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1304b62f6e
Function shnould be ok now. To be tested, with some input images. Example file to be written.
92 lines
3.4 KiB
Python
92 lines
3.4 KiB
Python
'''
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This method contains all functions segmentation related.
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Created on Nov 26, 2011
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@author: Julien Lengrand-Lambert
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@email: julien@lengrand.fr
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'''
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import sys
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import cv
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def simple_region_growing(img, seed, threshold=1):
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"""
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A (very) simple implementation of region growing.
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Extracts a region of the input image depending on a start position and a stop condition.
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The input should be a single channel 8 bits image and the seed a pixel position (x, y).
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The threshold corresponds to the difference between outside pixel intensity and mean intensity of region.
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In case no new pixel is found, the growing stops.
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Outputs a single channel 8 bits binary (0 or 255) image. Extracted region is highlighted in white.
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"""
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# img test
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if not(img.depth == cv.IPL_DEPTH_8U):
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raise TypeError("(%s) 8U image expected!" % (sys._getframe().f_code.co_name))
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elif not(img.nChannels is 1):
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raise TypeError("(%s) 1C image expected!" % (sys._getframe().f_code.co_name))
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# threshold tests
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if (not isinstance(threshold, int)) :
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raise TypeError("(%s) Int expected!" % (sys._getframe().f_code.co_name))
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elif threshold < 0:
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raise ValueError("(%s) Positive value expected!" % (sys._getframe().f_code.co_name))
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# seed tests
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if not((isinstance(seed, tuple)) and (len(seed) is 2) ) :
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raise ValueError("(%s) (x, y) variable expected!" % (sys._getframe().f_code.co_name))
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dims = cv.GetSize(img)
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if (seed[0] or seed[1] ) < 0 :
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raise ValueError("(%s) Seed should have positive values!" % (sys._getframe().f_code.co_name))
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elif ((seed[0] > dims[0]) or (seed[1] > dims[1])):
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raise ValueError("(%s) Seed values greater than img size!" % (sys._getframe().f_code.co_name))
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reg = cv.CreateImage( dims, cv.IPL_DEPTH_8U, 1)
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cv.Zero(reg)
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#parameters
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mean_reg = float(img[seed[1], seed[0]])
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size = 1
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pix_area = dims[0]*dims[1]
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contour = [] # will be [ [[x1, y1], val1],..., [[xn, yn], valn] ]
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contour_val = []
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dist = 0
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# TODO: may be enhanced later with 8th connectivity
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orient = [(1, 0), (0, 1), (-1, 0), (0, -1)] # 4 connectivity
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cur_pix = [seed[0], seed[1]]
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#Spreading
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while(dist<thres and size<pix_area):
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#adding pixels
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for j in range(4):
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#select new candidate
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temp_pix = [cur_pix[0] +orient[j][0], cur_pix[1] +orient[j][1]]
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#check if it belongs to the image
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is_in_img = dims[0]>temp_pix[0]>0 and dims[1]>temp_pix[1]>0 #returns boolean
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#candidate is taken if not already selected before
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if (is_in_img and (reg[temp_pix[1], temp_pix[0]]==0)):
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contour.append(temp_pix)
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contour_val.append(img[temp_pix[1], temp_pix[0]] )
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reg[temp_pix[1], temp_pix[0]] = 150
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#add the nearest pixel of the contour in it
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dist = abs(int(numpy.mean(contour_val)) - mean_reg)
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dist_list = [abs(i - mean_reg) for i in contour_val ]
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dist = min(dist_list) #get min distance
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index = dist_list.index(min(dist_list)) #mean distance index
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size += 1 # updating region size
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reg[cur_pix[1], cur_pix[0]] = 255
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#updating mean MUST BE FLOAT
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mean_reg = (mean_reg*size + float(contour_val[index]))/(size+1)
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#updating seed
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cur_pix = contour[index]
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#removing pixel from neigborhood
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del contour[index]
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del contour_val[index]
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return reg
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### |