我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用cv2.fitEllipse()。
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 lipSegment(self, img): # self.t1 = cv2.getTickCount() lipHull = self.dlib_obj.get_landmarks(img) cv2.drawContours(img, lipHull, -1, (255, 0, 0), 2) (x, y), (MA, ma), angle = cv2.fitEllipse(lipHull) a = ma/2 b = MA/2 eccentricity = sqrt(pow(a, 2)-pow(b, 2)) eccentricity = round(eccentricity/a, 2) cv2.putText(img, 'E = '+str(round(eccentricity, 3)), (10, 350), self.font, 1, (255, 0, 0), 1) if(eccentricity < 0.9): self.flags.cmd = 'b' else: self.flags.cmd = 'f' if angle < 80: self.flags.cmd = 'l' elif angle > 100: self.flags.cmd = 'r' cv2.putText(img, 'Cmd = ' + self.flags.cmd, (10, 300), self.font, 1, (0, 0, 255), 1, 16) # self.t2 = cv2.getTickCount() # print "Time = ", (self.t2-self.t1)/cv2.getTickFrequency() return img
def find_concetric_circles(gray_img,min_ring_count=3, visual_debug=False): # get threshold image used to get crisp-clean edges using blur to remove small features edges = cv2.adaptiveThreshold(cv2.blur(gray_img,(3,3)), 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 5, 11) _, contours, hierarchy = cv2.findContours(edges, mode=cv2.RETR_TREE, method=cv2.CHAIN_APPROX_NONE,offset=(0,0)) #TC89_KCOS if visual_debug is not False: cv2.drawContours(visual_debug,contours,-1,(200,0,0)) if contours is None or hierarchy is None: return [] clusters = get_nested_clusters(contours,hierarchy[0],min_nested_count=min_ring_count) concentric_cirlce_clusters = [] #speed up code by caching computed ellipses ellipses = {} # for each cluster fit ellipses and cull members that dont have good ellipse fit for cluster in clusters: if visual_debug is not False: cv2.drawContours(visual_debug, [contours[i] for i in cluster],-1, (0,0,255)) candidate_ellipses = [] for i in cluster: c = contours[i] if len(c)>5: if not i in ellipses: e = cv2.fitEllipse(c) fit = max(dist_pts_ellipse(e,c)) ellipses[i] = e,fit else: e,fit = ellipses[i] a,b = e[1][0]/2.,e[1][1]/2. if fit<max(2,max(e[1])/20): candidate_ellipses.append(e) if visual_debug is not False: cv2.ellipse(visual_debug, e, (0,255,0),1) if candidate_ellipses: cluster_center = np.mean(np.array([e[0] for e in candidate_ellipses]),axis=0) candidate_ellipses = [e for e in candidate_ellipses if np.linalg.norm(e[0]-cluster_center)<max(3,min(e[1])/20) ] if len(candidate_ellipses) >= min_ring_count: concentric_cirlce_clusters.append(candidate_ellipses) if visual_debug is not False: cv2.ellipse(visual_debug, candidate_ellipses[-1], (0,255,255),4) #return clusters sorted by size of outmost cirlce biggest first. return sorted(concentric_cirlce_clusters,key=lambda e:-max(e[-1][1]))
def circle_contour(image, contour): # Bounding ellipse image_with_ellipse = image.copy() #easy function ellipse = cv2.fitEllipse(contour) #add it cv2.ellipse(image_with_ellipse, ellipse, green, 2,cv2.LINE_AA) return image_with_ellipse
def lipSegment(img): img = imutils.resize(img,width=300) img_copy = img.copy() landmarks = dlib_obj.get_landmarks(img) dlib_obj.get_face_mask(img_copy, landmarks) output_img = img-img_copy output_img = cv2.cvtColor(output_img,cv2.COLOR_BGR2GRAY) contours,hierarchy = cv2.findContours(output_img.copy(), cv2.cv.CV_RETR_EXTERNAL, cv2.cv.CV_CHAIN_APPROX_SIMPLE) #cv2.findContours(image, mode, method cv2.drawContours(img, contours, -1, (0,255,0), 2,maxLevel=0) cnt = contours[0] ellipse = cv2.fitEllipse(cnt) (x,y),(MA,ma),angle = cv2.fitEllipse(cnt) a = ma/2 b = MA/2 eccentricity = sqrt(pow(a,2)-pow(b,2)) eccentricity = round(eccentricity/a,2) font = cv2.FONT_HERSHEY_SIMPLEX cv2.putText(img,'Eccentr= '+str(round(eccentricity,3)),(10,350), font, 1,(255,0,0),2,16) if(eccentricity < 0.9): cv2.putText(img,'Commands = O',(10,300), font, 1,(0,0,255),2,16) else: cv2.putText(img,'Commands = E',(10,300), font, 1,(0,0,255),2,16) return img
def draw_groupRect(self, img_arr, ID): ID = int(ID) img = Image.fromarray(img_arr) draw = ImageDraw.Draw(img, mode='RGBA') bi_ID = bi_t = np.zeros(self.final_BI.shape, np.uint8) bi_ID[self.final_ID == ID] = 255 # Image.fromarray(bi_ID).show() ## get contour -- cnt of polygon if self.parent().line2_mode == "polygonOtsu": bi_ID = cv2.GaussianBlur(bi_ID, (11, 11), 0) bi_ID = cv2.dilate(bi_ID, None, iterations=4) im2, contours, hierarchy = cv2.findContours(bi_ID, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnt_union = contours[0] for i, cnt in enumerate(contours): if i > 0: cnt_union = np.concatenate((cnt_union, cnt), axis=0) ellipse = cv2.fitEllipse(cnt_union) cv2.ellipse(img_arr,ellipse,(0,255,0,175),2) extBottom = tuple(cnt_union[cnt_union[:, :, 1].argmax()][0]) font = cv2.FONT_HERSHEY_PLAIN cv2.putText(img_arr, "ID_"+str(ID), extBottom, font, 1.5, (255, 49, 12), 2) return img_arr
def fit_ellipse(masks): ellipses = [] k = 0 for i in range(0,len(masks)): mask = masks[0] #cv2.imwrite("./input/ellipses/10_1_"+str(k)+"_mask.tif", mask) contours, hierarchy = cv2.findContours(mask.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) #immagine nera temp = np.zeros(mask.shape, dtype=np.uint8) #cv2.imwrite("./input/ellipses/10_1_mask_black.tif", temp) #disegno i contorni su sfondo nero cv2.drawContours(temp, contours, -1, (255,255,255), 3) #voglio scrivere quest'immagine che, in teoria, e' nera con #i contorni individuati in bianco.. ma esplode il kernel se usata #cv2.imwrite("./input/ellipses/10_1_mask_cont.tif", temp) #cv2.imwrite("./input/ellipses/10_1_mask_after.tif", mask) for cnt in contours: param_ellipse = [] #print "fitto" ellipse = cv2.fitEllipse(cnt) #cv2.imwrite("./input/ellipses/10_1_"+str(k)+"_mask.tif", mask) #disegna l'ellisse black = np.zeros(mask.shape, dtype=np.uint8) cv2.ellipse(black, ellipse, (255,255,255), 2) #cv2.imwrite("./input/ellipses/10_1_"+str(k)+"_mask_ellipse.tif", black) k=k+1 ''' Coordinate necessarie/ritornate: (ellipse.center.x, ellipse.center.y), (ellipse.size.height*2, ellipse.size.width*2), ellipse.angle ''' #print "ellipse" #print ellipse ellipses.append(ellipse) return np.array(ellipses)
