mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 03:03:07 +08:00
Johannes Schönberger
83 lines
1.9 KiB
Cython
83 lines
1.9 KiB
Cython
#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 cnp
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cdef inline float tri_max(float a, float b, float c):
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cdef float MAX
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if a > b:
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MAX = a
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else:
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MAX = b
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if MAX > c:
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return MAX
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else:
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return c
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def histograms(cnp.ndarray[cnp.uint8_t, ndim=3] img, int nbins):
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'''Calculate the channel histograms of the current image.
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Parameters
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----------
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img : ndarray, uint8, ndim=3
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The image to calculate the histogram.
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nbins : int
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The number of bins.
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Returns
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-------
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out : (rcounts, gcounts, bcounts, vcounts)
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The binned histograms of the RGB channels and grayscale intensity.
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This is a NAIVE histogram routine, meant primarily for fast display.
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'''
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cdef int width = img.shape[1]
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cdef int height = img.shape[0]
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cdef cnp.ndarray[cnp.int32_t, ndim=1] r, g, b, v
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r = np.zeros((nbins,), dtype=np.int32)
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g = np.zeros((nbins,), dtype=np.int32)
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b = np.zeros((nbins,), dtype=np.int32)
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v = np.zeros((nbins,), dtype=np.int32)
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cdef int i, j, k, rbin, gbin, bbin, vbin
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cdef float bin_width = 255./ nbins
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cdef float R, G, B, V
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for i in range(height):
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for j in range(width):
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R = <float>img[i, j, 0]
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G = <float>img[i, j, 1]
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B = <float>img[i, j, 2]
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V = tri_max(R, G, B)
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rbin = <int>(R / bin_width)
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gbin = <int>(G / bin_width)
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bbin = <int>(B / bin_width)
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vbin = <int>(V / bin_width)
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# fully open last bin
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if R == 255:
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rbin -= 1
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if G == 255:
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gbin -= 1
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if B == 255:
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bbin -= 1
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if V == 255:
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vbin -= 1
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r[rbin] += 1
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g[gbin] += 1
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b[bbin] += 1
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v[vbin] += 1
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return (r, g, b, v)
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