ShapeMatching Using Fourier Descriptor

github地址：https://github.com/AliceLeBrigant/ShapeMatching

这个代码上来就花轮廓，根据轮廓查找相似的，背景必须纯色，商用价值不高。

      import numpy as np
      import cv2
      print("Shape Matching Using Fourier Descriptor")
      distThreshold = 0.06
      ix, iy = -1, -1
      rect = (0, 0, 1, 1)
      manually = True
      temSeleteFlag = False
      temReadyFlag = False
      temConfirmFlag = False
      matchOverFlag = False
      templeteComVector = []
      sampleComVectors = []
      sampleContours = []
      # Manually select templete by mouse, On/Off by manually flag
      def selectTemplete(event, x, y, flags, param):
      global rect, temSeleteFlag, temReadyFlag, ix, iy
      if event == cv2.EVENT_LBUTTONDOWN and temReadyFlag == False:
       temSeleteFlag = True
       ix, iy = x, y
      elif event == cv2.EVENT_LBUTTONUP:
      if temReadyFlag == False and temSeleteFlag == True:
      # rect is selected templete ROI
       rect = (min(ix, x), min(iy, y), abs(ix - x), abs(iy - y))
      # draw a blue rectangle after selection
       cv2.rectangle(imgOri, (ix, iy), (x, y), (255, 0, 0), 2)
       temSeleteFlag = False
       temReadyFlag = True
      # Main findcontour function
      def getContours(img):
       imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
      # Threshold white paper(background) to white pixel(255), word is actully black(0)
       retvalth, imgthreshold = cv2.threshold(imgray, 50, 255, cv2.THRESH_BINARY)
      # We want words are white, backgournd is black, easy for opencv findcontour function
       imgthresholdNot = cv2.bitwise_not(imgthreshold)
      # Dilation make all 6 to form a closed loop
       kernel = np.ones((5, 5), np.uint8)
       imgdilation = cv2.dilate(imgthresholdNot, kernel, iterations=2)
      # Must use EXTERNAL outer contours, Must use CHAIN_APPROX_NONE method(not change points)
       imgcontours, contours, hierarchy = cv2.findContours(imgdilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
      return contours
      # Get complex vector of templete contour
      def getTempleteCV():
      # This is the templete region that we select by mouse or default
       templeteROI = imgOricpy[rect[1]:rect[1] + rect[3], rect[0]:rect[0] + rect[2]]
      # Automatically find templete contour
       tpContour = getContours(templeteROI)
      for contour in tpContour:
       x, y, w, h = cv2.boundingRect(contour)
      for point in contour:
      # -x and -y are to make left and upper boundry start from 0
       templeteComVector.append(complex(point[0][0] - x, (point[0][1] - y)))
      # Get complex vectors of testees contours
      def getSampleCV():
       spContours = getContours(imgOricpy)
      # cv2.drawContours(imgOri, spContours, -1, (0, 0, 255), 1)
      for contour in spContours:
       sampleComVector = []
       x, y, w, h = cv2.boundingRect(contour)
       cv2.rectangle(imgOri, (x, y), (x + w, y + h), (100, 100, 100), 1)
      for point in contour:
       sampleComVector.append(complex(point[0][0] - x, (point[0][1] - y)))
      # sampleComVectors store CV of all testees contours
       sampleComVectors.append(sampleComVector)
      # sampleContours store all testees contours, same order with sampleComVectors
       sampleContours.append(contour)
      # Calculate fourier transform of templete CV
      def getempleteFD():
      return np.fft.fft(templeteComVector)
      # Calculate fourier transform of sample CVs
      def getsampleFDs():
       FDs = []
      for sampleVector in sampleComVectors:
       sampleFD = np.fft.fft(sampleVector)
       FDs.append(sampleFD)
      return FDs
      # Make fourier descriptor invariant to rotaition and start point
      def rotataionInvariant(fourierDesc):
      for index, value in enumerate(fourierDesc):
       fourierDesc[index] = np.absolute(value)
      return fourierDesc
      # Make fourier descriptor invariant to scale
      def scaleInvariant(fourierDesc):
       firstVal = fourierDesc[0]
      for index, value in enumerate(fourierDesc):
       fourierDesc[index] = value / firstVal
      return fourierDesc
      # Make fourier descriptor invariant to translation
      def transInvariant(fourierDesc):
      return fourierDesc[1:len(fourierDesc)]
      # Get the lowest X of frequency values from the fourier values.
      def getLowFreqFDs(fourierDesc):
      # frequence order returned by np.fft is (0, 0.1, 0.2, 0.3, ...... , -0.3, -0.2, -0.1)
      # Note: in transInvariant(), we already remove first FD(0 frequency)
      return fourierDesc[:5]
      # Get the final FD that we want to use to calculate distance
      def finalFD(fourierDesc):
       fourierDesc = rotataionInvariant(fourierDesc)
       fourierDesc = scaleInvariant(fourierDesc)
       fourierDesc = transInvariant(fourierDesc)
       fourierDesc = getLowFreqFDs(fourierDesc)
      return fourierDesc
      # Core match function
      def match(tpFD, spFDs):
       tpFD = finalFD(tpFD)
      # dist store the distance, same order as spContours
       dist = []
       font = cv2.FONT_HERSHEY_SIMPLEX
      for spFD in spFDs:
       spFD = finalFD(spFD)
      # Calculate Euclidean distance between templete and testee
       dist.append(np.linalg.norm(np.array(spFD) - np.array(tpFD)))
       x, y, w, h = cv2.boundingRect(sampleContours[len(dist) - 1])
      # Draw distance on image
       distText = str(round(dist[len(dist) - 1], 2))
       cv2.putText(imgOri, distText, (x, y - 8), font, 0.5, (0, 0, 0), 1, cv2.LINE_AA)
      # print str(len(dist)) + ": " + str(dist[len(dist)-1])
      # if distance is less than threshold, it will be good match.
      if dist[len(dist) - 1] < distThreshold:
       cv2.rectangle(imgOri, (x - 5, y - 5), (x + w + 5, y + h + 5), (40, 255, 0), 2)
      # --------------------------------------------------------------------------
      # Main loop
      imgOri = cv2.imread(r"E:\new/a2.jpg", 1)
      # imOricpy is for processing, imgOri is for showing
      imgOricpy = imgOri.copy()
      cv2.namedWindow("Original Image")
      if manually == True:
      # Manually select templete by mouse
       cv2.setMouseCallback("Original Image", selectTemplete)
      else:
      # Default region: upper 6
       rect = (50, 100, 130, 160)
       cv2.rectangle(imgOri, (50, 100), (180, 260), (255, 0, 0), 2)
       temReadyFlag = True
       temConfirmFlag = True
      while (True):
       cv2.imshow("Original Image", imgOri)
      if temReadyFlag == True and matchOverFlag == False and temConfirmFlag == True:
      # Get complex vector
       getTempleteCV()
       getSampleCV()
      # Get fourider descriptor
       tpFD = getempleteFD()
       sampleFDs = getsampleFDs()
      # real match function
       match(tpFD, sampleFDs)
       matchOverFlag = True
       cv2.imwrite("result.jpg", imgOri)
      # Resize img for showing
       imgShow = cv2.resize(imgOri, None, fx=0.66, fy=0.66, interpolation=cv2.INTER_CUBIC)
       cv2.imshow("Small Size Show", imgShow)
       key = cv2.waitKey(1) & 0xFF
      if key == ord('y') or key == ord('Y'):
      # Press Y for templete confirm once mouse selection done
       temConfirmFlag = True
      elif key == ord('q') or key == ord('Q'):
      # Press q for quit
      break
      cv2.destroyAllWindows()
  
 

文章来源: blog.csdn.net，作者：网奇，版权归原作者所有，如需转载，请联系作者。

原文链接：blog.csdn.net/jacke121/article/details/95377492