我们从Python开源项目中，提取了以下13个代码示例，用于说明如何使用cv2.minEnclosingCircle()。
def find_center(self, name, frame, mask, min_radius): if name not in self.pts: self.pts[name] = deque(maxlen=self.params['tracking']['buffer_size']) # find contours in the mask and initialize the current (x, y) center of the ball cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2] # only proceed if at least one contour was found if len(cnts) > 0: # find the largest contour in the mask, then use it to compute the minimum enclosing circle and centroid c = max(cnts, key=cv2.contourArea) ((x, y), radius) = cv2.minEnclosingCircle(c) center = (int(x), int(y)) # only proceed if the radius meets a minimum size if radius > min_radius: # draw the circle and centroid on the frame, then update the list of tracked points cv2.circle(frame, center, int(radius), (0, 255, 255), 2) cv2.circle(frame, center, 5, (0, 0, 255), -1) self.pts[name].appendleft(center) smooth_points = 8 return (int(np.mean([self.pts[name][i][0] for i in range(min(smooth_points, len(self.pts[name])))])), int(np.mean([self.pts[name][i][1] for i in range(min(smooth_points, len(self.pts[name])))]))), radius return None, None
def getSunSize(self, p): """Get the size of the sun in screen pixels. This is a hack: it renders the sun, then finds the contour surrounding the sun and computes the center/radius of that. """ self.fbo.bind() self.clear_background() self.fbo.release() dt, sun_alt, sun_az, moon_alt, moon_az, sun_r, moon_r, sep, parallactic_angle, lat, lon = p sun_coords = horizontal_to_cartesian(deg(sun_alt), deg(sun_az)) sun_x, sun_y, sun_z = scale_vector(sun_coords, SUN_EARTH_DISTANCE) self.fbo.bind() self.draw_sun(sun_x, sun_y, sun_z) glFlush() self.fbo.release() image = self.fbo.toImage() # Find the contours of the sun in the image contours = self.find_contours(image) # Make a poly that fits the contour poly = cv2.approxPolyDP( np.array(contours[0]), 3, True ) # Find the minimum enclosing circle of the polygon c = cv2.minEnclosingCircle(poly) self.fbo.bind() self.clear_background() self.fbo.release() return c
def drawConvexHull(img, contours): cnt = contours[0] mask = np.zeros(img.shape, np.uint8) hull = cv2.convexHull(cnt,returnPoints = False) defects = cv2.convexityDefects(cnt,hull) for i in range(defects.shape[0]): s,e,f,d = defects[i,0] start = tuple(cnt[s][0]) end = tuple(cnt[e][0]) far = tuple(cnt[f][0]) cv2.line(mask,start,end,[255,255,255],5) cv2.circle(mask,far,5,[255,255,255],-1) (x,y),radius = cv2.minEnclosingCircle(cnt) center = (int(x),int(y)) radius = int(radius) cv2.circle(mask,center,radius,(255,255,255),-1) return mask
def drawLines(img, contours): cnt = contours[0] (x,y),radius = cv2.minEnclosingCircle(cnt) center = (int(x),int(y)) # radius = int(radius) # cv2.circle(mask,center,radius,(255,255,255),-1) mask = np.zeros(img.shape, np.uint8) cnt_points = [] for cnt in contours: for pts in cnt: cnt_points.append(pts[0]) cnt_points = np.array(cnt_points) # np.random.shuffle(cnt_points) # print cnt_points.shape # print cnt_points[0:10,:] for i in range(len(cnt_points)): cv2.line(mask,center,(cnt_points[i,0], cnt_points[i,1]),(255,255,255),5) return mask
def augment_graph(frame, contour): if contour is None: return None, None moments = cv2.moments(contour) #Central mass of first order moments #if moments['m00']!=0: # cx = int(moments['m10']/moments['m00']) # cx = M10/M00 # cy = int(moments['m01']/moments['m00']) # cy = M01/M00 #centerMass = (cx,cy) #Draw center mass #circle (x,y),radius = cv2.minEnclosingCircle(contour) center = (int(x),int(y)) radius = int(radius) cv2.circle(frame,center,7,[100,0,255],2) cv2.circle(frame,center,radius,(92, 66, 244),5) return center, radius
def find(self, image): hsv_frame = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) mask = cv2.inRange(hsv_frame, self.__hsv_bounds[0], self.__hsv_bounds[1]) mask = cv2.erode(mask, None, iterations=2) mask = cv2.dilate(mask, None, iterations=2) contours = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2] if len(contours) == 0: return (False, False) largest_contour = max(contours, key=cv2.contourArea) ((x, y), radius) = cv2.minEnclosingCircle(largest_contour) M = cv2.moments(largest_contour) center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"])) return (center, radius)
def is_detector(self, img): """ This uses color to determine if we have a detector, and if so, returns where the big screen and smaller screen is in the subimage """ lower = np.array([190, 190, 0], dtype = "uint8") upper = np.array([255, 255, 100], dtype = "uint8") mask = cv2.inRange(img, lower, upper) contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) contours = contours[0] if is_cv2() else contours[1] if len(contours) == 0: return None, False sorted_contours = sorted(contours, cmp=lambda a,b: int(cv2.contourArea(b)) - int(cv2.contourArea(a))) center, radius = cv2.minEnclosingCircle(sorted_contours[0]) up = True if len(contours) > 1: center2, radius = cv2.minEnclosingCircle(sorted_contours[1]) if center2[1] < center[1]: up = False if self.debug: debug_img = img.copy() cv2.drawContours(debug_img, [sorted_contours[0]], -1, (0, 255, 0), 2) cv2.imshow("cont", debug_img) cv2.waitKey(0) cv2.destroyAllWindows() return center, up
def is_repair_tool(self, img): """ This uses color to determine if we have a repair tool, and if so, returns where the button is located within the provided subimage """ lower = np.array([190, 0, 0], dtype = "uint8") upper = np.array([255, 125, 100], dtype = "uint8") mask = cv2.inRange(img, lower, upper) contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) contours = contours[0] if is_cv2() else contours[1] if len(contours) == 0: return None, False sorted_contours = sorted(contours, cmp=lambda a,b: int(cv2.contourArea(b)) - int(cv2.contourArea(a))) center, radius = cv2.minEnclosingCircle(sorted_contours[0]) up = True if center[1] > (img.shape[0] / 2): up = False if self.debug: debug_img = img.copy() cv2.drawContours(debug_img, [sorted_contours[0]], -1, (0, 255, 0), 2) cv2.imshow("cont", debug_img) cv2.waitKey(0) cv2.destroyAllWindows() return center, up
def detect_ball(frame): blurred = cv2.GaussianBlur(frame, (11, 11), 0) hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) mask = cv2.inRange(hsv, greenLower, greenUpper) mask = cv2.erode(mask, None, iterations=2) mask = cv2.dilate(mask, None, iterations=2) cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2] center = None # only proceed if at least one contour was found if len(cnts) == 0: return # find the largest contour in the mask, then use # it to compute the minimum enclosing circle and # centroid c = max(cnts, key=cv2.contourArea) ((x, y), radius) = cv2.minEnclosingCircle(c) M = cv2.moments(c) center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"])) if radius < 10: print('Too small') return return center, radius
def callback(self,data): try: imgOriginal = self.bridge.imgmsg_to_cv2(data, "bgr8") except CvBridgeError as e: print("==[CAMERA MANAGER]==", e) blurred = cv2.GaussianBlur(imgOriginal,(11,11),0) hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV) # lower = np.array([60,90,70]) # hsv range for green # upper = np.array([90,175,255]) lower = np.array([60,70,70]) # hsv range for green upper = np.array([90,255,255]) mask = cv2.inRange(hsv, lower, upper) mask = cv2.erode(mask, None, iterations=7) mask = cv2.dilate(mask, None, iterations=7) output = cv2.bitwise_and(imgOriginal, imgOriginal, mask = mask) outputGrayscale = cv2.cvtColor(output, cv2.COLOR_BGR2GRAY) if major_ver == '3': contours = cv2.findContours(outputGrayscale,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)[1] elif major_ver == '2': contours = cv2.findContours(outputGrayscale,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)[0] if len(contours) > 0: c = max(contours,key=cv2.contourArea) ((x,y),radius) = cv2.minEnclosingCircle(c) M = cv2.moments(c) treasureCenter = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"])) self.treasurePoint.x = treasureCenter[0] self.treasurePoint.y = treasureCenter[1] self.treasurePoint.flag = 1 self.pub.publish(self.treasurePoint) else: self.treasurePoint.flag = 0 self.pub.publish(self.treasurePoint) cv2.imshow("TreasureFilter", output) cv2.waitKey(3)
def __circle(self): (_, _), self._radius = cv2.minEnclosingCircle(self._ndarray)
def image_callback(self, msg): # convert ROS image to OpenCV image try: image = self.bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8') except CvBridgeError as e: print(e) # create hsv image of scene hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # find green objects in the image lower_green = numpy.array([50, 50, 177], numpy.uint8) # fluffy green ball upper_green = numpy.array([84, 150, 255], numpy.uint8) mask = cv2.inRange(hsv, lower_green, upper_green) # dilate and erode with kernel size 11x11 cv2.morphologyEx(mask, cv2.MORPH_CLOSE, numpy.ones((11,11))) # find all of the contours in the mask image contours, heirarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) self.contourLength = len(contours) # Check for at least one ball found if self.contourLength < 1: print "No objects found" sys.exit("No objects found") # if no Crazyflie in image, exit process ## Loop through all of the contours, and get their areas area = [0.0]*len(contours) for i in range(self.contourLength): area[i] = cv2.contourArea(contours[i]) #### Ball #### the largest "green" object ball_image = contours[area.index(max(area))] # Find the circumcircle of the green ball and draw a blue outline around it (self.cf_u,self.cf_v),radius = cv2.minEnclosingCircle(ball_image) ball_center = (int(self.cf_u),int(self.cf_v)) ball_radius = int(radius) cv2.circle(image, ball_center, ball_radius, (255,0,0), 2) # show image with green ball outlined with a blue circle cv2.imshow ("KinectV2", image) cv2.waitKey(3) # This callback function handles processing Kinect depth image, looking for the depth value # at the location of the center of the green ball on top of Crazyflie.
