mirror of
https://github.com/wassname/scikit-image.git
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Neil Yager
68 lines
2.3 KiB
Cython
68 lines
2.3 KiB
Cython
"""Cython implementation for computing a grey level co-occurance matrix
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"""
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import numpy as np
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cimport numpy as np
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cimport cython
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cdef extern from "math.h":
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double sin(double)
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double cos(double)
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@cython.boundscheck(False)
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def _glcm_loop(np.ndarray[dtype=np.uint8_t, ndim=2,
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negative_indices=False, mode='c'] image,
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np.ndarray[dtype=np.float64_t, ndim=1,
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negative_indices=False, mode='c'] distances,
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np.ndarray[dtype=np.float64_t, ndim=1,
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negative_indices=False, mode='c'] angles,
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int levels,
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np.ndarray[dtype=np.uint32_t, ndim=4,
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negative_indices=False, mode='c'] out
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):
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"""Perform co-occurnace matrix accumulation
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Parameters
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----------
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image : ndarray
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Input image, which is converted to the uint8 data type.
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distances : ndarray
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List of pixel pair distance offsets.
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angles : ndarray
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List of pixel pair angles in radians.
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levels : int
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The input image should contain integers in [0, levels-1],
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where levels indicate the number of grey-levels counted
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(typically 256 for an 8-bit image)
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out : ndarray
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On input a 4D array of zeros, and on output it contains
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the results of the GLCM computation.
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"""
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cdef:
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np.int32_t a_inx, d_idx
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np.int32_t r, c, rows, cols, row, col
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np.int32_t i, j
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rows = image.shape[0]
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cols = image.shape[1]
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for a_idx, angle in enumerate(angles):
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for d_idx, distance in enumerate(distances):
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for r in range(rows):
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for c in range(cols):
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i = image[r, c]
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# compute the location of the offset pixel
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row = r + <int>(sin(angle) * distance + 0.5)
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col = c + <int>(cos(angle) * distance + 0.5);
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# make sure the offset is within bounds
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if row >= 0 and row < rows and \
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col >= 0 and col < cols:
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j = image[row, col]
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if i >= 0 and i < levels and \
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j >= 0 and j < levels:
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out[i, j, d_idx, a_idx] += 1
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