mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-31 12:41:20 +08:00
271 lines
9.2 KiB
Cython
271 lines
9.2 KiB
Cython
""" to compile this use:
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>>> python setup.py build_ext --inplace
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to generate html report use:
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>>> cython -a core8.pxd
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"""
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#cython: cdivision=True
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#cython: boundscheck=False
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#cython: nonecheck=False
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#cython: wraparound=False
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import numpy as np
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cimport numpy as np
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from libc.stdlib cimport malloc, free
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#---------------------------------------------------------------------------
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# 8 bit core kernel
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#---------------------------------------------------------------------------
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cdef inline np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,Py_ssize_t r, Py_ssize_t c,np.uint8_t* mask):
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""" returns 1 if given(r,c) coordinate are within the image frame ([0-rows],[0-cols]) and
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inside the given mask
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returns 0 otherwise
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"""
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if r < 0 or r > rows - 1 or c < 0 or c > cols - 1:
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return 0
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else:
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if mask[r*cols+c]:
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return 1
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else:
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return 0
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cdef inline _core8(np.uint8_t kernel(Py_ssize_t*, float, np.uint8_t),
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np.ndarray[np.uint8_t, ndim=2] image,
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np.ndarray[np.uint8_t, ndim=2] selem,
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np.ndarray[np.uint8_t, ndim=2] mask,
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np.ndarray[np.uint8_t, ndim=2] out,
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char shift_x, char shift_y):
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""" Main loop, this function computes the histogram for each image point
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- data is uint8
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- result is uint8 casted
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"""
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cdef Py_ssize_t rows = image.shape[0]
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cdef Py_ssize_t cols = image.shape[1]
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cdef Py_ssize_t srows = selem.shape[0]
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cdef Py_ssize_t scols = selem.shape[1]
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cdef Py_ssize_t centre_r = int(selem.shape[0] / 2) + shift_y
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cdef Py_ssize_t centre_c = int(selem.shape[1] / 2) + shift_x
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# check that structuring element center is inside the element bounding box
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assert centre_r >= 0
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assert centre_c >= 0
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assert centre_r < srows
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assert centre_c < scols
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image = np.ascontiguousarray(image)
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if mask is None:
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mask = np.ones((rows, cols), dtype=np.uint8)
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else:
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mask = np.ascontiguousarray(mask)
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if out is None:
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out = np.zeros((rows, cols), dtype=np.uint8)
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else:
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out = np.ascontiguousarray(out)
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mask = np.ascontiguousarray(mask)
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# define pointers to the data
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cdef np.uint8_t* out_data = <np.uint8_t*>out.data
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cdef np.uint8_t* image_data = <np.uint8_t*>image.data
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cdef np.uint8_t* mask_data = <np.uint8_t*>mask.data
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# define local variable types
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cdef Py_ssize_t r, c, rr, cc, s, value, local_max, i, even_row
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cdef float pop # number of pixels actually inside the neighborhood (float)
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# allocate memory with malloc
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cdef Py_ssize_t max_se = srows*scols
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# number of element in each attack border
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cdef Py_ssize_t num_se_n, num_se_s, num_se_e, num_se_w
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# the current local histogram distribution
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cdef Py_ssize_t* histo = <Py_ssize_t*>malloc(256 * sizeof(Py_ssize_t))
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# these lists contain the relative pixel row and column for each of the 4 attack borders
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# east, west, north and south
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# e.g. se_e_r lists the rows of the east structuring element border
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cdef Py_ssize_t* se_e_r = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
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cdef Py_ssize_t* se_e_c = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
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cdef Py_ssize_t* se_w_r = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
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cdef Py_ssize_t* se_w_c = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
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cdef Py_ssize_t* se_n_r = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
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cdef Py_ssize_t* se_n_c = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
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cdef Py_ssize_t* se_s_r = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
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cdef Py_ssize_t* se_s_c = <Py_ssize_t*>malloc(max_se * sizeof(Py_ssize_t))
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# build attack and release borders
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# by using difference along axis
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t = np.hstack((selem,np.zeros((selem.shape[0],1))))
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t_e = np.diff(t,axis=1)==-1
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t = np.hstack((np.zeros((selem.shape[0],1)),selem))
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t_w = np.diff(t,axis=1)==1
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t = np.vstack((selem,np.zeros((1,selem.shape[1]))))
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t_s = np.diff(t,axis=0)==-1
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t = np.vstack((np.zeros((1,selem.shape[1])),selem))
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t_n = np.diff(t,axis=0)==1
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num_se_n = num_se_s = num_se_e = num_se_w = 0
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for r in range(srows):
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for c in range(scols):
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if t_e[r,c]:
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se_e_r[num_se_e] = r - centre_r
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se_e_c[num_se_e] = c - centre_c
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num_se_e += 1
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if t_w[r,c]:
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se_w_r[num_se_w] = r - centre_r
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se_w_c[num_se_w] = c - centre_c
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num_se_w += 1
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if t_n[r,c]:
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se_n_r[num_se_n] = r - centre_r
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se_n_c[num_se_n] = c - centre_c
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num_se_n += 1
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if t_s[r,c]:
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se_s_r[num_se_s] = r - centre_r
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se_s_c[num_se_s] = c - centre_c
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num_se_s += 1
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# initial population and histogram (kernel is centered on the first row and column)
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for i in range(256):
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histo[i] = 0
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pop = 0
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for r in range(srows):
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for c in range(scols):
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rr = r - centre_r
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cc = c - centre_c
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if selem[r, c]:
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if is_in_mask(rows,cols,rr,cc,mask_data):
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value = image_data[rr * cols + cc]
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histo[value] += 1
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pop += 1.
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r = 0
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c = 0
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# kernel --------------------------------------------------------------------
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out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c])
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# kernel --------------------------------------------------------------------
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# main loop
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r = 0
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for even_row in range(0,rows,2):
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# ---> west to east
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for c in range(1,cols):
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for s in range(num_se_e):
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rr = r + se_e_r[s]
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cc = c + se_e_c[s]
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if is_in_mask(rows,cols,rr,cc,mask_data):
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value = image_data[rr * cols + cc]
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histo[value] += 1
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pop += 1.
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for s in range(num_se_w):
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rr = r + se_w_r[s]
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cc = c + se_w_c[s] - 1
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if is_in_mask(rows,cols,rr,cc,mask_data):
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value = image_data[rr * cols + cc]
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histo[value] -= 1
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pop -= 1.
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# kernel --------------------------------------------------------------------
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out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c])
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# kernel --------------------------------------------------------------------
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r += 1 # pass to the next row
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if r>=rows:
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break
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# ---> north to south
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for s in range(num_se_s):
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rr = r + se_s_r[s]
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cc = c + se_s_c[s]
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if is_in_mask(rows,cols,rr,cc,mask_data):
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value = image_data[rr * cols + cc]
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histo[value] += 1
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pop += 1.
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for s in range(num_se_n):
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rr = r + se_n_r[s] - 1
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cc = c + se_n_c[s]
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if is_in_mask(rows,cols,rr,cc,mask_data):
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value = image_data[rr * cols + cc]
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histo[value] -= 1
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pop -= 1.
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# kernel --------------------------------------------------------------------
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out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c])
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# kernel --------------------------------------------------------------------
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# ---> east to west
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for c in range(cols-2,-1,-1):
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for s in range(num_se_w):
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rr = r + se_w_r[s]
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cc = c + se_w_c[s]
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if is_in_mask(rows,cols,rr,cc,mask_data):
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value = image_data[rr * cols + cc]
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histo[value] += 1
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pop += 1.
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for s in range(num_se_e):
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rr = r + se_e_r[s]
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cc = c + se_e_c[s] + 1
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if is_in_mask(rows,cols,rr,cc,mask_data):
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value = image_data[rr * cols + cc]
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histo[value] -= 1
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pop -= 1.
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# kernel --------------------------------------------------------------------
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out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c])
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# kernel --------------------------------------------------------------------
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r += 1 # pass to the next row
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if r>=rows:
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break
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# ---> north to south
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for s in range(num_se_s):
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rr = r + se_s_r[s]
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cc = c + se_s_c[s]
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if is_in_mask(rows,cols,rr,cc,mask_data):
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value = image_data[rr * cols + cc]
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histo[value] += 1
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pop += 1.
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for s in range(num_se_n):
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rr = r + se_n_r[s] - 1
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cc = c + se_n_c[s]
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if is_in_mask(rows,cols,rr,cc,mask_data):
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value = image_data[rr * cols + cc]
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histo[value] -= 1
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pop -= 1.
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# kernel --------------------------------------------------------------------
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out_data[r * cols + c] = kernel(histo,pop,image_data[r * cols + c])
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# kernel --------------------------------------------------------------------
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# release memory allocated by malloc
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free(se_e_r)
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free(se_e_c)
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free(se_w_r)
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free(se_w_c)
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free(se_n_r)
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free(se_n_c)
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free(se_s_r)
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free(se_s_c)
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free(histo)
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return out
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