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copied!<p>the entropy solution seems problematic and overly intensive computationally. Why not edge detect?</p> <p>I just wrote this python code to solve this same problem for myself. My background was dirty white-ish, so the criteria that I used was darkness and color. I simplified this criteria by just taking the smallest of the R, B or B value for each pixel, so that black or saturated red both stood out the same. I also used the average of the however many darkest pixels for each row or column. Then I started at each edge and worked my way in till I crossed a threshold.</p> <p>Here is my code:</p> <pre><code>#these values set how sensitive the bounding box detection is threshold = 200 #the average of the darkest values must be _below_ this to count (0 is darkest, 255 is lightest) obviousness = 50 #how many of the darkest pixels to include (1 would mean a single dark pixel triggers it) from PIL import Image def find_line(vals): #implement edge detection once, use many times for i,tmp in enumerate(vals): tmp.sort() average = float(sum(tmp[:obviousness]))/len(tmp[:obviousness]) if average <= threshold: return i return i #i is left over from failed threshold finding, it is the bounds def getbox(img): #get the bounding box of the interesting part of a PIL image object #this is done by getting the darekest of the R, G or B value of each pixel #and finding were the edge gest dark/colored enough #returns a tuple of (left,upper,right,lower) width, height = img.size #for making a 2d array retval = [0,0,width,height] #values will be disposed of, but this is a black image's box pixels = list(img.getdata()) vals = [] #store the value of the darkest color for pixel in pixels: vals.append(min(pixel)) #the darkest of the R,G or B values #make 2d array vals = np.array([vals[i * width:(i + 1) * width] for i in xrange(height)]) #start with upper bounds forupper = vals.copy() retval[1] = find_line(forupper) #next, do lower bounds forlower = vals.copy() forlower = np.flipud(forlower) retval[3] = height - find_line(forlower) #left edge, same as before but roatate the data so left edge is top edge forleft = vals.copy() forleft = np.swapaxes(forleft,0,1) retval[0] = find_line(forleft) #and right edge is bottom edge of rotated array forright = vals.copy() forright = np.swapaxes(forright,0,1) forright = np.flipud(forright) retval[2] = width - find_line(forright) if retval[0] >= retval[2] or retval[1] >= retval[3]: print "error, bounding box is not legit" return None return tuple(retval) if __name__ == '__main__': image = Image.open('cat.jpg') box = getbox(image) print "result is: ",box result = image.crop(box) result.show() </code></pre>

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