsNoMem, STS_NO_MEM, StsBadArg, STS_BAD_ARG, StsBadFunc, STS_BAD_FUNC, StsNoConv, STS_NO_CONV, StsAutoTrace, STS_AUTO_TRACE, HeaderIsNull, HEADER_IS_NULL, BadImageSize, BAD_IMAGE_SIZE, BadOffset, BAD_OFFSET, BadDataPtr, BAD_DATA_PTR, BadStep, BAD_STEP, BadModelOrChSeq, BAD_MODEL_OR_CH_SEQ, BadNumChannels, BAD_NUM_CHANNELS, BadNumChannel1U, BAD_NUM_CHANNEL1U, BadDepth, BAD_DEPTH, BadAlphaChannel, BAD_ALPHA_CHANNEL, BadOrder, BAD_ORDER, BadOrigin, BAD_ORIGIN, BadAlign, BAD_ALIGN, BadCallBack, BAD_CALL_BACK, BadTileSize, BAD_TILE_SIZE, BadCOI, BAD_COI, BadROISize, BAD_ROISIZE, MaskIsTiled, MASK_IS_TILED, StsNullPtr, STS_NULL_PTR, StsVecLengthErr, STS_VEC_LENGTH_ERR, StsFilterStructContentErr, STS_FILTER_STRUCT_CONTENT_ERR, StsKernelStructContentErr, STS_KERNEL_STRUCT_CONTENT_ERR, StsFilterOffsetErr, STS_FILTER_OFFSET_ERR, StsBadSize, STS_BAD_SIZE, StsDivByZero, STS_DIV_BY_ZERO, StsInplaceNotSupported, STS_INPLACE_NOT_SUPPORTED, StsObjectNotFound, STS_OBJECT_NOT_FOUND, StsUnmatchedFormats, STS_UNMATCHED_FORMATS, StsBadFlag, STS_BAD_FLAG, StsBadPoint, STS_BAD_POINT, StsBadMask, STS_BAD_MASK, StsUnmatchedSizes, STS_UNMATCHED_SIZES, StsUnsupportedFormat, STS_UNSUPPORTED_FORMAT, StsOutOfRange, STS_OUT_OF_RANGE, StsParseError, STS_PARSE_ERROR, StsNotImplemented, STS_NOT_IMPLEMENTED, StsBadMemBlock, STS_BAD_MEM_BLOCK, StsAssert, STS_ASSERT, GpuNotSupported, GPU_NOT_SUPPORTED, GpuApiCallError, GPU_API_CALL_ERROR, OpenGlNotSupported, OPEN_GL_NOT_SUPPORTED, OpenGlApiCallError, OPEN_GL_API_CALL_ERROR, OpenCLApiCallError, OPEN_CLAPI_CALL_ERROR, OpenCLDoubleNotSupported, OPEN_CLDOUBLE_NOT_SUPPORTED, OpenCLInitError, OPEN_CLINIT_ERROR, OpenCLNoAMDBlasFft, OPEN_CLNO_AMDBLAS_FFT]"""
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@ -1,181 +0,0 @@
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'''
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OpenCV Python binary extension loader
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'''
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import os
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import importlib
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import sys
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__all__ = []
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try:
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import numpy
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import numpy.core.multiarray
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except ImportError:
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print('OpenCV bindings requires "numpy" package.')
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print('Install it via command:')
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print(' pip install numpy')
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raise
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# TODO
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# is_x64 = sys.maxsize > 2**32
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def __load_extra_py_code_for_module(base, name, enable_debug_print=False):
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module_name = "{}.{}".format(__name__, name)
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export_module_name = "{}.{}".format(base, name)
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native_module = sys.modules.pop(module_name, None)
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try:
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py_module = importlib.import_module(module_name)
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except ImportError as err:
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if enable_debug_print:
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print("Can't load Python code for module:", module_name,
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". Reason:", err)
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# Extension doesn't contain extra py code
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return False
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if base in sys.modules and not hasattr(sys.modules[base], name):
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setattr(sys.modules[base], name, py_module)
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sys.modules[export_module_name] = py_module
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# If it is C extension module it is already loaded by cv2 package
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if native_module:
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setattr(py_module, "_native", native_module)
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for k, v in filter(lambda kv: not hasattr(py_module, kv[0]),
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native_module.__dict__.items()):
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if enable_debug_print: print(' symbol({}): {} = {}'.format(name, k, v))
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setattr(py_module, k, v)
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return True
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def __collect_extra_submodules(enable_debug_print=False):
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def modules_filter(module):
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return all((
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# module is not internal
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not module.startswith("_"),
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not module.startswith("python-"),
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# it is not a file
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os.path.isdir(os.path.join(_extra_submodules_init_path, module))
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))
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if sys.version_info[0] < 3:
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if enable_debug_print:
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print("Extra submodules is loaded only for Python 3")
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return []
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__INIT_FILE_PATH = os.path.abspath(__file__)
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_extra_submodules_init_path = os.path.dirname(__INIT_FILE_PATH)
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return filter(modules_filter, os.listdir(_extra_submodules_init_path))
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def bootstrap():
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import sys
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import copy
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save_sys_path = copy.copy(sys.path)
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if hasattr(sys, 'OpenCV_LOADER'):
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print(sys.path)
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raise ImportError('ERROR: recursion is detected during loading of "cv2" binary extensions. Check OpenCV installation.')
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sys.OpenCV_LOADER = True
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DEBUG = False
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if hasattr(sys, 'OpenCV_LOADER_DEBUG'):
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DEBUG = True
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import platform
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if DEBUG: print('OpenCV loader: os.name="{}" platform.system()="{}"'.format(os.name, str(platform.system())))
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LOADER_DIR = os.path.dirname(os.path.abspath(os.path.realpath(__file__)))
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PYTHON_EXTENSIONS_PATHS = []
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BINARIES_PATHS = []
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g_vars = globals()
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l_vars = locals().copy()
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if sys.version_info[:2] < (3, 0):
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from . load_config_py2 import exec_file_wrapper
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else:
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from . load_config_py3 import exec_file_wrapper
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def load_first_config(fnames, required=True):
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for fname in fnames:
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fpath = os.path.join(LOADER_DIR, fname)
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if not os.path.exists(fpath):
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if DEBUG: print('OpenCV loader: config not found, skip: {}'.format(fpath))
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continue
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if DEBUG: print('OpenCV loader: loading config: {}'.format(fpath))
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exec_file_wrapper(fpath, g_vars, l_vars)
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return True
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if required:
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raise ImportError('OpenCV loader: missing configuration file: {}. Check OpenCV installation.'.format(fnames))
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load_first_config(['config.py'], True)
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load_first_config([
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'config-{}.{}.py'.format(sys.version_info[0], sys.version_info[1]),
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'config-{}.py'.format(sys.version_info[0])
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], True)
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if DEBUG: print('OpenCV loader: PYTHON_EXTENSIONS_PATHS={}'.format(str(l_vars['PYTHON_EXTENSIONS_PATHS'])))
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if DEBUG: print('OpenCV loader: BINARIES_PATHS={}'.format(str(l_vars['BINARIES_PATHS'])))
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applySysPathWorkaround = False
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if hasattr(sys, 'OpenCV_REPLACE_SYS_PATH_0'):
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applySysPathWorkaround = True
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else:
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try:
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BASE_DIR = os.path.dirname(LOADER_DIR)
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if sys.path[0] == BASE_DIR or os.path.realpath(sys.path[0]) == BASE_DIR:
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applySysPathWorkaround = True
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except:
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if DEBUG: print('OpenCV loader: exception during checking workaround for sys.path[0]')
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pass # applySysPathWorkaround is False
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for p in reversed(l_vars['PYTHON_EXTENSIONS_PATHS']):
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sys.path.insert(1 if not applySysPathWorkaround else 0, p)
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if os.name == 'nt':
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if sys.version_info[:2] >= (3, 8): # https://github.com/python/cpython/pull/12302
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for p in l_vars['BINARIES_PATHS']:
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try:
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os.add_dll_directory(p)
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except Exception as e:
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if DEBUG: print('Failed os.add_dll_directory(): '+ str(e))
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pass
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os.environ['PATH'] = ';'.join(l_vars['BINARIES_PATHS']) + ';' + os.environ.get('PATH', '')
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if DEBUG: print('OpenCV loader: PATH={}'.format(str(os.environ['PATH'])))
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else:
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# amending of LD_LIBRARY_PATH works for sub-processes only
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os.environ['LD_LIBRARY_PATH'] = ':'.join(l_vars['BINARIES_PATHS']) + ':' + os.environ.get('LD_LIBRARY_PATH', '')
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if DEBUG: print("Relink everything from native cv2 module to cv2 package")
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py_module = sys.modules.pop("cv2")
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native_module = importlib.import_module("cv2")
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sys.modules["cv2"] = py_module
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setattr(py_module, "_native", native_module)
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for item_name, item in filter(lambda kv: kv[0] not in ("__file__", "__loader__", "__spec__",
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"__name__", "__package__"),
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native_module.__dict__.items()):
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if item_name not in g_vars:
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g_vars[item_name] = item
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sys.path = save_sys_path # multiprocessing should start from bootstrap code (https://github.com/opencv/opencv/issues/18502)
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try:
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del sys.OpenCV_LOADER
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except Exception as e:
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if DEBUG:
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print("Exception during delete OpenCV_LOADER:", e)
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if DEBUG: print('OpenCV loader: binary extension... OK')
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for submodule in __collect_extra_submodules(DEBUG):
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if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
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if DEBUG: print("Extra Python code for", submodule, "is loaded")
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if DEBUG: print('OpenCV loader: DONE')
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bootstrap()
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__all__: list[str] = []
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import cv2
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import cv2.typing
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import typing as _typing
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# Enumerations
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CORNER_REFINE_NONE: int
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CORNER_REFINE_SUBPIX: int
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CORNER_REFINE_CONTOUR: int
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CORNER_REFINE_APRILTAG: int
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CornerRefineMethod = int
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"""One of [CORNER_REFINE_NONE, CORNER_REFINE_SUBPIX, CORNER_REFINE_CONTOUR, CORNER_REFINE_APRILTAG]"""
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DICT_4X4_50: int
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DICT_4X4_100: int
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DICT_4X4_250: int
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DICT_4X4_1000: int
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DICT_5X5_50: int
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DICT_5X5_100: int
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DICT_5X5_250: int
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DICT_5X5_1000: int
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DICT_6X6_50: int
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DICT_6X6_100: int
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DICT_6X6_250: int
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DICT_6X6_1000: int
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DICT_7X7_50: int
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DICT_7X7_100: int
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DICT_7X7_250: int
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DICT_7X7_1000: int
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DICT_ARUCO_ORIGINAL: int
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DICT_APRILTAG_16h5: int
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DICT_APRILTAG_16H5: int
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DICT_APRILTAG_25h9: int
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DICT_APRILTAG_25H9: int
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DICT_APRILTAG_36h10: int
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DICT_APRILTAG_36H10: int
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DICT_APRILTAG_36h11: int
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DICT_APRILTAG_36H11: int
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DICT_ARUCO_MIP_36h12: int
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DICT_ARUCO_MIP_36H12: int
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PredefinedDictionaryType = int
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"""One of [DICT_4X4_50, DICT_4X4_100, DICT_4X4_250, DICT_4X4_1000, DICT_5X5_50, DICT_5X5_100, DICT_5X5_250, DICT_5X5_1000, DICT_6X6_50, DICT_6X6_100, DICT_6X6_250, DICT_6X6_1000, DICT_7X7_50, DICT_7X7_100, DICT_7X7_250, DICT_7X7_1000, DICT_ARUCO_ORIGINAL, DICT_APRILTAG_16h5, DICT_APRILTAG_16H5, DICT_APRILTAG_25h9, DICT_APRILTAG_25H9, DICT_APRILTAG_36h10, DICT_APRILTAG_36H10, DICT_APRILTAG_36h11, DICT_APRILTAG_36H11, DICT_ARUCO_MIP_36h12, DICT_ARUCO_MIP_36H12]"""
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ARUCO_CCW_CENTER: int
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ARUCO_CW_TOP_LEFT_CORNER: int
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PatternPositionType = int
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"""One of [ARUCO_CCW_CENTER, ARUCO_CW_TOP_LEFT_CORNER]"""
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# Classes
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class Board:
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# Functions
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@_typing.overload
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def __init__(self, objPoints: _typing.Sequence[cv2.typing.MatLike], dictionary: Dictionary, ids: cv2.typing.MatLike) -> None: ...
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@_typing.overload
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def __init__(self, objPoints: _typing.Sequence[cv2.UMat], dictionary: Dictionary, ids: cv2.UMat) -> None: ...
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def getDictionary(self) -> Dictionary: ...
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def getObjPoints(self) -> _typing.Sequence[_typing.Sequence[cv2.typing.Point3f]]: ...
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def getIds(self) -> _typing.Sequence[int]: ...
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def getRightBottomCorner(self) -> cv2.typing.Point3f: ...
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@_typing.overload
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def matchImagePoints(self, detectedCorners: _typing.Sequence[cv2.typing.MatLike], detectedIds: cv2.typing.MatLike, objPoints: cv2.typing.MatLike | None = ..., imgPoints: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
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@_typing.overload
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def matchImagePoints(self, detectedCorners: _typing.Sequence[cv2.UMat], detectedIds: cv2.UMat, objPoints: cv2.UMat | None = ..., imgPoints: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
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@_typing.overload
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def generateImage(self, outSize: cv2.typing.Size, img: cv2.typing.MatLike | None = ..., marginSize: int = ..., borderBits: int = ...) -> cv2.typing.MatLike: ...
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@_typing.overload
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def generateImage(self, outSize: cv2.typing.Size, img: cv2.UMat | None = ..., marginSize: int = ..., borderBits: int = ...) -> cv2.UMat: ...
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class GridBoard(Board):
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# Functions
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@_typing.overload
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def __init__(self, size: cv2.typing.Size, markerLength: float, markerSeparation: float, dictionary: Dictionary, ids: cv2.typing.MatLike | None = ...) -> None: ...
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@_typing.overload
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def __init__(self, size: cv2.typing.Size, markerLength: float, markerSeparation: float, dictionary: Dictionary, ids: cv2.UMat | None = ...) -> None: ...
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def getGridSize(self) -> cv2.typing.Size: ...
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def getMarkerLength(self) -> float: ...
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||||
|
||||
def getMarkerSeparation(self) -> float: ...
|
||||
|
||||
|
||||
class CharucoBoard(Board):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, size: cv2.typing.Size, squareLength: float, markerLength: float, dictionary: Dictionary, ids: cv2.typing.MatLike | None = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, size: cv2.typing.Size, squareLength: float, markerLength: float, dictionary: Dictionary, ids: cv2.UMat | None = ...) -> None: ...
|
||||
|
||||
def setLegacyPattern(self, legacyPattern: bool) -> None: ...
|
||||
|
||||
def getLegacyPattern(self) -> bool: ...
|
||||
|
||||
def getChessboardSize(self) -> cv2.typing.Size: ...
|
||||
|
||||
def getSquareLength(self) -> float: ...
|
||||
|
||||
def getMarkerLength(self) -> float: ...
|
||||
|
||||
def getChessboardCorners(self) -> _typing.Sequence[cv2.typing.Point3f]: ...
|
||||
|
||||
@_typing.overload
|
||||
def checkCharucoCornersCollinear(self, charucoIds: cv2.typing.MatLike) -> bool: ...
|
||||
@_typing.overload
|
||||
def checkCharucoCornersCollinear(self, charucoIds: cv2.UMat) -> bool: ...
|
||||
|
||||
|
||||
class DetectorParameters:
|
||||
adaptiveThreshWinSizeMin: int
|
||||
adaptiveThreshWinSizeMax: int
|
||||
adaptiveThreshWinSizeStep: int
|
||||
adaptiveThreshConstant: float
|
||||
minMarkerPerimeterRate: float
|
||||
maxMarkerPerimeterRate: float
|
||||
polygonalApproxAccuracyRate: float
|
||||
minCornerDistanceRate: float
|
||||
minDistanceToBorder: int
|
||||
minMarkerDistanceRate: float
|
||||
minGroupDistance: float
|
||||
cornerRefinementMethod: int
|
||||
cornerRefinementWinSize: int
|
||||
relativeCornerRefinmentWinSize: float
|
||||
cornerRefinementMaxIterations: int
|
||||
cornerRefinementMinAccuracy: float
|
||||
markerBorderBits: int
|
||||
perspectiveRemovePixelPerCell: int
|
||||
perspectiveRemoveIgnoredMarginPerCell: float
|
||||
maxErroneousBitsInBorderRate: float
|
||||
minOtsuStdDev: float
|
||||
errorCorrectionRate: float
|
||||
aprilTagQuadDecimate: float
|
||||
aprilTagQuadSigma: float
|
||||
aprilTagMinClusterPixels: int
|
||||
aprilTagMaxNmaxima: int
|
||||
aprilTagCriticalRad: float
|
||||
aprilTagMaxLineFitMse: float
|
||||
aprilTagMinWhiteBlackDiff: int
|
||||
aprilTagDeglitch: int
|
||||
detectInvertedMarker: bool
|
||||
useAruco3Detection: bool
|
||||
minSideLengthCanonicalImg: int
|
||||
minMarkerLengthRatioOriginalImg: float
|
||||
|
||||
# Functions
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
def readDetectorParameters(self, fn: cv2.FileNode) -> bool: ...
|
||||
|
||||
def writeDetectorParameters(self, fs: cv2.FileStorage, name: str = ...) -> bool: ...
|
||||
|
||||
|
||||
class RefineParameters:
|
||||
minRepDistance: float
|
||||
errorCorrectionRate: float
|
||||
checkAllOrders: bool
|
||||
|
||||
# Functions
|
||||
def __init__(self, minRepDistance: float = ..., errorCorrectionRate: float = ..., checkAllOrders: bool = ...) -> None: ...
|
||||
|
||||
def readRefineParameters(self, fn: cv2.FileNode) -> bool: ...
|
||||
|
||||
def writeRefineParameters(self, fs: cv2.FileStorage, name: str = ...) -> bool: ...
|
||||
|
||||
|
||||
class ArucoDetector(cv2.Algorithm):
|
||||
# Functions
|
||||
def __init__(self, dictionary: Dictionary = ..., detectorParams: DetectorParameters = ..., refineParams: RefineParameters = ...) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def detectMarkers(self, image: cv2.typing.MatLike, corners: _typing.Sequence[cv2.typing.MatLike] | None = ..., ids: cv2.typing.MatLike | None = ..., rejectedImgPoints: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike]]: ...
|
||||
@_typing.overload
|
||||
def detectMarkers(self, image: cv2.UMat, corners: _typing.Sequence[cv2.UMat] | None = ..., ids: cv2.UMat | None = ..., rejectedImgPoints: _typing.Sequence[cv2.UMat] | None = ...) -> tuple[_typing.Sequence[cv2.UMat], cv2.UMat, _typing.Sequence[cv2.UMat]]: ...
|
||||
|
||||
@_typing.overload
|
||||
def refineDetectedMarkers(self, image: cv2.typing.MatLike, board: Board, detectedCorners: _typing.Sequence[cv2.typing.MatLike], detectedIds: cv2.typing.MatLike, rejectedCorners: _typing.Sequence[cv2.typing.MatLike], cameraMatrix: cv2.typing.MatLike | None = ..., distCoeffs: cv2.typing.MatLike | None = ..., recoveredIdxs: cv2.typing.MatLike | None = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def refineDetectedMarkers(self, image: cv2.UMat, board: Board, detectedCorners: _typing.Sequence[cv2.UMat], detectedIds: cv2.UMat, rejectedCorners: _typing.Sequence[cv2.UMat], cameraMatrix: cv2.UMat | None = ..., distCoeffs: cv2.UMat | None = ..., recoveredIdxs: cv2.UMat | None = ...) -> tuple[_typing.Sequence[cv2.UMat], cv2.UMat, _typing.Sequence[cv2.UMat], cv2.UMat]: ...
|
||||
|
||||
def getDictionary(self) -> Dictionary: ...
|
||||
|
||||
def setDictionary(self, dictionary: Dictionary) -> None: ...
|
||||
|
||||
def getDetectorParameters(self) -> DetectorParameters: ...
|
||||
|
||||
def setDetectorParameters(self, detectorParameters: DetectorParameters) -> None: ...
|
||||
|
||||
def getRefineParameters(self) -> RefineParameters: ...
|
||||
|
||||
def setRefineParameters(self, refineParameters: RefineParameters) -> None: ...
|
||||
|
||||
def write(self, fs: cv2.FileStorage, name: str) -> None: ...
|
||||
|
||||
def read(self, fn: cv2.FileNode) -> None: ...
|
||||
|
||||
|
||||
class Dictionary:
|
||||
bytesList: cv2.typing.MatLike
|
||||
markerSize: int
|
||||
maxCorrectionBits: int
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, bytesList: cv2.typing.MatLike, _markerSize: int, maxcorr: int = ...) -> None: ...
|
||||
|
||||
def readDictionary(self, fn: cv2.FileNode) -> bool: ...
|
||||
|
||||
def writeDictionary(self, fs: cv2.FileStorage, name: str = ...) -> None: ...
|
||||
|
||||
def identify(self, onlyBits: cv2.typing.MatLike, maxCorrectionRate: float) -> tuple[bool, int, int]: ...
|
||||
|
||||
@_typing.overload
|
||||
def getDistanceToId(self, bits: cv2.typing.MatLike, id: int, allRotations: bool = ...) -> int: ...
|
||||
@_typing.overload
|
||||
def getDistanceToId(self, bits: cv2.UMat, id: int, allRotations: bool = ...) -> int: ...
|
||||
|
||||
@_typing.overload
|
||||
def generateImageMarker(self, id: int, sidePixels: int, _img: cv2.typing.MatLike | None = ..., borderBits: int = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def generateImageMarker(self, id: int, sidePixels: int, _img: cv2.UMat | None = ..., borderBits: int = ...) -> cv2.UMat: ...
|
||||
|
||||
@staticmethod
|
||||
def getByteListFromBits(bits: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
|
||||
|
||||
@staticmethod
|
||||
def getBitsFromByteList(byteList: cv2.typing.MatLike, markerSize: int) -> cv2.typing.MatLike: ...
|
||||
|
||||
|
||||
class CharucoParameters:
|
||||
cameraMatrix: cv2.typing.MatLike
|
||||
distCoeffs: cv2.typing.MatLike
|
||||
minMarkers: int
|
||||
tryRefineMarkers: bool
|
||||
|
||||
# Functions
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
|
||||
class CharucoDetector(cv2.Algorithm):
|
||||
# Functions
|
||||
def __init__(self, board: CharucoBoard, charucoParams: CharucoParameters = ..., detectorParams: DetectorParameters = ..., refineParams: RefineParameters = ...) -> None: ...
|
||||
|
||||
def getBoard(self) -> CharucoBoard: ...
|
||||
|
||||
def setBoard(self, board: CharucoBoard) -> None: ...
|
||||
|
||||
def getCharucoParameters(self) -> CharucoParameters: ...
|
||||
|
||||
def setCharucoParameters(self, charucoParameters: CharucoParameters) -> None: ...
|
||||
|
||||
def getDetectorParameters(self) -> DetectorParameters: ...
|
||||
|
||||
def setDetectorParameters(self, detectorParameters: DetectorParameters) -> None: ...
|
||||
|
||||
def getRefineParameters(self) -> RefineParameters: ...
|
||||
|
||||
def setRefineParameters(self, refineParameters: RefineParameters) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def detectBoard(self, image: cv2.typing.MatLike, charucoCorners: cv2.typing.MatLike | None = ..., charucoIds: cv2.typing.MatLike | None = ..., markerCorners: _typing.Sequence[cv2.typing.MatLike] | None = ..., markerIds: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def detectBoard(self, image: cv2.UMat, charucoCorners: cv2.UMat | None = ..., charucoIds: cv2.UMat | None = ..., markerCorners: _typing.Sequence[cv2.UMat] | None = ..., markerIds: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat, _typing.Sequence[cv2.UMat], cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def detectDiamonds(self, image: cv2.typing.MatLike, diamondCorners: _typing.Sequence[cv2.typing.MatLike] | None = ..., diamondIds: cv2.typing.MatLike | None = ..., markerCorners: _typing.Sequence[cv2.typing.MatLike] | None = ..., markerIds: cv2.typing.MatLike | None = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def detectDiamonds(self, image: cv2.UMat, diamondCorners: _typing.Sequence[cv2.UMat] | None = ..., diamondIds: cv2.UMat | None = ..., markerCorners: _typing.Sequence[cv2.UMat] | None = ..., markerIds: cv2.UMat | None = ...) -> tuple[_typing.Sequence[cv2.UMat], cv2.UMat, _typing.Sequence[cv2.UMat], cv2.UMat]: ...
|
||||
|
||||
|
||||
class EstimateParameters:
|
||||
pattern: PatternPositionType
|
||||
useExtrinsicGuess: bool
|
||||
solvePnPMethod: int
|
||||
|
||||
# Functions
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def calibrateCameraAruco(corners: _typing.Sequence[cv2.typing.MatLike], ids: cv2.typing.MatLike, counter: cv2.typing.MatLike, board: Board, imageSize: cv2.typing.Size, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike]]: ...
|
||||
@_typing.overload
|
||||
def calibrateCameraAruco(corners: _typing.Sequence[cv2.UMat], ids: cv2.UMat, counter: cv2.UMat, board: Board, imageSize: cv2.typing.Size, cameraMatrix: cv2.UMat, distCoeffs: cv2.UMat, rvecs: _typing.Sequence[cv2.UMat] | None = ..., tvecs: _typing.Sequence[cv2.UMat] | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.UMat, cv2.UMat, _typing.Sequence[cv2.UMat], _typing.Sequence[cv2.UMat]]: ...
|
||||
|
||||
@_typing.overload
|
||||
def calibrateCameraArucoExtended(corners: _typing.Sequence[cv2.typing.MatLike], ids: cv2.typing.MatLike, counter: cv2.typing.MatLike, board: Board, imageSize: cv2.typing.Size, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., stdDeviationsIntrinsics: cv2.typing.MatLike | None = ..., stdDeviationsExtrinsics: cv2.typing.MatLike | None = ..., perViewErrors: cv2.typing.MatLike | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def calibrateCameraArucoExtended(corners: _typing.Sequence[cv2.UMat], ids: cv2.UMat, counter: cv2.UMat, board: Board, imageSize: cv2.typing.Size, cameraMatrix: cv2.UMat, distCoeffs: cv2.UMat, rvecs: _typing.Sequence[cv2.UMat] | None = ..., tvecs: _typing.Sequence[cv2.UMat] | None = ..., stdDeviationsIntrinsics: cv2.UMat | None = ..., stdDeviationsExtrinsics: cv2.UMat | None = ..., perViewErrors: cv2.UMat | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.UMat, cv2.UMat, _typing.Sequence[cv2.UMat], _typing.Sequence[cv2.UMat], cv2.UMat, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def calibrateCameraCharuco(charucoCorners: _typing.Sequence[cv2.typing.MatLike], charucoIds: _typing.Sequence[cv2.typing.MatLike], board: CharucoBoard, imageSize: cv2.typing.Size, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike]]: ...
|
||||
@_typing.overload
|
||||
def calibrateCameraCharuco(charucoCorners: _typing.Sequence[cv2.UMat], charucoIds: _typing.Sequence[cv2.UMat], board: CharucoBoard, imageSize: cv2.typing.Size, cameraMatrix: cv2.UMat, distCoeffs: cv2.UMat, rvecs: _typing.Sequence[cv2.UMat] | None = ..., tvecs: _typing.Sequence[cv2.UMat] | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.UMat, cv2.UMat, _typing.Sequence[cv2.UMat], _typing.Sequence[cv2.UMat]]: ...
|
||||
|
||||
@_typing.overload
|
||||
def calibrateCameraCharucoExtended(charucoCorners: _typing.Sequence[cv2.typing.MatLike], charucoIds: _typing.Sequence[cv2.typing.MatLike], board: CharucoBoard, imageSize: cv2.typing.Size, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., stdDeviationsIntrinsics: cv2.typing.MatLike | None = ..., stdDeviationsExtrinsics: cv2.typing.MatLike | None = ..., perViewErrors: cv2.typing.MatLike | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def calibrateCameraCharucoExtended(charucoCorners: _typing.Sequence[cv2.UMat], charucoIds: _typing.Sequence[cv2.UMat], board: CharucoBoard, imageSize: cv2.typing.Size, cameraMatrix: cv2.UMat, distCoeffs: cv2.UMat, rvecs: _typing.Sequence[cv2.UMat] | None = ..., tvecs: _typing.Sequence[cv2.UMat] | None = ..., stdDeviationsIntrinsics: cv2.UMat | None = ..., stdDeviationsExtrinsics: cv2.UMat | None = ..., perViewErrors: cv2.UMat | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.UMat, cv2.UMat, _typing.Sequence[cv2.UMat], _typing.Sequence[cv2.UMat], cv2.UMat, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def detectCharucoDiamond(image: cv2.typing.MatLike, markerCorners: _typing.Sequence[cv2.typing.MatLike], markerIds: cv2.typing.MatLike, squareMarkerLengthRate: float, diamondCorners: _typing.Sequence[cv2.typing.MatLike] | None = ..., diamondIds: cv2.typing.MatLike | None = ..., cameraMatrix: cv2.typing.MatLike | None = ..., distCoeffs: cv2.typing.MatLike | None = ..., dictionary: Dictionary = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def detectCharucoDiamond(image: cv2.UMat, markerCorners: _typing.Sequence[cv2.UMat], markerIds: cv2.UMat, squareMarkerLengthRate: float, diamondCorners: _typing.Sequence[cv2.UMat] | None = ..., diamondIds: cv2.UMat | None = ..., cameraMatrix: cv2.UMat | None = ..., distCoeffs: cv2.UMat | None = ..., dictionary: Dictionary = ...) -> tuple[_typing.Sequence[cv2.UMat], cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def detectMarkers(image: cv2.typing.MatLike, dictionary: Dictionary, corners: _typing.Sequence[cv2.typing.MatLike] | None = ..., ids: cv2.typing.MatLike | None = ..., parameters: DetectorParameters = ..., rejectedImgPoints: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike]]: ...
|
||||
@_typing.overload
|
||||
def detectMarkers(image: cv2.UMat, dictionary: Dictionary, corners: _typing.Sequence[cv2.UMat] | None = ..., ids: cv2.UMat | None = ..., parameters: DetectorParameters = ..., rejectedImgPoints: _typing.Sequence[cv2.UMat] | None = ...) -> tuple[_typing.Sequence[cv2.UMat], cv2.UMat, _typing.Sequence[cv2.UMat]]: ...
|
||||
|
||||
@_typing.overload
|
||||
def drawCharucoDiamond(dictionary: Dictionary, ids: cv2.typing.Vec4i, squareLength: int, markerLength: int, img: cv2.typing.MatLike | None = ..., marginSize: int = ..., borderBits: int = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def drawCharucoDiamond(dictionary: Dictionary, ids: cv2.typing.Vec4i, squareLength: int, markerLength: int, img: cv2.UMat | None = ..., marginSize: int = ..., borderBits: int = ...) -> cv2.UMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def drawDetectedCornersCharuco(image: cv2.typing.MatLike, charucoCorners: cv2.typing.MatLike, charucoIds: cv2.typing.MatLike | None = ..., cornerColor: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def drawDetectedCornersCharuco(image: cv2.UMat, charucoCorners: cv2.UMat, charucoIds: cv2.UMat | None = ..., cornerColor: cv2.typing.Scalar = ...) -> cv2.UMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def drawDetectedDiamonds(image: cv2.typing.MatLike, diamondCorners: _typing.Sequence[cv2.typing.MatLike], diamondIds: cv2.typing.MatLike | None = ..., borderColor: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def drawDetectedDiamonds(image: cv2.UMat, diamondCorners: _typing.Sequence[cv2.UMat], diamondIds: cv2.UMat | None = ..., borderColor: cv2.typing.Scalar = ...) -> cv2.UMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def drawDetectedMarkers(image: cv2.typing.MatLike, corners: _typing.Sequence[cv2.typing.MatLike], ids: cv2.typing.MatLike | None = ..., borderColor: cv2.typing.Scalar = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def drawDetectedMarkers(image: cv2.UMat, corners: _typing.Sequence[cv2.UMat], ids: cv2.UMat | None = ..., borderColor: cv2.typing.Scalar = ...) -> cv2.UMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def drawPlanarBoard(board: Board, outSize: cv2.typing.Size, marginSize: int, borderBits: int, img: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def drawPlanarBoard(board: Board, outSize: cv2.typing.Size, marginSize: int, borderBits: int, img: cv2.UMat | None = ...) -> cv2.UMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def estimatePoseBoard(corners: _typing.Sequence[cv2.typing.MatLike], ids: cv2.typing.MatLike, board: Board, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvec: cv2.typing.MatLike, tvec: cv2.typing.MatLike, useExtrinsicGuess: bool = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def estimatePoseBoard(corners: _typing.Sequence[cv2.UMat], ids: cv2.UMat, board: Board, cameraMatrix: cv2.UMat, distCoeffs: cv2.UMat, rvec: cv2.UMat, tvec: cv2.UMat, useExtrinsicGuess: bool = ...) -> tuple[int, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def estimatePoseCharucoBoard(charucoCorners: cv2.typing.MatLike, charucoIds: cv2.typing.MatLike, board: CharucoBoard, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvec: cv2.typing.MatLike, tvec: cv2.typing.MatLike, useExtrinsicGuess: bool = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def estimatePoseCharucoBoard(charucoCorners: cv2.UMat, charucoIds: cv2.UMat, board: CharucoBoard, cameraMatrix: cv2.UMat, distCoeffs: cv2.UMat, rvec: cv2.UMat, tvec: cv2.UMat, useExtrinsicGuess: bool = ...) -> tuple[bool, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def estimatePoseSingleMarkers(corners: _typing.Sequence[cv2.typing.MatLike], markerLength: float, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvecs: cv2.typing.MatLike | None = ..., tvecs: cv2.typing.MatLike | None = ..., objPoints: cv2.typing.MatLike | None = ..., estimateParameters: EstimateParameters = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def estimatePoseSingleMarkers(corners: _typing.Sequence[cv2.UMat], markerLength: float, cameraMatrix: cv2.UMat, distCoeffs: cv2.UMat, rvecs: cv2.UMat | None = ..., tvecs: cv2.UMat | None = ..., objPoints: cv2.UMat | None = ..., estimateParameters: EstimateParameters = ...) -> tuple[cv2.UMat, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
def extendDictionary(nMarkers: int, markerSize: int, baseDictionary: Dictionary = ..., randomSeed: int = ...) -> Dictionary: ...
|
||||
|
||||
@_typing.overload
|
||||
def generateImageMarker(dictionary: Dictionary, id: int, sidePixels: int, img: cv2.typing.MatLike | None = ..., borderBits: int = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def generateImageMarker(dictionary: Dictionary, id: int, sidePixels: int, img: cv2.UMat | None = ..., borderBits: int = ...) -> cv2.UMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def getBoardObjectAndImagePoints(board: Board, detectedCorners: _typing.Sequence[cv2.typing.MatLike], detectedIds: cv2.typing.MatLike, objPoints: cv2.typing.MatLike | None = ..., imgPoints: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def getBoardObjectAndImagePoints(board: Board, detectedCorners: _typing.Sequence[cv2.UMat], detectedIds: cv2.UMat, objPoints: cv2.UMat | None = ..., imgPoints: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
def getPredefinedDictionary(dict: int) -> Dictionary: ...
|
||||
|
||||
@_typing.overload
|
||||
def interpolateCornersCharuco(markerCorners: _typing.Sequence[cv2.typing.MatLike], markerIds: cv2.typing.MatLike, image: cv2.typing.MatLike, board: CharucoBoard, charucoCorners: cv2.typing.MatLike | None = ..., charucoIds: cv2.typing.MatLike | None = ..., cameraMatrix: cv2.typing.MatLike | None = ..., distCoeffs: cv2.typing.MatLike | None = ..., minMarkers: int = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def interpolateCornersCharuco(markerCorners: _typing.Sequence[cv2.UMat], markerIds: cv2.UMat, image: cv2.UMat, board: CharucoBoard, charucoCorners: cv2.UMat | None = ..., charucoIds: cv2.UMat | None = ..., cameraMatrix: cv2.UMat | None = ..., distCoeffs: cv2.UMat | None = ..., minMarkers: int = ...) -> tuple[int, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def refineDetectedMarkers(image: cv2.typing.MatLike, board: Board, detectedCorners: _typing.Sequence[cv2.typing.MatLike], detectedIds: cv2.typing.MatLike, rejectedCorners: _typing.Sequence[cv2.typing.MatLike], cameraMatrix: cv2.typing.MatLike | None = ..., distCoeffs: cv2.typing.MatLike | None = ..., minRepDistance: float = ..., errorCorrectionRate: float = ..., checkAllOrders: bool = ..., recoveredIdxs: cv2.typing.MatLike | None = ..., parameters: DetectorParameters = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def refineDetectedMarkers(image: cv2.UMat, board: Board, detectedCorners: _typing.Sequence[cv2.UMat], detectedIds: cv2.UMat, rejectedCorners: _typing.Sequence[cv2.UMat], cameraMatrix: cv2.UMat | None = ..., distCoeffs: cv2.UMat | None = ..., minRepDistance: float = ..., errorCorrectionRate: float = ..., checkAllOrders: bool = ..., recoveredIdxs: cv2.UMat | None = ..., parameters: DetectorParameters = ...) -> tuple[_typing.Sequence[cv2.UMat], cv2.UMat, _typing.Sequence[cv2.UMat], cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def testCharucoCornersCollinear(board: CharucoBoard, charucoIds: cv2.typing.MatLike) -> bool: ...
|
||||
@_typing.overload
|
||||
def testCharucoCornersCollinear(board: CharucoBoard, charucoIds: cv2.UMat) -> bool: ...
|
||||
|
||||
|
||||
@ -1,39 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
import cv2.typing
|
||||
import typing as _typing
|
||||
|
||||
|
||||
# Classes
|
||||
class BarcodeDetector(cv2.GraphicalCodeDetector):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, prototxt_path: str, model_path: str) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def decodeWithType(self, img: cv2.typing.MatLike, points: cv2.typing.MatLike) -> tuple[bool, _typing.Sequence[str], _typing.Sequence[str]]: ...
|
||||
@_typing.overload
|
||||
def decodeWithType(self, img: cv2.UMat, points: cv2.UMat) -> tuple[bool, _typing.Sequence[str], _typing.Sequence[str]]: ...
|
||||
|
||||
@_typing.overload
|
||||
def detectAndDecodeWithType(self, img: cv2.typing.MatLike, points: cv2.typing.MatLike | None = ...) -> tuple[bool, _typing.Sequence[str], _typing.Sequence[str], cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def detectAndDecodeWithType(self, img: cv2.UMat, points: cv2.UMat | None = ...) -> tuple[bool, _typing.Sequence[str], _typing.Sequence[str], cv2.UMat]: ...
|
||||
|
||||
def getDownsamplingThreshold(self) -> float: ...
|
||||
|
||||
def setDownsamplingThreshold(self, thresh: float) -> BarcodeDetector: ...
|
||||
|
||||
def getDetectorScales(self) -> _typing.Sequence[float]: ...
|
||||
|
||||
def setDetectorScales(self, sizes: _typing.Sequence[float]) -> BarcodeDetector: ...
|
||||
|
||||
def getGradientThreshold(self) -> float: ...
|
||||
|
||||
def setGradientThreshold(self, thresh: float) -> BarcodeDetector: ...
|
||||
|
||||
|
||||
|
||||
@ -1,24 +0,0 @@
|
||||
PYTHON_EXTENSIONS_PATHS = [
|
||||
LOADER_DIR
|
||||
] + PYTHON_EXTENSIONS_PATHS
|
||||
|
||||
ci_and_not_headless = False
|
||||
|
||||
try:
|
||||
from .version import ci_build, headless
|
||||
|
||||
ci_and_not_headless = ci_build and not headless
|
||||
except:
|
||||
pass
|
||||
|
||||
# the Qt plugin is included currently only in the pre-built wheels
|
||||
if sys.platform.startswith("linux") and ci_and_not_headless:
|
||||
os.environ["QT_QPA_PLATFORM_PLUGIN_PATH"] = os.path.join(
|
||||
os.path.dirname(os.path.abspath(__file__)), "qt", "plugins"
|
||||
)
|
||||
|
||||
# Qt will throw warning on Linux if fonts are not found
|
||||
if sys.platform.startswith("linux") and ci_and_not_headless:
|
||||
os.environ["QT_QPA_FONTDIR"] = os.path.join(
|
||||
os.path.dirname(os.path.abspath(__file__)), "qt", "fonts"
|
||||
)
|
||||
@ -1,5 +0,0 @@
|
||||
import os
|
||||
|
||||
BINARIES_PATHS = [
|
||||
os.path.join(os.path.join(LOADER_DIR, '../../'), 'lib64')
|
||||
] + BINARIES_PATHS
|
||||
@ -1,548 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
import cv2.typing
|
||||
import typing as _typing
|
||||
|
||||
|
||||
# Enumerations
|
||||
FEATURE_SET_COMPUTE_10: int
|
||||
FEATURE_SET_COMPUTE_11: int
|
||||
FEATURE_SET_COMPUTE_12: int
|
||||
FEATURE_SET_COMPUTE_13: int
|
||||
FEATURE_SET_COMPUTE_20: int
|
||||
FEATURE_SET_COMPUTE_21: int
|
||||
FEATURE_SET_COMPUTE_30: int
|
||||
FEATURE_SET_COMPUTE_32: int
|
||||
FEATURE_SET_COMPUTE_35: int
|
||||
FEATURE_SET_COMPUTE_50: int
|
||||
GLOBAL_ATOMICS: int
|
||||
SHARED_ATOMICS: int
|
||||
NATIVE_DOUBLE: int
|
||||
WARP_SHUFFLE_FUNCTIONS: int
|
||||
DYNAMIC_PARALLELISM: int
|
||||
FeatureSet = int
|
||||
"""One of [FEATURE_SET_COMPUTE_10, FEATURE_SET_COMPUTE_11, FEATURE_SET_COMPUTE_12, FEATURE_SET_COMPUTE_13, FEATURE_SET_COMPUTE_20, FEATURE_SET_COMPUTE_21, FEATURE_SET_COMPUTE_30, FEATURE_SET_COMPUTE_32, FEATURE_SET_COMPUTE_35, FEATURE_SET_COMPUTE_50, GLOBAL_ATOMICS, SHARED_ATOMICS, NATIVE_DOUBLE, WARP_SHUFFLE_FUNCTIONS, DYNAMIC_PARALLELISM]"""
|
||||
|
||||
|
||||
HostMem_PAGE_LOCKED: int
|
||||
HOST_MEM_PAGE_LOCKED: int
|
||||
HostMem_SHARED: int
|
||||
HOST_MEM_SHARED: int
|
||||
HostMem_WRITE_COMBINED: int
|
||||
HOST_MEM_WRITE_COMBINED: int
|
||||
HostMem_AllocType = int
|
||||
"""One of [HostMem_PAGE_LOCKED, HOST_MEM_PAGE_LOCKED, HostMem_SHARED, HOST_MEM_SHARED, HostMem_WRITE_COMBINED, HOST_MEM_WRITE_COMBINED]"""
|
||||
|
||||
Event_DEFAULT: int
|
||||
EVENT_DEFAULT: int
|
||||
Event_BLOCKING_SYNC: int
|
||||
EVENT_BLOCKING_SYNC: int
|
||||
Event_DISABLE_TIMING: int
|
||||
EVENT_DISABLE_TIMING: int
|
||||
Event_INTERPROCESS: int
|
||||
EVENT_INTERPROCESS: int
|
||||
Event_CreateFlags = int
|
||||
"""One of [Event_DEFAULT, EVENT_DEFAULT, Event_BLOCKING_SYNC, EVENT_BLOCKING_SYNC, Event_DISABLE_TIMING, EVENT_DISABLE_TIMING, Event_INTERPROCESS, EVENT_INTERPROCESS]"""
|
||||
|
||||
DeviceInfo_ComputeModeDefault: int
|
||||
DEVICE_INFO_COMPUTE_MODE_DEFAULT: int
|
||||
DeviceInfo_ComputeModeExclusive: int
|
||||
DEVICE_INFO_COMPUTE_MODE_EXCLUSIVE: int
|
||||
DeviceInfo_ComputeModeProhibited: int
|
||||
DEVICE_INFO_COMPUTE_MODE_PROHIBITED: int
|
||||
DeviceInfo_ComputeModeExclusiveProcess: int
|
||||
DEVICE_INFO_COMPUTE_MODE_EXCLUSIVE_PROCESS: int
|
||||
DeviceInfo_ComputeMode = int
|
||||
"""One of [DeviceInfo_ComputeModeDefault, DEVICE_INFO_COMPUTE_MODE_DEFAULT, DeviceInfo_ComputeModeExclusive, DEVICE_INFO_COMPUTE_MODE_EXCLUSIVE, DeviceInfo_ComputeModeProhibited, DEVICE_INFO_COMPUTE_MODE_PROHIBITED, DeviceInfo_ComputeModeExclusiveProcess, DEVICE_INFO_COMPUTE_MODE_EXCLUSIVE_PROCESS]"""
|
||||
|
||||
SURF_CUDA_X_ROW: int
|
||||
SURF_CUDA_Y_ROW: int
|
||||
SURF_CUDA_LAPLACIAN_ROW: int
|
||||
SURF_CUDA_OCTAVE_ROW: int
|
||||
SURF_CUDA_SIZE_ROW: int
|
||||
SURF_CUDA_ANGLE_ROW: int
|
||||
SURF_CUDA_HESSIAN_ROW: int
|
||||
SURF_CUDA_ROWS_COUNT: int
|
||||
SURF_CUDA_KeypointLayout = int
|
||||
"""One of [SURF_CUDA_X_ROW, SURF_CUDA_Y_ROW, SURF_CUDA_LAPLACIAN_ROW, SURF_CUDA_OCTAVE_ROW, SURF_CUDA_SIZE_ROW, SURF_CUDA_ANGLE_ROW, SURF_CUDA_HESSIAN_ROW, SURF_CUDA_ROWS_COUNT]"""
|
||||
|
||||
|
||||
# Classes
|
||||
class GpuMat:
|
||||
@property
|
||||
def step(self) -> int: ...
|
||||
|
||||
# Classes
|
||||
class Allocator:
|
||||
...
|
||||
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, allocator: GpuMat.Allocator = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, rows: int, cols: int, type: int, allocator: GpuMat.Allocator = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, size: cv2.typing.Size, type: int, allocator: GpuMat.Allocator = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, rows: int, cols: int, type: int, s: cv2.typing.Scalar, allocator: GpuMat.Allocator = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, size: cv2.typing.Size, type: int, s: cv2.typing.Scalar, allocator: GpuMat.Allocator = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, m: GpuMat) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, m: GpuMat, rowRange: cv2.typing.Range, colRange: cv2.typing.Range) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, m: GpuMat, roi: cv2.typing.Rect) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, arr: cv2.typing.MatLike, allocator: GpuMat.Allocator = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, arr: GpuMat, allocator: GpuMat.Allocator = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, arr: cv2.UMat, allocator: GpuMat.Allocator = ...) -> None: ...
|
||||
|
||||
@staticmethod
|
||||
def defaultAllocator() -> GpuMat.Allocator: ...
|
||||
|
||||
@staticmethod
|
||||
def setDefaultAllocator(allocator: GpuMat.Allocator) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def create(self, rows: int, cols: int, type: int) -> None: ...
|
||||
@_typing.overload
|
||||
def create(self, size: cv2.typing.Size, type: int) -> None: ...
|
||||
|
||||
def release(self) -> None: ...
|
||||
|
||||
def swap(self, mat: GpuMat) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def upload(self, arr: cv2.typing.MatLike) -> None: ...
|
||||
@_typing.overload
|
||||
def upload(self, arr: GpuMat) -> None: ...
|
||||
@_typing.overload
|
||||
def upload(self, arr: cv2.UMat) -> None: ...
|
||||
@_typing.overload
|
||||
def upload(self, arr: cv2.typing.MatLike, stream: Stream) -> None: ...
|
||||
@_typing.overload
|
||||
def upload(self, arr: GpuMat, stream: Stream) -> None: ...
|
||||
@_typing.overload
|
||||
def upload(self, arr: cv2.UMat, stream: Stream) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def download(self, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def download(self, dst: GpuMat | None = ...) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def download(self, dst: cv2.UMat | None = ...) -> cv2.UMat: ...
|
||||
@_typing.overload
|
||||
def download(self, stream: Stream, dst: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def download(self, stream: Stream, dst: GpuMat | None = ...) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def download(self, stream: Stream, dst: cv2.UMat | None = ...) -> cv2.UMat: ...
|
||||
|
||||
def clone(self) -> GpuMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def copyTo(self, dst: GpuMat | None = ...) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def copyTo(self, stream: Stream, dst: GpuMat | None = ...) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def copyTo(self, mask: GpuMat, dst: GpuMat | None = ...) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def copyTo(self, mask: GpuMat, stream: Stream, dst: GpuMat | None = ...) -> GpuMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def setTo(self, s: cv2.typing.Scalar) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def setTo(self, s: cv2.typing.Scalar, stream: Stream) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def setTo(self, s: cv2.typing.Scalar, mask: cv2.typing.MatLike) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def setTo(self, s: cv2.typing.Scalar, mask: GpuMat) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def setTo(self, s: cv2.typing.Scalar, mask: cv2.UMat) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def setTo(self, s: cv2.typing.Scalar, mask: cv2.typing.MatLike, stream: Stream) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def setTo(self, s: cv2.typing.Scalar, mask: GpuMat, stream: Stream) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def setTo(self, s: cv2.typing.Scalar, mask: cv2.UMat, stream: Stream) -> GpuMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def convertTo(self, rtype: int, stream: Stream, dst: GpuMat | None = ...) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def convertTo(self, rtype: int, dst: GpuMat | None = ..., alpha: float = ..., beta: float = ...) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def convertTo(self, rtype: int, alpha: float, beta: float, stream: Stream, dst: GpuMat | None = ...) -> GpuMat: ...
|
||||
|
||||
def assignTo(self, m: GpuMat, type: int = ...) -> None: ...
|
||||
|
||||
def row(self, y: int) -> GpuMat: ...
|
||||
|
||||
def col(self, x: int) -> GpuMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def rowRange(self, startrow: int, endrow: int) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def rowRange(self, r: cv2.typing.Range) -> GpuMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def colRange(self, startcol: int, endcol: int) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def colRange(self, r: cv2.typing.Range) -> GpuMat: ...
|
||||
|
||||
def reshape(self, cn: int, rows: int = ...) -> GpuMat: ...
|
||||
|
||||
def locateROI(self, wholeSize: cv2.typing.Size, ofs: cv2.typing.Point) -> None: ...
|
||||
|
||||
def adjustROI(self, dtop: int, dbottom: int, dleft: int, dright: int) -> GpuMat: ...
|
||||
|
||||
def isContinuous(self) -> bool: ...
|
||||
|
||||
def elemSize(self) -> int: ...
|
||||
|
||||
def elemSize1(self) -> int: ...
|
||||
|
||||
def type(self) -> int: ...
|
||||
|
||||
def depth(self) -> int: ...
|
||||
|
||||
def channels(self) -> int: ...
|
||||
|
||||
def step1(self) -> int: ...
|
||||
|
||||
def size(self) -> cv2.typing.Size: ...
|
||||
|
||||
def empty(self) -> bool: ...
|
||||
|
||||
def cudaPtr(self) -> cv2.typing.IntPointer: ...
|
||||
|
||||
def updateContinuityFlag(self) -> None: ...
|
||||
|
||||
|
||||
class GpuData:
|
||||
...
|
||||
|
||||
class GpuMatND:
|
||||
...
|
||||
|
||||
class BufferPool:
|
||||
# Functions
|
||||
def __init__(self, stream: Stream) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def getBuffer(self, rows: int, cols: int, type: int) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def getBuffer(self, size: cv2.typing.Size, type: int) -> GpuMat: ...
|
||||
|
||||
def getAllocator(self) -> GpuMat.Allocator: ...
|
||||
|
||||
|
||||
class HostMem:
|
||||
@property
|
||||
def step(self) -> int: ...
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, alloc_type: HostMem_AllocType = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, rows: int, cols: int, type: int, alloc_type: HostMem_AllocType = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, size: cv2.typing.Size, type: int, alloc_type: HostMem_AllocType = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, arr: cv2.typing.MatLike, alloc_type: HostMem_AllocType = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, arr: GpuMat, alloc_type: HostMem_AllocType = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, arr: cv2.UMat, alloc_type: HostMem_AllocType = ...) -> None: ...
|
||||
|
||||
def swap(self, b: HostMem) -> None: ...
|
||||
|
||||
def clone(self) -> HostMem: ...
|
||||
|
||||
def create(self, rows: int, cols: int, type: int) -> None: ...
|
||||
|
||||
def reshape(self, cn: int, rows: int = ...) -> HostMem: ...
|
||||
|
||||
def createMatHeader(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def isContinuous(self) -> bool: ...
|
||||
|
||||
def elemSize(self) -> int: ...
|
||||
|
||||
def elemSize1(self) -> int: ...
|
||||
|
||||
def type(self) -> int: ...
|
||||
|
||||
def depth(self) -> int: ...
|
||||
|
||||
def channels(self) -> int: ...
|
||||
|
||||
def step1(self) -> int: ...
|
||||
|
||||
def size(self) -> cv2.typing.Size: ...
|
||||
|
||||
def empty(self) -> bool: ...
|
||||
|
||||
|
||||
class Stream:
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, allocator: GpuMat.Allocator) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, cudaFlags: int) -> None: ...
|
||||
|
||||
def queryIfComplete(self) -> bool: ...
|
||||
|
||||
def waitForCompletion(self) -> None: ...
|
||||
|
||||
def waitEvent(self, event: Event) -> None: ...
|
||||
|
||||
@classmethod
|
||||
def Null(cls) -> Stream: ...
|
||||
|
||||
def cudaPtr(self) -> cv2.typing.IntPointer: ...
|
||||
|
||||
|
||||
class Event:
|
||||
# Functions
|
||||
def __init__(self, flags: Event_CreateFlags = ...) -> None: ...
|
||||
|
||||
def record(self, stream: Stream = ...) -> None: ...
|
||||
|
||||
def queryIfComplete(self) -> bool: ...
|
||||
|
||||
def waitForCompletion(self) -> None: ...
|
||||
|
||||
@staticmethod
|
||||
def elapsedTime(start: Event, end: Event) -> float: ...
|
||||
|
||||
|
||||
class TargetArchs:
|
||||
# Functions
|
||||
@staticmethod
|
||||
def has(major: int, minor: int) -> bool: ...
|
||||
|
||||
@staticmethod
|
||||
def hasPtx(major: int, minor: int) -> bool: ...
|
||||
|
||||
@staticmethod
|
||||
def hasBin(major: int, minor: int) -> bool: ...
|
||||
|
||||
@staticmethod
|
||||
def hasEqualOrLessPtx(major: int, minor: int) -> bool: ...
|
||||
|
||||
@staticmethod
|
||||
def hasEqualOrGreater(major: int, minor: int) -> bool: ...
|
||||
|
||||
@staticmethod
|
||||
def hasEqualOrGreaterPtx(major: int, minor: int) -> bool: ...
|
||||
|
||||
@staticmethod
|
||||
def hasEqualOrGreaterBin(major: int, minor: int) -> bool: ...
|
||||
|
||||
|
||||
class DeviceInfo:
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, device_id: int) -> None: ...
|
||||
|
||||
def deviceID(self) -> int: ...
|
||||
|
||||
def totalGlobalMem(self) -> int: ...
|
||||
|
||||
def sharedMemPerBlock(self) -> int: ...
|
||||
|
||||
def regsPerBlock(self) -> int: ...
|
||||
|
||||
def warpSize(self) -> int: ...
|
||||
|
||||
def memPitch(self) -> int: ...
|
||||
|
||||
def maxThreadsPerBlock(self) -> int: ...
|
||||
|
||||
def maxThreadsDim(self) -> cv2.typing.Vec3i: ...
|
||||
|
||||
def maxGridSize(self) -> cv2.typing.Vec3i: ...
|
||||
|
||||
def clockRate(self) -> int: ...
|
||||
|
||||
def totalConstMem(self) -> int: ...
|
||||
|
||||
def majorVersion(self) -> int: ...
|
||||
|
||||
def minorVersion(self) -> int: ...
|
||||
|
||||
def textureAlignment(self) -> int: ...
|
||||
|
||||
def texturePitchAlignment(self) -> int: ...
|
||||
|
||||
def multiProcessorCount(self) -> int: ...
|
||||
|
||||
def kernelExecTimeoutEnabled(self) -> bool: ...
|
||||
|
||||
def integrated(self) -> bool: ...
|
||||
|
||||
def canMapHostMemory(self) -> bool: ...
|
||||
|
||||
def computeMode(self) -> DeviceInfo_ComputeMode: ...
|
||||
|
||||
def maxTexture1D(self) -> int: ...
|
||||
|
||||
def maxTexture1DMipmap(self) -> int: ...
|
||||
|
||||
def maxTexture1DLinear(self) -> int: ...
|
||||
|
||||
def maxTexture2D(self) -> cv2.typing.Vec2i: ...
|
||||
|
||||
def maxTexture2DMipmap(self) -> cv2.typing.Vec2i: ...
|
||||
|
||||
def maxTexture2DLinear(self) -> cv2.typing.Vec3i: ...
|
||||
|
||||
def maxTexture2DGather(self) -> cv2.typing.Vec2i: ...
|
||||
|
||||
def maxTexture3D(self) -> cv2.typing.Vec3i: ...
|
||||
|
||||
def maxTextureCubemap(self) -> int: ...
|
||||
|
||||
def maxTexture1DLayered(self) -> cv2.typing.Vec2i: ...
|
||||
|
||||
def maxTexture2DLayered(self) -> cv2.typing.Vec3i: ...
|
||||
|
||||
def maxTextureCubemapLayered(self) -> cv2.typing.Vec2i: ...
|
||||
|
||||
def maxSurface1D(self) -> int: ...
|
||||
|
||||
def maxSurface2D(self) -> cv2.typing.Vec2i: ...
|
||||
|
||||
def maxSurface3D(self) -> cv2.typing.Vec3i: ...
|
||||
|
||||
def maxSurface1DLayered(self) -> cv2.typing.Vec2i: ...
|
||||
|
||||
def maxSurface2DLayered(self) -> cv2.typing.Vec3i: ...
|
||||
|
||||
def maxSurfaceCubemap(self) -> int: ...
|
||||
|
||||
def maxSurfaceCubemapLayered(self) -> cv2.typing.Vec2i: ...
|
||||
|
||||
def surfaceAlignment(self) -> int: ...
|
||||
|
||||
def concurrentKernels(self) -> bool: ...
|
||||
|
||||
def ECCEnabled(self) -> bool: ...
|
||||
|
||||
def pciBusID(self) -> int: ...
|
||||
|
||||
def pciDeviceID(self) -> int: ...
|
||||
|
||||
def pciDomainID(self) -> int: ...
|
||||
|
||||
def tccDriver(self) -> bool: ...
|
||||
|
||||
def asyncEngineCount(self) -> int: ...
|
||||
|
||||
def unifiedAddressing(self) -> bool: ...
|
||||
|
||||
def memoryClockRate(self) -> int: ...
|
||||
|
||||
def memoryBusWidth(self) -> int: ...
|
||||
|
||||
def l2CacheSize(self) -> int: ...
|
||||
|
||||
def maxThreadsPerMultiProcessor(self) -> int: ...
|
||||
|
||||
def queryMemory(self, totalMemory: int, freeMemory: int) -> None: ...
|
||||
|
||||
def freeMemory(self) -> int: ...
|
||||
|
||||
def totalMemory(self) -> int: ...
|
||||
|
||||
def isCompatible(self) -> bool: ...
|
||||
|
||||
|
||||
class SURF_CUDA:
|
||||
@property
|
||||
def hessianThreshold(self) -> float: ...
|
||||
@property
|
||||
def nOctaves(self) -> int: ...
|
||||
@property
|
||||
def nOctaveLayers(self) -> int: ...
|
||||
@property
|
||||
def extended(self) -> bool: ...
|
||||
@property
|
||||
def upright(self) -> bool: ...
|
||||
@property
|
||||
def keypointsRatio(self) -> float: ...
|
||||
|
||||
# Functions
|
||||
@classmethod
|
||||
def create(cls, _hessianThreshold: float, _nOctaves: int = ..., _nOctaveLayers: int = ..., _extended: bool = ..., _keypointsRatio: float = ..., _upright: bool = ...) -> SURF_CUDA: ...
|
||||
|
||||
def descriptorSize(self) -> int: ...
|
||||
|
||||
def defaultNorm(self) -> int: ...
|
||||
|
||||
def downloadKeypoints(self, keypointsGPU: GpuMat) -> _typing.Sequence[cv2.KeyPoint]: ...
|
||||
|
||||
def detect(self, img: GpuMat, mask: GpuMat, keypoints: GpuMat | None = ...) -> GpuMat: ...
|
||||
|
||||
def detectWithDescriptors(self, img: GpuMat, mask: GpuMat, keypoints: GpuMat | None = ..., descriptors: GpuMat | None = ..., useProvidedKeypoints: bool = ...) -> tuple[GpuMat, GpuMat]: ...
|
||||
|
||||
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def createContinuous(rows: int, cols: int, type: int, arr: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def createContinuous(rows: int, cols: int, type: int, arr: GpuMat | None = ...) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def createContinuous(rows: int, cols: int, type: int, arr: cv2.UMat | None = ...) -> cv2.UMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def createGpuMatFromCudaMemory(rows: int, cols: int, type: int, cudaMemoryAddress: int, step: int = ...) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def createGpuMatFromCudaMemory(size: cv2.typing.Size, type: int, cudaMemoryAddress: int, step: int = ...) -> GpuMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def ensureSizeIsEnough(rows: int, cols: int, type: int, arr: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def ensureSizeIsEnough(rows: int, cols: int, type: int, arr: GpuMat | None = ...) -> GpuMat: ...
|
||||
@_typing.overload
|
||||
def ensureSizeIsEnough(rows: int, cols: int, type: int, arr: cv2.UMat | None = ...) -> cv2.UMat: ...
|
||||
|
||||
def fastNlMeansDenoising(src: GpuMat, h: float, dst: GpuMat | None = ..., search_window: int = ..., block_size: int = ..., stream: Stream = ...) -> GpuMat: ...
|
||||
|
||||
def fastNlMeansDenoisingColored(src: GpuMat, h_luminance: float, photo_render: float, dst: GpuMat | None = ..., search_window: int = ..., block_size: int = ..., stream: Stream = ...) -> GpuMat: ...
|
||||
|
||||
def getCudaEnabledDeviceCount() -> int: ...
|
||||
|
||||
def getDevice() -> int: ...
|
||||
|
||||
def nonLocalMeans(src: GpuMat, h: float, dst: GpuMat | None = ..., search_window: int = ..., block_size: int = ..., borderMode: int = ..., stream: Stream = ...) -> GpuMat: ...
|
||||
|
||||
def printCudaDeviceInfo(device: int) -> None: ...
|
||||
|
||||
def printShortCudaDeviceInfo(device: int) -> None: ...
|
||||
|
||||
def registerPageLocked(m: cv2.typing.MatLike) -> None: ...
|
||||
|
||||
def resetDevice() -> None: ...
|
||||
|
||||
def setBufferPoolConfig(deviceId: int, stackSize: int, stackCount: int) -> None: ...
|
||||
|
||||
def setBufferPoolUsage(on: bool) -> None: ...
|
||||
|
||||
def setDevice(device: int) -> None: ...
|
||||
|
||||
def unregisterPageLocked(m: cv2.typing.MatLike) -> None: ...
|
||||
|
||||
def wrapStream(cudaStreamMemoryAddress: int) -> Stream: ...
|
||||
|
||||
|
||||
Binary file not shown.
@ -1,3 +0,0 @@
|
||||
import os
|
||||
|
||||
haarcascades = os.path.join(os.path.dirname(__file__), "")
|
||||
Binary file not shown.
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,627 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
import cv2.gapi
|
||||
import cv2.gapi.ie
|
||||
import cv2.gapi.onnx
|
||||
import cv2.gapi.ov
|
||||
import cv2.typing
|
||||
import numpy
|
||||
import typing as _typing
|
||||
|
||||
|
||||
# Enumerations
|
||||
TEST_CUSTOM: int
|
||||
TEST_EQ: int
|
||||
TEST_NE: int
|
||||
TEST_LE: int
|
||||
TEST_LT: int
|
||||
TEST_GE: int
|
||||
TEST_GT: int
|
||||
TestOp = int
|
||||
"""One of [TEST_CUSTOM, TEST_EQ, TEST_NE, TEST_LE, TEST_LT, TEST_GE, TEST_GT]"""
|
||||
|
||||
OpaqueKind_CV_UNKNOWN: int
|
||||
OPAQUE_KIND_CV_UNKNOWN: int
|
||||
OpaqueKind_CV_BOOL: int
|
||||
OPAQUE_KIND_CV_BOOL: int
|
||||
OpaqueKind_CV_INT: int
|
||||
OPAQUE_KIND_CV_INT: int
|
||||
OpaqueKind_CV_INT64: int
|
||||
OPAQUE_KIND_CV_INT64: int
|
||||
OpaqueKind_CV_DOUBLE: int
|
||||
OPAQUE_KIND_CV_DOUBLE: int
|
||||
OpaqueKind_CV_FLOAT: int
|
||||
OPAQUE_KIND_CV_FLOAT: int
|
||||
OpaqueKind_CV_UINT64: int
|
||||
OPAQUE_KIND_CV_UINT64: int
|
||||
OpaqueKind_CV_STRING: int
|
||||
OPAQUE_KIND_CV_STRING: int
|
||||
OpaqueKind_CV_POINT: int
|
||||
OPAQUE_KIND_CV_POINT: int
|
||||
OpaqueKind_CV_POINT2F: int
|
||||
OPAQUE_KIND_CV_POINT2F: int
|
||||
OpaqueKind_CV_POINT3F: int
|
||||
OPAQUE_KIND_CV_POINT3F: int
|
||||
OpaqueKind_CV_SIZE: int
|
||||
OPAQUE_KIND_CV_SIZE: int
|
||||
OpaqueKind_CV_RECT: int
|
||||
OPAQUE_KIND_CV_RECT: int
|
||||
OpaqueKind_CV_SCALAR: int
|
||||
OPAQUE_KIND_CV_SCALAR: int
|
||||
OpaqueKind_CV_MAT: int
|
||||
OPAQUE_KIND_CV_MAT: int
|
||||
OpaqueKind_CV_DRAW_PRIM: int
|
||||
OPAQUE_KIND_CV_DRAW_PRIM: int
|
||||
OpaqueKind = int
|
||||
"""One of [OpaqueKind_CV_UNKNOWN, OPAQUE_KIND_CV_UNKNOWN, OpaqueKind_CV_BOOL, OPAQUE_KIND_CV_BOOL, OpaqueKind_CV_INT, OPAQUE_KIND_CV_INT, OpaqueKind_CV_INT64, OPAQUE_KIND_CV_INT64, OpaqueKind_CV_DOUBLE, OPAQUE_KIND_CV_DOUBLE, OpaqueKind_CV_FLOAT, OPAQUE_KIND_CV_FLOAT, OpaqueKind_CV_UINT64, OPAQUE_KIND_CV_UINT64, OpaqueKind_CV_STRING, OPAQUE_KIND_CV_STRING, OpaqueKind_CV_POINT, OPAQUE_KIND_CV_POINT, OpaqueKind_CV_POINT2F, OPAQUE_KIND_CV_POINT2F, OpaqueKind_CV_POINT3F, OPAQUE_KIND_CV_POINT3F, OpaqueKind_CV_SIZE, OPAQUE_KIND_CV_SIZE, OpaqueKind_CV_RECT, OPAQUE_KIND_CV_RECT, OpaqueKind_CV_SCALAR, OPAQUE_KIND_CV_SCALAR, OpaqueKind_CV_MAT, OPAQUE_KIND_CV_MAT, OpaqueKind_CV_DRAW_PRIM, OPAQUE_KIND_CV_DRAW_PRIM]"""
|
||||
|
||||
ArgKind_OPAQUE_VAL: int
|
||||
ARG_KIND_OPAQUE_VAL: int
|
||||
ArgKind_OPAQUE: int
|
||||
ARG_KIND_OPAQUE: int
|
||||
ArgKind_GOBJREF: int
|
||||
ARG_KIND_GOBJREF: int
|
||||
ArgKind_GMAT: int
|
||||
ARG_KIND_GMAT: int
|
||||
ArgKind_GMATP: int
|
||||
ARG_KIND_GMATP: int
|
||||
ArgKind_GFRAME: int
|
||||
ARG_KIND_GFRAME: int
|
||||
ArgKind_GSCALAR: int
|
||||
ARG_KIND_GSCALAR: int
|
||||
ArgKind_GARRAY: int
|
||||
ARG_KIND_GARRAY: int
|
||||
ArgKind_GOPAQUE: int
|
||||
ARG_KIND_GOPAQUE: int
|
||||
ArgKind = int
|
||||
"""One of [ArgKind_OPAQUE_VAL, ARG_KIND_OPAQUE_VAL, ArgKind_OPAQUE, ARG_KIND_OPAQUE, ArgKind_GOBJREF, ARG_KIND_GOBJREF, ArgKind_GMAT, ARG_KIND_GMAT, ArgKind_GMATP, ARG_KIND_GMATP, ArgKind_GFRAME, ARG_KIND_GFRAME, ArgKind_GSCALAR, ARG_KIND_GSCALAR, ArgKind_GARRAY, ARG_KIND_GARRAY, ArgKind_GOPAQUE, ARG_KIND_GOPAQUE]"""
|
||||
|
||||
WAVE_CORRECT_HORIZ: int
|
||||
WAVE_CORRECT_VERT: int
|
||||
WAVE_CORRECT_AUTO: int
|
||||
WaveCorrectKind = int
|
||||
"""One of [WAVE_CORRECT_HORIZ, WAVE_CORRECT_VERT, WAVE_CORRECT_AUTO]"""
|
||||
|
||||
|
||||
Blender_NO: int
|
||||
BLENDER_NO: int
|
||||
Blender_FEATHER: int
|
||||
BLENDER_FEATHER: int
|
||||
Blender_MULTI_BAND: int
|
||||
BLENDER_MULTI_BAND: int
|
||||
|
||||
ExposureCompensator_NO: int
|
||||
EXPOSURE_COMPENSATOR_NO: int
|
||||
ExposureCompensator_GAIN: int
|
||||
EXPOSURE_COMPENSATOR_GAIN: int
|
||||
ExposureCompensator_GAIN_BLOCKS: int
|
||||
EXPOSURE_COMPENSATOR_GAIN_BLOCKS: int
|
||||
ExposureCompensator_CHANNELS: int
|
||||
EXPOSURE_COMPENSATOR_CHANNELS: int
|
||||
ExposureCompensator_CHANNELS_BLOCKS: int
|
||||
EXPOSURE_COMPENSATOR_CHANNELS_BLOCKS: int
|
||||
|
||||
SeamFinder_NO: int
|
||||
SEAM_FINDER_NO: int
|
||||
SeamFinder_VORONOI_SEAM: int
|
||||
SEAM_FINDER_VORONOI_SEAM: int
|
||||
SeamFinder_DP_SEAM: int
|
||||
SEAM_FINDER_DP_SEAM: int
|
||||
|
||||
DpSeamFinder_COLOR: int
|
||||
DP_SEAM_FINDER_COLOR: int
|
||||
DpSeamFinder_COLOR_GRAD: int
|
||||
DP_SEAM_FINDER_COLOR_GRAD: int
|
||||
DpSeamFinder_CostFunction = int
|
||||
"""One of [DpSeamFinder_COLOR, DP_SEAM_FINDER_COLOR, DpSeamFinder_COLOR_GRAD, DP_SEAM_FINDER_COLOR_GRAD]"""
|
||||
|
||||
Timelapser_AS_IS: int
|
||||
TIMELAPSER_AS_IS: int
|
||||
Timelapser_CROP: int
|
||||
TIMELAPSER_CROP: int
|
||||
|
||||
TrackerSamplerCSC_MODE_INIT_POS: int
|
||||
TRACKER_SAMPLER_CSC_MODE_INIT_POS: int
|
||||
TrackerSamplerCSC_MODE_INIT_NEG: int
|
||||
TRACKER_SAMPLER_CSC_MODE_INIT_NEG: int
|
||||
TrackerSamplerCSC_MODE_TRACK_POS: int
|
||||
TRACKER_SAMPLER_CSC_MODE_TRACK_POS: int
|
||||
TrackerSamplerCSC_MODE_TRACK_NEG: int
|
||||
TRACKER_SAMPLER_CSC_MODE_TRACK_NEG: int
|
||||
TrackerSamplerCSC_MODE_DETECT: int
|
||||
TRACKER_SAMPLER_CSC_MODE_DETECT: int
|
||||
TrackerSamplerCSC_MODE = int
|
||||
"""One of [TrackerSamplerCSC_MODE_INIT_POS, TRACKER_SAMPLER_CSC_MODE_INIT_POS, TrackerSamplerCSC_MODE_INIT_NEG, TRACKER_SAMPLER_CSC_MODE_INIT_NEG, TrackerSamplerCSC_MODE_TRACK_POS, TRACKER_SAMPLER_CSC_MODE_TRACK_POS, TrackerSamplerCSC_MODE_TRACK_NEG, TRACKER_SAMPLER_CSC_MODE_TRACK_NEG, TrackerSamplerCSC_MODE_DETECT, TRACKER_SAMPLER_CSC_MODE_DETECT]"""
|
||||
|
||||
GraphCutSeamFinderBase_COST_COLOR: int
|
||||
GRAPH_CUT_SEAM_FINDER_BASE_COST_COLOR: int
|
||||
GraphCutSeamFinderBase_COST_COLOR_GRAD: int
|
||||
GRAPH_CUT_SEAM_FINDER_BASE_COST_COLOR_GRAD: int
|
||||
GraphCutSeamFinderBase_CostType = int
|
||||
"""One of [GraphCutSeamFinderBase_COST_COLOR, GRAPH_CUT_SEAM_FINDER_BASE_COST_COLOR, GraphCutSeamFinderBase_COST_COLOR_GRAD, GRAPH_CUT_SEAM_FINDER_BASE_COST_COLOR_GRAD]"""
|
||||
|
||||
CvFeatureParams_HAAR: int
|
||||
CV_FEATURE_PARAMS_HAAR: int
|
||||
CvFeatureParams_LBP: int
|
||||
CV_FEATURE_PARAMS_LBP: int
|
||||
CvFeatureParams_HOG: int
|
||||
CV_FEATURE_PARAMS_HOG: int
|
||||
CvFeatureParams_FeatureType = int
|
||||
"""One of [CvFeatureParams_HAAR, CV_FEATURE_PARAMS_HAAR, CvFeatureParams_LBP, CV_FEATURE_PARAMS_LBP, CvFeatureParams_HOG, CV_FEATURE_PARAMS_HOG]"""
|
||||
|
||||
TrackerContribSamplerCSC_MODE_INIT_POS: int
|
||||
TRACKER_CONTRIB_SAMPLER_CSC_MODE_INIT_POS: int
|
||||
TrackerContribSamplerCSC_MODE_INIT_NEG: int
|
||||
TRACKER_CONTRIB_SAMPLER_CSC_MODE_INIT_NEG: int
|
||||
TrackerContribSamplerCSC_MODE_TRACK_POS: int
|
||||
TRACKER_CONTRIB_SAMPLER_CSC_MODE_TRACK_POS: int
|
||||
TrackerContribSamplerCSC_MODE_TRACK_NEG: int
|
||||
TRACKER_CONTRIB_SAMPLER_CSC_MODE_TRACK_NEG: int
|
||||
TrackerContribSamplerCSC_MODE_DETECT: int
|
||||
TRACKER_CONTRIB_SAMPLER_CSC_MODE_DETECT: int
|
||||
|
||||
TrackerSamplerCS_MODE_POSITIVE: int
|
||||
TRACKER_SAMPLER_CS_MODE_POSITIVE: int
|
||||
TrackerSamplerCS_MODE_NEGATIVE: int
|
||||
TRACKER_SAMPLER_CS_MODE_NEGATIVE: int
|
||||
TrackerSamplerCS_MODE_CLASSIFY: int
|
||||
TRACKER_SAMPLER_CS_MODE_CLASSIFY: int
|
||||
|
||||
|
||||
# Classes
|
||||
class Blender:
|
||||
# Functions
|
||||
@classmethod
|
||||
def createDefault(cls, type: int, try_gpu: bool = ...) -> Blender: ...
|
||||
|
||||
@_typing.overload
|
||||
def prepare(self, corners: _typing.Sequence[cv2.typing.Point], sizes: _typing.Sequence[cv2.typing.Size]) -> None: ...
|
||||
@_typing.overload
|
||||
def prepare(self, dst_roi: cv2.typing.Rect) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def feed(self, img: cv2.typing.MatLike, mask: cv2.typing.MatLike, tl: cv2.typing.Point) -> None: ...
|
||||
@_typing.overload
|
||||
def feed(self, img: cv2.UMat, mask: cv2.UMat, tl: cv2.typing.Point) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def blend(self, dst: cv2.typing.MatLike, dst_mask: cv2.typing.MatLike) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def blend(self, dst: cv2.UMat, dst_mask: cv2.UMat) -> tuple[cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
|
||||
class FeatherBlender(Blender):
|
||||
# Functions
|
||||
def __init__(self, sharpness: float = ...) -> None: ...
|
||||
|
||||
def sharpness(self) -> float: ...
|
||||
|
||||
def setSharpness(self, val: float) -> None: ...
|
||||
|
||||
def prepare(self, dst_roi: cv2.typing.Rect) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def feed(self, img: cv2.typing.MatLike, mask: cv2.typing.MatLike, tl: cv2.typing.Point) -> None: ...
|
||||
@_typing.overload
|
||||
def feed(self, img: cv2.UMat, mask: cv2.UMat, tl: cv2.typing.Point) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def blend(self, dst: cv2.typing.MatLike, dst_mask: cv2.typing.MatLike) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def blend(self, dst: cv2.UMat, dst_mask: cv2.UMat) -> tuple[cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
def createWeightMaps(self, masks: _typing.Sequence[cv2.UMat], corners: _typing.Sequence[cv2.typing.Point], weight_maps: _typing.Sequence[cv2.UMat]) -> tuple[cv2.typing.Rect, _typing.Sequence[cv2.UMat]]: ...
|
||||
|
||||
|
||||
class MultiBandBlender(Blender):
|
||||
# Functions
|
||||
def __init__(self, try_gpu: int = ..., num_bands: int = ..., weight_type: int = ...) -> None: ...
|
||||
|
||||
def numBands(self) -> int: ...
|
||||
|
||||
def setNumBands(self, val: int) -> None: ...
|
||||
|
||||
def prepare(self, dst_roi: cv2.typing.Rect) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def feed(self, img: cv2.typing.MatLike, mask: cv2.typing.MatLike, tl: cv2.typing.Point) -> None: ...
|
||||
@_typing.overload
|
||||
def feed(self, img: cv2.UMat, mask: cv2.UMat, tl: cv2.typing.Point) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def blend(self, dst: cv2.typing.MatLike, dst_mask: cv2.typing.MatLike) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def blend(self, dst: cv2.UMat, dst_mask: cv2.UMat) -> tuple[cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
|
||||
class CameraParams:
|
||||
focal: float
|
||||
aspect: float
|
||||
ppx: float
|
||||
ppy: float
|
||||
R: cv2.typing.MatLike
|
||||
t: cv2.typing.MatLike
|
||||
|
||||
# Functions
|
||||
def K(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
|
||||
class ExposureCompensator:
|
||||
# Functions
|
||||
@classmethod
|
||||
def createDefault(cls, type: int) -> ExposureCompensator: ...
|
||||
|
||||
def feed(self, corners: _typing.Sequence[cv2.typing.Point], images: _typing.Sequence[cv2.UMat], masks: _typing.Sequence[cv2.UMat]) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def apply(self, index: int, corner: cv2.typing.Point, image: cv2.typing.MatLike, mask: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def apply(self, index: int, corner: cv2.typing.Point, image: cv2.UMat, mask: cv2.UMat) -> cv2.UMat: ...
|
||||
|
||||
def getMatGains(self, arg1: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
|
||||
def setMatGains(self, arg1: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
|
||||
|
||||
def setUpdateGain(self, b: bool) -> None: ...
|
||||
|
||||
def getUpdateGain(self) -> bool: ...
|
||||
|
||||
|
||||
class NoExposureCompensator(ExposureCompensator):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def apply(self, arg1: int, arg2: cv2.typing.Point, arg3: cv2.typing.MatLike, arg4: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def apply(self, arg1: int, arg2: cv2.typing.Point, arg3: cv2.UMat, arg4: cv2.UMat) -> cv2.UMat: ...
|
||||
|
||||
def getMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
|
||||
def setMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
|
||||
|
||||
|
||||
class GainCompensator(ExposureCompensator):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, nr_feeds: int) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def apply(self, index: int, corner: cv2.typing.Point, image: cv2.typing.MatLike, mask: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def apply(self, index: int, corner: cv2.typing.Point, image: cv2.UMat, mask: cv2.UMat) -> cv2.UMat: ...
|
||||
|
||||
def getMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
|
||||
def setMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
|
||||
|
||||
def setNrFeeds(self, nr_feeds: int) -> None: ...
|
||||
|
||||
def getNrFeeds(self) -> int: ...
|
||||
|
||||
def setSimilarityThreshold(self, similarity_threshold: float) -> None: ...
|
||||
|
||||
def getSimilarityThreshold(self) -> float: ...
|
||||
|
||||
|
||||
class ChannelsCompensator(ExposureCompensator):
|
||||
# Functions
|
||||
def __init__(self, nr_feeds: int = ...) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def apply(self, index: int, corner: cv2.typing.Point, image: cv2.typing.MatLike, mask: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def apply(self, index: int, corner: cv2.typing.Point, image: cv2.UMat, mask: cv2.UMat) -> cv2.UMat: ...
|
||||
|
||||
def getMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
|
||||
def setMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
|
||||
|
||||
def setNrFeeds(self, nr_feeds: int) -> None: ...
|
||||
|
||||
def getNrFeeds(self) -> int: ...
|
||||
|
||||
def setSimilarityThreshold(self, similarity_threshold: float) -> None: ...
|
||||
|
||||
def getSimilarityThreshold(self) -> float: ...
|
||||
|
||||
|
||||
class BlocksCompensator(ExposureCompensator):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def apply(self, index: int, corner: cv2.typing.Point, image: cv2.typing.MatLike, mask: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def apply(self, index: int, corner: cv2.typing.Point, image: cv2.UMat, mask: cv2.UMat) -> cv2.UMat: ...
|
||||
|
||||
def getMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
|
||||
def setMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
|
||||
|
||||
def setNrFeeds(self, nr_feeds: int) -> None: ...
|
||||
|
||||
def getNrFeeds(self) -> int: ...
|
||||
|
||||
def setSimilarityThreshold(self, similarity_threshold: float) -> None: ...
|
||||
|
||||
def getSimilarityThreshold(self) -> float: ...
|
||||
|
||||
@_typing.overload
|
||||
def setBlockSize(self, width: int, height: int) -> None: ...
|
||||
@_typing.overload
|
||||
def setBlockSize(self, size: cv2.typing.Size) -> None: ...
|
||||
|
||||
def getBlockSize(self) -> cv2.typing.Size: ...
|
||||
|
||||
def setNrGainsFilteringIterations(self, nr_iterations: int) -> None: ...
|
||||
|
||||
def getNrGainsFilteringIterations(self) -> int: ...
|
||||
|
||||
|
||||
class BlocksGainCompensator(BlocksCompensator):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, bl_width: int = ..., bl_height: int = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, bl_width: int, bl_height: int, nr_feeds: int) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def apply(self, index: int, corner: cv2.typing.Point, image: cv2.typing.MatLike, mask: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def apply(self, index: int, corner: cv2.typing.Point, image: cv2.UMat, mask: cv2.UMat) -> cv2.UMat: ...
|
||||
|
||||
def getMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
|
||||
def setMatGains(self, umv: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
|
||||
|
||||
|
||||
class BlocksChannelsCompensator(BlocksCompensator):
|
||||
# Functions
|
||||
def __init__(self, bl_width: int = ..., bl_height: int = ..., nr_feeds: int = ...) -> None: ...
|
||||
|
||||
|
||||
class ImageFeatures:
|
||||
img_idx: int
|
||||
img_size: cv2.typing.Size
|
||||
keypoints: _typing.Sequence[cv2.KeyPoint]
|
||||
descriptors: cv2.UMat
|
||||
|
||||
# Functions
|
||||
def getKeypoints(self) -> _typing.Sequence[cv2.KeyPoint]: ...
|
||||
|
||||
|
||||
class MatchesInfo:
|
||||
src_img_idx: int
|
||||
dst_img_idx: int
|
||||
matches: _typing.Sequence[cv2.DMatch]
|
||||
inliers_mask: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]
|
||||
num_inliers: int
|
||||
H: cv2.typing.MatLike
|
||||
confidence: float
|
||||
|
||||
# Functions
|
||||
def getMatches(self) -> _typing.Sequence[cv2.DMatch]: ...
|
||||
|
||||
def getInliers(self) -> numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]: ...
|
||||
|
||||
|
||||
class FeaturesMatcher:
|
||||
# Functions
|
||||
def apply(self, features1: ImageFeatures, features2: ImageFeatures) -> MatchesInfo: ...
|
||||
|
||||
def apply2(self, features: _typing.Sequence[ImageFeatures], mask: cv2.UMat | None = ...) -> _typing.Sequence[MatchesInfo]: ...
|
||||
|
||||
def isThreadSafe(self) -> bool: ...
|
||||
|
||||
def collectGarbage(self) -> None: ...
|
||||
|
||||
|
||||
class BestOf2NearestMatcher(FeaturesMatcher):
|
||||
# Functions
|
||||
def __init__(self, try_use_gpu: bool = ..., match_conf: float = ..., num_matches_thresh1: int = ..., num_matches_thresh2: int = ..., matches_confindece_thresh: float = ...) -> None: ...
|
||||
|
||||
def collectGarbage(self) -> None: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls, try_use_gpu: bool = ..., match_conf: float = ..., num_matches_thresh1: int = ..., num_matches_thresh2: int = ..., matches_confindece_thresh: float = ...) -> BestOf2NearestMatcher: ...
|
||||
|
||||
|
||||
class BestOf2NearestRangeMatcher(BestOf2NearestMatcher):
|
||||
# Functions
|
||||
def __init__(self, range_width: int = ..., try_use_gpu: bool = ..., match_conf: float = ..., num_matches_thresh1: int = ..., num_matches_thresh2: int = ...) -> None: ...
|
||||
|
||||
|
||||
class AffineBestOf2NearestMatcher(BestOf2NearestMatcher):
|
||||
# Functions
|
||||
def __init__(self, full_affine: bool = ..., try_use_gpu: bool = ..., match_conf: float = ..., num_matches_thresh1: int = ...) -> None: ...
|
||||
|
||||
|
||||
class Estimator:
|
||||
# Functions
|
||||
def apply(self, features: _typing.Sequence[ImageFeatures], pairwise_matches: _typing.Sequence[MatchesInfo], cameras: _typing.Sequence[CameraParams]) -> tuple[bool, _typing.Sequence[CameraParams]]: ...
|
||||
|
||||
|
||||
class HomographyBasedEstimator(Estimator):
|
||||
# Functions
|
||||
def __init__(self, is_focals_estimated: bool = ...) -> None: ...
|
||||
|
||||
|
||||
class AffineBasedEstimator(Estimator):
|
||||
# Functions
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
|
||||
class BundleAdjusterBase(Estimator):
|
||||
# Functions
|
||||
def refinementMask(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def setRefinementMask(self, mask: cv2.typing.MatLike) -> None: ...
|
||||
|
||||
def confThresh(self) -> float: ...
|
||||
|
||||
def setConfThresh(self, conf_thresh: float) -> None: ...
|
||||
|
||||
def termCriteria(self) -> cv2.typing.TermCriteria: ...
|
||||
|
||||
def setTermCriteria(self, term_criteria: cv2.typing.TermCriteria) -> None: ...
|
||||
|
||||
|
||||
class NoBundleAdjuster(BundleAdjusterBase):
|
||||
# Functions
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
|
||||
class BundleAdjusterReproj(BundleAdjusterBase):
|
||||
# Functions
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
|
||||
class BundleAdjusterRay(BundleAdjusterBase):
|
||||
# Functions
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
|
||||
class BundleAdjusterAffine(BundleAdjusterBase):
|
||||
# Functions
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
|
||||
class BundleAdjusterAffinePartial(BundleAdjusterBase):
|
||||
# Functions
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
|
||||
class SeamFinder:
|
||||
# Functions
|
||||
def find(self, src: _typing.Sequence[cv2.UMat], corners: _typing.Sequence[cv2.typing.Point], masks: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
|
||||
|
||||
@classmethod
|
||||
def createDefault(cls, type: int) -> SeamFinder: ...
|
||||
|
||||
|
||||
class NoSeamFinder(SeamFinder):
|
||||
# Functions
|
||||
def find(self, arg1: _typing.Sequence[cv2.UMat], arg2: _typing.Sequence[cv2.typing.Point], arg3: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
|
||||
|
||||
|
||||
class PairwiseSeamFinder(SeamFinder):
|
||||
# Functions
|
||||
def find(self, src: _typing.Sequence[cv2.UMat], corners: _typing.Sequence[cv2.typing.Point], masks: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
|
||||
|
||||
|
||||
class VoronoiSeamFinder(PairwiseSeamFinder):
|
||||
# Functions
|
||||
def find(self, src: _typing.Sequence[cv2.UMat], corners: _typing.Sequence[cv2.typing.Point], masks: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
|
||||
|
||||
|
||||
class DpSeamFinder(SeamFinder):
|
||||
# Functions
|
||||
def __init__(self, costFunc: str) -> None: ...
|
||||
|
||||
def setCostFunction(self, val: str) -> None: ...
|
||||
|
||||
|
||||
class GraphCutSeamFinder:
|
||||
# Functions
|
||||
def __init__(self, cost_type: str, terminal_cost: float = ..., bad_region_penalty: float = ...) -> None: ...
|
||||
|
||||
def find(self, src: _typing.Sequence[cv2.UMat], corners: _typing.Sequence[cv2.typing.Point], masks: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
|
||||
|
||||
|
||||
class Timelapser:
|
||||
# Functions
|
||||
@classmethod
|
||||
def createDefault(cls, type: int) -> Timelapser: ...
|
||||
|
||||
def initialize(self, corners: _typing.Sequence[cv2.typing.Point], sizes: _typing.Sequence[cv2.typing.Size]) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def process(self, img: cv2.typing.MatLike, mask: cv2.typing.MatLike, tl: cv2.typing.Point) -> None: ...
|
||||
@_typing.overload
|
||||
def process(self, img: cv2.UMat, mask: cv2.UMat, tl: cv2.typing.Point) -> None: ...
|
||||
|
||||
def getDst(self) -> cv2.UMat: ...
|
||||
|
||||
|
||||
class TimelapserCrop(Timelapser):
|
||||
...
|
||||
|
||||
class ProjectorBase:
|
||||
...
|
||||
|
||||
class SphericalProjector(ProjectorBase):
|
||||
# Functions
|
||||
def mapForward(self, x: float, y: float, u: float, v: float) -> None: ...
|
||||
|
||||
def mapBackward(self, u: float, v: float, x: float, y: float) -> None: ...
|
||||
|
||||
|
||||
|
||||
# Functions
|
||||
def calibrateRotatingCamera(Hs: _typing.Sequence[cv2.typing.MatLike], K: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike]: ...
|
||||
|
||||
@_typing.overload
|
||||
def computeImageFeatures(featuresFinder: cv2.Feature2D, images: _typing.Sequence[cv2.typing.MatLike], masks: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[ImageFeatures]: ...
|
||||
@_typing.overload
|
||||
def computeImageFeatures(featuresFinder: cv2.Feature2D, images: _typing.Sequence[cv2.UMat], masks: _typing.Sequence[cv2.UMat] | None = ...) -> _typing.Sequence[ImageFeatures]: ...
|
||||
|
||||
@_typing.overload
|
||||
def computeImageFeatures2(featuresFinder: cv2.Feature2D, image: cv2.typing.MatLike, mask: cv2.typing.MatLike | None = ...) -> ImageFeatures: ...
|
||||
@_typing.overload
|
||||
def computeImageFeatures2(featuresFinder: cv2.Feature2D, image: cv2.UMat, mask: cv2.UMat | None = ...) -> ImageFeatures: ...
|
||||
|
||||
@_typing.overload
|
||||
def createLaplacePyr(img: cv2.typing.MatLike, num_levels: int, pyr: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
|
||||
@_typing.overload
|
||||
def createLaplacePyr(img: cv2.UMat, num_levels: int, pyr: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def createLaplacePyrGpu(img: cv2.typing.MatLike, num_levels: int, pyr: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
|
||||
@_typing.overload
|
||||
def createLaplacePyrGpu(img: cv2.UMat, num_levels: int, pyr: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def createWeightMap(mask: cv2.typing.MatLike, sharpness: float, weight: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def createWeightMap(mask: cv2.UMat, sharpness: float, weight: cv2.UMat) -> cv2.UMat: ...
|
||||
|
||||
def focalsFromHomography(H: cv2.typing.MatLike, f0: float, f1: float, f0_ok: bool, f1_ok: bool) -> None: ...
|
||||
|
||||
def leaveBiggestComponent(features: _typing.Sequence[ImageFeatures], pairwise_matches: _typing.Sequence[MatchesInfo], conf_threshold: float) -> _typing.Sequence[int]: ...
|
||||
|
||||
def matchesGraphAsString(paths: _typing.Sequence[str], pairwise_matches: _typing.Sequence[MatchesInfo], conf_threshold: float) -> str: ...
|
||||
|
||||
@_typing.overload
|
||||
def normalizeUsingWeightMap(weight: cv2.typing.MatLike, src: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def normalizeUsingWeightMap(weight: cv2.UMat, src: cv2.UMat) -> cv2.UMat: ...
|
||||
|
||||
def overlapRoi(tl1: cv2.typing.Point, tl2: cv2.typing.Point, sz1: cv2.typing.Size, sz2: cv2.typing.Size, roi: cv2.typing.Rect) -> bool: ...
|
||||
|
||||
def restoreImageFromLaplacePyr(pyr: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
|
||||
|
||||
def restoreImageFromLaplacePyrGpu(pyr: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def resultRoi(corners: _typing.Sequence[cv2.typing.Point], images: _typing.Sequence[cv2.UMat]) -> cv2.typing.Rect: ...
|
||||
@_typing.overload
|
||||
def resultRoi(corners: _typing.Sequence[cv2.typing.Point], sizes: _typing.Sequence[cv2.typing.Size]) -> cv2.typing.Rect: ...
|
||||
|
||||
def resultRoiIntersection(corners: _typing.Sequence[cv2.typing.Point], sizes: _typing.Sequence[cv2.typing.Size]) -> cv2.typing.Rect: ...
|
||||
|
||||
def resultTl(corners: _typing.Sequence[cv2.typing.Point]) -> cv2.typing.Point: ...
|
||||
|
||||
def selectRandomSubset(count: int, size: int, subset: _typing.Sequence[int]) -> None: ...
|
||||
|
||||
def stitchingLogLevel() -> int: ...
|
||||
|
||||
@_typing.overload
|
||||
def strip(params: cv2.gapi.ie.PyParams) -> cv2.gapi.GNetParam: ...
|
||||
@_typing.overload
|
||||
def strip(params: cv2.gapi.onnx.PyParams) -> cv2.gapi.GNetParam: ...
|
||||
@_typing.overload
|
||||
def strip(params: cv2.gapi.ov.PyParams) -> cv2.gapi.GNetParam: ...
|
||||
|
||||
def waveCorrect(rmats: _typing.Sequence[cv2.typing.MatLike], kind: WaveCorrectKind) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
|
||||
|
||||
@ -1,530 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
import cv2.typing
|
||||
import numpy
|
||||
import sys
|
||||
import typing as _typing
|
||||
if sys.version_info >= (3, 8):
|
||||
from typing import Protocol
|
||||
else:
|
||||
from typing_extensions import Protocol
|
||||
|
||||
|
||||
# Enumerations
|
||||
DNN_BACKEND_DEFAULT: int
|
||||
DNN_BACKEND_HALIDE: int
|
||||
DNN_BACKEND_INFERENCE_ENGINE: int
|
||||
DNN_BACKEND_OPENCV: int
|
||||
DNN_BACKEND_VKCOM: int
|
||||
DNN_BACKEND_CUDA: int
|
||||
DNN_BACKEND_WEBNN: int
|
||||
DNN_BACKEND_TIMVX: int
|
||||
DNN_BACKEND_CANN: int
|
||||
Backend = int
|
||||
"""One of [DNN_BACKEND_DEFAULT, DNN_BACKEND_HALIDE, DNN_BACKEND_INFERENCE_ENGINE, DNN_BACKEND_OPENCV, DNN_BACKEND_VKCOM, DNN_BACKEND_CUDA, DNN_BACKEND_WEBNN, DNN_BACKEND_TIMVX, DNN_BACKEND_CANN]"""
|
||||
|
||||
DNN_TARGET_CPU: int
|
||||
DNN_TARGET_OPENCL: int
|
||||
DNN_TARGET_OPENCL_FP16: int
|
||||
DNN_TARGET_MYRIAD: int
|
||||
DNN_TARGET_VULKAN: int
|
||||
DNN_TARGET_FPGA: int
|
||||
DNN_TARGET_CUDA: int
|
||||
DNN_TARGET_CUDA_FP16: int
|
||||
DNN_TARGET_HDDL: int
|
||||
DNN_TARGET_NPU: int
|
||||
DNN_TARGET_CPU_FP16: int
|
||||
Target = int
|
||||
"""One of [DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16, DNN_TARGET_MYRIAD, DNN_TARGET_VULKAN, DNN_TARGET_FPGA, DNN_TARGET_CUDA, DNN_TARGET_CUDA_FP16, DNN_TARGET_HDDL, DNN_TARGET_NPU, DNN_TARGET_CPU_FP16]"""
|
||||
|
||||
DNN_LAYOUT_UNKNOWN: int
|
||||
DNN_LAYOUT_ND: int
|
||||
DNN_LAYOUT_NCHW: int
|
||||
DNN_LAYOUT_NCDHW: int
|
||||
DNN_LAYOUT_NHWC: int
|
||||
DNN_LAYOUT_NDHWC: int
|
||||
DNN_LAYOUT_PLANAR: int
|
||||
DataLayout = int
|
||||
"""One of [DNN_LAYOUT_UNKNOWN, DNN_LAYOUT_ND, DNN_LAYOUT_NCHW, DNN_LAYOUT_NCDHW, DNN_LAYOUT_NHWC, DNN_LAYOUT_NDHWC, DNN_LAYOUT_PLANAR]"""
|
||||
|
||||
DNN_PMODE_NULL: int
|
||||
DNN_PMODE_CROP_CENTER: int
|
||||
DNN_PMODE_LETTERBOX: int
|
||||
ImagePaddingMode = int
|
||||
"""One of [DNN_PMODE_NULL, DNN_PMODE_CROP_CENTER, DNN_PMODE_LETTERBOX]"""
|
||||
|
||||
SoftNMSMethod_SOFTNMS_LINEAR: int
|
||||
SOFT_NMSMETHOD_SOFTNMS_LINEAR: int
|
||||
SoftNMSMethod_SOFTNMS_GAUSSIAN: int
|
||||
SOFT_NMSMETHOD_SOFTNMS_GAUSSIAN: int
|
||||
SoftNMSMethod = int
|
||||
"""One of [SoftNMSMethod_SOFTNMS_LINEAR, SOFT_NMSMETHOD_SOFTNMS_LINEAR, SoftNMSMethod_SOFTNMS_GAUSSIAN, SOFT_NMSMETHOD_SOFTNMS_GAUSSIAN]"""
|
||||
|
||||
|
||||
|
||||
# Classes
|
||||
class DictValue:
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, i: int) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, p: float) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, s: str) -> None: ...
|
||||
|
||||
def isInt(self) -> bool: ...
|
||||
|
||||
def isString(self) -> bool: ...
|
||||
|
||||
def isReal(self) -> bool: ...
|
||||
|
||||
def getIntValue(self, idx: int = ...) -> int: ...
|
||||
|
||||
def getRealValue(self, idx: int = ...) -> float: ...
|
||||
|
||||
def getStringValue(self, idx: int = ...) -> str: ...
|
||||
|
||||
|
||||
class Layer(cv2.Algorithm):
|
||||
blobs: _typing.Sequence[cv2.typing.MatLike]
|
||||
@property
|
||||
def name(self) -> str: ...
|
||||
@property
|
||||
def type(self) -> str: ...
|
||||
@property
|
||||
def preferableTarget(self) -> int: ...
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def finalize(self, inputs: _typing.Sequence[cv2.typing.MatLike], outputs: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def finalize(self, inputs: _typing.Sequence[cv2.UMat], outputs: _typing.Sequence[cv2.UMat] | None = ...) -> _typing.Sequence[cv2.UMat]: ...
|
||||
|
||||
def run(self, inputs: _typing.Sequence[cv2.typing.MatLike], internals: _typing.Sequence[cv2.typing.MatLike], outputs: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> tuple[_typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike]]: ...
|
||||
|
||||
def outputNameToIndex(self, outputName: str) -> int: ...
|
||||
|
||||
|
||||
class Net:
|
||||
# Functions
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
@classmethod
|
||||
@_typing.overload
|
||||
def readFromModelOptimizer(cls, xml: str, bin: str) -> Net: ...
|
||||
@classmethod
|
||||
@_typing.overload
|
||||
def readFromModelOptimizer(cls, bufferModelConfig: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], bufferWeights: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]) -> Net: ...
|
||||
|
||||
def empty(self) -> bool: ...
|
||||
|
||||
def dump(self) -> str: ...
|
||||
|
||||
def dumpToFile(self, path: str) -> None: ...
|
||||
|
||||
def dumpToPbtxt(self, path: str) -> None: ...
|
||||
|
||||
def getLayerId(self, layer: str) -> int: ...
|
||||
|
||||
def getLayerNames(self) -> _typing.Sequence[str]: ...
|
||||
|
||||
@_typing.overload
|
||||
def getLayer(self, layerId: int) -> Layer: ...
|
||||
@_typing.overload
|
||||
def getLayer(self, layerName: str) -> Layer: ...
|
||||
@_typing.overload
|
||||
def getLayer(self, layerId: cv2.typing.LayerId) -> Layer: ...
|
||||
|
||||
def connect(self, outPin: str, inpPin: str) -> None: ...
|
||||
|
||||
def setInputsNames(self, inputBlobNames: _typing.Sequence[str]) -> None: ...
|
||||
|
||||
def setInputShape(self, inputName: str, shape: cv2.typing.MatShape) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def forward(self, outputName: str = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def forward(self, outputBlobs: _typing.Sequence[cv2.typing.MatLike] | None = ..., outputName: str = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def forward(self, outputBlobs: _typing.Sequence[cv2.UMat] | None = ..., outputName: str = ...) -> _typing.Sequence[cv2.UMat]: ...
|
||||
@_typing.overload
|
||||
def forward(self, outBlobNames: _typing.Sequence[str], outputBlobs: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def forward(self, outBlobNames: _typing.Sequence[str], outputBlobs: _typing.Sequence[cv2.UMat] | None = ...) -> _typing.Sequence[cv2.UMat]: ...
|
||||
|
||||
def forwardAsync(self, outputName: str = ...) -> cv2.AsyncArray: ...
|
||||
|
||||
def forwardAndRetrieve(self, outBlobNames: _typing.Sequence[str]) -> _typing.Sequence[_typing.Sequence[cv2.typing.MatLike]]: ...
|
||||
|
||||
@_typing.overload
|
||||
def quantize(self, calibData: _typing.Sequence[cv2.typing.MatLike], inputsDtype: int, outputsDtype: int, perChannel: bool = ...) -> Net: ...
|
||||
@_typing.overload
|
||||
def quantize(self, calibData: _typing.Sequence[cv2.UMat], inputsDtype: int, outputsDtype: int, perChannel: bool = ...) -> Net: ...
|
||||
|
||||
def getInputDetails(self) -> tuple[_typing.Sequence[float], _typing.Sequence[int]]: ...
|
||||
|
||||
def getOutputDetails(self) -> tuple[_typing.Sequence[float], _typing.Sequence[int]]: ...
|
||||
|
||||
def setHalideScheduler(self, scheduler: str) -> None: ...
|
||||
|
||||
def setPreferableBackend(self, backendId: int) -> None: ...
|
||||
|
||||
def setPreferableTarget(self, targetId: int) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def setInput(self, blob: cv2.typing.MatLike, name: str = ..., scalefactor: float = ..., mean: cv2.typing.Scalar = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def setInput(self, blob: cv2.UMat, name: str = ..., scalefactor: float = ..., mean: cv2.typing.Scalar = ...) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def setParam(self, layer: int, numParam: int, blob: cv2.typing.MatLike) -> None: ...
|
||||
@_typing.overload
|
||||
def setParam(self, layerName: str, numParam: int, blob: cv2.typing.MatLike) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def getParam(self, layer: int, numParam: int = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def getParam(self, layerName: str, numParam: int = ...) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getUnconnectedOutLayers(self) -> _typing.Sequence[int]: ...
|
||||
|
||||
def getUnconnectedOutLayersNames(self) -> _typing.Sequence[str]: ...
|
||||
|
||||
@_typing.overload
|
||||
def getLayersShapes(self, netInputShapes: _typing.Sequence[cv2.typing.MatShape]) -> tuple[_typing.Sequence[int], _typing.Sequence[_typing.Sequence[cv2.typing.MatShape]], _typing.Sequence[_typing.Sequence[cv2.typing.MatShape]]]: ...
|
||||
@_typing.overload
|
||||
def getLayersShapes(self, netInputShape: cv2.typing.MatShape) -> tuple[_typing.Sequence[int], _typing.Sequence[_typing.Sequence[cv2.typing.MatShape]], _typing.Sequence[_typing.Sequence[cv2.typing.MatShape]]]: ...
|
||||
|
||||
@_typing.overload
|
||||
def getFLOPS(self, netInputShapes: _typing.Sequence[cv2.typing.MatShape]) -> int: ...
|
||||
@_typing.overload
|
||||
def getFLOPS(self, netInputShape: cv2.typing.MatShape) -> int: ...
|
||||
@_typing.overload
|
||||
def getFLOPS(self, layerId: int, netInputShapes: _typing.Sequence[cv2.typing.MatShape]) -> int: ...
|
||||
@_typing.overload
|
||||
def getFLOPS(self, layerId: int, netInputShape: cv2.typing.MatShape) -> int: ...
|
||||
|
||||
def getLayerTypes(self) -> _typing.Sequence[str]: ...
|
||||
|
||||
def getLayersCount(self, layerType: str) -> int: ...
|
||||
|
||||
@_typing.overload
|
||||
def getMemoryConsumption(self, netInputShape: cv2.typing.MatShape) -> tuple[int, int]: ...
|
||||
@_typing.overload
|
||||
def getMemoryConsumption(self, layerId: int, netInputShapes: _typing.Sequence[cv2.typing.MatShape]) -> tuple[int, int]: ...
|
||||
@_typing.overload
|
||||
def getMemoryConsumption(self, layerId: int, netInputShape: cv2.typing.MatShape) -> tuple[int, int]: ...
|
||||
|
||||
def enableFusion(self, fusion: bool) -> None: ...
|
||||
|
||||
def enableWinograd(self, useWinograd: bool) -> None: ...
|
||||
|
||||
def getPerfProfile(self) -> tuple[int, _typing.Sequence[float]]: ...
|
||||
|
||||
|
||||
class Image2BlobParams:
|
||||
scalefactor: cv2.typing.Scalar
|
||||
size: cv2.typing.Size
|
||||
mean: cv2.typing.Scalar
|
||||
swapRB: bool
|
||||
ddepth: int
|
||||
datalayout: DataLayout
|
||||
paddingmode: ImagePaddingMode
|
||||
borderValue: cv2.typing.Scalar
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, scalefactor: cv2.typing.Scalar, size: cv2.typing.Size = ..., mean: cv2.typing.Scalar = ..., swapRB: bool = ..., ddepth: int = ..., datalayout: DataLayout = ..., mode: ImagePaddingMode = ..., borderValue: cv2.typing.Scalar = ...) -> None: ...
|
||||
|
||||
def blobRectToImageRect(self, rBlob: cv2.typing.Rect, size: cv2.typing.Size) -> cv2.typing.Rect: ...
|
||||
|
||||
def blobRectsToImageRects(self, rBlob: _typing.Sequence[cv2.typing.Rect], size: cv2.typing.Size) -> _typing.Sequence[cv2.typing.Rect]: ...
|
||||
|
||||
|
||||
class Model:
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, model: str, config: str = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, network: Net) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def setInputSize(self, size: cv2.typing.Size) -> Model: ...
|
||||
@_typing.overload
|
||||
def setInputSize(self, width: int, height: int) -> Model: ...
|
||||
|
||||
def setInputMean(self, mean: cv2.typing.Scalar) -> Model: ...
|
||||
|
||||
def setInputScale(self, scale: cv2.typing.Scalar) -> Model: ...
|
||||
|
||||
def setInputCrop(self, crop: bool) -> Model: ...
|
||||
|
||||
def setInputSwapRB(self, swapRB: bool) -> Model: ...
|
||||
|
||||
def setOutputNames(self, outNames: _typing.Sequence[str]) -> Model: ...
|
||||
|
||||
def setInputParams(self, scale: float = ..., size: cv2.typing.Size = ..., mean: cv2.typing.Scalar = ..., swapRB: bool = ..., crop: bool = ...) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def predict(self, frame: cv2.typing.MatLike, outs: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def predict(self, frame: cv2.UMat, outs: _typing.Sequence[cv2.UMat] | None = ...) -> _typing.Sequence[cv2.UMat]: ...
|
||||
|
||||
def setPreferableBackend(self, backendId: Backend) -> Model: ...
|
||||
|
||||
def setPreferableTarget(self, targetId: Target) -> Model: ...
|
||||
|
||||
def enableWinograd(self, useWinograd: bool) -> Model: ...
|
||||
|
||||
|
||||
class ClassificationModel(Model):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, model: str, config: str = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, network: Net) -> None: ...
|
||||
|
||||
def setEnableSoftmaxPostProcessing(self, enable: bool) -> ClassificationModel: ...
|
||||
|
||||
def getEnableSoftmaxPostProcessing(self) -> bool: ...
|
||||
|
||||
@_typing.overload
|
||||
def classify(self, frame: cv2.typing.MatLike) -> tuple[int, float]: ...
|
||||
@_typing.overload
|
||||
def classify(self, frame: cv2.UMat) -> tuple[int, float]: ...
|
||||
|
||||
|
||||
class KeypointsModel(Model):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, model: str, config: str = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, network: Net) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def estimate(self, frame: cv2.typing.MatLike, thresh: float = ...) -> _typing.Sequence[cv2.typing.Point2f]: ...
|
||||
@_typing.overload
|
||||
def estimate(self, frame: cv2.UMat, thresh: float = ...) -> _typing.Sequence[cv2.typing.Point2f]: ...
|
||||
|
||||
|
||||
class SegmentationModel(Model):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, model: str, config: str = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, network: Net) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def segment(self, frame: cv2.typing.MatLike, mask: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def segment(self, frame: cv2.UMat, mask: cv2.UMat | None = ...) -> cv2.UMat: ...
|
||||
|
||||
|
||||
class DetectionModel(Model):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, model: str, config: str = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, network: Net) -> None: ...
|
||||
|
||||
def setNmsAcrossClasses(self, value: bool) -> DetectionModel: ...
|
||||
|
||||
def getNmsAcrossClasses(self) -> bool: ...
|
||||
|
||||
@_typing.overload
|
||||
def detect(self, frame: cv2.typing.MatLike, confThreshold: float = ..., nmsThreshold: float = ...) -> tuple[_typing.Sequence[int], _typing.Sequence[float], _typing.Sequence[cv2.typing.Rect]]: ...
|
||||
@_typing.overload
|
||||
def detect(self, frame: cv2.UMat, confThreshold: float = ..., nmsThreshold: float = ...) -> tuple[_typing.Sequence[int], _typing.Sequence[float], _typing.Sequence[cv2.typing.Rect]]: ...
|
||||
|
||||
|
||||
class TextRecognitionModel(Model):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, network: Net) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, model: str, config: str = ...) -> None: ...
|
||||
|
||||
def setDecodeType(self, decodeType: str) -> TextRecognitionModel: ...
|
||||
|
||||
def getDecodeType(self) -> str: ...
|
||||
|
||||
def setDecodeOptsCTCPrefixBeamSearch(self, beamSize: int, vocPruneSize: int = ...) -> TextRecognitionModel: ...
|
||||
|
||||
def setVocabulary(self, vocabulary: _typing.Sequence[str]) -> TextRecognitionModel: ...
|
||||
|
||||
def getVocabulary(self) -> _typing.Sequence[str]: ...
|
||||
|
||||
@_typing.overload
|
||||
def recognize(self, frame: cv2.typing.MatLike) -> str: ...
|
||||
@_typing.overload
|
||||
def recognize(self, frame: cv2.UMat) -> str: ...
|
||||
@_typing.overload
|
||||
def recognize(self, frame: cv2.typing.MatLike, roiRects: _typing.Sequence[cv2.typing.MatLike]) -> _typing.Sequence[str]: ...
|
||||
@_typing.overload
|
||||
def recognize(self, frame: cv2.UMat, roiRects: _typing.Sequence[cv2.UMat]) -> _typing.Sequence[str]: ...
|
||||
|
||||
|
||||
class TextDetectionModel(Model):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def detect(self, frame: cv2.typing.MatLike) -> tuple[_typing.Sequence[_typing.Sequence[cv2.typing.Point]], _typing.Sequence[float]]: ...
|
||||
@_typing.overload
|
||||
def detect(self, frame: cv2.UMat) -> tuple[_typing.Sequence[_typing.Sequence[cv2.typing.Point]], _typing.Sequence[float]]: ...
|
||||
@_typing.overload
|
||||
def detect(self, frame: cv2.typing.MatLike) -> _typing.Sequence[_typing.Sequence[cv2.typing.Point]]: ...
|
||||
@_typing.overload
|
||||
def detect(self, frame: cv2.UMat) -> _typing.Sequence[_typing.Sequence[cv2.typing.Point]]: ...
|
||||
|
||||
@_typing.overload
|
||||
def detectTextRectangles(self, frame: cv2.typing.MatLike) -> tuple[_typing.Sequence[cv2.typing.RotatedRect], _typing.Sequence[float]]: ...
|
||||
@_typing.overload
|
||||
def detectTextRectangles(self, frame: cv2.UMat) -> tuple[_typing.Sequence[cv2.typing.RotatedRect], _typing.Sequence[float]]: ...
|
||||
@_typing.overload
|
||||
def detectTextRectangles(self, frame: cv2.typing.MatLike) -> _typing.Sequence[cv2.typing.RotatedRect]: ...
|
||||
@_typing.overload
|
||||
def detectTextRectangles(self, frame: cv2.UMat) -> _typing.Sequence[cv2.typing.RotatedRect]: ...
|
||||
|
||||
|
||||
class TextDetectionModel_EAST(TextDetectionModel):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, network: Net) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, model: str, config: str = ...) -> None: ...
|
||||
|
||||
def setConfidenceThreshold(self, confThreshold: float) -> TextDetectionModel_EAST: ...
|
||||
|
||||
def getConfidenceThreshold(self) -> float: ...
|
||||
|
||||
def setNMSThreshold(self, nmsThreshold: float) -> TextDetectionModel_EAST: ...
|
||||
|
||||
def getNMSThreshold(self) -> float: ...
|
||||
|
||||
|
||||
class TextDetectionModel_DB(TextDetectionModel):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, network: Net) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, model: str, config: str = ...) -> None: ...
|
||||
|
||||
def setBinaryThreshold(self, binaryThreshold: float) -> TextDetectionModel_DB: ...
|
||||
|
||||
def getBinaryThreshold(self) -> float: ...
|
||||
|
||||
def setPolygonThreshold(self, polygonThreshold: float) -> TextDetectionModel_DB: ...
|
||||
|
||||
def getPolygonThreshold(self) -> float: ...
|
||||
|
||||
def setUnclipRatio(self, unclipRatio: float) -> TextDetectionModel_DB: ...
|
||||
|
||||
def getUnclipRatio(self) -> float: ...
|
||||
|
||||
def setMaxCandidates(self, maxCandidates: int) -> TextDetectionModel_DB: ...
|
||||
|
||||
def getMaxCandidates(self) -> int: ...
|
||||
|
||||
|
||||
class LayerProtocol(Protocol):
|
||||
# Functions
|
||||
def __init__(self, params: dict[str, DictValue], blobs: _typing.Sequence[cv2.typing.MatLike]) -> None: ...
|
||||
|
||||
def getMemoryShapes(self, inputs: _typing.Sequence[_typing.Sequence[int]]) -> _typing.Sequence[_typing.Sequence[int]]: ...
|
||||
|
||||
def forward(self, inputs: _typing.Sequence[cv2.typing.MatLike]) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
|
||||
|
||||
|
||||
# Functions
|
||||
def NMSBoxes(bboxes: _typing.Sequence[cv2.typing.Rect2d], scores: _typing.Sequence[float], score_threshold: float, nms_threshold: float, eta: float = ..., top_k: int = ...) -> _typing.Sequence[int]: ...
|
||||
|
||||
def NMSBoxesBatched(bboxes: _typing.Sequence[cv2.typing.Rect2d], scores: _typing.Sequence[float], class_ids: _typing.Sequence[int], score_threshold: float, nms_threshold: float, eta: float = ..., top_k: int = ...) -> _typing.Sequence[int]: ...
|
||||
|
||||
def NMSBoxesRotated(bboxes: _typing.Sequence[cv2.typing.RotatedRect], scores: _typing.Sequence[float], score_threshold: float, nms_threshold: float, eta: float = ..., top_k: int = ...) -> _typing.Sequence[int]: ...
|
||||
|
||||
@_typing.overload
|
||||
def blobFromImage(image: cv2.typing.MatLike, scalefactor: float = ..., size: cv2.typing.Size = ..., mean: cv2.typing.Scalar = ..., swapRB: bool = ..., crop: bool = ..., ddepth: int = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def blobFromImage(image: cv2.UMat, scalefactor: float = ..., size: cv2.typing.Size = ..., mean: cv2.typing.Scalar = ..., swapRB: bool = ..., crop: bool = ..., ddepth: int = ...) -> cv2.typing.MatLike: ...
|
||||
|
||||
@_typing.overload
|
||||
def blobFromImageWithParams(image: cv2.typing.MatLike, param: Image2BlobParams = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def blobFromImageWithParams(image: cv2.UMat, param: Image2BlobParams = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def blobFromImageWithParams(image: cv2.typing.MatLike, blob: cv2.typing.MatLike | None = ..., param: Image2BlobParams = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def blobFromImageWithParams(image: cv2.UMat, blob: cv2.UMat | None = ..., param: Image2BlobParams = ...) -> cv2.UMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def blobFromImages(images: _typing.Sequence[cv2.typing.MatLike], scalefactor: float = ..., size: cv2.typing.Size = ..., mean: cv2.typing.Scalar = ..., swapRB: bool = ..., crop: bool = ..., ddepth: int = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def blobFromImages(images: _typing.Sequence[cv2.UMat], scalefactor: float = ..., size: cv2.typing.Size = ..., mean: cv2.typing.Scalar = ..., swapRB: bool = ..., crop: bool = ..., ddepth: int = ...) -> cv2.typing.MatLike: ...
|
||||
|
||||
@_typing.overload
|
||||
def blobFromImagesWithParams(images: _typing.Sequence[cv2.typing.MatLike], param: Image2BlobParams = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def blobFromImagesWithParams(images: _typing.Sequence[cv2.UMat], param: Image2BlobParams = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def blobFromImagesWithParams(images: _typing.Sequence[cv2.typing.MatLike], blob: cv2.typing.MatLike | None = ..., param: Image2BlobParams = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def blobFromImagesWithParams(images: _typing.Sequence[cv2.UMat], blob: cv2.UMat | None = ..., param: Image2BlobParams = ...) -> cv2.UMat: ...
|
||||
|
||||
def getAvailableTargets(be: Backend) -> _typing.Sequence[Target]: ...
|
||||
|
||||
@_typing.overload
|
||||
def imagesFromBlob(blob_: cv2.typing.MatLike, images_: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def imagesFromBlob(blob_: cv2.typing.MatLike, images_: _typing.Sequence[cv2.UMat] | None = ...) -> _typing.Sequence[cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def readNet(model: str, config: str = ..., framework: str = ...) -> Net: ...
|
||||
@_typing.overload
|
||||
def readNet(framework: str, bufferModel: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], bufferConfig: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]] = ...) -> Net: ...
|
||||
|
||||
@_typing.overload
|
||||
def readNetFromCaffe(prototxt: str, caffeModel: str = ...) -> Net: ...
|
||||
@_typing.overload
|
||||
def readNetFromCaffe(bufferProto: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], bufferModel: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]] = ...) -> Net: ...
|
||||
|
||||
@_typing.overload
|
||||
def readNetFromDarknet(cfgFile: str, darknetModel: str = ...) -> Net: ...
|
||||
@_typing.overload
|
||||
def readNetFromDarknet(bufferCfg: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], bufferModel: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]] = ...) -> Net: ...
|
||||
|
||||
@_typing.overload
|
||||
def readNetFromModelOptimizer(xml: str, bin: str = ...) -> Net: ...
|
||||
@_typing.overload
|
||||
def readNetFromModelOptimizer(bufferModelConfig: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], bufferWeights: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]) -> Net: ...
|
||||
|
||||
@_typing.overload
|
||||
def readNetFromONNX(onnxFile: str) -> Net: ...
|
||||
@_typing.overload
|
||||
def readNetFromONNX(buffer: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]) -> Net: ...
|
||||
|
||||
@_typing.overload
|
||||
def readNetFromTFLite(model: str) -> Net: ...
|
||||
@_typing.overload
|
||||
def readNetFromTFLite(bufferModel: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]) -> Net: ...
|
||||
|
||||
@_typing.overload
|
||||
def readNetFromTensorflow(model: str, config: str = ...) -> Net: ...
|
||||
@_typing.overload
|
||||
def readNetFromTensorflow(bufferModel: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]], bufferConfig: numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]] = ...) -> Net: ...
|
||||
|
||||
def readNetFromTorch(model: str, isBinary: bool = ..., evaluate: bool = ...) -> Net: ...
|
||||
|
||||
def readTensorFromONNX(path: str) -> cv2.typing.MatLike: ...
|
||||
|
||||
def readTorchBlob(filename: str, isBinary: bool = ...) -> cv2.typing.MatLike: ...
|
||||
|
||||
def shrinkCaffeModel(src: str, dst: str, layersTypes: _typing.Sequence[str] = ...) -> None: ...
|
||||
|
||||
def softNMSBoxes(bboxes: _typing.Sequence[cv2.typing.Rect], scores: _typing.Sequence[float], score_threshold: float, nms_threshold: float, top_k: int = ..., sigma: float = ..., method: SoftNMSMethod = ...) -> tuple[_typing.Sequence[float], _typing.Sequence[int]]: ...
|
||||
|
||||
def writeTextGraph(model: str, output: str) -> None: ...
|
||||
|
||||
|
||||
@ -1,79 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
import cv2.typing
|
||||
import typing as _typing
|
||||
|
||||
|
||||
# Enumerations
|
||||
CALIB_USE_INTRINSIC_GUESS: int
|
||||
CALIB_RECOMPUTE_EXTRINSIC: int
|
||||
CALIB_CHECK_COND: int
|
||||
CALIB_FIX_SKEW: int
|
||||
CALIB_FIX_K1: int
|
||||
CALIB_FIX_K2: int
|
||||
CALIB_FIX_K3: int
|
||||
CALIB_FIX_K4: int
|
||||
CALIB_FIX_INTRINSIC: int
|
||||
CALIB_FIX_PRINCIPAL_POINT: int
|
||||
CALIB_ZERO_DISPARITY: int
|
||||
CALIB_FIX_FOCAL_LENGTH: int
|
||||
|
||||
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def calibrate(objectPoints: _typing.Sequence[cv2.typing.MatLike], imagePoints: _typing.Sequence[cv2.typing.MatLike], image_size: cv2.typing.Size, K: cv2.typing.MatLike, D: cv2.typing.MatLike, rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike]]: ...
|
||||
@_typing.overload
|
||||
def calibrate(objectPoints: _typing.Sequence[cv2.UMat], imagePoints: _typing.Sequence[cv2.UMat], image_size: cv2.typing.Size, K: cv2.UMat, D: cv2.UMat, rvecs: _typing.Sequence[cv2.UMat] | None = ..., tvecs: _typing.Sequence[cv2.UMat] | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.UMat, cv2.UMat, _typing.Sequence[cv2.UMat], _typing.Sequence[cv2.UMat]]: ...
|
||||
|
||||
@_typing.overload
|
||||
def distortPoints(undistorted: cv2.typing.MatLike, K: cv2.typing.MatLike, D: cv2.typing.MatLike, distorted: cv2.typing.MatLike | None = ..., alpha: float = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def distortPoints(undistorted: cv2.UMat, K: cv2.UMat, D: cv2.UMat, distorted: cv2.UMat | None = ..., alpha: float = ...) -> cv2.UMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def estimateNewCameraMatrixForUndistortRectify(K: cv2.typing.MatLike, D: cv2.typing.MatLike, image_size: cv2.typing.Size, R: cv2.typing.MatLike, P: cv2.typing.MatLike | None = ..., balance: float = ..., new_size: cv2.typing.Size = ..., fov_scale: float = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def estimateNewCameraMatrixForUndistortRectify(K: cv2.UMat, D: cv2.UMat, image_size: cv2.typing.Size, R: cv2.UMat, P: cv2.UMat | None = ..., balance: float = ..., new_size: cv2.typing.Size = ..., fov_scale: float = ...) -> cv2.UMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def initUndistortRectifyMap(K: cv2.typing.MatLike, D: cv2.typing.MatLike, R: cv2.typing.MatLike, P: cv2.typing.MatLike, size: cv2.typing.Size, m1type: int, map1: cv2.typing.MatLike | None = ..., map2: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def initUndistortRectifyMap(K: cv2.UMat, D: cv2.UMat, R: cv2.UMat, P: cv2.UMat, size: cv2.typing.Size, m1type: int, map1: cv2.UMat | None = ..., map2: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def projectPoints(objectPoints: cv2.typing.MatLike, rvec: cv2.typing.MatLike, tvec: cv2.typing.MatLike, K: cv2.typing.MatLike, D: cv2.typing.MatLike, imagePoints: cv2.typing.MatLike | None = ..., alpha: float = ..., jacobian: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def projectPoints(objectPoints: cv2.UMat, rvec: cv2.UMat, tvec: cv2.UMat, K: cv2.UMat, D: cv2.UMat, imagePoints: cv2.UMat | None = ..., alpha: float = ..., jacobian: cv2.UMat | None = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def solvePnP(objectPoints: cv2.typing.MatLike, imagePoints: cv2.typing.MatLike, cameraMatrix: cv2.typing.MatLike, distCoeffs: cv2.typing.MatLike, rvec: cv2.typing.MatLike | None = ..., tvec: cv2.typing.MatLike | None = ..., useExtrinsicGuess: bool = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def solvePnP(objectPoints: cv2.UMat, imagePoints: cv2.UMat, cameraMatrix: cv2.UMat, distCoeffs: cv2.UMat, rvec: cv2.UMat | None = ..., tvec: cv2.UMat | None = ..., useExtrinsicGuess: bool = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[bool, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def stereoCalibrate(objectPoints: _typing.Sequence[cv2.typing.MatLike], imagePoints1: _typing.Sequence[cv2.typing.MatLike], imagePoints2: _typing.Sequence[cv2.typing.MatLike], K1: cv2.typing.MatLike, D1: cv2.typing.MatLike, K2: cv2.typing.MatLike, D2: cv2.typing.MatLike, imageSize: cv2.typing.Size, R: cv2.typing.MatLike | None = ..., T: cv2.typing.MatLike | None = ..., rvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., tvecs: _typing.Sequence[cv2.typing.MatLike] | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike]]: ...
|
||||
@_typing.overload
|
||||
def stereoCalibrate(objectPoints: _typing.Sequence[cv2.UMat], imagePoints1: _typing.Sequence[cv2.UMat], imagePoints2: _typing.Sequence[cv2.UMat], K1: cv2.UMat, D1: cv2.UMat, K2: cv2.UMat, D2: cv2.UMat, imageSize: cv2.typing.Size, R: cv2.UMat | None = ..., T: cv2.UMat | None = ..., rvecs: _typing.Sequence[cv2.UMat] | None = ..., tvecs: _typing.Sequence[cv2.UMat] | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat, _typing.Sequence[cv2.UMat], _typing.Sequence[cv2.UMat]]: ...
|
||||
@_typing.overload
|
||||
def stereoCalibrate(objectPoints: _typing.Sequence[cv2.typing.MatLike], imagePoints1: _typing.Sequence[cv2.typing.MatLike], imagePoints2: _typing.Sequence[cv2.typing.MatLike], K1: cv2.typing.MatLike, D1: cv2.typing.MatLike, K2: cv2.typing.MatLike, D2: cv2.typing.MatLike, imageSize: cv2.typing.Size, R: cv2.typing.MatLike | None = ..., T: cv2.typing.MatLike | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def stereoCalibrate(objectPoints: _typing.Sequence[cv2.UMat], imagePoints1: _typing.Sequence[cv2.UMat], imagePoints2: _typing.Sequence[cv2.UMat], K1: cv2.UMat, D1: cv2.UMat, K2: cv2.UMat, D2: cv2.UMat, imageSize: cv2.typing.Size, R: cv2.UMat | None = ..., T: cv2.UMat | None = ..., flags: int = ..., criteria: cv2.typing.TermCriteria = ...) -> tuple[float, cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def stereoRectify(K1: cv2.typing.MatLike, D1: cv2.typing.MatLike, K2: cv2.typing.MatLike, D2: cv2.typing.MatLike, imageSize: cv2.typing.Size, R: cv2.typing.MatLike, tvec: cv2.typing.MatLike, flags: int, R1: cv2.typing.MatLike | None = ..., R2: cv2.typing.MatLike | None = ..., P1: cv2.typing.MatLike | None = ..., P2: cv2.typing.MatLike | None = ..., Q: cv2.typing.MatLike | None = ..., newImageSize: cv2.typing.Size = ..., balance: float = ..., fov_scale: float = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def stereoRectify(K1: cv2.UMat, D1: cv2.UMat, K2: cv2.UMat, D2: cv2.UMat, imageSize: cv2.typing.Size, R: cv2.UMat, tvec: cv2.UMat, flags: int, R1: cv2.UMat | None = ..., R2: cv2.UMat | None = ..., P1: cv2.UMat | None = ..., P2: cv2.UMat | None = ..., Q: cv2.UMat | None = ..., newImageSize: cv2.typing.Size = ..., balance: float = ..., fov_scale: float = ...) -> tuple[cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def undistortImage(distorted: cv2.typing.MatLike, K: cv2.typing.MatLike, D: cv2.typing.MatLike, undistorted: cv2.typing.MatLike | None = ..., Knew: cv2.typing.MatLike | None = ..., new_size: cv2.typing.Size = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def undistortImage(distorted: cv2.UMat, K: cv2.UMat, D: cv2.UMat, undistorted: cv2.UMat | None = ..., Knew: cv2.UMat | None = ..., new_size: cv2.typing.Size = ...) -> cv2.UMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def undistortPoints(distorted: cv2.typing.MatLike, K: cv2.typing.MatLike, D: cv2.typing.MatLike, undistorted: cv2.typing.MatLike | None = ..., R: cv2.typing.MatLike | None = ..., P: cv2.typing.MatLike | None = ..., criteria: cv2.typing.TermCriteria = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def undistortPoints(distorted: cv2.UMat, K: cv2.UMat, D: cv2.UMat, undistorted: cv2.UMat | None = ..., R: cv2.UMat | None = ..., P: cv2.UMat | None = ..., criteria: cv2.typing.TermCriteria = ...) -> cv2.UMat: ...
|
||||
|
||||
|
||||
@ -1,64 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
import cv2.typing
|
||||
import typing as _typing
|
||||
|
||||
|
||||
# Enumerations
|
||||
FLANN_INDEX_TYPE_8U: int
|
||||
FLANN_INDEX_TYPE_8S: int
|
||||
FLANN_INDEX_TYPE_16U: int
|
||||
FLANN_INDEX_TYPE_16S: int
|
||||
FLANN_INDEX_TYPE_32S: int
|
||||
FLANN_INDEX_TYPE_32F: int
|
||||
FLANN_INDEX_TYPE_64F: int
|
||||
FLANN_INDEX_TYPE_STRING: int
|
||||
FLANN_INDEX_TYPE_BOOL: int
|
||||
FLANN_INDEX_TYPE_ALGORITHM: int
|
||||
LAST_VALUE_FLANN_INDEX_TYPE: int
|
||||
FlannIndexType = int
|
||||
"""One of [FLANN_INDEX_TYPE_8U, FLANN_INDEX_TYPE_8S, FLANN_INDEX_TYPE_16U, FLANN_INDEX_TYPE_16S, FLANN_INDEX_TYPE_32S, FLANN_INDEX_TYPE_32F, FLANN_INDEX_TYPE_64F, FLANN_INDEX_TYPE_STRING, FLANN_INDEX_TYPE_BOOL, FLANN_INDEX_TYPE_ALGORITHM, LAST_VALUE_FLANN_INDEX_TYPE]"""
|
||||
|
||||
|
||||
|
||||
# Classes
|
||||
class Index:
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, features: cv2.typing.MatLike, params: cv2.typing.IndexParams, distType: int = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, features: cv2.UMat, params: cv2.typing.IndexParams, distType: int = ...) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def build(self, features: cv2.typing.MatLike, params: cv2.typing.IndexParams, distType: int = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def build(self, features: cv2.UMat, params: cv2.typing.IndexParams, distType: int = ...) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def knnSearch(self, query: cv2.typing.MatLike, knn: int, indices: cv2.typing.MatLike | None = ..., dists: cv2.typing.MatLike | None = ..., params: cv2.typing.SearchParams = ...) -> tuple[cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def knnSearch(self, query: cv2.UMat, knn: int, indices: cv2.UMat | None = ..., dists: cv2.UMat | None = ..., params: cv2.typing.SearchParams = ...) -> tuple[cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def radiusSearch(self, query: cv2.typing.MatLike, radius: float, maxResults: int, indices: cv2.typing.MatLike | None = ..., dists: cv2.typing.MatLike | None = ..., params: cv2.typing.SearchParams = ...) -> tuple[int, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def radiusSearch(self, query: cv2.UMat, radius: float, maxResults: int, indices: cv2.UMat | None = ..., dists: cv2.UMat | None = ..., params: cv2.typing.SearchParams = ...) -> tuple[int, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
def save(self, filename: str) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def load(self, features: cv2.typing.MatLike, filename: str) -> bool: ...
|
||||
@_typing.overload
|
||||
def load(self, features: cv2.UMat, filename: str) -> bool: ...
|
||||
|
||||
def release(self) -> None: ...
|
||||
|
||||
def getDistance(self) -> int: ...
|
||||
|
||||
def getAlgorithm(self) -> int: ...
|
||||
|
||||
|
||||
|
||||
@ -1,323 +0,0 @@
|
||||
__all__ = ['op', 'kernel']
|
||||
|
||||
import sys
|
||||
import cv2 as cv
|
||||
|
||||
# NB: Register function in specific module
|
||||
def register(mname):
|
||||
def parameterized(func):
|
||||
sys.modules[mname].__dict__[func.__name__] = func
|
||||
return func
|
||||
return parameterized
|
||||
|
||||
|
||||
@register('cv2.gapi')
|
||||
def networks(*args):
|
||||
return cv.gapi_GNetPackage(list(map(cv.detail.strip, args)))
|
||||
|
||||
|
||||
@register('cv2.gapi')
|
||||
def compile_args(*args):
|
||||
return list(map(cv.GCompileArg, args))
|
||||
|
||||
|
||||
@register('cv2')
|
||||
def GIn(*args):
|
||||
return [*args]
|
||||
|
||||
|
||||
@register('cv2')
|
||||
def GOut(*args):
|
||||
return [*args]
|
||||
|
||||
|
||||
@register('cv2')
|
||||
def gin(*args):
|
||||
return [*args]
|
||||
|
||||
|
||||
@register('cv2.gapi')
|
||||
def descr_of(*args):
|
||||
return [*args]
|
||||
|
||||
|
||||
@register('cv2')
|
||||
class GOpaque():
|
||||
# NB: Inheritance from c++ class cause segfault.
|
||||
# So just aggregate cv.GOpaqueT instead of inheritance
|
||||
def __new__(cls, argtype):
|
||||
return cv.GOpaqueT(argtype)
|
||||
|
||||
class Bool():
|
||||
def __new__(self):
|
||||
return cv.GOpaqueT(cv.gapi.CV_BOOL)
|
||||
|
||||
class Int():
|
||||
def __new__(self):
|
||||
return cv.GOpaqueT(cv.gapi.CV_INT)
|
||||
|
||||
class Int64():
|
||||
def __new__(self):
|
||||
return cv.GOpaqueT(cv.gapi.CV_INT64)
|
||||
|
||||
class UInt64():
|
||||
def __new__(self):
|
||||
return cv.GOpaqueT(cv.gapi.CV_UINT64)
|
||||
|
||||
class Double():
|
||||
def __new__(self):
|
||||
return cv.GOpaqueT(cv.gapi.CV_DOUBLE)
|
||||
|
||||
class Float():
|
||||
def __new__(self):
|
||||
return cv.GOpaqueT(cv.gapi.CV_FLOAT)
|
||||
|
||||
class String():
|
||||
def __new__(self):
|
||||
return cv.GOpaqueT(cv.gapi.CV_STRING)
|
||||
|
||||
class Point():
|
||||
def __new__(self):
|
||||
return cv.GOpaqueT(cv.gapi.CV_POINT)
|
||||
|
||||
class Point2f():
|
||||
def __new__(self):
|
||||
return cv.GOpaqueT(cv.gapi.CV_POINT2F)
|
||||
|
||||
class Point3f():
|
||||
def __new__(self):
|
||||
return cv.GOpaqueT(cv.gapi.CV_POINT3F)
|
||||
|
||||
class Size():
|
||||
def __new__(self):
|
||||
return cv.GOpaqueT(cv.gapi.CV_SIZE)
|
||||
|
||||
class Rect():
|
||||
def __new__(self):
|
||||
return cv.GOpaqueT(cv.gapi.CV_RECT)
|
||||
|
||||
class Prim():
|
||||
def __new__(self):
|
||||
return cv.GOpaqueT(cv.gapi.CV_DRAW_PRIM)
|
||||
|
||||
class Any():
|
||||
def __new__(self):
|
||||
return cv.GOpaqueT(cv.gapi.CV_ANY)
|
||||
|
||||
@register('cv2')
|
||||
class GArray():
|
||||
# NB: Inheritance from c++ class cause segfault.
|
||||
# So just aggregate cv.GArrayT instead of inheritance
|
||||
def __new__(cls, argtype):
|
||||
return cv.GArrayT(argtype)
|
||||
|
||||
class Bool():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_BOOL)
|
||||
|
||||
class Int():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_INT)
|
||||
|
||||
class Int64():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_INT64)
|
||||
|
||||
class UInt64():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_UINT64)
|
||||
|
||||
class Double():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_DOUBLE)
|
||||
|
||||
class Float():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_FLOAT)
|
||||
|
||||
class String():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_STRING)
|
||||
|
||||
class Point():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_POINT)
|
||||
|
||||
class Point2f():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_POINT2F)
|
||||
|
||||
class Point3f():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_POINT3F)
|
||||
|
||||
class Size():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_SIZE)
|
||||
|
||||
class Rect():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_RECT)
|
||||
|
||||
class Scalar():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_SCALAR)
|
||||
|
||||
class Mat():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_MAT)
|
||||
|
||||
class GMat():
|
||||
def __new__(self):
|
||||
return cv.GArrayT(cv.gapi.CV_GMAT)
|
||||
|
||||
class Prim():
|
||||
def __new__(self):
|
||||
return cv.GArray(cv.gapi.CV_DRAW_PRIM)
|
||||
|
||||
class Any():
|
||||
def __new__(self):
|
||||
return cv.GArray(cv.gapi.CV_ANY)
|
||||
|
||||
|
||||
# NB: Top lvl decorator takes arguments
|
||||
def op(op_id, in_types, out_types):
|
||||
|
||||
garray_types= {
|
||||
cv.GArray.Bool: cv.gapi.CV_BOOL,
|
||||
cv.GArray.Int: cv.gapi.CV_INT,
|
||||
cv.GArray.Int64: cv.gapi.CV_INT64,
|
||||
cv.GArray.UInt64: cv.gapi.CV_UINT64,
|
||||
cv.GArray.Double: cv.gapi.CV_DOUBLE,
|
||||
cv.GArray.Float: cv.gapi.CV_FLOAT,
|
||||
cv.GArray.String: cv.gapi.CV_STRING,
|
||||
cv.GArray.Point: cv.gapi.CV_POINT,
|
||||
cv.GArray.Point2f: cv.gapi.CV_POINT2F,
|
||||
cv.GArray.Point3f: cv.gapi.CV_POINT3F,
|
||||
cv.GArray.Size: cv.gapi.CV_SIZE,
|
||||
cv.GArray.Rect: cv.gapi.CV_RECT,
|
||||
cv.GArray.Scalar: cv.gapi.CV_SCALAR,
|
||||
cv.GArray.Mat: cv.gapi.CV_MAT,
|
||||
cv.GArray.GMat: cv.gapi.CV_GMAT,
|
||||
cv.GArray.Prim: cv.gapi.CV_DRAW_PRIM,
|
||||
cv.GArray.Any: cv.gapi.CV_ANY
|
||||
}
|
||||
|
||||
gopaque_types= {
|
||||
cv.GOpaque.Size: cv.gapi.CV_SIZE,
|
||||
cv.GOpaque.Rect: cv.gapi.CV_RECT,
|
||||
cv.GOpaque.Bool: cv.gapi.CV_BOOL,
|
||||
cv.GOpaque.Int: cv.gapi.CV_INT,
|
||||
cv.GOpaque.Int64: cv.gapi.CV_INT64,
|
||||
cv.GOpaque.UInt64: cv.gapi.CV_UINT64,
|
||||
cv.GOpaque.Double: cv.gapi.CV_DOUBLE,
|
||||
cv.GOpaque.Float: cv.gapi.CV_FLOAT,
|
||||
cv.GOpaque.String: cv.gapi.CV_STRING,
|
||||
cv.GOpaque.Point: cv.gapi.CV_POINT,
|
||||
cv.GOpaque.Point2f: cv.gapi.CV_POINT2F,
|
||||
cv.GOpaque.Point3f: cv.gapi.CV_POINT3F,
|
||||
cv.GOpaque.Size: cv.gapi.CV_SIZE,
|
||||
cv.GOpaque.Rect: cv.gapi.CV_RECT,
|
||||
cv.GOpaque.Prim: cv.gapi.CV_DRAW_PRIM,
|
||||
cv.GOpaque.Any: cv.gapi.CV_ANY
|
||||
}
|
||||
|
||||
type2str = {
|
||||
cv.gapi.CV_BOOL: 'cv.gapi.CV_BOOL' ,
|
||||
cv.gapi.CV_INT: 'cv.gapi.CV_INT' ,
|
||||
cv.gapi.CV_INT64: 'cv.gapi.CV_INT64' ,
|
||||
cv.gapi.CV_UINT64: 'cv.gapi.CV_UINT64' ,
|
||||
cv.gapi.CV_DOUBLE: 'cv.gapi.CV_DOUBLE' ,
|
||||
cv.gapi.CV_FLOAT: 'cv.gapi.CV_FLOAT' ,
|
||||
cv.gapi.CV_STRING: 'cv.gapi.CV_STRING' ,
|
||||
cv.gapi.CV_POINT: 'cv.gapi.CV_POINT' ,
|
||||
cv.gapi.CV_POINT2F: 'cv.gapi.CV_POINT2F' ,
|
||||
cv.gapi.CV_POINT3F: 'cv.gapi.CV_POINT3F' ,
|
||||
cv.gapi.CV_SIZE: 'cv.gapi.CV_SIZE',
|
||||
cv.gapi.CV_RECT: 'cv.gapi.CV_RECT',
|
||||
cv.gapi.CV_SCALAR: 'cv.gapi.CV_SCALAR',
|
||||
cv.gapi.CV_MAT: 'cv.gapi.CV_MAT',
|
||||
cv.gapi.CV_GMAT: 'cv.gapi.CV_GMAT',
|
||||
cv.gapi.CV_DRAW_PRIM: 'cv.gapi.CV_DRAW_PRIM'
|
||||
}
|
||||
|
||||
# NB: Second lvl decorator takes class to decorate
|
||||
def op_with_params(cls):
|
||||
if not in_types:
|
||||
raise Exception('{} operation should have at least one input!'.format(cls.__name__))
|
||||
|
||||
if not out_types:
|
||||
raise Exception('{} operation should have at least one output!'.format(cls.__name__))
|
||||
|
||||
for i, t in enumerate(out_types):
|
||||
if t not in [cv.GMat, cv.GScalar, *garray_types, *gopaque_types]:
|
||||
raise Exception('{} unsupported output type: {} in position: {}'
|
||||
.format(cls.__name__, t.__name__, i))
|
||||
|
||||
def on(*args):
|
||||
if len(in_types) != len(args):
|
||||
raise Exception('Invalid number of input elements!\nExpected: {}, Actual: {}'
|
||||
.format(len(in_types), len(args)))
|
||||
|
||||
for i, (t, a) in enumerate(zip(in_types, args)):
|
||||
if t in garray_types:
|
||||
if not isinstance(a, cv.GArrayT):
|
||||
raise Exception("{} invalid type for argument {}.\nExpected: {}, Actual: {}"
|
||||
.format(cls.__name__, i, cv.GArrayT.__name__, type(a).__name__))
|
||||
|
||||
elif a.type() != garray_types[t]:
|
||||
raise Exception("{} invalid GArrayT type for argument {}.\nExpected: {}, Actual: {}"
|
||||
.format(cls.__name__, i, type2str[garray_types[t]], type2str[a.type()]))
|
||||
|
||||
elif t in gopaque_types:
|
||||
if not isinstance(a, cv.GOpaqueT):
|
||||
raise Exception("{} invalid type for argument {}.\nExpected: {}, Actual: {}"
|
||||
.format(cls.__name__, i, cv.GOpaqueT.__name__, type(a).__name__))
|
||||
|
||||
elif a.type() != gopaque_types[t]:
|
||||
raise Exception("{} invalid GOpaque type for argument {}.\nExpected: {}, Actual: {}"
|
||||
.format(cls.__name__, i, type2str[gopaque_types[t]], type2str[a.type()]))
|
||||
|
||||
else:
|
||||
if t != type(a):
|
||||
raise Exception('{} invalid input type for argument {}.\nExpected: {}, Actual: {}'
|
||||
.format(cls.__name__, i, t.__name__, type(a).__name__))
|
||||
|
||||
op = cv.gapi.__op(op_id, cls.outMeta, *args)
|
||||
|
||||
out_protos = []
|
||||
for i, out_type in enumerate(out_types):
|
||||
if out_type == cv.GMat:
|
||||
out_protos.append(op.getGMat())
|
||||
elif out_type == cv.GScalar:
|
||||
out_protos.append(op.getGScalar())
|
||||
elif out_type in gopaque_types:
|
||||
out_protos.append(op.getGOpaque(gopaque_types[out_type]))
|
||||
elif out_type in garray_types:
|
||||
out_protos.append(op.getGArray(garray_types[out_type]))
|
||||
else:
|
||||
raise Exception("""In {}: G-API operation can't produce the output with type: {} in position: {}"""
|
||||
.format(cls.__name__, out_type.__name__, i))
|
||||
|
||||
return tuple(out_protos) if len(out_protos) != 1 else out_protos[0]
|
||||
|
||||
# NB: Extend operation class
|
||||
cls.id = op_id
|
||||
cls.on = staticmethod(on)
|
||||
return cls
|
||||
|
||||
return op_with_params
|
||||
|
||||
|
||||
def kernel(op_cls):
|
||||
# NB: Second lvl decorator takes class to decorate
|
||||
def kernel_with_params(cls):
|
||||
# NB: Add new members to kernel class
|
||||
cls.id = op_cls.id
|
||||
cls.outMeta = op_cls.outMeta
|
||||
return cls
|
||||
|
||||
return kernel_with_params
|
||||
|
||||
|
||||
cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
|
||||
@ -1,349 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
import cv2.typing
|
||||
import typing as _typing
|
||||
|
||||
|
||||
from cv2.gapi import core as core
|
||||
from cv2.gapi import ie as ie
|
||||
from cv2.gapi import imgproc as imgproc
|
||||
from cv2.gapi import oak as oak
|
||||
from cv2.gapi import onnx as onnx
|
||||
from cv2.gapi import ot as ot
|
||||
from cv2.gapi import ov as ov
|
||||
from cv2.gapi import own as own
|
||||
from cv2.gapi import render as render
|
||||
from cv2.gapi import streaming as streaming
|
||||
from cv2.gapi import video as video
|
||||
from cv2.gapi import wip as wip
|
||||
|
||||
|
||||
# Enumerations
|
||||
StereoOutputFormat_DEPTH_FLOAT16: int
|
||||
STEREO_OUTPUT_FORMAT_DEPTH_FLOAT16: int
|
||||
StereoOutputFormat_DEPTH_FLOAT32: int
|
||||
STEREO_OUTPUT_FORMAT_DEPTH_FLOAT32: int
|
||||
StereoOutputFormat_DISPARITY_FIXED16_11_5: int
|
||||
STEREO_OUTPUT_FORMAT_DISPARITY_FIXED16_11_5: int
|
||||
StereoOutputFormat_DISPARITY_FIXED16_12_4: int
|
||||
STEREO_OUTPUT_FORMAT_DISPARITY_FIXED16_12_4: int
|
||||
StereoOutputFormat_DEPTH_16F: int
|
||||
STEREO_OUTPUT_FORMAT_DEPTH_16F: int
|
||||
StereoOutputFormat_DEPTH_32F: int
|
||||
STEREO_OUTPUT_FORMAT_DEPTH_32F: int
|
||||
StereoOutputFormat_DISPARITY_16Q_10_5: int
|
||||
STEREO_OUTPUT_FORMAT_DISPARITY_16Q_10_5: int
|
||||
StereoOutputFormat_DISPARITY_16Q_11_4: int
|
||||
STEREO_OUTPUT_FORMAT_DISPARITY_16Q_11_4: int
|
||||
StereoOutputFormat = int
|
||||
"""One of [StereoOutputFormat_DEPTH_FLOAT16, STEREO_OUTPUT_FORMAT_DEPTH_FLOAT16, StereoOutputFormat_DEPTH_FLOAT32, STEREO_OUTPUT_FORMAT_DEPTH_FLOAT32, StereoOutputFormat_DISPARITY_FIXED16_11_5, STEREO_OUTPUT_FORMAT_DISPARITY_FIXED16_11_5, StereoOutputFormat_DISPARITY_FIXED16_12_4, STEREO_OUTPUT_FORMAT_DISPARITY_FIXED16_12_4, StereoOutputFormat_DEPTH_16F, STEREO_OUTPUT_FORMAT_DEPTH_16F, StereoOutputFormat_DEPTH_32F, STEREO_OUTPUT_FORMAT_DEPTH_32F, StereoOutputFormat_DISPARITY_16Q_10_5, STEREO_OUTPUT_FORMAT_DISPARITY_16Q_10_5, StereoOutputFormat_DISPARITY_16Q_11_4, STEREO_OUTPUT_FORMAT_DISPARITY_16Q_11_4]"""
|
||||
|
||||
CV_BOOL: int
|
||||
CV_INT: int
|
||||
CV_INT64: int
|
||||
CV_UINT64: int
|
||||
CV_DOUBLE: int
|
||||
CV_FLOAT: int
|
||||
CV_STRING: int
|
||||
CV_POINT: int
|
||||
CV_POINT2F: int
|
||||
CV_POINT3F: int
|
||||
CV_SIZE: int
|
||||
CV_RECT: int
|
||||
CV_SCALAR: int
|
||||
CV_MAT: int
|
||||
CV_GMAT: int
|
||||
CV_DRAW_PRIM: int
|
||||
CV_ANY: int
|
||||
ArgType = int
|
||||
"""One of [CV_BOOL, CV_INT, CV_INT64, CV_UINT64, CV_DOUBLE, CV_FLOAT, CV_STRING, CV_POINT, CV_POINT2F, CV_POINT3F, CV_SIZE, CV_RECT, CV_SCALAR, CV_MAT, CV_GMAT, CV_DRAW_PRIM, CV_ANY]"""
|
||||
|
||||
|
||||
|
||||
# Classes
|
||||
class GNetParam:
|
||||
...
|
||||
|
||||
class GNetPackage:
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, nets: _typing.Sequence[GNetParam]) -> None: ...
|
||||
|
||||
|
||||
|
||||
# Functions
|
||||
def BGR2Gray(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def BGR2I420(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def BGR2LUV(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def BGR2RGB(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def BGR2YUV(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def BayerGR2RGB(src_gr: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def Canny(image: cv2.GMat, threshold1: float, threshold2: float, apertureSize: int = ..., L2gradient: bool = ...) -> cv2.GMat: ...
|
||||
|
||||
def I4202BGR(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def I4202RGB(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def LUT(src: cv2.GMat, lut: cv2.typing.MatLike) -> cv2.GMat: ...
|
||||
|
||||
def LUV2BGR(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def Laplacian(src: cv2.GMat, ddepth: int, ksize: int = ..., scale: float = ..., delta: float = ..., borderType: int = ...) -> cv2.GMat: ...
|
||||
|
||||
def NV12toBGR(src_y: cv2.GMat, src_uv: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def NV12toGray(src_y: cv2.GMat, src_uv: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def NV12toRGB(src_y: cv2.GMat, src_uv: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def RGB2Gray(src: cv2.GMat) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def RGB2Gray(src: cv2.GMat, rY: float, gY: float, bY: float) -> cv2.GMat: ...
|
||||
|
||||
def RGB2HSV(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def RGB2I420(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def RGB2Lab(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def RGB2YUV(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def RGB2YUV422(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def Sobel(src: cv2.GMat, ddepth: int, dx: int, dy: int, ksize: int = ..., scale: float = ..., delta: float = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
|
||||
|
||||
def SobelXY(src: cv2.GMat, ddepth: int, order: int, ksize: int = ..., scale: float = ..., delta: float = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> tuple[cv2.GMat, cv2.GMat]: ...
|
||||
|
||||
def YUV2BGR(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def YUV2RGB(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def absDiff(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def absDiffC(src: cv2.GMat, c: cv2.GScalar) -> cv2.GMat: ...
|
||||
|
||||
def add(src1: cv2.GMat, src2: cv2.GMat, ddepth: int = ...) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def addC(src1: cv2.GMat, c: cv2.GScalar, ddepth: int = ...) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def addC(c: cv2.GScalar, src1: cv2.GMat, ddepth: int = ...) -> cv2.GMat: ...
|
||||
|
||||
def addWeighted(src1: cv2.GMat, alpha: float, src2: cv2.GMat, beta: float, gamma: float, ddepth: int = ...) -> cv2.GMat: ...
|
||||
|
||||
def bilateralFilter(src: cv2.GMat, d: int, sigmaColor: float, sigmaSpace: float, borderType: int = ...) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def bitwise_and(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def bitwise_and(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
|
||||
|
||||
def bitwise_not(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def bitwise_or(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def bitwise_or(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def bitwise_xor(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def bitwise_xor(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
|
||||
|
||||
def blur(src: cv2.GMat, ksize: cv2.typing.Size, anchor: cv2.typing.Point = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def boundingRect(src: cv2.GMat) -> cv2.GOpaqueT: ...
|
||||
@_typing.overload
|
||||
def boundingRect(src: cv2.GArrayT) -> cv2.GOpaqueT: ...
|
||||
@_typing.overload
|
||||
def boundingRect(src: cv2.GArrayT) -> cv2.GOpaqueT: ...
|
||||
|
||||
def boxFilter(src: cv2.GMat, dtype: int, ksize: cv2.typing.Size, anchor: cv2.typing.Point = ..., normalize: bool = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
|
||||
|
||||
def cartToPolar(x: cv2.GMat, y: cv2.GMat, angleInDegrees: bool = ...) -> tuple[cv2.GMat, cv2.GMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def cmpEQ(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def cmpEQ(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def cmpGE(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def cmpGE(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def cmpGT(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def cmpGT(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def cmpLE(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def cmpLE(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def cmpLT(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def cmpLT(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def cmpNE(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def cmpNE(src1: cv2.GMat, src2: cv2.GScalar) -> cv2.GMat: ...
|
||||
|
||||
def combine(lhs: cv2.GKernelPackage, rhs: cv2.GKernelPackage) -> cv2.GKernelPackage: ...
|
||||
|
||||
@_typing.overload
|
||||
def concatHor(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def concatHor(v: _typing.Sequence[cv2.GMat]) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def concatVert(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def concatVert(v: _typing.Sequence[cv2.GMat]) -> cv2.GMat: ...
|
||||
|
||||
def convertTo(src: cv2.GMat, rdepth: int, alpha: float = ..., beta: float = ...) -> cv2.GMat: ...
|
||||
|
||||
def copy(in_: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def countNonZero(src: cv2.GMat) -> cv2.GOpaqueT: ...
|
||||
|
||||
def crop(src: cv2.GMat, rect: cv2.typing.Rect) -> cv2.GMat: ...
|
||||
|
||||
def dilate(src: cv2.GMat, kernel: cv2.typing.MatLike, anchor: cv2.typing.Point = ..., iterations: int = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
|
||||
|
||||
def dilate3x3(src: cv2.GMat, iterations: int = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
|
||||
|
||||
def div(src1: cv2.GMat, src2: cv2.GMat, scale: float, ddepth: int = ...) -> cv2.GMat: ...
|
||||
|
||||
def divC(src: cv2.GMat, divisor: cv2.GScalar, scale: float, ddepth: int = ...) -> cv2.GMat: ...
|
||||
|
||||
def divRC(divident: cv2.GScalar, src: cv2.GMat, scale: float, ddepth: int = ...) -> cv2.GMat: ...
|
||||
|
||||
def equalizeHist(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def erode(src: cv2.GMat, kernel: cv2.typing.MatLike, anchor: cv2.typing.Point = ..., iterations: int = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
|
||||
|
||||
def erode3x3(src: cv2.GMat, iterations: int = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
|
||||
|
||||
def filter2D(src: cv2.GMat, ddepth: int, kernel: cv2.typing.MatLike, anchor: cv2.typing.Point = ..., delta: cv2.typing.Scalar = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
|
||||
|
||||
def flip(src: cv2.GMat, flipCode: int) -> cv2.GMat: ...
|
||||
|
||||
def gaussianBlur(src: cv2.GMat, ksize: cv2.typing.Size, sigmaX: float, sigmaY: float = ..., borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
|
||||
|
||||
def goodFeaturesToTrack(image: cv2.GMat, maxCorners: int, qualityLevel: float, minDistance: float, mask: cv2.typing.MatLike | None = ..., blockSize: int = ..., useHarrisDetector: bool = ..., k: float = ...) -> cv2.GArrayT: ...
|
||||
|
||||
def inRange(src: cv2.GMat, threshLow: cv2.GScalar, threshUp: cv2.GScalar) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def infer(name: str, inputs: cv2.GInferInputs) -> cv2.GInferOutputs: ...
|
||||
@_typing.overload
|
||||
def infer(name: str, roi: cv2.GOpaqueT, inputs: cv2.GInferInputs) -> cv2.GInferOutputs: ...
|
||||
@_typing.overload
|
||||
def infer(name: str, rois: cv2.GArrayT, inputs: cv2.GInferInputs) -> cv2.GInferListOutputs: ...
|
||||
|
||||
def infer2(name: str, in_: cv2.GMat, inputs: cv2.GInferListInputs) -> cv2.GInferListOutputs: ...
|
||||
|
||||
def integral(src: cv2.GMat, sdepth: int = ..., sqdepth: int = ...) -> tuple[cv2.GMat, cv2.GMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def kmeans(data: cv2.GMat, K: int, bestLabels: cv2.GMat, criteria: cv2.typing.TermCriteria, attempts: int, flags: cv2.KmeansFlags) -> tuple[cv2.GOpaqueT, cv2.GMat, cv2.GMat]: ...
|
||||
@_typing.overload
|
||||
def kmeans(data: cv2.GMat, K: int, criteria: cv2.typing.TermCriteria, attempts: int, flags: cv2.KmeansFlags) -> tuple[cv2.GOpaqueT, cv2.GMat, cv2.GMat]: ...
|
||||
@_typing.overload
|
||||
def kmeans(data: cv2.GArrayT, K: int, bestLabels: cv2.GArrayT, criteria: cv2.typing.TermCriteria, attempts: int, flags: cv2.KmeansFlags) -> tuple[cv2.GOpaqueT, cv2.GArrayT, cv2.GArrayT]: ...
|
||||
@_typing.overload
|
||||
def kmeans(data: cv2.GArrayT, K: int, bestLabels: cv2.GArrayT, criteria: cv2.typing.TermCriteria, attempts: int, flags: cv2.KmeansFlags) -> tuple[cv2.GOpaqueT, cv2.GArrayT, cv2.GArrayT]: ...
|
||||
|
||||
def mask(src: cv2.GMat, mask: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def max(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def mean(src: cv2.GMat) -> cv2.GScalar: ...
|
||||
|
||||
def medianBlur(src: cv2.GMat, ksize: int) -> cv2.GMat: ...
|
||||
|
||||
def merge3(src1: cv2.GMat, src2: cv2.GMat, src3: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def merge4(src1: cv2.GMat, src2: cv2.GMat, src3: cv2.GMat, src4: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def min(src1: cv2.GMat, src2: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def morphologyEx(src: cv2.GMat, op: cv2.MorphTypes, kernel: cv2.typing.MatLike, anchor: cv2.typing.Point = ..., iterations: int = ..., borderType: cv2.BorderTypes = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
|
||||
|
||||
def mul(src1: cv2.GMat, src2: cv2.GMat, scale: float = ..., ddepth: int = ...) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def mulC(src: cv2.GMat, multiplier: float, ddepth: int = ...) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def mulC(src: cv2.GMat, multiplier: cv2.GScalar, ddepth: int = ...) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def mulC(multiplier: cv2.GScalar, src: cv2.GMat, ddepth: int = ...) -> cv2.GMat: ...
|
||||
|
||||
def normInf(src: cv2.GMat) -> cv2.GScalar: ...
|
||||
|
||||
def normL1(src: cv2.GMat) -> cv2.GScalar: ...
|
||||
|
||||
def normL2(src: cv2.GMat) -> cv2.GScalar: ...
|
||||
|
||||
def normalize(src: cv2.GMat, alpha: float, beta: float, norm_type: int, ddepth: int = ...) -> cv2.GMat: ...
|
||||
|
||||
@_typing.overload
|
||||
def parseSSD(in_: cv2.GMat, inSz: cv2.GOpaqueT, confidenceThreshold: float = ..., filterLabel: int = ...) -> tuple[cv2.GArrayT, cv2.GArrayT]: ...
|
||||
@_typing.overload
|
||||
def parseSSD(in_: cv2.GMat, inSz: cv2.GOpaqueT, confidenceThreshold: float, alignmentToSquare: bool, filterOutOfBounds: bool) -> cv2.GArrayT: ...
|
||||
|
||||
def parseYolo(in_: cv2.GMat, inSz: cv2.GOpaqueT, confidenceThreshold: float = ..., nmsThreshold: float = ..., anchors: _typing.Sequence[float] = ...) -> tuple[cv2.GArrayT, cv2.GArrayT]: ...
|
||||
|
||||
def phase(x: cv2.GMat, y: cv2.GMat, angleInDegrees: bool = ...) -> cv2.GMat: ...
|
||||
|
||||
def polarToCart(magnitude: cv2.GMat, angle: cv2.GMat, angleInDegrees: bool = ...) -> tuple[cv2.GMat, cv2.GMat]: ...
|
||||
|
||||
def remap(src: cv2.GMat, map1: cv2.typing.MatLike, map2: cv2.typing.MatLike, interpolation: int, borderMode: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
|
||||
|
||||
def resize(src: cv2.GMat, dsize: cv2.typing.Size, fx: float = ..., fy: float = ..., interpolation: int = ...) -> cv2.GMat: ...
|
||||
|
||||
def select(src1: cv2.GMat, src2: cv2.GMat, mask: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def sepFilter(src: cv2.GMat, ddepth: int, kernelX: cv2.typing.MatLike, kernelY: cv2.typing.MatLike, anchor: cv2.typing.Point, delta: cv2.typing.Scalar, borderType: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
|
||||
|
||||
def split3(src: cv2.GMat) -> tuple[cv2.GMat, cv2.GMat, cv2.GMat]: ...
|
||||
|
||||
def split4(src: cv2.GMat) -> tuple[cv2.GMat, cv2.GMat, cv2.GMat, cv2.GMat]: ...
|
||||
|
||||
def sqrt(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def sub(src1: cv2.GMat, src2: cv2.GMat, ddepth: int = ...) -> cv2.GMat: ...
|
||||
|
||||
def subC(src: cv2.GMat, c: cv2.GScalar, ddepth: int = ...) -> cv2.GMat: ...
|
||||
|
||||
def subRC(c: cv2.GScalar, src: cv2.GMat, ddepth: int = ...) -> cv2.GMat: ...
|
||||
|
||||
def sum(src: cv2.GMat) -> cv2.GScalar: ...
|
||||
|
||||
@_typing.overload
|
||||
def threshold(src: cv2.GMat, thresh: cv2.GScalar, maxval: cv2.GScalar, type: int) -> cv2.GMat: ...
|
||||
@_typing.overload
|
||||
def threshold(src: cv2.GMat, maxval: cv2.GScalar, type: int) -> tuple[cv2.GMat, cv2.GScalar]: ...
|
||||
|
||||
def transpose(src: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def warpAffine(src: cv2.GMat, M: cv2.typing.MatLike, dsize: cv2.typing.Size, flags: int = ..., borderMode: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
|
||||
|
||||
def warpPerspective(src: cv2.GMat, M: cv2.typing.MatLike, dsize: cv2.typing.Size, flags: int = ..., borderMode: int = ..., borderValue: cv2.typing.Scalar = ...) -> cv2.GMat: ...
|
||||
|
||||
|
||||
Binary file not shown.
@ -1,7 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
from cv2.gapi.core import cpu as cpu
|
||||
from cv2.gapi.core import fluid as fluid
|
||||
from cv2.gapi.core import ocl as ocl
|
||||
|
||||
|
||||
@ -1,9 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
|
||||
|
||||
# Functions
|
||||
def kernels() -> cv2.GKernelPackage: ...
|
||||
|
||||
|
||||
@ -1,9 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
|
||||
|
||||
# Functions
|
||||
def kernels() -> cv2.GKernelPackage: ...
|
||||
|
||||
|
||||
@ -1,9 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
|
||||
|
||||
# Functions
|
||||
def kernels() -> cv2.GKernelPackage: ...
|
||||
|
||||
|
||||
@ -1,51 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2.typing
|
||||
import typing as _typing
|
||||
|
||||
|
||||
from cv2.gapi.ie import detail as detail
|
||||
|
||||
|
||||
# Enumerations
|
||||
TraitAs_TENSOR: int
|
||||
TRAIT_AS_TENSOR: int
|
||||
TraitAs_IMAGE: int
|
||||
TRAIT_AS_IMAGE: int
|
||||
TraitAs = int
|
||||
"""One of [TraitAs_TENSOR, TRAIT_AS_TENSOR, TraitAs_IMAGE, TRAIT_AS_IMAGE]"""
|
||||
|
||||
Sync: int
|
||||
SYNC: int
|
||||
Async: int
|
||||
ASYNC: int
|
||||
InferMode = int
|
||||
"""One of [Sync, SYNC, Async, ASYNC]"""
|
||||
|
||||
|
||||
|
||||
# Classes
|
||||
class PyParams:
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, tag: str, model: str, weights: str, device: str) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, tag: str, model: str, device: str) -> None: ...
|
||||
|
||||
def constInput(self, layer_name: str, data: cv2.typing.MatLike, hint: TraitAs = ...) -> PyParams: ...
|
||||
|
||||
def cfgNumRequests(self, nireq: int) -> PyParams: ...
|
||||
|
||||
def cfgBatchSize(self, size: int) -> PyParams: ...
|
||||
|
||||
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def params(tag: str, model: str, weights: str, device: str) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def params(tag: str, model: str, device: str) -> PyParams: ...
|
||||
|
||||
|
||||
@ -1,12 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
ParamDesc_Kind_Load: int
|
||||
PARAM_DESC_KIND_LOAD: int
|
||||
ParamDesc_Kind_Import: int
|
||||
PARAM_DESC_KIND_IMPORT: int
|
||||
ParamDesc_Kind = int
|
||||
"""One of [ParamDesc_Kind_Load, PARAM_DESC_KIND_LOAD, ParamDesc_Kind_Import, PARAM_DESC_KIND_IMPORT]"""
|
||||
|
||||
|
||||
# Classes
|
||||
|
||||
@ -1,5 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
from cv2.gapi.imgproc import fluid as fluid
|
||||
|
||||
|
||||
@ -1,9 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
|
||||
|
||||
# Functions
|
||||
def kernels() -> cv2.GKernelPackage: ...
|
||||
|
||||
|
||||
@ -1,37 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
EncoderConfig_RateControlMode_CBR: int
|
||||
ENCODER_CONFIG_RATE_CONTROL_MODE_CBR: int
|
||||
EncoderConfig_RateControlMode_VBR: int
|
||||
ENCODER_CONFIG_RATE_CONTROL_MODE_VBR: int
|
||||
EncoderConfig_RateControlMode = int
|
||||
"""One of [EncoderConfig_RateControlMode_CBR, ENCODER_CONFIG_RATE_CONTROL_MODE_CBR, EncoderConfig_RateControlMode_VBR, ENCODER_CONFIG_RATE_CONTROL_MODE_VBR]"""
|
||||
|
||||
EncoderConfig_Profile_H264_BASELINE: int
|
||||
ENCODER_CONFIG_PROFILE_H264_BASELINE: int
|
||||
EncoderConfig_Profile_H264_HIGH: int
|
||||
ENCODER_CONFIG_PROFILE_H264_HIGH: int
|
||||
EncoderConfig_Profile_H264_MAIN: int
|
||||
ENCODER_CONFIG_PROFILE_H264_MAIN: int
|
||||
EncoderConfig_Profile_H265_MAIN: int
|
||||
ENCODER_CONFIG_PROFILE_H265_MAIN: int
|
||||
EncoderConfig_Profile_MJPEG: int
|
||||
ENCODER_CONFIG_PROFILE_MJPEG: int
|
||||
EncoderConfig_Profile = int
|
||||
"""One of [EncoderConfig_Profile_H264_BASELINE, ENCODER_CONFIG_PROFILE_H264_BASELINE, EncoderConfig_Profile_H264_HIGH, ENCODER_CONFIG_PROFILE_H264_HIGH, EncoderConfig_Profile_H264_MAIN, ENCODER_CONFIG_PROFILE_H264_MAIN, EncoderConfig_Profile_H265_MAIN, ENCODER_CONFIG_PROFILE_H265_MAIN, EncoderConfig_Profile_MJPEG, ENCODER_CONFIG_PROFILE_MJPEG]"""
|
||||
|
||||
ColorCameraParams_BoardSocket_RGB: int
|
||||
COLOR_CAMERA_PARAMS_BOARD_SOCKET_RGB: int
|
||||
ColorCameraParams_BoardSocket_BGR: int
|
||||
COLOR_CAMERA_PARAMS_BOARD_SOCKET_BGR: int
|
||||
ColorCameraParams_BoardSocket = int
|
||||
"""One of [ColorCameraParams_BoardSocket_RGB, COLOR_CAMERA_PARAMS_BOARD_SOCKET_RGB, ColorCameraParams_BoardSocket_BGR, COLOR_CAMERA_PARAMS_BOARD_SOCKET_BGR]"""
|
||||
|
||||
ColorCameraParams_Resolution_THE_1080_P: int
|
||||
COLOR_CAMERA_PARAMS_RESOLUTION_THE_1080_P: int
|
||||
ColorCameraParams_Resolution = int
|
||||
"""One of [ColorCameraParams_Resolution_THE_1080_P, COLOR_CAMERA_PARAMS_RESOLUTION_THE_1080_P]"""
|
||||
|
||||
|
||||
# Classes
|
||||
|
||||
@ -1,51 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2.gapi.onnx.ep
|
||||
import cv2.typing
|
||||
import typing as _typing
|
||||
|
||||
|
||||
from cv2.gapi.onnx import ep as ep
|
||||
|
||||
|
||||
# Enumerations
|
||||
TraitAs_TENSOR: int
|
||||
TRAIT_AS_TENSOR: int
|
||||
TraitAs_IMAGE: int
|
||||
TRAIT_AS_IMAGE: int
|
||||
TraitAs = int
|
||||
"""One of [TraitAs_TENSOR, TRAIT_AS_TENSOR, TraitAs_IMAGE, TRAIT_AS_IMAGE]"""
|
||||
|
||||
|
||||
|
||||
# Classes
|
||||
class PyParams:
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, tag: str, model_path: str) -> None: ...
|
||||
|
||||
def cfgMeanStd(self, layer_name: str, m: cv2.typing.Scalar, s: cv2.typing.Scalar) -> PyParams: ...
|
||||
|
||||
def cfgNormalize(self, layer_name: str, flag: bool) -> PyParams: ...
|
||||
|
||||
@_typing.overload
|
||||
def cfgAddExecutionProvider(self, ep: cv2.gapi.onnx.ep.OpenVINO) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def cfgAddExecutionProvider(self, ep: cv2.gapi.onnx.ep.DirectML) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def cfgAddExecutionProvider(self, ep: cv2.gapi.onnx.ep.CoreML) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def cfgAddExecutionProvider(self, ep: cv2.gapi.onnx.ep.CUDA) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def cfgAddExecutionProvider(self, ep: cv2.gapi.onnx.ep.TensorRT) -> PyParams: ...
|
||||
|
||||
def cfgDisableMemPattern(self) -> PyParams: ...
|
||||
|
||||
|
||||
|
||||
# Functions
|
||||
def params(tag: str, model_path: str) -> PyParams: ...
|
||||
|
||||
|
||||
@ -1,63 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2.typing
|
||||
import typing as _typing
|
||||
|
||||
|
||||
# Classes
|
||||
class CoreML:
|
||||
# Functions
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
def cfgUseCPUOnly(self) -> CoreML: ...
|
||||
|
||||
def cfgEnableOnSubgraph(self) -> CoreML: ...
|
||||
|
||||
def cfgEnableOnlyNeuralEngine(self) -> CoreML: ...
|
||||
|
||||
|
||||
class CUDA:
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, dev_id: int) -> None: ...
|
||||
|
||||
|
||||
class TensorRT:
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, dev_id: int) -> None: ...
|
||||
|
||||
|
||||
class OpenVINO:
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, dev_type: str) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, params: cv2.typing.map_string_and_string) -> None: ...
|
||||
|
||||
def cfgCacheDir(self, dir: str) -> OpenVINO: ...
|
||||
|
||||
def cfgNumThreads(self, nthreads: int) -> OpenVINO: ...
|
||||
|
||||
def cfgEnableOpenCLThrottling(self) -> OpenVINO: ...
|
||||
|
||||
def cfgEnableDynamicShapes(self) -> OpenVINO: ...
|
||||
|
||||
|
||||
class DirectML:
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, device_id: int) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, adapter_name: str) -> None: ...
|
||||
|
||||
|
||||
|
||||
@ -1,32 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
import typing as _typing
|
||||
|
||||
|
||||
from cv2.gapi.ot import cpu as cpu
|
||||
|
||||
|
||||
# Enumerations
|
||||
NEW: int
|
||||
TRACKED: int
|
||||
LOST: int
|
||||
TrackingStatus = int
|
||||
"""One of [NEW, TRACKED, LOST]"""
|
||||
|
||||
|
||||
|
||||
# Classes
|
||||
class ObjectTrackerParams:
|
||||
max_num_objects: int
|
||||
input_image_format: int
|
||||
tracking_per_class: bool
|
||||
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def track(mat: cv2.GMat, detected_rects: cv2.GArrayT, detected_class_labels: cv2.GArrayT, delta: float) -> tuple[cv2.GArrayT, cv2.GArrayT, cv2.GArrayT, cv2.GArrayT]: ...
|
||||
@_typing.overload
|
||||
def track(frame: cv2.GFrame, detected_rects: cv2.GArrayT, detected_class_labels: cv2.GArrayT, delta: float) -> tuple[cv2.GArrayT, cv2.GArrayT, cv2.GArrayT, cv2.GArrayT]: ...
|
||||
|
||||
|
||||
@ -1,9 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
|
||||
|
||||
# Functions
|
||||
def kernels() -> cv2.GKernelPackage: ...
|
||||
|
||||
|
||||
@ -1,74 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2.typing
|
||||
import typing as _typing
|
||||
|
||||
|
||||
# Classes
|
||||
class PyParams:
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, tag: str, model_path: str, bin_path: str, device: str) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, tag: str, blob_path: str, device: str) -> None: ...
|
||||
|
||||
def cfgPluginConfig(self, config: cv2.typing.map_string_and_string) -> PyParams: ...
|
||||
|
||||
@_typing.overload
|
||||
def cfgInputTensorLayout(self, tensor_layout: str) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def cfgInputTensorLayout(self, layout_map: cv2.typing.map_string_and_string) -> PyParams: ...
|
||||
|
||||
@_typing.overload
|
||||
def cfgInputModelLayout(self, tensor_layout: str) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def cfgInputModelLayout(self, layout_map: cv2.typing.map_string_and_string) -> PyParams: ...
|
||||
|
||||
@_typing.overload
|
||||
def cfgOutputTensorLayout(self, tensor_layout: str) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def cfgOutputTensorLayout(self, layout_map: cv2.typing.map_string_and_string) -> PyParams: ...
|
||||
|
||||
@_typing.overload
|
||||
def cfgOutputModelLayout(self, tensor_layout: str) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def cfgOutputModelLayout(self, layout_map: cv2.typing.map_string_and_string) -> PyParams: ...
|
||||
|
||||
@_typing.overload
|
||||
def cfgOutputTensorPrecision(self, precision: int) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def cfgOutputTensorPrecision(self, precision_map: cv2.typing.map_string_and_int) -> PyParams: ...
|
||||
|
||||
@_typing.overload
|
||||
def cfgReshape(self, new_shape: _typing.Sequence[int]) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def cfgReshape(self, new_shape_map: cv2.typing.map_string_and_vector_size_t) -> PyParams: ...
|
||||
|
||||
def cfgNumRequests(self, nireq: int) -> PyParams: ...
|
||||
|
||||
@_typing.overload
|
||||
def cfgMean(self, mean_values: _typing.Sequence[float]) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def cfgMean(self, mean_map: cv2.typing.map_string_and_vector_float) -> PyParams: ...
|
||||
|
||||
@_typing.overload
|
||||
def cfgScale(self, scale_values: _typing.Sequence[float]) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def cfgScale(self, scale_map: cv2.typing.map_string_and_vector_float) -> PyParams: ...
|
||||
|
||||
@_typing.overload
|
||||
def cfgResize(self, interpolation: int) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def cfgResize(self, interpolation: cv2.typing.map_string_and_int) -> PyParams: ...
|
||||
|
||||
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def params(tag: str, model_path: str, weights: str, device: str) -> PyParams: ...
|
||||
@_typing.overload
|
||||
def params(tag: str, bin_path: str, device: str) -> PyParams: ...
|
||||
|
||||
|
||||
@ -1,5 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
from cv2.gapi.own import detail as detail
|
||||
|
||||
|
||||
@ -1,10 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
MatHeader_AUTO_STEP: int
|
||||
MAT_HEADER_AUTO_STEP: int
|
||||
MatHeader_TYPE_MASK: int
|
||||
MAT_HEADER_TYPE_MASK: int
|
||||
|
||||
|
||||
# Classes
|
||||
|
||||
@ -1,5 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
from cv2.gapi.render import ocv as ocv
|
||||
|
||||
|
||||
@ -1,9 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
|
||||
|
||||
# Functions
|
||||
def kernels() -> cv2.GKernelPackage: ...
|
||||
|
||||
|
||||
@ -1,42 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
import typing as _typing
|
||||
|
||||
|
||||
# Enumerations
|
||||
sync_policy_dont_sync: int
|
||||
SYNC_POLICY_DONT_SYNC: int
|
||||
sync_policy_drop: int
|
||||
SYNC_POLICY_DROP: int
|
||||
sync_policy = int
|
||||
"""One of [sync_policy_dont_sync, SYNC_POLICY_DONT_SYNC, sync_policy_drop, SYNC_POLICY_DROP]"""
|
||||
|
||||
|
||||
|
||||
# Classes
|
||||
class queue_capacity:
|
||||
capacity: int
|
||||
|
||||
# Functions
|
||||
def __init__(self, cap: int = ...) -> None: ...
|
||||
|
||||
|
||||
|
||||
# Functions
|
||||
def desync(g: cv2.GMat) -> cv2.GMat: ...
|
||||
|
||||
def seqNo(arg1: cv2.GMat) -> cv2.GOpaqueT: ...
|
||||
|
||||
def seq_id(arg1: cv2.GMat) -> cv2.GOpaqueT: ...
|
||||
|
||||
@_typing.overload
|
||||
def size(src: cv2.GMat) -> cv2.GOpaqueT: ...
|
||||
@_typing.overload
|
||||
def size(r: cv2.GOpaqueT) -> cv2.GOpaqueT: ...
|
||||
@_typing.overload
|
||||
def size(src: cv2.GFrame) -> cv2.GOpaqueT: ...
|
||||
|
||||
def timestamp(arg1: cv2.GMat) -> cv2.GOpaqueT: ...
|
||||
|
||||
|
||||
@ -1,10 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
# Enumerations
|
||||
TYPE_BS_MOG2: int
|
||||
TYPE_BS_KNN: int
|
||||
BackgroundSubtractorType = int
|
||||
"""One of [TYPE_BS_MOG2, TYPE_BS_KNN]"""
|
||||
|
||||
|
||||
|
||||
@ -1,41 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
import cv2.gapi
|
||||
import cv2.gapi.wip.gst
|
||||
import cv2.typing
|
||||
import typing as _typing
|
||||
|
||||
|
||||
from cv2.gapi.wip import draw as draw
|
||||
from cv2.gapi.wip import gst as gst
|
||||
from cv2.gapi.wip import onevpl as onevpl
|
||||
|
||||
|
||||
# Classes
|
||||
class GOutputs:
|
||||
# Functions
|
||||
def getGMat(self) -> cv2.GMat: ...
|
||||
|
||||
def getGScalar(self) -> cv2.GScalar: ...
|
||||
|
||||
def getGArray(self, type: cv2.gapi.ArgType) -> cv2.GArrayT: ...
|
||||
|
||||
def getGOpaque(self, type: cv2.gapi.ArgType) -> cv2.GOpaqueT: ...
|
||||
|
||||
|
||||
class IStreamSource:
|
||||
...
|
||||
|
||||
|
||||
# Functions
|
||||
def get_streaming_source(pipeline: cv2.gapi.wip.gst.GStreamerPipeline, appsinkName: str, outputType: cv2.gapi.wip.gst.GStreamerSource_OutputType = ...) -> IStreamSource: ...
|
||||
|
||||
@_typing.overload
|
||||
def make_capture_src(path: str, properties: cv2.typing.map_int_and_double = ...) -> IStreamSource: ...
|
||||
@_typing.overload
|
||||
def make_capture_src(id: int, properties: cv2.typing.map_int_and_double = ...) -> IStreamSource: ...
|
||||
|
||||
def make_gst_src(pipeline: str, outputType: cv2.gapi.wip.gst.GStreamerSource_OutputType = ...) -> IStreamSource: ...
|
||||
|
||||
|
||||
@ -1,119 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
import cv2.typing
|
||||
import typing as _typing
|
||||
|
||||
|
||||
# Classes
|
||||
class Text:
|
||||
text: str
|
||||
org: cv2.typing.Point
|
||||
ff: int
|
||||
fs: float
|
||||
color: cv2.typing.Scalar
|
||||
thick: int
|
||||
lt: int
|
||||
bottom_left_origin: bool
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, text_: str, org_: cv2.typing.Point, ff_: int, fs_: float, color_: cv2.typing.Scalar, thick_: int = ..., lt_: int = ..., bottom_left_origin_: bool = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
|
||||
class Rect:
|
||||
rect: cv2.typing.Rect
|
||||
color: cv2.typing.Scalar
|
||||
thick: int
|
||||
lt: int
|
||||
shift: int
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, rect_: cv2.typing.Rect2i, color_: cv2.typing.Scalar, thick_: int = ..., lt_: int = ..., shift_: int = ...) -> None: ...
|
||||
|
||||
|
||||
class Circle:
|
||||
center: cv2.typing.Point
|
||||
radius: int
|
||||
color: cv2.typing.Scalar
|
||||
thick: int
|
||||
lt: int
|
||||
shift: int
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, center_: cv2.typing.Point, radius_: int, color_: cv2.typing.Scalar, thick_: int = ..., lt_: int = ..., shift_: int = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
|
||||
class Line:
|
||||
pt1: cv2.typing.Point
|
||||
pt2: cv2.typing.Point
|
||||
color: cv2.typing.Scalar
|
||||
thick: int
|
||||
lt: int
|
||||
shift: int
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, pt1_: cv2.typing.Point, pt2_: cv2.typing.Point, color_: cv2.typing.Scalar, thick_: int = ..., lt_: int = ..., shift_: int = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
|
||||
class Mosaic:
|
||||
mos: cv2.typing.Rect
|
||||
cellSz: int
|
||||
decim: int
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self, mos_: cv2.typing.Rect2i, cellSz_: int, decim_: int) -> None: ...
|
||||
|
||||
|
||||
class Image:
|
||||
org: cv2.typing.Point
|
||||
img: cv2.typing.MatLike
|
||||
alpha: cv2.typing.MatLike
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, org_: cv2.typing.Point, img_: cv2.typing.MatLike, alpha_: cv2.typing.MatLike) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
|
||||
class Poly:
|
||||
points: _typing.Sequence[cv2.typing.Point]
|
||||
color: cv2.typing.Scalar
|
||||
thick: int
|
||||
lt: int
|
||||
shift: int
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def __init__(self, points_: _typing.Sequence[cv2.typing.Point], color_: cv2.typing.Scalar, thick_: int = ..., lt_: int = ..., shift_: int = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def render(bgr: cv2.typing.MatLike, prims: _typing.Sequence[cv2.typing.Prim], args: _typing.Sequence[cv2.GCompileArg] = ...) -> None: ...
|
||||
@_typing.overload
|
||||
def render(y_plane: cv2.typing.MatLike, uv_plane: cv2.typing.MatLike, prims: _typing.Sequence[cv2.typing.Prim], args: _typing.Sequence[cv2.GCompileArg] = ...) -> None: ...
|
||||
|
||||
def render3ch(src: cv2.GMat, prims: cv2.GArrayT) -> cv2.GMat: ...
|
||||
|
||||
def renderNV12(y: cv2.GMat, uv: cv2.GMat, prims: cv2.GArrayT) -> tuple[cv2.GMat, cv2.GMat]: ...
|
||||
|
||||
|
||||
@ -1,17 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
GStreamerSource_OutputType_FRAME: int
|
||||
GSTREAMER_SOURCE_OUTPUT_TYPE_FRAME: int
|
||||
GStreamerSource_OutputType_MAT: int
|
||||
GSTREAMER_SOURCE_OUTPUT_TYPE_MAT: int
|
||||
GStreamerSource_OutputType = int
|
||||
"""One of [GStreamerSource_OutputType_FRAME, GSTREAMER_SOURCE_OUTPUT_TYPE_FRAME, GStreamerSource_OutputType_MAT, GSTREAMER_SOURCE_OUTPUT_TYPE_MAT]"""
|
||||
|
||||
|
||||
# Classes
|
||||
class GStreamerPipeline:
|
||||
# Functions
|
||||
def __init__(self, pipeline: str) -> None: ...
|
||||
|
||||
|
||||
|
||||
@ -1,16 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
# Enumerations
|
||||
AccelType_HOST: int
|
||||
ACCEL_TYPE_HOST: int
|
||||
AccelType_DX11: int
|
||||
ACCEL_TYPE_DX11: int
|
||||
AccelType_VAAPI: int
|
||||
ACCEL_TYPE_VAAPI: int
|
||||
AccelType_LAST_VALUE: int
|
||||
ACCEL_TYPE_LAST_VALUE: int
|
||||
AccelType = int
|
||||
"""One of [AccelType_HOST, ACCEL_TYPE_HOST, AccelType_DX11, ACCEL_TYPE_DX11, AccelType_VAAPI, ACCEL_TYPE_VAAPI, AccelType_LAST_VALUE, ACCEL_TYPE_LAST_VALUE]"""
|
||||
|
||||
|
||||
|
||||
@ -1,14 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
# Functions
|
||||
def getIppVersion() -> str: ...
|
||||
|
||||
def setUseIPP(flag: bool) -> None: ...
|
||||
|
||||
def setUseIPP_NotExact(flag: bool) -> None: ...
|
||||
|
||||
def useIPP() -> bool: ...
|
||||
|
||||
def useIPP_NotExact() -> bool: ...
|
||||
|
||||
|
||||
@ -1,6 +0,0 @@
|
||||
# flake8: noqa
|
||||
import sys
|
||||
|
||||
if sys.version_info[:2] < (3, 0):
|
||||
def exec_file_wrapper(fpath, g_vars, l_vars):
|
||||
execfile(fpath, g_vars, l_vars)
|
||||
@ -1,9 +0,0 @@
|
||||
# flake8: noqa
|
||||
import os
|
||||
import sys
|
||||
|
||||
if sys.version_info[:2] >= (3, 0):
|
||||
def exec_file_wrapper(fpath, g_vars, l_vars):
|
||||
with open(fpath) as f:
|
||||
code = compile(f.read(), os.path.basename(fpath), 'exec')
|
||||
exec(code, g_vars, l_vars)
|
||||
@ -1,40 +0,0 @@
|
||||
__all__ = []
|
||||
|
||||
import numpy as np
|
||||
import cv2 as cv
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
# Same as cv2.typing.NumPyArrayNumeric, but avoids circular dependencies
|
||||
if TYPE_CHECKING:
|
||||
_NumPyArrayNumeric = np.ndarray[Any, np.dtype[np.integer[Any] | np.floating[Any]]]
|
||||
else:
|
||||
_NumPyArrayNumeric = np.ndarray
|
||||
|
||||
# NumPy documentation: https://numpy.org/doc/stable/user/basics.subclassing.html
|
||||
|
||||
|
||||
class Mat(_NumPyArrayNumeric):
|
||||
'''
|
||||
cv.Mat wrapper for numpy array.
|
||||
|
||||
Stores extra metadata information how to interpret and process of numpy array for underlying C++ code.
|
||||
'''
|
||||
|
||||
def __new__(cls, arr, **kwargs):
|
||||
obj = arr.view(Mat)
|
||||
return obj
|
||||
|
||||
def __init__(self, arr, **kwargs):
|
||||
self.wrap_channels = kwargs.pop('wrap_channels', getattr(arr, 'wrap_channels', False))
|
||||
if len(kwargs) > 0:
|
||||
raise TypeError('Unknown parameters: {}'.format(repr(kwargs)))
|
||||
|
||||
def __array_finalize__(self, obj):
|
||||
if obj is None:
|
||||
return
|
||||
self.wrap_channels = getattr(obj, 'wrap_channels', None)
|
||||
|
||||
|
||||
Mat.__module__ = cv.__name__
|
||||
cv.Mat = Mat
|
||||
cv._registerMatType(Mat)
|
||||
Binary file not shown.
@ -1 +0,0 @@
|
||||
from .version import get_ocv_version
|
||||
Binary file not shown.
Binary file not shown.
@ -1,5 +0,0 @@
|
||||
import cv2
|
||||
|
||||
|
||||
def get_ocv_version():
|
||||
return getattr(cv2, "__version__", "unavailable")
|
||||
@ -1,695 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
import cv2.typing
|
||||
import typing as _typing
|
||||
|
||||
|
||||
# Enumerations
|
||||
VAR_NUMERICAL: int
|
||||
VAR_ORDERED: int
|
||||
VAR_CATEGORICAL: int
|
||||
VariableTypes = int
|
||||
"""One of [VAR_NUMERICAL, VAR_ORDERED, VAR_CATEGORICAL]"""
|
||||
|
||||
TEST_ERROR: int
|
||||
TRAIN_ERROR: int
|
||||
ErrorTypes = int
|
||||
"""One of [TEST_ERROR, TRAIN_ERROR]"""
|
||||
|
||||
ROW_SAMPLE: int
|
||||
COL_SAMPLE: int
|
||||
SampleTypes = int
|
||||
"""One of [ROW_SAMPLE, COL_SAMPLE]"""
|
||||
|
||||
|
||||
StatModel_UPDATE_MODEL: int
|
||||
STAT_MODEL_UPDATE_MODEL: int
|
||||
StatModel_RAW_OUTPUT: int
|
||||
STAT_MODEL_RAW_OUTPUT: int
|
||||
StatModel_COMPRESSED_INPUT: int
|
||||
STAT_MODEL_COMPRESSED_INPUT: int
|
||||
StatModel_PREPROCESSED_INPUT: int
|
||||
STAT_MODEL_PREPROCESSED_INPUT: int
|
||||
StatModel_Flags = int
|
||||
"""One of [StatModel_UPDATE_MODEL, STAT_MODEL_UPDATE_MODEL, StatModel_RAW_OUTPUT, STAT_MODEL_RAW_OUTPUT, StatModel_COMPRESSED_INPUT, STAT_MODEL_COMPRESSED_INPUT, StatModel_PREPROCESSED_INPUT, STAT_MODEL_PREPROCESSED_INPUT]"""
|
||||
|
||||
KNearest_BRUTE_FORCE: int
|
||||
KNEAREST_BRUTE_FORCE: int
|
||||
KNearest_KDTREE: int
|
||||
KNEAREST_KDTREE: int
|
||||
KNearest_Types = int
|
||||
"""One of [KNearest_BRUTE_FORCE, KNEAREST_BRUTE_FORCE, KNearest_KDTREE, KNEAREST_KDTREE]"""
|
||||
|
||||
SVM_C_SVC: int
|
||||
SVM_NU_SVC: int
|
||||
SVM_ONE_CLASS: int
|
||||
SVM_EPS_SVR: int
|
||||
SVM_NU_SVR: int
|
||||
SVM_Types = int
|
||||
"""One of [SVM_C_SVC, SVM_NU_SVC, SVM_ONE_CLASS, SVM_EPS_SVR, SVM_NU_SVR]"""
|
||||
|
||||
SVM_CUSTOM: int
|
||||
SVM_LINEAR: int
|
||||
SVM_POLY: int
|
||||
SVM_RBF: int
|
||||
SVM_SIGMOID: int
|
||||
SVM_CHI2: int
|
||||
SVM_INTER: int
|
||||
SVM_KernelTypes = int
|
||||
"""One of [SVM_CUSTOM, SVM_LINEAR, SVM_POLY, SVM_RBF, SVM_SIGMOID, SVM_CHI2, SVM_INTER]"""
|
||||
|
||||
SVM_C: int
|
||||
SVM_GAMMA: int
|
||||
SVM_P: int
|
||||
SVM_NU: int
|
||||
SVM_COEF: int
|
||||
SVM_DEGREE: int
|
||||
SVM_ParamTypes = int
|
||||
"""One of [SVM_C, SVM_GAMMA, SVM_P, SVM_NU, SVM_COEF, SVM_DEGREE]"""
|
||||
|
||||
EM_COV_MAT_SPHERICAL: int
|
||||
EM_COV_MAT_DIAGONAL: int
|
||||
EM_COV_MAT_GENERIC: int
|
||||
EM_COV_MAT_DEFAULT: int
|
||||
EM_Types = int
|
||||
"""One of [EM_COV_MAT_SPHERICAL, EM_COV_MAT_DIAGONAL, EM_COV_MAT_GENERIC, EM_COV_MAT_DEFAULT]"""
|
||||
|
||||
EM_DEFAULT_NCLUSTERS: int
|
||||
EM_DEFAULT_MAX_ITERS: int
|
||||
EM_START_E_STEP: int
|
||||
EM_START_M_STEP: int
|
||||
EM_START_AUTO_STEP: int
|
||||
|
||||
DTrees_PREDICT_AUTO: int
|
||||
DTREES_PREDICT_AUTO: int
|
||||
DTrees_PREDICT_SUM: int
|
||||
DTREES_PREDICT_SUM: int
|
||||
DTrees_PREDICT_MAX_VOTE: int
|
||||
DTREES_PREDICT_MAX_VOTE: int
|
||||
DTrees_PREDICT_MASK: int
|
||||
DTREES_PREDICT_MASK: int
|
||||
DTrees_Flags = int
|
||||
"""One of [DTrees_PREDICT_AUTO, DTREES_PREDICT_AUTO, DTrees_PREDICT_SUM, DTREES_PREDICT_SUM, DTrees_PREDICT_MAX_VOTE, DTREES_PREDICT_MAX_VOTE, DTrees_PREDICT_MASK, DTREES_PREDICT_MASK]"""
|
||||
|
||||
Boost_DISCRETE: int
|
||||
BOOST_DISCRETE: int
|
||||
Boost_REAL: int
|
||||
BOOST_REAL: int
|
||||
Boost_LOGIT: int
|
||||
BOOST_LOGIT: int
|
||||
Boost_GENTLE: int
|
||||
BOOST_GENTLE: int
|
||||
Boost_Types = int
|
||||
"""One of [Boost_DISCRETE, BOOST_DISCRETE, Boost_REAL, BOOST_REAL, Boost_LOGIT, BOOST_LOGIT, Boost_GENTLE, BOOST_GENTLE]"""
|
||||
|
||||
ANN_MLP_BACKPROP: int
|
||||
ANN_MLP_RPROP: int
|
||||
ANN_MLP_ANNEAL: int
|
||||
ANN_MLP_TrainingMethods = int
|
||||
"""One of [ANN_MLP_BACKPROP, ANN_MLP_RPROP, ANN_MLP_ANNEAL]"""
|
||||
|
||||
ANN_MLP_IDENTITY: int
|
||||
ANN_MLP_SIGMOID_SYM: int
|
||||
ANN_MLP_GAUSSIAN: int
|
||||
ANN_MLP_RELU: int
|
||||
ANN_MLP_LEAKYRELU: int
|
||||
ANN_MLP_ActivationFunctions = int
|
||||
"""One of [ANN_MLP_IDENTITY, ANN_MLP_SIGMOID_SYM, ANN_MLP_GAUSSIAN, ANN_MLP_RELU, ANN_MLP_LEAKYRELU]"""
|
||||
|
||||
ANN_MLP_UPDATE_WEIGHTS: int
|
||||
ANN_MLP_NO_INPUT_SCALE: int
|
||||
ANN_MLP_NO_OUTPUT_SCALE: int
|
||||
ANN_MLP_TrainFlags = int
|
||||
"""One of [ANN_MLP_UPDATE_WEIGHTS, ANN_MLP_NO_INPUT_SCALE, ANN_MLP_NO_OUTPUT_SCALE]"""
|
||||
|
||||
LogisticRegression_REG_DISABLE: int
|
||||
LOGISTIC_REGRESSION_REG_DISABLE: int
|
||||
LogisticRegression_REG_L1: int
|
||||
LOGISTIC_REGRESSION_REG_L1: int
|
||||
LogisticRegression_REG_L2: int
|
||||
LOGISTIC_REGRESSION_REG_L2: int
|
||||
LogisticRegression_RegKinds = int
|
||||
"""One of [LogisticRegression_REG_DISABLE, LOGISTIC_REGRESSION_REG_DISABLE, LogisticRegression_REG_L1, LOGISTIC_REGRESSION_REG_L1, LogisticRegression_REG_L2, LOGISTIC_REGRESSION_REG_L2]"""
|
||||
|
||||
LogisticRegression_BATCH: int
|
||||
LOGISTIC_REGRESSION_BATCH: int
|
||||
LogisticRegression_MINI_BATCH: int
|
||||
LOGISTIC_REGRESSION_MINI_BATCH: int
|
||||
LogisticRegression_Methods = int
|
||||
"""One of [LogisticRegression_BATCH, LOGISTIC_REGRESSION_BATCH, LogisticRegression_MINI_BATCH, LOGISTIC_REGRESSION_MINI_BATCH]"""
|
||||
|
||||
SVMSGD_SGD: int
|
||||
SVMSGD_ASGD: int
|
||||
SVMSGD_SvmsgdType = int
|
||||
"""One of [SVMSGD_SGD, SVMSGD_ASGD]"""
|
||||
|
||||
SVMSGD_SOFT_MARGIN: int
|
||||
SVMSGD_HARD_MARGIN: int
|
||||
SVMSGD_MarginType = int
|
||||
"""One of [SVMSGD_SOFT_MARGIN, SVMSGD_HARD_MARGIN]"""
|
||||
|
||||
|
||||
# Classes
|
||||
class ParamGrid:
|
||||
minVal: float
|
||||
maxVal: float
|
||||
logStep: float
|
||||
|
||||
# Functions
|
||||
@classmethod
|
||||
def create(cls, minVal: float = ..., maxVal: float = ..., logstep: float = ...) -> ParamGrid: ...
|
||||
|
||||
|
||||
class TrainData:
|
||||
# Functions
|
||||
def getLayout(self) -> int: ...
|
||||
|
||||
def getNTrainSamples(self) -> int: ...
|
||||
|
||||
def getNTestSamples(self) -> int: ...
|
||||
|
||||
def getNSamples(self) -> int: ...
|
||||
|
||||
def getNVars(self) -> int: ...
|
||||
|
||||
def getNAllVars(self) -> int: ...
|
||||
|
||||
@_typing.overload
|
||||
def getSample(self, varIdx: cv2.typing.MatLike, sidx: int, buf: float) -> None: ...
|
||||
@_typing.overload
|
||||
def getSample(self, varIdx: cv2.UMat, sidx: int, buf: float) -> None: ...
|
||||
|
||||
def getSamples(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getMissing(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getTrainSamples(self, layout: int = ..., compressSamples: bool = ..., compressVars: bool = ...) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getTrainResponses(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getTrainNormCatResponses(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getTestResponses(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getTestNormCatResponses(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getResponses(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getNormCatResponses(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getSampleWeights(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getTrainSampleWeights(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getTestSampleWeights(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getVarIdx(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getVarType(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getVarSymbolFlags(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getResponseType(self) -> int: ...
|
||||
|
||||
def getTrainSampleIdx(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getTestSampleIdx(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
@_typing.overload
|
||||
def getValues(self, vi: int, sidx: cv2.typing.MatLike, values: float) -> None: ...
|
||||
@_typing.overload
|
||||
def getValues(self, vi: int, sidx: cv2.UMat, values: float) -> None: ...
|
||||
|
||||
def getDefaultSubstValues(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getCatCount(self, vi: int) -> int: ...
|
||||
|
||||
def getClassLabels(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getCatOfs(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getCatMap(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def setTrainTestSplit(self, count: int, shuffle: bool = ...) -> None: ...
|
||||
|
||||
def setTrainTestSplitRatio(self, ratio: float, shuffle: bool = ...) -> None: ...
|
||||
|
||||
def shuffleTrainTest(self) -> None: ...
|
||||
|
||||
def getTestSamples(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getNames(self, names: _typing.Sequence[str]) -> None: ...
|
||||
|
||||
@staticmethod
|
||||
def getSubVector(vec: cv2.typing.MatLike, idx: cv2.typing.MatLike) -> cv2.typing.MatLike: ...
|
||||
|
||||
@staticmethod
|
||||
def getSubMatrix(matrix: cv2.typing.MatLike, idx: cv2.typing.MatLike, layout: int) -> cv2.typing.MatLike: ...
|
||||
|
||||
@classmethod
|
||||
@_typing.overload
|
||||
def create(cls, samples: cv2.typing.MatLike, layout: int, responses: cv2.typing.MatLike, varIdx: cv2.typing.MatLike | None = ..., sampleIdx: cv2.typing.MatLike | None = ..., sampleWeights: cv2.typing.MatLike | None = ..., varType: cv2.typing.MatLike | None = ...) -> TrainData: ...
|
||||
@classmethod
|
||||
@_typing.overload
|
||||
def create(cls, samples: cv2.UMat, layout: int, responses: cv2.UMat, varIdx: cv2.UMat | None = ..., sampleIdx: cv2.UMat | None = ..., sampleWeights: cv2.UMat | None = ..., varType: cv2.UMat | None = ...) -> TrainData: ...
|
||||
|
||||
|
||||
class StatModel(cv2.Algorithm):
|
||||
# Functions
|
||||
def getVarCount(self) -> int: ...
|
||||
|
||||
def empty(self) -> bool: ...
|
||||
|
||||
def isTrained(self) -> bool: ...
|
||||
|
||||
def isClassifier(self) -> bool: ...
|
||||
|
||||
@_typing.overload
|
||||
def train(self, trainData: TrainData, flags: int = ...) -> bool: ...
|
||||
@_typing.overload
|
||||
def train(self, samples: cv2.typing.MatLike, layout: int, responses: cv2.typing.MatLike) -> bool: ...
|
||||
@_typing.overload
|
||||
def train(self, samples: cv2.UMat, layout: int, responses: cv2.UMat) -> bool: ...
|
||||
|
||||
@_typing.overload
|
||||
def calcError(self, data: TrainData, test: bool, resp: cv2.typing.MatLike | None = ...) -> tuple[float, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def calcError(self, data: TrainData, test: bool, resp: cv2.UMat | None = ...) -> tuple[float, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def predict(self, samples: cv2.typing.MatLike, results: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[float, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def predict(self, samples: cv2.UMat, results: cv2.UMat | None = ..., flags: int = ...) -> tuple[float, cv2.UMat]: ...
|
||||
|
||||
|
||||
class NormalBayesClassifier(StatModel):
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def predictProb(self, inputs: cv2.typing.MatLike, outputs: cv2.typing.MatLike | None = ..., outputProbs: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def predictProb(self, inputs: cv2.UMat, outputs: cv2.UMat | None = ..., outputProbs: cv2.UMat | None = ..., flags: int = ...) -> tuple[float, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> NormalBayesClassifier: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str, nodeName: str = ...) -> NormalBayesClassifier: ...
|
||||
|
||||
|
||||
class KNearest(StatModel):
|
||||
# Functions
|
||||
def getDefaultK(self) -> int: ...
|
||||
|
||||
def setDefaultK(self, val: int) -> None: ...
|
||||
|
||||
def getIsClassifier(self) -> bool: ...
|
||||
|
||||
def setIsClassifier(self, val: bool) -> None: ...
|
||||
|
||||
def getEmax(self) -> int: ...
|
||||
|
||||
def setEmax(self, val: int) -> None: ...
|
||||
|
||||
def getAlgorithmType(self) -> int: ...
|
||||
|
||||
def setAlgorithmType(self, val: int) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def findNearest(self, samples: cv2.typing.MatLike, k: int, results: cv2.typing.MatLike | None = ..., neighborResponses: cv2.typing.MatLike | None = ..., dist: cv2.typing.MatLike | None = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def findNearest(self, samples: cv2.UMat, k: int, results: cv2.UMat | None = ..., neighborResponses: cv2.UMat | None = ..., dist: cv2.UMat | None = ...) -> tuple[float, cv2.UMat, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> KNearest: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str) -> KNearest: ...
|
||||
|
||||
|
||||
class SVM(StatModel):
|
||||
# Functions
|
||||
def getType(self) -> int: ...
|
||||
|
||||
def setType(self, val: int) -> None: ...
|
||||
|
||||
def getGamma(self) -> float: ...
|
||||
|
||||
def setGamma(self, val: float) -> None: ...
|
||||
|
||||
def getCoef0(self) -> float: ...
|
||||
|
||||
def setCoef0(self, val: float) -> None: ...
|
||||
|
||||
def getDegree(self) -> float: ...
|
||||
|
||||
def setDegree(self, val: float) -> None: ...
|
||||
|
||||
def getC(self) -> float: ...
|
||||
|
||||
def setC(self, val: float) -> None: ...
|
||||
|
||||
def getNu(self) -> float: ...
|
||||
|
||||
def setNu(self, val: float) -> None: ...
|
||||
|
||||
def getP(self) -> float: ...
|
||||
|
||||
def setP(self, val: float) -> None: ...
|
||||
|
||||
def getClassWeights(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def setClassWeights(self, val: cv2.typing.MatLike) -> None: ...
|
||||
|
||||
def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
|
||||
|
||||
def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
|
||||
|
||||
def getKernelType(self) -> int: ...
|
||||
|
||||
def setKernel(self, kernelType: int) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def trainAuto(self, samples: cv2.typing.MatLike, layout: int, responses: cv2.typing.MatLike, kFold: int = ..., Cgrid: ParamGrid = ..., gammaGrid: ParamGrid = ..., pGrid: ParamGrid = ..., nuGrid: ParamGrid = ..., coeffGrid: ParamGrid = ..., degreeGrid: ParamGrid = ..., balanced: bool = ...) -> bool: ...
|
||||
@_typing.overload
|
||||
def trainAuto(self, samples: cv2.UMat, layout: int, responses: cv2.UMat, kFold: int = ..., Cgrid: ParamGrid = ..., gammaGrid: ParamGrid = ..., pGrid: ParamGrid = ..., nuGrid: ParamGrid = ..., coeffGrid: ParamGrid = ..., degreeGrid: ParamGrid = ..., balanced: bool = ...) -> bool: ...
|
||||
|
||||
def getSupportVectors(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getUncompressedSupportVectors(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
@_typing.overload
|
||||
def getDecisionFunction(self, i: int, alpha: cv2.typing.MatLike | None = ..., svidx: cv2.typing.MatLike | None = ...) -> tuple[float, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def getDecisionFunction(self, i: int, alpha: cv2.UMat | None = ..., svidx: cv2.UMat | None = ...) -> tuple[float, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@staticmethod
|
||||
def getDefaultGridPtr(param_id: int) -> ParamGrid: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> SVM: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str) -> SVM: ...
|
||||
|
||||
|
||||
class EM(StatModel):
|
||||
# Functions
|
||||
def getClustersNumber(self) -> int: ...
|
||||
|
||||
def setClustersNumber(self, val: int) -> None: ...
|
||||
|
||||
def getCovarianceMatrixType(self) -> int: ...
|
||||
|
||||
def setCovarianceMatrixType(self, val: int) -> None: ...
|
||||
|
||||
def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
|
||||
|
||||
def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
|
||||
|
||||
def getWeights(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getMeans(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getCovs(self, covs: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
|
||||
@_typing.overload
|
||||
def predict(self, samples: cv2.typing.MatLike, results: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[float, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def predict(self, samples: cv2.UMat, results: cv2.UMat | None = ..., flags: int = ...) -> tuple[float, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def predict2(self, sample: cv2.typing.MatLike, probs: cv2.typing.MatLike | None = ...) -> tuple[cv2.typing.Vec2d, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def predict2(self, sample: cv2.UMat, probs: cv2.UMat | None = ...) -> tuple[cv2.typing.Vec2d, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def trainEM(self, samples: cv2.typing.MatLike, logLikelihoods: cv2.typing.MatLike | None = ..., labels: cv2.typing.MatLike | None = ..., probs: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def trainEM(self, samples: cv2.UMat, logLikelihoods: cv2.UMat | None = ..., labels: cv2.UMat | None = ..., probs: cv2.UMat | None = ...) -> tuple[bool, cv2.UMat, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def trainE(self, samples: cv2.typing.MatLike, means0: cv2.typing.MatLike, covs0: cv2.typing.MatLike | None = ..., weights0: cv2.typing.MatLike | None = ..., logLikelihoods: cv2.typing.MatLike | None = ..., labels: cv2.typing.MatLike | None = ..., probs: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def trainE(self, samples: cv2.UMat, means0: cv2.UMat, covs0: cv2.UMat | None = ..., weights0: cv2.UMat | None = ..., logLikelihoods: cv2.UMat | None = ..., labels: cv2.UMat | None = ..., probs: cv2.UMat | None = ...) -> tuple[bool, cv2.UMat, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def trainM(self, samples: cv2.typing.MatLike, probs0: cv2.typing.MatLike, logLikelihoods: cv2.typing.MatLike | None = ..., labels: cv2.typing.MatLike | None = ..., probs: cv2.typing.MatLike | None = ...) -> tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def trainM(self, samples: cv2.UMat, probs0: cv2.UMat, logLikelihoods: cv2.UMat | None = ..., labels: cv2.UMat | None = ..., probs: cv2.UMat | None = ...) -> tuple[bool, cv2.UMat, cv2.UMat, cv2.UMat]: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> EM: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str, nodeName: str = ...) -> EM: ...
|
||||
|
||||
|
||||
class DTrees(StatModel):
|
||||
# Functions
|
||||
def getMaxCategories(self) -> int: ...
|
||||
|
||||
def setMaxCategories(self, val: int) -> None: ...
|
||||
|
||||
def getMaxDepth(self) -> int: ...
|
||||
|
||||
def setMaxDepth(self, val: int) -> None: ...
|
||||
|
||||
def getMinSampleCount(self) -> int: ...
|
||||
|
||||
def setMinSampleCount(self, val: int) -> None: ...
|
||||
|
||||
def getCVFolds(self) -> int: ...
|
||||
|
||||
def setCVFolds(self, val: int) -> None: ...
|
||||
|
||||
def getUseSurrogates(self) -> bool: ...
|
||||
|
||||
def setUseSurrogates(self, val: bool) -> None: ...
|
||||
|
||||
def getUse1SERule(self) -> bool: ...
|
||||
|
||||
def setUse1SERule(self, val: bool) -> None: ...
|
||||
|
||||
def getTruncatePrunedTree(self) -> bool: ...
|
||||
|
||||
def setTruncatePrunedTree(self, val: bool) -> None: ...
|
||||
|
||||
def getRegressionAccuracy(self) -> float: ...
|
||||
|
||||
def setRegressionAccuracy(self, val: float) -> None: ...
|
||||
|
||||
def getPriors(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def setPriors(self, val: cv2.typing.MatLike) -> None: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> DTrees: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str, nodeName: str = ...) -> DTrees: ...
|
||||
|
||||
|
||||
class RTrees(DTrees):
|
||||
# Functions
|
||||
def getCalculateVarImportance(self) -> bool: ...
|
||||
|
||||
def setCalculateVarImportance(self, val: bool) -> None: ...
|
||||
|
||||
def getActiveVarCount(self) -> int: ...
|
||||
|
||||
def setActiveVarCount(self, val: int) -> None: ...
|
||||
|
||||
def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
|
||||
|
||||
def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
|
||||
|
||||
def getVarImportance(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
@_typing.overload
|
||||
def getVotes(self, samples: cv2.typing.MatLike, flags: int, results: cv2.typing.MatLike | None = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def getVotes(self, samples: cv2.UMat, flags: int, results: cv2.UMat | None = ...) -> cv2.UMat: ...
|
||||
|
||||
def getOOBError(self) -> float: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> RTrees: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str, nodeName: str = ...) -> RTrees: ...
|
||||
|
||||
|
||||
class Boost(DTrees):
|
||||
# Functions
|
||||
def getBoostType(self) -> int: ...
|
||||
|
||||
def setBoostType(self, val: int) -> None: ...
|
||||
|
||||
def getWeakCount(self) -> int: ...
|
||||
|
||||
def setWeakCount(self, val: int) -> None: ...
|
||||
|
||||
def getWeightTrimRate(self) -> float: ...
|
||||
|
||||
def setWeightTrimRate(self, val: float) -> None: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> Boost: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str, nodeName: str = ...) -> Boost: ...
|
||||
|
||||
|
||||
class ANN_MLP(StatModel):
|
||||
# Functions
|
||||
def setTrainMethod(self, method: int, param1: float = ..., param2: float = ...) -> None: ...
|
||||
|
||||
def getTrainMethod(self) -> int: ...
|
||||
|
||||
def setActivationFunction(self, type: int, param1: float = ..., param2: float = ...) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def setLayerSizes(self, _layer_sizes: cv2.typing.MatLike) -> None: ...
|
||||
@_typing.overload
|
||||
def setLayerSizes(self, _layer_sizes: cv2.UMat) -> None: ...
|
||||
|
||||
def getLayerSizes(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
|
||||
|
||||
def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
|
||||
|
||||
def getBackpropWeightScale(self) -> float: ...
|
||||
|
||||
def setBackpropWeightScale(self, val: float) -> None: ...
|
||||
|
||||
def getBackpropMomentumScale(self) -> float: ...
|
||||
|
||||
def setBackpropMomentumScale(self, val: float) -> None: ...
|
||||
|
||||
def getRpropDW0(self) -> float: ...
|
||||
|
||||
def setRpropDW0(self, val: float) -> None: ...
|
||||
|
||||
def getRpropDWPlus(self) -> float: ...
|
||||
|
||||
def setRpropDWPlus(self, val: float) -> None: ...
|
||||
|
||||
def getRpropDWMinus(self) -> float: ...
|
||||
|
||||
def setRpropDWMinus(self, val: float) -> None: ...
|
||||
|
||||
def getRpropDWMin(self) -> float: ...
|
||||
|
||||
def setRpropDWMin(self, val: float) -> None: ...
|
||||
|
||||
def getRpropDWMax(self) -> float: ...
|
||||
|
||||
def setRpropDWMax(self, val: float) -> None: ...
|
||||
|
||||
def getAnnealInitialT(self) -> float: ...
|
||||
|
||||
def setAnnealInitialT(self, val: float) -> None: ...
|
||||
|
||||
def getAnnealFinalT(self) -> float: ...
|
||||
|
||||
def setAnnealFinalT(self, val: float) -> None: ...
|
||||
|
||||
def getAnnealCoolingRatio(self) -> float: ...
|
||||
|
||||
def setAnnealCoolingRatio(self, val: float) -> None: ...
|
||||
|
||||
def getAnnealItePerStep(self) -> int: ...
|
||||
|
||||
def setAnnealItePerStep(self, val: int) -> None: ...
|
||||
|
||||
def getWeights(self, layerIdx: int) -> cv2.typing.MatLike: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> ANN_MLP: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str) -> ANN_MLP: ...
|
||||
|
||||
|
||||
class LogisticRegression(StatModel):
|
||||
# Functions
|
||||
def getLearningRate(self) -> float: ...
|
||||
|
||||
def setLearningRate(self, val: float) -> None: ...
|
||||
|
||||
def getIterations(self) -> int: ...
|
||||
|
||||
def setIterations(self, val: int) -> None: ...
|
||||
|
||||
def getRegularization(self) -> int: ...
|
||||
|
||||
def setRegularization(self, val: int) -> None: ...
|
||||
|
||||
def getTrainMethod(self) -> int: ...
|
||||
|
||||
def setTrainMethod(self, val: int) -> None: ...
|
||||
|
||||
def getMiniBatchSize(self) -> int: ...
|
||||
|
||||
def setMiniBatchSize(self, val: int) -> None: ...
|
||||
|
||||
def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
|
||||
|
||||
def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def predict(self, samples: cv2.typing.MatLike, results: cv2.typing.MatLike | None = ..., flags: int = ...) -> tuple[float, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def predict(self, samples: cv2.UMat, results: cv2.UMat | None = ..., flags: int = ...) -> tuple[float, cv2.UMat]: ...
|
||||
|
||||
def get_learnt_thetas(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> LogisticRegression: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str, nodeName: str = ...) -> LogisticRegression: ...
|
||||
|
||||
|
||||
class SVMSGD(StatModel):
|
||||
# Functions
|
||||
def getWeights(self) -> cv2.typing.MatLike: ...
|
||||
|
||||
def getShift(self) -> float: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls) -> SVMSGD: ...
|
||||
|
||||
@classmethod
|
||||
def load(cls, filepath: str, nodeName: str = ...) -> SVMSGD: ...
|
||||
|
||||
def setOptimalParameters(self, svmsgdType: int = ..., marginType: int = ...) -> None: ...
|
||||
|
||||
def getSvmsgdType(self) -> int: ...
|
||||
|
||||
def setSvmsgdType(self, svmsgdType: int) -> None: ...
|
||||
|
||||
def getMarginType(self) -> int: ...
|
||||
|
||||
def setMarginType(self, marginType: int) -> None: ...
|
||||
|
||||
def getMarginRegularization(self) -> float: ...
|
||||
|
||||
def setMarginRegularization(self, marginRegularization: float) -> None: ...
|
||||
|
||||
def getInitialStepSize(self) -> float: ...
|
||||
|
||||
def setInitialStepSize(self, InitialStepSize: float) -> None: ...
|
||||
|
||||
def getStepDecreasingPower(self) -> float: ...
|
||||
|
||||
def setStepDecreasingPower(self, stepDecreasingPower: float) -> None: ...
|
||||
|
||||
def getTermCriteria(self) -> cv2.typing.TermCriteria: ...
|
||||
|
||||
def setTermCriteria(self, val: cv2.typing.TermCriteria) -> None: ...
|
||||
|
||||
|
||||
|
||||
@ -1,252 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
# Enumerations
|
||||
OCL_VECTOR_OWN: int
|
||||
OCL_VECTOR_MAX: int
|
||||
OCL_VECTOR_DEFAULT: int
|
||||
OclVectorStrategy = int
|
||||
"""One of [OCL_VECTOR_OWN, OCL_VECTOR_MAX, OCL_VECTOR_DEFAULT]"""
|
||||
|
||||
|
||||
Device_TYPE_DEFAULT: int
|
||||
DEVICE_TYPE_DEFAULT: int
|
||||
Device_TYPE_CPU: int
|
||||
DEVICE_TYPE_CPU: int
|
||||
Device_TYPE_GPU: int
|
||||
DEVICE_TYPE_GPU: int
|
||||
Device_TYPE_ACCELERATOR: int
|
||||
DEVICE_TYPE_ACCELERATOR: int
|
||||
Device_TYPE_DGPU: int
|
||||
DEVICE_TYPE_DGPU: int
|
||||
Device_TYPE_IGPU: int
|
||||
DEVICE_TYPE_IGPU: int
|
||||
Device_TYPE_ALL: int
|
||||
DEVICE_TYPE_ALL: int
|
||||
Device_FP_DENORM: int
|
||||
DEVICE_FP_DENORM: int
|
||||
Device_FP_INF_NAN: int
|
||||
DEVICE_FP_INF_NAN: int
|
||||
Device_FP_ROUND_TO_NEAREST: int
|
||||
DEVICE_FP_ROUND_TO_NEAREST: int
|
||||
Device_FP_ROUND_TO_ZERO: int
|
||||
DEVICE_FP_ROUND_TO_ZERO: int
|
||||
Device_FP_ROUND_TO_INF: int
|
||||
DEVICE_FP_ROUND_TO_INF: int
|
||||
Device_FP_FMA: int
|
||||
DEVICE_FP_FMA: int
|
||||
Device_FP_SOFT_FLOAT: int
|
||||
DEVICE_FP_SOFT_FLOAT: int
|
||||
Device_FP_CORRECTLY_ROUNDED_DIVIDE_SQRT: int
|
||||
DEVICE_FP_CORRECTLY_ROUNDED_DIVIDE_SQRT: int
|
||||
Device_EXEC_KERNEL: int
|
||||
DEVICE_EXEC_KERNEL: int
|
||||
Device_EXEC_NATIVE_KERNEL: int
|
||||
DEVICE_EXEC_NATIVE_KERNEL: int
|
||||
Device_NO_CACHE: int
|
||||
DEVICE_NO_CACHE: int
|
||||
Device_READ_ONLY_CACHE: int
|
||||
DEVICE_READ_ONLY_CACHE: int
|
||||
Device_READ_WRITE_CACHE: int
|
||||
DEVICE_READ_WRITE_CACHE: int
|
||||
Device_NO_LOCAL_MEM: int
|
||||
DEVICE_NO_LOCAL_MEM: int
|
||||
Device_LOCAL_IS_LOCAL: int
|
||||
DEVICE_LOCAL_IS_LOCAL: int
|
||||
Device_LOCAL_IS_GLOBAL: int
|
||||
DEVICE_LOCAL_IS_GLOBAL: int
|
||||
Device_UNKNOWN_VENDOR: int
|
||||
DEVICE_UNKNOWN_VENDOR: int
|
||||
Device_VENDOR_AMD: int
|
||||
DEVICE_VENDOR_AMD: int
|
||||
Device_VENDOR_INTEL: int
|
||||
DEVICE_VENDOR_INTEL: int
|
||||
Device_VENDOR_NVIDIA: int
|
||||
DEVICE_VENDOR_NVIDIA: int
|
||||
|
||||
KernelArg_LOCAL: int
|
||||
KERNEL_ARG_LOCAL: int
|
||||
KernelArg_READ_ONLY: int
|
||||
KERNEL_ARG_READ_ONLY: int
|
||||
KernelArg_WRITE_ONLY: int
|
||||
KERNEL_ARG_WRITE_ONLY: int
|
||||
KernelArg_READ_WRITE: int
|
||||
KERNEL_ARG_READ_WRITE: int
|
||||
KernelArg_CONSTANT: int
|
||||
KERNEL_ARG_CONSTANT: int
|
||||
KernelArg_PTR_ONLY: int
|
||||
KERNEL_ARG_PTR_ONLY: int
|
||||
KernelArg_NO_SIZE: int
|
||||
KERNEL_ARG_NO_SIZE: int
|
||||
|
||||
|
||||
# Classes
|
||||
class Device:
|
||||
# Functions
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
def name(self) -> str: ...
|
||||
|
||||
def extensions(self) -> str: ...
|
||||
|
||||
def isExtensionSupported(self, extensionName: str) -> bool: ...
|
||||
|
||||
def version(self) -> str: ...
|
||||
|
||||
def vendorName(self) -> str: ...
|
||||
|
||||
def OpenCL_C_Version(self) -> str: ...
|
||||
|
||||
def OpenCLVersion(self) -> str: ...
|
||||
|
||||
def deviceVersionMajor(self) -> int: ...
|
||||
|
||||
def deviceVersionMinor(self) -> int: ...
|
||||
|
||||
def driverVersion(self) -> str: ...
|
||||
|
||||
def type(self) -> int: ...
|
||||
|
||||
def addressBits(self) -> int: ...
|
||||
|
||||
def available(self) -> bool: ...
|
||||
|
||||
def compilerAvailable(self) -> bool: ...
|
||||
|
||||
def linkerAvailable(self) -> bool: ...
|
||||
|
||||
def doubleFPConfig(self) -> int: ...
|
||||
|
||||
def singleFPConfig(self) -> int: ...
|
||||
|
||||
def halfFPConfig(self) -> int: ...
|
||||
|
||||
def hasFP64(self) -> bool: ...
|
||||
|
||||
def hasFP16(self) -> bool: ...
|
||||
|
||||
def endianLittle(self) -> bool: ...
|
||||
|
||||
def errorCorrectionSupport(self) -> bool: ...
|
||||
|
||||
def executionCapabilities(self) -> int: ...
|
||||
|
||||
def globalMemCacheSize(self) -> int: ...
|
||||
|
||||
def globalMemCacheType(self) -> int: ...
|
||||
|
||||
def globalMemCacheLineSize(self) -> int: ...
|
||||
|
||||
def globalMemSize(self) -> int: ...
|
||||
|
||||
def localMemSize(self) -> int: ...
|
||||
|
||||
def localMemType(self) -> int: ...
|
||||
|
||||
def hostUnifiedMemory(self) -> bool: ...
|
||||
|
||||
def imageSupport(self) -> bool: ...
|
||||
|
||||
def imageFromBufferSupport(self) -> bool: ...
|
||||
|
||||
def intelSubgroupsSupport(self) -> bool: ...
|
||||
|
||||
def image2DMaxWidth(self) -> int: ...
|
||||
|
||||
def image2DMaxHeight(self) -> int: ...
|
||||
|
||||
def image3DMaxWidth(self) -> int: ...
|
||||
|
||||
def image3DMaxHeight(self) -> int: ...
|
||||
|
||||
def image3DMaxDepth(self) -> int: ...
|
||||
|
||||
def imageMaxBufferSize(self) -> int: ...
|
||||
|
||||
def imageMaxArraySize(self) -> int: ...
|
||||
|
||||
def vendorID(self) -> int: ...
|
||||
|
||||
def isAMD(self) -> bool: ...
|
||||
|
||||
def isIntel(self) -> bool: ...
|
||||
|
||||
def isNVidia(self) -> bool: ...
|
||||
|
||||
def maxClockFrequency(self) -> int: ...
|
||||
|
||||
def maxComputeUnits(self) -> int: ...
|
||||
|
||||
def maxConstantArgs(self) -> int: ...
|
||||
|
||||
def maxConstantBufferSize(self) -> int: ...
|
||||
|
||||
def maxMemAllocSize(self) -> int: ...
|
||||
|
||||
def maxParameterSize(self) -> int: ...
|
||||
|
||||
def maxReadImageArgs(self) -> int: ...
|
||||
|
||||
def maxWriteImageArgs(self) -> int: ...
|
||||
|
||||
def maxSamplers(self) -> int: ...
|
||||
|
||||
def maxWorkGroupSize(self) -> int: ...
|
||||
|
||||
def maxWorkItemDims(self) -> int: ...
|
||||
|
||||
def memBaseAddrAlign(self) -> int: ...
|
||||
|
||||
def nativeVectorWidthChar(self) -> int: ...
|
||||
|
||||
def nativeVectorWidthShort(self) -> int: ...
|
||||
|
||||
def nativeVectorWidthInt(self) -> int: ...
|
||||
|
||||
def nativeVectorWidthLong(self) -> int: ...
|
||||
|
||||
def nativeVectorWidthFloat(self) -> int: ...
|
||||
|
||||
def nativeVectorWidthDouble(self) -> int: ...
|
||||
|
||||
def nativeVectorWidthHalf(self) -> int: ...
|
||||
|
||||
def preferredVectorWidthChar(self) -> int: ...
|
||||
|
||||
def preferredVectorWidthShort(self) -> int: ...
|
||||
|
||||
def preferredVectorWidthInt(self) -> int: ...
|
||||
|
||||
def preferredVectorWidthLong(self) -> int: ...
|
||||
|
||||
def preferredVectorWidthFloat(self) -> int: ...
|
||||
|
||||
def preferredVectorWidthDouble(self) -> int: ...
|
||||
|
||||
def preferredVectorWidthHalf(self) -> int: ...
|
||||
|
||||
def printfBufferSize(self) -> int: ...
|
||||
|
||||
def profilingTimerResolution(self) -> int: ...
|
||||
|
||||
@classmethod
|
||||
def getDefault(cls) -> Device: ...
|
||||
|
||||
|
||||
class OpenCLExecutionContext:
|
||||
...
|
||||
|
||||
|
||||
# Functions
|
||||
def finish() -> None: ...
|
||||
|
||||
def haveAmdBlas() -> bool: ...
|
||||
|
||||
def haveAmdFft() -> bool: ...
|
||||
|
||||
def haveOpenCL() -> bool: ...
|
||||
|
||||
def setUseOpenCL(flag: bool) -> None: ...
|
||||
|
||||
def useOpenCL() -> bool: ...
|
||||
|
||||
|
||||
@ -1,51 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
# Enumerations
|
||||
POINTS: int
|
||||
LINES: int
|
||||
LINE_LOOP: int
|
||||
LINE_STRIP: int
|
||||
TRIANGLES: int
|
||||
TRIANGLE_STRIP: int
|
||||
TRIANGLE_FAN: int
|
||||
QUADS: int
|
||||
QUAD_STRIP: int
|
||||
POLYGON: int
|
||||
RenderModes = int
|
||||
"""One of [POINTS, LINES, LINE_LOOP, LINE_STRIP, TRIANGLES, TRIANGLE_STRIP, TRIANGLE_FAN, QUADS, QUAD_STRIP, POLYGON]"""
|
||||
|
||||
|
||||
Buffer_ARRAY_BUFFER: int
|
||||
BUFFER_ARRAY_BUFFER: int
|
||||
Buffer_ELEMENT_ARRAY_BUFFER: int
|
||||
BUFFER_ELEMENT_ARRAY_BUFFER: int
|
||||
Buffer_PIXEL_PACK_BUFFER: int
|
||||
BUFFER_PIXEL_PACK_BUFFER: int
|
||||
Buffer_PIXEL_UNPACK_BUFFER: int
|
||||
BUFFER_PIXEL_UNPACK_BUFFER: int
|
||||
Buffer_Target = int
|
||||
"""One of [Buffer_ARRAY_BUFFER, BUFFER_ARRAY_BUFFER, Buffer_ELEMENT_ARRAY_BUFFER, BUFFER_ELEMENT_ARRAY_BUFFER, Buffer_PIXEL_PACK_BUFFER, BUFFER_PIXEL_PACK_BUFFER, Buffer_PIXEL_UNPACK_BUFFER, BUFFER_PIXEL_UNPACK_BUFFER]"""
|
||||
|
||||
Buffer_READ_ONLY: int
|
||||
BUFFER_READ_ONLY: int
|
||||
Buffer_WRITE_ONLY: int
|
||||
BUFFER_WRITE_ONLY: int
|
||||
Buffer_READ_WRITE: int
|
||||
BUFFER_READ_WRITE: int
|
||||
Buffer_Access = int
|
||||
"""One of [Buffer_READ_ONLY, BUFFER_READ_ONLY, Buffer_WRITE_ONLY, BUFFER_WRITE_ONLY, Buffer_READ_WRITE, BUFFER_READ_WRITE]"""
|
||||
|
||||
Texture2D_NONE: int
|
||||
TEXTURE2D_NONE: int
|
||||
Texture2D_DEPTH_COMPONENT: int
|
||||
TEXTURE2D_DEPTH_COMPONENT: int
|
||||
Texture2D_RGB: int
|
||||
TEXTURE2D_RGB: int
|
||||
Texture2D_RGBA: int
|
||||
TEXTURE2D_RGBA: int
|
||||
Texture2D_Format = int
|
||||
"""One of [Texture2D_NONE, TEXTURE2D_NONE, Texture2D_DEPTH_COMPONENT, TEXTURE2D_DEPTH_COMPONENT, Texture2D_RGB, TEXTURE2D_RGB, Texture2D_RGBA, TEXTURE2D_RGBA]"""
|
||||
|
||||
|
||||
# Classes
|
||||
|
||||
@ -1,6 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
# Functions
|
||||
def setParallelForBackend(backendName: str, propagateNumThreads: bool = ...) -> bool: ...
|
||||
|
||||
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
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Binary file not shown.
Binary file not shown.
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@ -1,12 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
# Functions
|
||||
def addSamplesDataSearchPath(path: str) -> None: ...
|
||||
|
||||
def addSamplesDataSearchSubDirectory(subdir: str) -> None: ...
|
||||
|
||||
def findFile(relative_path: str, required: bool = ..., silentMode: bool = ...) -> str: ...
|
||||
|
||||
def findFileOrKeep(relative_path: str, silentMode: bool = ...) -> str: ...
|
||||
|
||||
|
||||
@ -1,39 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
import cv2.typing
|
||||
import typing as _typing
|
||||
|
||||
|
||||
# Classes
|
||||
class IntelligentScissorsMB:
|
||||
# Functions
|
||||
def __init__(self) -> None: ...
|
||||
|
||||
def setWeights(self, weight_non_edge: float, weight_gradient_direction: float, weight_gradient_magnitude: float) -> IntelligentScissorsMB: ...
|
||||
|
||||
def setGradientMagnitudeMaxLimit(self, gradient_magnitude_threshold_max: float = ...) -> IntelligentScissorsMB: ...
|
||||
|
||||
def setEdgeFeatureZeroCrossingParameters(self, gradient_magnitude_min_value: float = ...) -> IntelligentScissorsMB: ...
|
||||
|
||||
def setEdgeFeatureCannyParameters(self, threshold1: float, threshold2: float, apertureSize: int = ..., L2gradient: bool = ...) -> IntelligentScissorsMB: ...
|
||||
|
||||
@_typing.overload
|
||||
def applyImage(self, image: cv2.typing.MatLike) -> IntelligentScissorsMB: ...
|
||||
@_typing.overload
|
||||
def applyImage(self, image: cv2.UMat) -> IntelligentScissorsMB: ...
|
||||
|
||||
@_typing.overload
|
||||
def applyImageFeatures(self, non_edge: cv2.typing.MatLike, gradient_direction: cv2.typing.MatLike, gradient_magnitude: cv2.typing.MatLike, image: cv2.typing.MatLike | None = ...) -> IntelligentScissorsMB: ...
|
||||
@_typing.overload
|
||||
def applyImageFeatures(self, non_edge: cv2.UMat, gradient_direction: cv2.UMat, gradient_magnitude: cv2.UMat, image: cv2.UMat | None = ...) -> IntelligentScissorsMB: ...
|
||||
|
||||
def buildMap(self, sourcePt: cv2.typing.Point) -> None: ...
|
||||
|
||||
@_typing.overload
|
||||
def getContour(self, targetPt: cv2.typing.Point, contour: cv2.typing.MatLike | None = ..., backward: bool = ...) -> cv2.typing.MatLike: ...
|
||||
@_typing.overload
|
||||
def getContour(self, targetPt: cv2.typing.Point, contour: cv2.UMat | None = ..., backward: bool = ...) -> cv2.UMat: ...
|
||||
|
||||
|
||||
|
||||
@ -1,178 +0,0 @@
|
||||
__all__ = [
|
||||
"IntPointer",
|
||||
"MatLike",
|
||||
"MatShape",
|
||||
"Size",
|
||||
"Size2f",
|
||||
"Scalar",
|
||||
"Point",
|
||||
"Point2i",
|
||||
"Point2f",
|
||||
"Point2d",
|
||||
"Point3i",
|
||||
"Point3f",
|
||||
"Point3d",
|
||||
"Range",
|
||||
"Rect",
|
||||
"Rect2i",
|
||||
"Rect2f",
|
||||
"Rect2d",
|
||||
"Moments",
|
||||
"RotatedRect",
|
||||
"TermCriteria",
|
||||
"Vec2i",
|
||||
"Vec2f",
|
||||
"Vec2d",
|
||||
"Vec3i",
|
||||
"Vec3f",
|
||||
"Vec3d",
|
||||
"Vec4i",
|
||||
"Vec4f",
|
||||
"Vec4d",
|
||||
"Vec6f",
|
||||
"FeatureDetector",
|
||||
"DescriptorExtractor",
|
||||
"FeatureExtractor",
|
||||
"GProtoArg",
|
||||
"GProtoInputArgs",
|
||||
"GProtoOutputArgs",
|
||||
"GRunArg",
|
||||
"GOptRunArg",
|
||||
"GMetaArg",
|
||||
"Prim",
|
||||
"Matx33f",
|
||||
"Matx33d",
|
||||
"Matx44f",
|
||||
"Matx44d",
|
||||
"GTypeInfo",
|
||||
"ExtractArgsCallback",
|
||||
"ExtractMetaCallback",
|
||||
"LayerId",
|
||||
"IndexParams",
|
||||
"SearchParams",
|
||||
"map_string_and_string",
|
||||
"map_string_and_int",
|
||||
"map_string_and_vector_size_t",
|
||||
"map_string_and_vector_float",
|
||||
"map_int_and_double",
|
||||
]
|
||||
|
||||
import cv2.mat_wrapper
|
||||
import cv2.dnn
|
||||
import numpy
|
||||
import cv2
|
||||
import cv2.gapi.wip.draw
|
||||
import typing as _typing
|
||||
|
||||
|
||||
if _typing.TYPE_CHECKING:
|
||||
NumPyArrayNumeric = numpy.ndarray[_typing.Any, numpy.dtype[numpy.integer[_typing.Any] | numpy.floating[_typing.Any]]]
|
||||
else:
|
||||
NumPyArrayNumeric = numpy.ndarray
|
||||
|
||||
|
||||
if _typing.TYPE_CHECKING:
|
||||
NumPyArrayFloat32 = numpy.ndarray[_typing.Any, numpy.dtype[numpy.float32]]
|
||||
else:
|
||||
NumPyArrayFloat32 = numpy.ndarray
|
||||
|
||||
|
||||
if _typing.TYPE_CHECKING:
|
||||
NumPyArrayFloat64 = numpy.ndarray[_typing.Any, numpy.dtype[numpy.float64]]
|
||||
else:
|
||||
NumPyArrayFloat64 = numpy.ndarray
|
||||
|
||||
|
||||
if _typing.TYPE_CHECKING:
|
||||
TermCriteria_Type = cv2.TermCriteria_Type
|
||||
else:
|
||||
TermCriteria_Type = int
|
||||
|
||||
|
||||
IntPointer = int
|
||||
"""Represents an arbitrary pointer"""
|
||||
MatLike = _typing.Union[cv2.mat_wrapper.Mat, NumPyArrayNumeric]
|
||||
MatShape = _typing.Sequence[int]
|
||||
Size = _typing.Sequence[int]
|
||||
"""Required length is 2"""
|
||||
Size2f = _typing.Sequence[float]
|
||||
"""Required length is 2"""
|
||||
Scalar = _typing.Sequence[float]
|
||||
"""Required length is at most 4"""
|
||||
Point = _typing.Sequence[int]
|
||||
"""Required length is 2"""
|
||||
Point2i = Point
|
||||
Point2f = _typing.Sequence[float]
|
||||
"""Required length is 2"""
|
||||
Point2d = _typing.Sequence[float]
|
||||
"""Required length is 2"""
|
||||
Point3i = _typing.Sequence[int]
|
||||
"""Required length is 3"""
|
||||
Point3f = _typing.Sequence[float]
|
||||
"""Required length is 3"""
|
||||
Point3d = _typing.Sequence[float]
|
||||
"""Required length is 3"""
|
||||
Range = _typing.Sequence[int]
|
||||
"""Required length is 2"""
|
||||
Rect = _typing.Sequence[int]
|
||||
"""Required length is 4"""
|
||||
Rect2i = _typing.Sequence[int]
|
||||
"""Required length is 4"""
|
||||
Rect2f = _typing.Sequence[float]
|
||||
"""Required length is 4"""
|
||||
Rect2d = _typing.Sequence[float]
|
||||
"""Required length is 4"""
|
||||
Moments = _typing.Dict[str, float]
|
||||
RotatedRect = _typing.Tuple[Point2f, Size2f, float]
|
||||
"""Any type providing sequence protocol is supported"""
|
||||
TermCriteria = _typing.Tuple[TermCriteria_Type, int, float]
|
||||
"""Any type providing sequence protocol is supported"""
|
||||
Vec2i = _typing.Sequence[int]
|
||||
"""Required length is 2"""
|
||||
Vec2f = _typing.Sequence[float]
|
||||
"""Required length is 2"""
|
||||
Vec2d = _typing.Sequence[float]
|
||||
"""Required length is 2"""
|
||||
Vec3i = _typing.Sequence[int]
|
||||
"""Required length is 3"""
|
||||
Vec3f = _typing.Sequence[float]
|
||||
"""Required length is 3"""
|
||||
Vec3d = _typing.Sequence[float]
|
||||
"""Required length is 3"""
|
||||
Vec4i = _typing.Sequence[int]
|
||||
"""Required length is 4"""
|
||||
Vec4f = _typing.Sequence[float]
|
||||
"""Required length is 4"""
|
||||
Vec4d = _typing.Sequence[float]
|
||||
"""Required length is 4"""
|
||||
Vec6f = _typing.Sequence[float]
|
||||
"""Required length is 6"""
|
||||
FeatureDetector = cv2.Feature2D
|
||||
DescriptorExtractor = cv2.Feature2D
|
||||
FeatureExtractor = cv2.Feature2D
|
||||
GProtoArg = _typing.Union[Scalar, cv2.GMat, cv2.GOpaqueT, cv2.GArrayT]
|
||||
GProtoInputArgs = _typing.Sequence[GProtoArg]
|
||||
GProtoOutputArgs = _typing.Sequence[GProtoArg]
|
||||
GRunArg = _typing.Union[MatLike, Scalar, cv2.GOpaqueT, cv2.GArrayT, _typing.Sequence[_typing.Any], None]
|
||||
GOptRunArg = _typing.Optional[GRunArg]
|
||||
GMetaArg = _typing.Union[cv2.GMat, Scalar, cv2.GOpaqueT, cv2.GArrayT]
|
||||
Prim = _typing.Union[cv2.gapi.wip.draw.Text, cv2.gapi.wip.draw.Circle, cv2.gapi.wip.draw.Image, cv2.gapi.wip.draw.Line, cv2.gapi.wip.draw.Rect, cv2.gapi.wip.draw.Mosaic, cv2.gapi.wip.draw.Poly]
|
||||
Matx33f = NumPyArrayFloat32
|
||||
"""NDArray(shape=(3, 3), dtype=numpy.float32)"""
|
||||
Matx33d = NumPyArrayFloat64
|
||||
"""NDArray(shape=(3, 3), dtype=numpy.float64)"""
|
||||
Matx44f = NumPyArrayFloat32
|
||||
"""NDArray(shape=(4, 4), dtype=numpy.float32)"""
|
||||
Matx44d = NumPyArrayFloat64
|
||||
"""NDArray(shape=(4, 4), dtype=numpy.float64)"""
|
||||
GTypeInfo = _typing.Union[cv2.GMat, Scalar, cv2.GOpaqueT, cv2.GArrayT]
|
||||
ExtractArgsCallback = _typing.Callable[[_typing.Sequence[GTypeInfo]], _typing.Sequence[GRunArg]]
|
||||
ExtractMetaCallback = _typing.Callable[[_typing.Sequence[GTypeInfo]], _typing.Sequence[GMetaArg]]
|
||||
LayerId = cv2.dnn.DictValue
|
||||
IndexParams = _typing.Dict[str, _typing.Union[bool, int, float, str]]
|
||||
SearchParams = _typing.Dict[str, _typing.Union[bool, int, float, str]]
|
||||
map_string_and_string = _typing.Dict[str, str]
|
||||
map_string_and_int = _typing.Dict[str, int]
|
||||
map_string_and_vector_size_t = _typing.Dict[str, _typing.Sequence[int]]
|
||||
map_string_and_vector_float = _typing.Dict[str, _typing.Sequence[float]]
|
||||
map_int_and_double = _typing.Dict[int, float]
|
||||
Binary file not shown.
@ -1,14 +0,0 @@
|
||||
from collections import namedtuple
|
||||
|
||||
import cv2
|
||||
|
||||
|
||||
NativeMethodPatchedResult = namedtuple("NativeMethodPatchedResult",
|
||||
("py", "native"))
|
||||
|
||||
|
||||
def testOverwriteNativeMethod(arg):
|
||||
return NativeMethodPatchedResult(
|
||||
arg + 1,
|
||||
cv2.utils._native.testOverwriteNativeMethod(arg)
|
||||
)
|
||||
@ -1,109 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
import cv2.typing
|
||||
import typing as _typing
|
||||
|
||||
|
||||
from cv2.utils import fs as fs
|
||||
from cv2.utils import nested as nested
|
||||
|
||||
|
||||
# Classes
|
||||
class ClassWithKeywordProperties:
|
||||
lambda_: int
|
||||
@property
|
||||
def except_(self) -> int: ...
|
||||
|
||||
# Functions
|
||||
def __init__(self, lambda_arg: int = ..., except_arg: int = ...) -> None: ...
|
||||
|
||||
|
||||
|
||||
# Functions
|
||||
@_typing.overload
|
||||
def copyMatAndDumpNamedArguments(src: cv2.typing.MatLike, dst: cv2.typing.MatLike | None = ..., lambda_: int = ..., sigma: float = ...) -> tuple[str, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def copyMatAndDumpNamedArguments(src: cv2.UMat, dst: cv2.UMat | None = ..., lambda_: int = ..., sigma: float = ...) -> tuple[str, cv2.UMat]: ...
|
||||
|
||||
def dumpBool(argument: bool) -> str: ...
|
||||
|
||||
def dumpCString(argument: str) -> str: ...
|
||||
|
||||
def dumpDouble(argument: float) -> str: ...
|
||||
|
||||
def dumpFloat(argument: float) -> str: ...
|
||||
|
||||
@_typing.overload
|
||||
def dumpInputArray(argument: cv2.typing.MatLike) -> str: ...
|
||||
@_typing.overload
|
||||
def dumpInputArray(argument: cv2.UMat) -> str: ...
|
||||
|
||||
@_typing.overload
|
||||
def dumpInputArrayOfArrays(argument: _typing.Sequence[cv2.typing.MatLike]) -> str: ...
|
||||
@_typing.overload
|
||||
def dumpInputArrayOfArrays(argument: _typing.Sequence[cv2.UMat]) -> str: ...
|
||||
|
||||
@_typing.overload
|
||||
def dumpInputOutputArray(argument: cv2.typing.MatLike) -> tuple[str, cv2.typing.MatLike]: ...
|
||||
@_typing.overload
|
||||
def dumpInputOutputArray(argument: cv2.UMat) -> tuple[str, cv2.UMat]: ...
|
||||
|
||||
@_typing.overload
|
||||
def dumpInputOutputArrayOfArrays(argument: _typing.Sequence[cv2.typing.MatLike]) -> tuple[str, _typing.Sequence[cv2.typing.MatLike]]: ...
|
||||
@_typing.overload
|
||||
def dumpInputOutputArrayOfArrays(argument: _typing.Sequence[cv2.UMat]) -> tuple[str, _typing.Sequence[cv2.UMat]]: ...
|
||||
|
||||
def dumpInt(argument: int) -> str: ...
|
||||
|
||||
def dumpInt64(argument: int) -> str: ...
|
||||
|
||||
def dumpRange(argument: cv2.typing.Range) -> str: ...
|
||||
|
||||
def dumpRect(argument: cv2.typing.Rect) -> str: ...
|
||||
|
||||
def dumpRotatedRect(argument: cv2.typing.RotatedRect) -> str: ...
|
||||
|
||||
def dumpSizeT(argument: int) -> str: ...
|
||||
|
||||
def dumpString(argument: str) -> str: ...
|
||||
|
||||
def dumpTermCriteria(argument: cv2.typing.TermCriteria) -> str: ...
|
||||
|
||||
def dumpVec2i(value: cv2.typing.Vec2i = ...) -> str: ...
|
||||
|
||||
def dumpVectorOfDouble(vec: _typing.Sequence[float]) -> str: ...
|
||||
|
||||
def dumpVectorOfInt(vec: _typing.Sequence[int]) -> str: ...
|
||||
|
||||
def dumpVectorOfRect(vec: _typing.Sequence[cv2.typing.Rect]) -> str: ...
|
||||
|
||||
def generateVectorOfInt(len: int) -> _typing.Sequence[int]: ...
|
||||
|
||||
def generateVectorOfMat(len: int, rows: int, cols: int, dtype: int, vec: _typing.Sequence[cv2.typing.MatLike] | None = ...) -> _typing.Sequence[cv2.typing.MatLike]: ...
|
||||
|
||||
def generateVectorOfRect(len: int) -> _typing.Sequence[cv2.typing.Rect]: ...
|
||||
|
||||
@_typing.overload
|
||||
def testAsyncArray(argument: cv2.typing.MatLike) -> cv2.AsyncArray: ...
|
||||
@_typing.overload
|
||||
def testAsyncArray(argument: cv2.UMat) -> cv2.AsyncArray: ...
|
||||
|
||||
def testAsyncException() -> cv2.AsyncArray: ...
|
||||
|
||||
@_typing.overload
|
||||
def testOverloadResolution(value: int, point: cv2.typing.Point = ...) -> str: ...
|
||||
@_typing.overload
|
||||
def testOverloadResolution(rect: cv2.typing.Rect) -> str: ...
|
||||
|
||||
def testOverwriteNativeMethod(argument: int) -> int: ...
|
||||
|
||||
def testRaiseGeneralException() -> None: ...
|
||||
|
||||
def testReservedKeywordConversion(positional_argument: int, lambda_: int = ..., from_: int = ...) -> str: ...
|
||||
|
||||
def testRotatedRect(x: float, y: float, w: float, h: float, angle: float) -> cv2.typing.RotatedRect: ...
|
||||
|
||||
def testRotatedRectVector(x: float, y: float, w: float, h: float, angle: float) -> _typing.Sequence[cv2.typing.RotatedRect]: ...
|
||||
|
||||
|
||||
Binary file not shown.
@ -1,6 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
# Functions
|
||||
def getCacheDirectoryForDownloads() -> str: ...
|
||||
|
||||
|
||||
@ -1,31 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
# Classes
|
||||
class ExportClassName:
|
||||
# Classes
|
||||
class Params:
|
||||
int_value: int
|
||||
float_value: float
|
||||
|
||||
# Functions
|
||||
def __init__(self, int_param: int = ..., float_param: float = ...) -> None: ...
|
||||
|
||||
|
||||
|
||||
# Functions
|
||||
def getIntParam(self) -> int: ...
|
||||
|
||||
def getFloatParam(self) -> float: ...
|
||||
|
||||
@staticmethod
|
||||
def originalName() -> str: ...
|
||||
|
||||
@classmethod
|
||||
def create(cls, params: ExportClassName.Params = ...) -> ExportClassName: ...
|
||||
|
||||
|
||||
|
||||
# Functions
|
||||
def testEchoBooleanFunction(flag: bool) -> bool: ...
|
||||
|
||||
|
||||
@ -1,5 +0,0 @@
|
||||
opencv_version = "4.10.0.82"
|
||||
contrib = True
|
||||
headless = False
|
||||
rolling = False
|
||||
ci_build = True
|
||||
@ -1,27 +0,0 @@
|
||||
__all__: list[str] = []
|
||||
|
||||
import cv2
|
||||
|
||||
|
||||
# Functions
|
||||
def getBackendName(api: cv2.VideoCaptureAPIs) -> str: ...
|
||||
|
||||
def getBackends() -> _typing.Sequence[cv2.VideoCaptureAPIs]: ...
|
||||
|
||||
def getCameraBackendPluginVersion(api: cv2.VideoCaptureAPIs) -> tuple[str, int, int]: ...
|
||||
|
||||
def getCameraBackends() -> _typing.Sequence[cv2.VideoCaptureAPIs]: ...
|
||||
|
||||
def getStreamBackendPluginVersion(api: cv2.VideoCaptureAPIs) -> tuple[str, int, int]: ...
|
||||
|
||||
def getStreamBackends() -> _typing.Sequence[cv2.VideoCaptureAPIs]: ...
|
||||
|
||||
def getWriterBackendPluginVersion(api: cv2.VideoCaptureAPIs) -> tuple[str, int, int]: ...
|
||||
|
||||
def getWriterBackends() -> _typing.Sequence[cv2.VideoCaptureAPIs]: ...
|
||||
|
||||
def hasBackend(api: cv2.VideoCaptureAPIs) -> bool: ...
|
||||
|
||||
def isBackendBuiltIn(api: cv2.VideoCaptureAPIs) -> bool: ...
|
||||
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user