mirror of
https://github.com/wassname/scikit-image.git
synced 2026-08-12 12:30:16 +08:00
Clean up long lines and whitespace in opencv_backend.
This commit is contained in:
Stefan van der Walt
@@ -7,14 +7,14 @@ from opencv_type cimport *
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np.import_array()
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#-------------------------------------------------------------------------------
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#-----------------------------------------------------------------------------
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# Data Type Handling
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#-------------------------------------------------------------------------------
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#-----------------------------------------------------------------------------
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# for some reason these have to declared as dtype objects rather than just the
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# for some reason these have to declared as dtype objects rather than just the
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# dtype itself....
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UINT8 = np.dtype('uint8')
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INT8 = np.dtype('int8')
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UINT8 = np.dtype('uint8')
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INT8 = np.dtype('int8')
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INT16 = np.dtype('int16')
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INT32 = np.dtype('int32')
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FLOAT32 = np.dtype('float32')
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@@ -29,16 +29,16 @@ cdef int IPL_DEPTH_32F = 32
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cdef int IPL_DEPTH_64F = 64
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# I'd like a better to associate the IPL data type flag to the proper numpy
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# I'd like a better to associate the IPL data type flag to the proper numpy
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# types without using a dictionary.
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_ipltypes = {UINT8: IPL_DEPTH_8U, INT8: IPL_DEPTH_8S, INT16: IPL_DEPTH_16S,
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INT32: IPL_DEPTH_32S, FLOAT32: IPL_DEPTH_32F,
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FLOAT64: IPL_DEPTH_64F}
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_ipltypes = {UINT8: IPL_DEPTH_8U, INT8: IPL_DEPTH_8S, INT16: IPL_DEPTH_16S,
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INT32: IPL_DEPTH_32S, FLOAT32: IPL_DEPTH_32F,
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FLOAT64: IPL_DEPTH_64F}
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#-------------------------------------------------------------------------------
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#-----------------------------------------------------------------------------
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# Utility functions for IplImage creation, array validation, etc...
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#-------------------------------------------------------------------------------
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#-----------------------------------------------------------------------------
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cdef int IPLIMAGE_SIZE = sizeof(IplImage)
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@@ -48,7 +48,7 @@ cdef void populate_iplimage(np.ndarray arr, IplImage* img):
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# function before using this function.
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# This function assumes that the array has successfully passed
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# validation
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# everything that will never change
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img.nSize = IPLIMAGE_SIZE
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img.ID = 0
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@@ -58,29 +58,29 @@ cdef void populate_iplimage(np.ndarray arr, IplImage* img):
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img.maskROI = NULL
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img.imageId = NULL
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img.tileInfo = NULL
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cdef int channels
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cdef int ndim = arr.ndim
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cdef np.npy_intp* shape = arr.shape
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cdef np.npy_intp* strides = arr.strides
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cdef np.npy_intp* strides = arr.strides
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# nChannels is essentially the value of np.shape[2] of a 3D numpy array
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# for a 2D array, nChannels is 1
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if ndim == 2:
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img.nChannels = 1
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else:
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img.nChannels = shape[2]
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img.depth = _ipltypes[arr.dtype]
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img.width = shape[1]
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img.height = shape[0]
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img.height = shape[0]
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img.imageSize = arr.nbytes
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img.imageData = <char*>arr.data
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img.widthStep = strides[0]
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# really doesn't matter what this is set to, because opencv only uses it to
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# deallocate images, but it will never attempt to deallocate images we
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# create ourselves.
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# really doesn't matter what this is set to, because opencv only uses it to
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# deallocate images, but it will never attempt to deallocate images we
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# create ourselves.
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img.imageDataOrigin = <char*>NULL
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cdef int validate_array(np.ndarray arr) except -1:
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@@ -90,47 +90,49 @@ cdef int validate_array(np.ndarray arr) except -1:
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if arr.shape[2] > 4:
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raise ValueError('A 3D array must have 4 or less channels')
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if arr.dtype not in _ipltypes:
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raise ValueError('Arrays must have one of the following dtypes: uint8, int8, int16, int32, float32, float64')
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raise ValueError('Arrays must have one of the following dtypes: '
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'uint8, int8, int16, int32, float32, float64')
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return 1
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cdef int assert_dtype(np.ndarray arr, dtypes) except -1:
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if arr.dtype not in dtypes:
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raise ValueError('Unsupported dtype for this operation. \
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Supported dtypes are %s' % str(dtypes))
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return 1
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cdef int assert_ndims(np.ndarray arr, dims) except -1:
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if arr.ndim not in dims:
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raise ValueError('Incorrect number of dimensions')
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return 1
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cdef int assert_nchannels(np.ndarray arr, channels) except -1:
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cdef int nchannels
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if arr.ndim == 2:
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nchannels = 1
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else:
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nchannels = arr.shape[2]
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nchannels = arr.shape[2]
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if nchannels not in channels:
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raise ValueError('Incorrect number of channels')
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return 1
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cdef int assert_same_dtype(np.ndarray arr1, np.ndarray arr2) except -1:
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if arr1.dtype != arr2.dtype:
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raise ValueError('dtypes not same')
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return 1
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cdef int assert_same_shape(np.ndarray arr1, np.ndarray arr2) except -1:
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if not np.PyArray_SAMESHAPE(arr1, arr2):
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raise ValueError('arrays not same shape')
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return 1
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cdef int assert_same_width_and_height(np.ndarray arr1, np.ndarray arr2) except -1:
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cdef int assert_same_width_and_height(np.ndarray arr1, np.ndarray arr2) \
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except -1:
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cdef np.npy_intp* shape1 = arr1.shape
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cdef np.npy_intp* shape2 = arr2.shape
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if (shape1[0] != shape2[0]) or (shape1[1] != shape2[1]):
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raise ValueError('Arrays must have same width and height')
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return 1
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cdef int assert_like(np.ndarray arr1, np.ndarray arr2) except -1:
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assert_same_dtype(arr1, arr2)
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assert_same_shape(arr1, arr2)
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@@ -142,9 +144,9 @@ cdef int assert_not_sharing_data(np.ndarray arr1, np.ndarray arr2) except -1:
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the out array is not just a view of src array')
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return 1
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#-------------------------------------------------------------------------------
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# NumPy array convienences
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#-------------------------------------------------------------------------------
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#-----------------------------------------------------------------------------
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# NumPy array convienences
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#-----------------------------------------------------------------------------
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cdef np.ndarray new_array(int ndim, np.npy_intp* shape, dtype):
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# need to incref because numpy will apprently steal a dtype reference
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Py_INCREF(<object>dtype)
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@@ -154,35 +156,36 @@ cdef np.ndarray new_array_like(np.ndarray arr):
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# need to incref because numpy will apprently steal a dtype reference
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Py_INCREF(<object>arr.dtype)
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return PyArray_Empty(arr.ndim, arr.shape, arr.dtype, 0)
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cdef np.ndarray new_array_like_diff_dtype(np.ndarray arr, dtype):
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# need to incref because numpy will apprently steal a dtype reference
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# need to incref because numpy will apprently steal a dtype reference
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Py_INCREF(<object>dtype)
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return PyArray_Empty(arr.ndim, arr.shape, dtype, 0)
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cdef np.npy_intp* clone_array_shape(np.ndarray arr):
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# make sure you call PyMem_Free after your done with the shape
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cdef int ndim = arr.ndim
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cdef np.npy_intp* shape = <np.npy_intp*>PyMem_Malloc(ndim * sizeof(np.npy_intp))
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cdef np.npy_intp* shape = <np.npy_intp*>PyMem_Malloc(
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ndim * sizeof(np.npy_intp))
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cdef int i
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for i in range(ndim):
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shape[i] = arr.shape[i]
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return shape
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cdef np.npy_intp get_array_nbytes(np.ndarray arr):
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cdef np.npy_intp nbytes = np.PyArray_NBYTES(arr)
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return nbytes
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#-------------------------------------------------------------------------------
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#-----------------------------------------------------------------------------
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# OpenCV convienences
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#-------------------------------------------------------------------------------
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#-----------------------------------------------------------------------------
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cdef CvPoint2D32f* array_as_cvPoint2D32f_ptr(np.ndarray arr):
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cdef CvPoint2D32f* point2Darr
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cdef CvPoint2D32f* point2Darr
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point2Darr = <CvPoint2D32f*>arr.data
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return point2Darr
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cdef CvTermCriteria get_cvTermCriteria(int iterations, double epsilon):
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cdef CvTermCriteria crit
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cdef CvTermCriteria crit
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if iterations and epsilon:
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crit.type = <int>(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS)
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crit.max_iter = iterations
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@@ -196,10 +199,3 @@ cdef CvTermCriteria get_cvTermCriteria(int iterations, double epsilon):
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crit.max_iter = 0
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crit.epsilon = epsilon
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return crit
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