我们从Python开源项目中，提取了以下4个代码示例，用于说明如何使用cv2.matchShapes()。
def findEllipses(edges): contours, _ = cv2.findContours(edges.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) ellipseMask = np.zeros(edges.shape, dtype=np.uint8) contourMask = np.zeros(edges.shape, dtype=np.uint8) pi_4 = np.pi * 4 for i, contour in enumerate(contours): if len(contour) < 5: continue area = cv2.contourArea(contour) if area <= 100: # skip ellipses smaller then 10x10 continue arclen = cv2.arcLength(contour, True) circularity = (pi_4 * area) / (arclen * arclen) ellipse = cv2.fitEllipse(contour) poly = cv2.ellipse2Poly((int(ellipse[0][0]), int(ellipse[0][1])), (int(ellipse[1][0] / 2), int(ellipse[1][1] / 2)), int(ellipse[2]), 0, 360, 5) # if contour is circular enough if circularity > 0.6: cv2.fillPoly(ellipseMask, [poly], 255) continue # if contour has enough similarity to an ellipse similarity = cv2.matchShapes(poly.reshape((poly.shape[0], 1, poly.shape[1])), contour, cv2.cv.CV_CONTOURS_MATCH_I2, 0) if similarity <= 0.2: cv2.fillPoly(contourMask, [poly], 255) return ellipseMask, contourMask
def average_goal_matching(contour): global DEFINITE_GOALS total_score = 0 min_score = 9999999999999999 # number_of_things = 0 if len(contour) < 8: return 9999999999999999 for definite_goal in DEFINITE_GOALS: # number_of_things += 1 this_score = cv2.matchShapes(contour, definite_goal, 1, 0.0) # print("Score:", this_score) total_score += this_score if this_score < min_score: min_score = this_score # print(number_of_things) # print("Smallest score:", min_score) # return total_score / DEFINITE_GOALS.size return min_score
def single_finger_check(self, cnt): # use single finger image to check current fame has single finger grey_fin1 = cv2.cvtColor(self.fin1, cv2.COLOR_BGR2GRAY) _, thresh_fin1 = cv2.threshold(grey_fin1, 127, 255, 0) contour_fin1, hierarchy = cv2.findContours(thresh_fin1.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) cnt1 = contour_fin1[0] ret1 = cv2.matchShapes(cnt, cnt1, 1, 0) grey_fin2 = cv2.cvtColor(self.fin2, cv2.COLOR_BGR2GRAY) _, thresh_fin2 = cv2.threshold(grey_fin2, 127, 255, 0) contour_fin2, hierarchy = cv2.findContours(thresh_fin2.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) cnt2 = contour_fin2[0] ret2 = cv2.matchShapes(cnt, cnt2, 1, 0) grey_fin3 = cv2.cvtColor(self.fin3, cv2.COLOR_BGR2GRAY) _, thresh_fin3 = cv2.threshold(grey_fin3, 127, 255, 0) contour_fin3, hierarchy = cv2.findContours(thresh_fin3.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) cnt3 = contour_fin3[0] ret3 = cv2.matchShapes(cnt, cnt3, 1, 0) reta = (ret1 + ret2 + ret3)/3 if reta <= 0.3: return 5 # set as one-finger module else: return 0 # not detect, still 0 # Use PyAutoGUI to control mouse event
def contouring(img,match_coeff): #Defining coefficients #---------------------------------- #Max value of contour shape matching coefficient match_coeff = 0.1 #max contour area max_cont_area = 100 #---------------------------------- #find countours im2, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) #truncate contours less than predefined maximum area c_counter = 0 for c in contours: A = cv2.contourArea(c) if A<max_cont_area: contours=np.delete(contours,c_counter,0) c_counter=c_counter-1 c_counter=c_counter+1 #length of truncated contour array clen=c_counter #create match_array [dimension = len x len] with 0s match_array=np.zeros((clen,clen),np.uint8) #loop over the contours and compare two by two icounter = 0 for i in contours: jcounter = 0 for j in contours: #If difference has index <0.01 then regard as TRUE ret=cv2.matchShapes(i,j,1,0.0) if ret<match_coeff: match_array[icounter,jcounter]=1 else: match_array[icounter,jcounter]=0 jcounter=jcounter+1 icounter=icounter+1 #sum each row of the array (for TRUEs and FALSEs] sum_array=np.sum(match_array,axis=1,dtype=np.uint16) #finding mean of the comparison value sum_all=np.sum(sum_array,axis=0,dtype=np.uint16) ave_sim_val=sum_all/clen #Assumption: there is a lot of 1s return contours,sum_array,ave_sim_val
