def detectEdgesInColorPlanes(self, imageToSplit, colorPlane="red"): morpher = ImageMorpher() channels = cv2.split(imageToSplit) colorEdges = [] for i in range(len(channels)): blurred = cv2.GaussianBlur(channels[i], (7, 7), 3) colorEdges.append(morpher.dilateWithSquare(cv2.Canny(blurred, 50, 100), 3)) if (colorPlane == "red"): print 'anding' result = cv2.bitwise_and(colorEdges[0], colorEdges[1]) result = morpher.closeWithSquare(result, 2) #self.showImage(result) result = cv2.bitwise_and(result, cv2.bitwise_not(colorEdges[2])) elif (colorPlane == "green"): result = cv2.bitwise_and(colorEdges[0], colorEdges[2]) result = morpher.closeWithSquare(result, 2) result = cv2.bitwise_and(result, cv2.bitwise_not(colorEdges[1])) elif (colorPlane == "blue"): result = cv2.bitwise_and(colorEdges[1], colorEdges[2]) result = morpher.closeWithSquare(result, 2) result = cv2.bitwise_and(result, cv2.bitwise_not(colorEdges[0])) #self.showImage(result) result = morpher.openWithSquare(result, 2) #self.showImage(result) return result
def cutOutNonRed(self, imageToCut): redHighlighted = self.highlightDarkRed(imageToCut, 160, -1, 180, 20, False) #self.showImage(redHighlighted) morpher = ImageMorpher() #self.showImage(redHighlighted) mask1 = morpher.dilateWithSquare(redHighlighted, 10) #self.showImage(mask1) mask1 = morpher.closeWithSquare(mask1, 50) #self.showImage(mask1) maskedImg = cv2.bitwise_and(imageToCut, imageToCut, mask=mask1) #self.showImage(maskedImg) gray = self.convertColorToGrayscale(imageToCut) blurred = cv2.GaussianBlur(gray, (5, 5), 3) edges = cv2.Canny(blurred, 50, 100) #self.showImage(edges) mask2 = morpher.dilateWithSquare(edges, 10) #self.showImage(mask2) mask2 = morpher.closeWithSquare(mask2, 50) #self.showImage(mask2) secondMaskedImg = cv2.bitwise_and(maskedImg, maskedImg, mask=mask2) #self.showImage(secondMaskedImg) totalMask = cv2.bitwise_and(mask1, mask2) #self.showImage(totalMask) totalMask = morpher.operateWithVerticalLine(totalMask, morpher.DILATION, 15) totalMask = morpher.operateWithHorizontalLine(totalMask, morpher.DILATION, 15) #self.showImage(totalMask) totalMask = morpher.openWithSquare(totalMask, 20) #self.showImage(totalMask) totalMask = morpher.closeWithSquare(totalMask, 40) #self.showImage(totalMask) result = cv2.bitwise_and(imageToCut, imageToCut, mask=totalMask) #self.showImage(result) return result
def combined(inputfile, debug=False): start_file = inputfile img = cv2.imread(start_file) full_img = cv2.imread(start_file) original_img = deepcopy(img) fullheight, fullwidth, fullchannels = full_img.shape print "fullwidth %s" % fullwidth print "fullheight %s" % fullheight img, scale = ScaleImage.scale(full_img,1000) height, width, channels = img.shape scaled_img = deepcopy(img) ##First blur image in order to reduce noise blurred_img = deepcopy(img) print "Blurring image for filtering" blurred_img = cv2.GaussianBlur(blurred_img,(9,9),0) blurred_img = cv2.GaussianBlur(blurred_img,(9,9),0) blurred_img = cv2.GaussianBlur(blurred_img,(9,9),0) print "Blurring done" cv2.imwrite("Test_Images/Output_Images/blurred_img.jpg", blurred_img) ##Then find the average background color print "Calibrating color filtration" red,green,blue = Calibrate.findRed(blurred_img) ##Filter the image based on that average color print "Filtering blurred image" blurred_img = FilterImage.filter(blurred_img,red,green,blue) if (debug): showImage.showImage(blurred_img) ##Mask the original image based on the the blurred filter print "Masking original image based on blurred image" for y in range(0,height): for x in range(0,width): pxR = blurred_img[y,x,2] pxB = blurred_img[y,x,1] pxG = blurred_img[y,x,0] if ( (pxR == 0) and (pxG == 0) and (pxB == 0) ): img[y,x] = 0 cv2.imwrite("Test_Images/Output_Images/justFiltered.jpg", img) if (debug): showImage.showImage(img, "Just filtered") morpher = ImageMorpher() openimg = deepcopy(img) ## Use morphology to get rid of erratic blobs and specs print "Doing morphology to fix blobbies" openimg = morpher.openWithSquare(openimg,7) openimg = morpher.closeWithSquare(openimg,7) openimg = cv2.cvtColor(openimg,cv2.COLOR_BGR2GRAY) for x in range(0,height): for y in range(0,width): px = openimg[x,y] if ( (px == 0) ): openimg[x,y] = 0 else: openimg[x,y] = 255 ##Get contours for remaining blobs print "Contouring blobbies" contoured_img, contours, hierarchy = cv2.findContours(openimg,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) ##Get rid of the tiny remaining specs print "Removing small blobs" for i in range(0, len(contours)): cnt = contours[i] if (cv2.contourArea(cnt) < 2000): cv2.drawContours(contoured_img,cnt,-1,0,thickness=cv2.FILLED) ##Create a new mask to remove contours print "Masking image based on removed contours" for x in range(0,height): for y in range(0,width): px = contoured_img[x,y] if ( (px == 0) ): img[x,y] = [0,0,0] else: continue if (debug): showImage.showImage(img, "Filtered and corrected") color_filtered_img = deepcopy(img) cv2.imwrite("Test_Images/Output_Images/1_Template_Color_Filtered.jpg",img) temp = deepcopy(color_filtered_img) ##Watershed works best with blurred image print "Blurring image for watershed algorithm" img = cv2.GaussianBlur(img,(9,9),0) img = cv2.GaussianBlur(img,(9,9),0) ##### #Watershed segmentation b,g,r = cv2.split(img) rgb_img = cv2.merge([r,g,b]) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) # noise removal kernel = np.ones((2,2),np.uint8) closing = cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,kernel, iterations = 2) # sure background area sure_bg = cv2.dilate(closing,kernel,iterations=1) # Finding sure foreground area dist_transform = cv2.distanceTransform(sure_bg,cv2.DIST_L2,3) # Threshold ret, sure_fg = cv2.threshold(dist_transform,0.1*dist_transform.max(),255,0) # Finding unknown region sure_fg = np.uint8(sure_fg) unknown = cv2.subtract(sure_bg,sure_fg) # Marker labelling ret, markers = cv2.connectedComponents(sure_fg) # Add one to all labels so that sure background is not 0, but 1 markers = markers+1 # Now, mark the region of unknown with zero markers[unknown==255] = 0 markers = cv2.watershed(img,markers) img[markers == -1] = [255,0,0] cv2.imwrite('Test_Images/Output_Images/2_Template_Watershedded.jpg',img) if (debug): showImage.showImage(img,"Template_Watershedded") water_img = deepcopy(img) new_img = img for x in range(1,height-1): for y in range(1,width-1): if markers[x,y] == -1: new_img[x,y] = [255,255,255] else: new_img[x,y] = [0,0,0] cv2.imwrite('Test_Images/Output_Images/3_Template_Just_Watershed_Edges.jpg',new_img) ##### #Hough Circles print "Doing Hough cirlces on watershedded edges" scimg = cv2.imread('Test_Images/Output_Images/3_Template_Just_Watershed_Edges.jpg',0) #scimg = cv2.medianBlur(scimg,5) sccimg = cv2.cvtColor(scimg,cv2.COLOR_GRAY2BGR) circles = cv2.HoughCircles(scimg,cv2.HOUGH_GRADIENT,1,100, param1=50,param2=20,minRadius=30,maxRadius=100) ##circles = cv2.HoughCircles(scimg,cv2.HOUGH_GRADIENT,1,50, ## param1=50,param2=15,minRadius=15,maxRadius=50) counter = 0 radsum = 0 try: print circles.shape except Exception, e: print e