我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用cv2.cornerHarris()。
def animpingpong(self): print self print self.Object print self.Object.Name obj=self.Object img = cv2.imread(obj.imageFile) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) gray = np.float32(gray) dst = cv2.cornerHarris(gray,3,3,0.00001) dst = cv2.dilate(dst,None) img[dst>0.01*dst.max()]=[0,0,255] from matplotlib import pyplot as plt plt.subplot(121),plt.imshow(img,cmap = 'gray') plt.title('Edge Image'), plt.xticks([]), plt.yticks([]) plt.subplot(122),plt.imshow(dst,cmap = 'gray') plt.title('Corner Image'), plt.xticks([]), plt.yticks([]) plt.show()
def animpingpong(self): obj=self.Object img=None if not obj.imageFromNode: img = cv2.imread(obj.imageFile) else: img = obj.imageNode.ViewObject.Proxy.img.copy() print (obj.blockSize,obj.ksize,obj.k) try: gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) gray = np.float32(gray) print "normale" except: im2=cv2.cvtColor(img,cv2.COLOR_GRAY2RGB) gray = cv2.cvtColor(im2,cv2.COLOR_RGB2GRAY) print "except" dst = cv2.cornerHarris(gray,obj.blockSize,obj.ksize*2+1,obj.k/10000) dst = cv2.dilate(dst,None) img[dst>0.01*dst.max()]=[0,0,255] dst2=img.copy() dst2[dst<0.01*dst.max()]=[255,255,255] dst2[dst>0.01*dst.max()]=[0,0,255] if not obj.matplotlib: cv2.imshow(obj.Label,img) else: from matplotlib import pyplot as plt plt.subplot(121),plt.imshow(img,cmap = 'gray') plt.title('Edge Image'), plt.xticks([]), plt.yticks([]) plt.subplot(122),plt.imshow(dst2,cmap = 'gray') plt.title('Corner Image'), plt.xticks([]), plt.yticks([]) plt.show() self.img=img
def createCV_cornerharris(): print "create CV cornerharris ... 2" obj= createCV(True) obj.Label='Harris' obj.addProperty('App::PropertyInteger','blockSize',"cornerHarris").blockSize=2 obj.addProperty('App::PropertyInteger','ksize',"cornerHarris").ksize=3 obj.addProperty('App::PropertyFloat','k',"cornerHarris").k=1.0 _CV_cornerharris(obj,__dir__+ '/icons/icon2.svg') miki2=miki.Miki2(MyApp,s6,obj) return obj
def animpingpong(self): obj=self.Object img=None if not obj.imageFromNode: img = cv2.imread(obj.imageFile) else: print "copy image ..." img = obj.imageNode.ViewObject.Proxy.img.copy() print "cpied" print " loaded" print (obj.blockSize,obj.ksize,obj.k) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) gray = np.float32(gray) # dst = cv2.cornerHarris(gray,3,3,0.00001) dst = cv2.cornerHarris(gray,obj.blockSize,obj.ksize*2+1,obj.k/10000) dst = cv2.dilate(dst,None) img[dst>0.01*dst.max()]=[0,0,255] if True: print "zeige" cv2.imshow(obj.Label,img) print "gezeigt" else: from matplotlib import pyplot as plt plt.subplot(121),plt.imshow(img,cmap = 'gray') plt.title('Edge Image'), plt.xticks([]), plt.yticks([]) plt.subplot(122),plt.imshow(dst,cmap = 'gray') plt.title('Corner Image'), plt.xticks([]), plt.yticks([]) plt.show() print "fertig" self.img=img
def createCV_demo(): print "create CV demo ..." obj=FreeCAD.ActiveDocument.addObject('App::DocumentObjectGroupPython','Image') obj.addProperty('App::PropertyFile','imageFile',"base").imageFile='/home/thomas/Bilder/c1.png' obj.addProperty('App::PropertyLink','imageNode',"base") obj.addProperty('App::PropertyBool','imageFromNode',"base").imageFromNode=False obj.addProperty('App::PropertyInteger','blockSize',"cornerHarris").blockSize=2 obj.addProperty('App::PropertyInteger','ksize',"cornerHarris").ksize=3 obj.addProperty('App::PropertyFloat','k',"cornerHarris").k=1.0 _CV_demo(obj,'/icons/bounder.png') _ViewProviderCV_demo(obj.ViewObject,__dir__+ '/icons/icon1.svg') app=MyApp() miki2=miki.Miki() miki2.app=app app.root=miki2 app.obj=obj obj.ViewObject.Proxy.cmenu.append(["Dialog",lambda:miki2.run(MyApp.s6)]) obj.ViewObject.Proxy.edit= lambda:miki2.run(MyApp.s6) return obj # # derived classes #
def harris(gray): dst = cv2.cornerHarris(gray, 2, 3, 0.04) dst = cv2.dilate(dst,None) return dst
def Harris_Corner(self): self.threshold = 0.999999999999 temp_i = self.image_i.copy() temp1_i = self.image_i.copy() gray_i = cv2.cvtColor(temp_i, cv2.COLOR_BGR2GRAY) gray_i = numpy.float32(gray_i) dst_i = cv2.cornerHarris(gray_i,2,3,0.025) dst_i = cv2.dilate(dst_i,None) # Threshold for an optimal value, it may vary depending on the image. temp_i[dst_i<0.01*dst_i.max()]=[0,0,0] temp_i[dst_i>=0.01*dst_i.max()]=[255,255,255] temp1_i[dst_i>0.01*dst_i.max()]=[0,0,255] hist_i = cv2.calcHist([temp_i], [0], None, [256], [0, 256]) temp_j = self.image_j.copy() temp1_j = self.image_j.copy() gray_j = cv2.cvtColor(temp_j, cv2.COLOR_BGR2GRAY) gray_j = numpy.float32(gray_j) dst_j = cv2.cornerHarris(gray_j,2,3,0.025) dst_j = cv2.dilate(dst_j,None) # Threshold for an optimal value, it may vary depending on the image. temp_j[dst_j<0.01*dst_j.max()]=[0,0,0] temp_j[dst_j>=0.01*dst_j.max()]=[255,255,255] temp1_j[dst_j>0.01*dst_j.max()]=[0,0,255] hist_j = cv2.calcHist([temp_j], [0], None, [256], [0, 256]) self.measure = cv2.compareHist(hist_i, hist_j, cv.CV_COMP_CORREL) self.assertGreater(self.measure, self.threshold) print self.measure cv2.imshow('Input X',temp1_i) cv2.waitKey(0) cv2.imshow('Input Y',temp1_j) cv2.waitKey(0)
def Harris_Corner(self): self.threshold = 0.999999999999 temp_i = self.image_i.copy() temp1_i = self.image_i.copy() gray_i = cv2.cvtColor(temp_i, cv2.COLOR_BGR2GRAY) gray_i = numpy.float32(gray_i) dst_i = cv2.cornerHarris(gray_i,2,3,0.025) dst_i = cv2.dilate(dst_i,None) # Threshold for an optimal value, it may vary depending on the image. temp_i[dst_i<0.01*dst_i.max()]=[0,0,0] temp1_i[dst_i>0.01*dst_i.max()]=[0,0,255] hist_i = cv2.calcHist([temp_i], [0], None, [256], [0, 256]) temp_j = self.image_j.copy() temp1_j = self.image_j.copy() gray_j = cv2.cvtColor(temp_j, cv2.COLOR_BGR2GRAY) gray_j = numpy.float32(gray_j) dst_j = cv2.cornerHarris(gray_j,2,3,0.025) dst_j = cv2.dilate(dst_j,None) # Threshold for an optimal value, it may vary depending on the image. temp_j[dst_j<0.01*dst_j.max()]=[0,0,0] temp1_j[dst_j>0.01*dst_j.max()]=[0,0,255] hist_j = cv2.calcHist([temp_j], [0], None, [256], [0, 256]) self.measure = cv2.compareHist(hist_i, hist_j, cv.CV_COMP_CORREL) self.assertGreater(self.measure, self.threshold)
def harris_corners(image): i = 2 j = 11 k = 4 copy = np.copy(image) corners = cv2.cornerHarris(grayscale(copy), i, j, k/20) # dst = cv2.dilate(dst, None) # copy[dst > 0.01 * dst.max()] = [0, 0, 255] # cv2.imwrite('{}-{}-{}.png'.format(i, j, k), copy) return [cv2.KeyPoint(corner[0], corner[1], 10) for corner in corners]
def detect(self, image): harrisResponse = cv2.cornerHarris(image, self._block_size, self._aperture_size, self._alpha) points = np.argwhere(harrisResponse > harrisResponse.max() * self._quality_level) return [cv2.KeyPoint(point[0], point[1], self._feature_size) for point in points]
def retrieve_subsections(img): """Yield coordinates of boxes that contain interesting images Yields x, y coordinates; width and height as a tuple An example use: images = [] for x, y, w, h in retrieve_subsections(img): image.append(img[y:y+h,x:x+w]) """ if len(img.shape) == 3: gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) else: gray = img results = cv2.cornerHarris(gray, 9, 3, 0.04) # Normalise harris points between 0 and 1 hmin = results.min() hmax = results.max() results = (results - hmin)/(hmax-hmin) # Blur so we retrieve the surrounding details results = cv2.GaussianBlur(results, (31, 31), 5) # Create a threshold collecting the most interesting areas threshold = np.zeros(results.shape, dtype=np.uint8) threshold[results>results.mean() * 1.01] = 255 # Find the bounding box of each threshold, and yield the image contour_response = cv2.findContours(threshold, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) # Different versions of cv2 return a different number of attributes if len(contour_response) == 3: contours = contour_response[1] else: contours = contour_response[0] for contour in contours: # x, y, w, h yield cv2.boundingRect(contour)
