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Open Source Computer Vision Library
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110 lines
3.5 KiB
110 lines
3.5 KiB
''' This is a sample for histogram plotting for RGB images and grayscale images for better understanding of colour distribution |
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Benefit : Learn how to draw histogram of images |
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Get familier with cv2.calcHist, cv2.equalizeHist,cv2.normalize and some drawing functions |
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Level : Beginner or Intermediate |
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Functions : 1) hist_curve : returns histogram of an image drawn as curves |
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2) hist_lines : return histogram of an image drawn as bins ( only for grayscale images ) |
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Usage : python hist.py <image_file> |
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Abid Rahman 3/14/12 debug Gary Bradski |
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''' |
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import cv2 |
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import numpy as np |
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bins = np.arange(256).reshape(256,1) |
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def hist_curve(im): |
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h = np.zeros((300,256,3)) |
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if len(im.shape) == 2: |
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color = [(255,255,255)] |
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elif im.shape[2] == 3: |
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color = [ (255,0,0),(0,255,0),(0,0,255) ] |
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for ch, col in enumerate(color): |
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hist_item = cv2.calcHist([im],[ch],None,[256],[0,255]) |
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cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX) |
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hist=np.int32(np.around(hist_item)) |
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pts = np.int32(np.column_stack((bins,hist))) |
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cv2.polylines(h,[pts],False,col) |
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y=np.flipud(h) |
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return y |
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def hist_lines(im): |
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h = np.zeros((300,256,3)) |
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if len(im.shape)!=2: |
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print "hist_lines applicable only for grayscale images" |
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#print "so converting image to grayscale for representation" |
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im = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY) |
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hist_item = cv2.calcHist([im],[0],None,[256],[0,255]) |
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cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX) |
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hist=np.int32(np.around(hist_item)) |
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for x,y in enumerate(hist): |
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cv2.line(h,(x,0),(x,y),(255,255,255)) |
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y = np.flipud(h) |
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return y |
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if __name__ == '__main__': |
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import sys |
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if len(sys.argv)>1: |
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im = cv2.imread(sys.argv[1]) |
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else : |
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im = cv2.imread('../cpp/lena.jpg') |
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print "usage : python hist.py <image_file>" |
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gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY) |
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print ''' Histogram plotting \n |
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Keymap :\n |
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a - show histogram for color image in curve mode \n |
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b - show histogram in bin mode \n |
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c - show equalized histogram (always in bin mode) \n |
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d - show histogram for color image in curve mode \n |
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e - show histogram for a normalized image in curve mode \n |
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Esc - exit \n |
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''' |
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cv2.imshow('image',im) |
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while True: |
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k = cv2.waitKey(0)&0xFF |
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if k == ord('a'): |
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curve = hist_curve(im) |
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cv2.imshow('histogram',curve) |
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cv2.imshow('image',im) |
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print 'a' |
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elif k == ord('b'): |
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print 'b' |
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lines = hist_lines(im) |
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cv2.imshow('histogram',lines) |
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cv2.imshow('image',gray) |
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elif k == ord('c'): |
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print 'c' |
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equ = cv2.equalizeHist(gray) |
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lines = hist_lines(equ) |
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cv2.imshow('histogram',lines) |
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cv2.imshow('image',equ) |
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elif k == ord('d'): |
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print 'd' |
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curve = hist_curve(gray) |
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cv2.imshow('histogram',curve) |
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cv2.imshow('image',gray) |
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elif k == ord('e'): |
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print 'e' |
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norm = cv2.normalize(gray,alpha = 0,beta = 255,norm_type = cv2.NORM_MINMAX) |
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lines = hist_lines(norm) |
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cv2.imshow('histogram',lines) |
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cv2.imshow('image',norm) |
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elif k == 27: |
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print 'ESC' |
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cv2.destroyAllWindows() |
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break |
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cv2.destroyAllWindows() |
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