Completed generated modules

python · Avasam · Oct 10, 2022 · Oct 10, 2022 · Oct 10, 2022 · Oct 11, 2022
commit 28dc455c4a9dda7906c6ff3b1e7f9a62fd0cd4cb
diff --git a/stubs/opencv-python/cv2/__init__.pyi b/stubs/opencv-python/cv2/__init__.pyi
@@ -10,12 +10,12 @@ from cv2 import (
     version as version,
 )
 from cv2.cv2 import *
-from cv2.mat_wrapper import Mat as _WrappedMat, _NDArray
+from cv2.mat_wrapper import Mat as _WrappedMat, _NDArray, _NDArrayF
 
 __all__: list[str] = []
 
 def bootstrap() -> None: ...
 
 Mat: TypeAlias = _WrappedMat | _NDArray
 # TODO: Make Mat generic with int or float
-_MatF: TypeAlias = _WrappedMat | _NDArray  # noqa: Y047
+_MatF: TypeAlias = _WrappedMat | _NDArrayF  # noqa: Y047
diff --git a/stubs/opencv-python/cv2/cuda.pyi b/stubs/opencv-python/cv2/cuda.pyi
@@ -0,0 +1,78 @@
+from typing import TypeVar, overload
+
+from cv2 import Mat
+from cv2.cv2 import _Boolean, _NumericScalar, _UMat, cuda_Event, cuda_GpuMat, cuda_GpuMat_Allocator, cuda_Stream
+
+_TGpuMat = TypeVar("_TGpuMat", bound=cuda_GpuMat | _UMat)
+
+DEVICE_INFO_COMPUTE_MODE_DEFAULT: int
+DEVICE_INFO_COMPUTE_MODE_EXCLUSIVE: int
+DEVICE_INFO_COMPUTE_MODE_EXCLUSIVE_PROCESS: int
+DEVICE_INFO_COMPUTE_MODE_PROHIBITED: int
+DYNAMIC_PARALLELISM: int
+DeviceInfo_ComputeModeDefault: int
+DeviceInfo_ComputeModeExclusive: int
+DeviceInfo_ComputeModeExclusiveProcess: int
+DeviceInfo_ComputeModeProhibited: int
+EVENT_BLOCKING_SYNC: int
+EVENT_DEFAULT: int
+EVENT_DISABLE_TIMING: int
+EVENT_INTERPROCESS: int
+Event_BLOCKING_SYNC: int
+Event_DEFAULT: int
+Event_DISABLE_TIMING: int
+Event_INTERPROCESS: int
+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
+HOST_MEM_PAGE_LOCKED: int
+HOST_MEM_SHARED: int
+HOST_MEM_WRITE_COMBINED: int
+HostMem_PAGE_LOCKED: int
+HostMem_SHARED: int
+HostMem_WRITE_COMBINED: int
+NATIVE_DOUBLE: int
+SHARED_ATOMICS: int
+WARP_SHUFFLE_FUNCTIONS: int
+
+def Even
10000
t_elapsedTime(start: cuda_Event, end: cuda_Event) -> float: ...
+def GpuMat_defaultAllocator() -> cuda_GpuMat_Allocator: ...
+def GpuMat_setDefaultAllocator(allocator: cuda_GpuMat_Allocator) -> None: ...
+def Stream_Null() -> cuda_Stream: ...
+def TargetArchs_has(major: int | None, minor: int | None) -> bool: ...
+def TargetArchs_hasBin(major: int | None, minor: int | None) -> bool: ...
+def TargetArchs_hasEqualOrGreater(major: int | None, minor: int | None) -> bool: ...
+def TargetArchs_hasEqualOrGreaterBin(major: int | None, minor: int | None) -> bool: ...
+def TargetArchs_hasEqualOrGreaterPtx(major: int | None, minor: int | None) -> bool: ...
+def TargetArchs_hasEqualOrLessPtx(major: int | None, minor: int | None) -> bool: ...
+def TargetArchs_hasPtx(major: int | None, minor: int | None) -> bool: ...
+@overload
+def createContinuous(rows: int, cols: int, type: int, arr: _NumericScalar) -> Mat: ...  # type: ignore[misc]  # https://github.com/python/mypy/issues/8881
+@overload
+def createContinuous(rows: int, cols: int, type: int, arr: _TGpuMat) -> _TGpuMat: ...  # type: ignore[misc]  # https://github.com/python/mypy/issues/8881
+@overload
+def createContinuous(rows: int | None, cols: int | None, type: int | None, arr: cuda_GpuMat | _UMat = ...) -> None: ...
+@overload
+def ensureSizeIsEnough(rows: int, cols: int, type: int, arr: _NumericScalar) -> Mat: ...  # type: ignore[misc]  # https://github.com/python/mypy/issues/8881
+@overload
+def ensureSizeIsEnough(rows: int, cols: int, type: int, arr: _TGpuMat) -> _TGpuMat: ...  # type: ignore[misc]  # https://github.com/python/mypy/issues/8881
+@overload
+def ensureSizeIsEnough(rows: int | None, cols: int | None, type: int | None, arr: cuda_GpuMat | _UMat = ...) -> None: ...
+def getCudaEnabledDeviceCount() -> int: ...
+def getDevice() -> int: ...
+def printCudaDeviceInfo(device: int | None) -> None: ...
+def printShortCudaDeviceInfo(device: int | None) -> None: ...
+def registerPageLocked(m: Mat | _NumericScalar) -> None: ...
+def resetDevice() -> None: ...
+def setBufferPoolConfig(deviceId: int | None, stackSize: int | None, stackCount: int | None) -> None: ...
+def setBufferPoolUsage(on: _Boolean) -> None: ...
+def setDevice(device: int | None) -> None: ...
+def unregisterPageLocked(m: Mat | _NumericScalar) -> None: ...
diff --git a/stubs/opencv-python/cv2/cv2.pyi b/stubs/opencv-python/cv2/cv2.pyi
diff --git a/stubs/opencv-python/cv2/detail.pyi b/stubs/opencv-python/cv2/detail.pyi
@@ -10,10 +10,7 @@ from cv2.cv2 import (
     _Point,
     _Rect,
     _Size,
-    _TUMat,
-    _TUMatF,
     _UMat,
-    _UMatF,
     detail_BestOf2NearestMatcher,
     detail_Blender,
     detail_ExposureCompensator,
@@ -151,13 +148,19 @@ def computeImageFeatures(
 def computeImageFeatures2(featuresFinder: Feature2D, image: _UMat, mask: _UMat = ...) -> detail_ImageFeatures: ...
 def createLaplacePyr(img: _UMat, num_levels: int, pyr: Sequence[_UMat]) -> tuple[UMat, ...]: ...
 def createLaplacePyrGpu(img: _UMat, num_levels: int, pyr: Sequence[_UMat]) -> tuple[UMat, ...]: ...
-def createWeightMap(mask: _TUMat, sharpness: float, weight: _TUMat) -> _TUMat: ...
+@overload
+def createWeightMap(mask: Mat, sharpness: float, weight: Mat) -> Mat: ...
+@overload
+def createWeightMap(mask: _UMat, sharpness: float, weight: _UMat) -> UMat: ...
 def focalsFromHomography(H: Mat, f0: float, f1: float, f0_ok: bool, f1_ok: bool) -> None: ...
 def leaveBiggestComponent(
     features: Sequence[detail_ImageFeatures], pairwise_matches: Sequence[detail_MatchesInfo], conf_threshold: float
 ) -> tuple[int, ...]: ...
 def matchesGraphAsString(pathes: Sequence[str], pairwise_matches: Sequence[detail_MatchesInfo], conf_threshold: float) -> str: ...
-def normalizeUsingWeightMap(weight: _UMatF, src: _TUMatF) -> _TUMatF: ...
+@overload
+def normalizeUsingWeightMap(weight: Mat, src: Mat) -> Mat: ...
+@overload
+def normalizeUsingWeightMap(weight: _UMat, src: _UMat) -> UMat: ...
 def overlapRoi(tl1: _Point, tl2: _Point, sz1: _Size, sz2: _Size, roi: _Rect) -> bool: ...
 def restoreImageFromLaplacePyr(pyr: Sequence[_UMat]) -> tuple[UMat, ...]: ...
 def restoreImageFromLaplacePyrGpu(pyr: Sequence[_UMat]) -> tuple[UMat, ...]: ...

diff --git a/stubs/opencv-python/cv2/dnn.pyi b/stubs/opencv-python/cv2/dnn.pyi
@@ -0,0 +1,110 @@
+from collections.abc import Sequence
+from typing import overload
+from typing_extensions import TypeAlias
+
+from cv2 import Mat, _MatF
+from cv2.cv2 import _Boolean, _NumericScalar, _RectFloat, _RotatedRect, _Scalar, _Size, _UMat, dnn_Net
+from cv2.mat_wrapper import _NDArray, _NDArrayF
+
+_Buffer: TypeAlias = Sequence[_NumericScalar] | bytes | None
+
+DNN_BACKEND_CUDA: int
+DNN_BACKEND_DEFAULT: int
+DNN_BACKEND_HALIDE: int
+DNN_BACKEND_INFERENCE_ENGINE: int
+DNN_BACKEND_OPENCV: int
+DNN_BACKEND_VKCOM: int
+DNN_BACKEND_WEBNN: int
+DNN_TARGET_CPU: int
+DNN_TARGET_CUDA: int
+DNN_TARGET_CUDA_FP16: int
+DNN_TARGET_FPGA: int
+DNN_TARGET_HDDL: int
+DNN_TARGET_MYRIAD: int
+DNN_TARGET_OPENCL: int
+DNN_TARGET_OPENCL_FP16: int
+DNN_TARGET_VULKAN: int
+SOFT_NMSMETHOD_SOFTNMS_GAUSSIAN: int
+SOFT_NMSMETHOD_SOFTNMS_LINEAR: int
+SoftNMSMethod_SOFTNMS_GAUSSIAN: int
+SoftNMSMethod_SOFTNMS_LINEAR: int
+
+def NMSBoxes(
+    bboxes: Sequence[_RectFloat | None] | None,
+    scores: Sequence[float | None] | None,
+    score_threshold: float | None,
+    nms_threshold: float | None,
+    eta: float | None = ...,
+    top_k: int | None = ...,
+) -> _NDArray: ...
+def NMSBoxesRotated(
+    bboxes: Sequence[_RotatedRect | None] | None,
+    scores: Sequence[float | None] | None,
+    score_threshold: float | None,
+    nms_threshold: float | None,
+    eta: float | None = ...,
+    top_k: int | None = ...,
+) -> _NDArray: ...
+@overload
+def Net_readFromModelOptimizer(xml: str, bin: str) -> dnn_Net: ...
+@overload
+def Net_readFromModelOptimizer(bufferModelConfig: _Buffer, bufferWeights: _Buffer) -> dnn_Net: ...
+def blobFromImage(
+    image: _UMat | None,
+    scalefactor: float | None = ...,
+    size: _Size | None = ...,
+    mean: _Scalar = ...,
+    swapRB: _Boolean = ...,
+    crop: _Boolean = ...,
+    ddepth: int | None = ...,
+) -> Mat: ...
+def blobFromImages(
+    images: Sequence[_UMat | None],
+    scalefactor: float | None = ...,
+    size: _Size | None = ...,
+    mean: _Scalar = ...,
+    swapRB: _Boolean = ...,
+    crop: _Boolean = ...,
+    ddepth: int | None = ...,
+) -> Mat: ...
+def getAvailableTargets(be: int | None) -> _NDArray: ...
+def imagesFromBlob(blob_: _MatF, images_: Sequence[_MatF | Mat] = ...) -> tuple[_MatF, ...]: ...
+@overload
+def readNet(model: str, config: str = ..., framework: str = ...) -> dnn_Net: ...
+@overload
+def readNet(framework: str, bufferModel: _Buffer, bufferConfig: _Buffer = ...) -> dnn_Net: ...
+@overload
+def readNetFromCaffe(prototxt: str, caffeModel: str = ...) -> dnn_Net: ...
+@overload
+def readNetFromCaffe(bufferProto: _Buffer, bufferModel: _Buffer = ...) -> dnn_Net: ...
+@overload
+def readNetFromDarknet(cfgFile: str, darknetModel: str = ...) -> dnn_Net: ...
+@overload
+def readNetFromDarknet(bufferCfg: _Buffer, bufferModel: _Buffer = ...) -> dnn_Net: ...
+@overload
+def readNetFromModelOptimizer(xml: str, bin: str) -> dnn_Net: ...
+@overload
+def readNetFromModelOptimizer(bufferModelConfig: _Buffer, bufferWeights: _Buffer) -> dnn_Net: ...
+@overload
+def readNetFromONNX(onnxFile: str) -> dnn_Net: ...
+@overload
+def readNetFromONNX(buffer: _Buffer) -> dnn_Net: ...
+@overload
+def readNetFromTensorflow(model: str, config: str = ...) -> dnn_Net: ...
+@overload
+def readNetFromTensorflow(bufferModel: _Buffer, bufferConfig: _Buffer = ...) -> dnn_Net: ...
+def readNetFromTorch(model: str, isBinary: _Boolean = ..., evaluate: _Boolean = ...) -> dnn_Net: ...
+def readTensorFromONNX(path: str) -> Mat: ...
+def readTorchBlob(filename: str, isBinary: _Boolean = ...) -> Mat: ...
+def shrinkCaffeModel(src: str, dst: str, layersTypes: Sequence[str] = ...) -> None: ...
+def softNMSBoxes(
+    bboxes: Sequence[_RectFloat | None] | None,
+    scores: Sequence[float | None] | None,
+    score_threshold: float | None,
+    nms_threshold: float | None,
+    eta: float | None = ...,
+    top_k: int | None = ...,
+    sigma: float | None = ...,
+    method: int | None = ...,
+) -> tuple[_NDArrayF, _NDArray]: ...
+def writeTextGraph(model: str, output: str) -> None: ...