我们从Python开源项目中，提取了以下12个代码示例，用于说明如何使用cv2.HoughCircles()。
def findCircles(fname, image, circles_directory): f = os.path.join(circles_directory, os.path.basename(fname) + ".pkl") if os.path.exists(f): circles = pickle.load(open(f, "rb")) return circles image_cols, image_rows, _ = image.shape gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) blurred = cv2.bilateralFilter(gray, 9, 75, 75) gray = cv2.addWeighted(gray, 1.5, blurred, -0.5, 0) gray = cv2.bilateralFilter(gray, 9, 75, 75) # # detect circles in the image dp = 1 c1 = 100 c2 = 15 print "start hough", fname circles = cv2.HoughCircles(gray, cv2.cv.CV_HOUGH_GRADIENT, dp, image_cols / 8, param1=c1, param2=c2) print "finish hough", fname pickle.dump(circles, open(f, "wb")) if circles is None or not len(circles): return None return circles
def process_img(img): original_image=img processed_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) processed_img = cv2.Canny(processed_img, threshold1=200, threshold2=300) processed_img = cv2.GaussianBlur(processed_img, (3,3), 0 ) copy=processed_img vertices = np.array([[30, 240], [30, 100], [195, 100], [195, 240]]) processed_img = roi(processed_img, np.int32([vertices])) verticesP = np.array([[30, 270], [30, 230], [197, 230], [197, 270]]) platform = roi(copy, np.int32([verticesP])) # edges #lines = cv2.HoughLinesP(platform, 1, np.pi/180, 180,np.array([]), 3, 2) #draw_lines(processed_img,lines) #draw_lines(original_image,lines) #Platform lines #imgray = cv2.cvtColor(platform,cv2.COLOR_BGR2GRAY) ret,thresh = cv2.threshold(platform,127,255,0) im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) cv2.drawContours(original_image, contours, -1, (0,255,0), 3) try: platformpos=contours[0][0][0] except: platformpos=[[0]] circles = cv2.HoughCircles(processed_img, cv2.HOUGH_GRADIENT, 1, 20, param1=90, param2=5, minRadius=1, maxRadius=3) ballpos=draw_circles(original_image,circles=circles) return processed_img,original_image,platform,platformpos,ballpos
def read_captured_circles(self): img = cv2.cvtColor(self.query, cv2.COLOR_BGR2GRAY) img = cv2.medianBlur(img, 7) cimg = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 30, param1=50, param2=30, minRadius=20, maxRadius=50) if circles is None: return circles = np.uint16(np.around(circles)) for i in circles[0, :]: if i[1] < 400: continue self.circlePoints.append((i[0], i[1])) if self._debug: self.draw_circles(circles, cimg)
def findCircles(image): image_cols, image_rows, _ = image.shape gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) first, second = getRescaledDimensions(gray.shape[1], gray.shape[0], HD_MAX_X, HD_MAX_Y) gray = cv2.resize(gray, (first, second)) blurred = cv2.bilateralFilter(gray, 9, 75, 75) gray = cv2.addWeighted(gray, 1.5, blurred, -0.5, 0) gray = cv2.bilateralFilter(gray, 9, 75, 75) # # detect circles in the image dp = 1 c1 = 100 c2 = 15 circles = cv2.HoughCircles(gray, cv2.cv.CV_HOUGH_GRADIENT, dp, second / 8, param1=c1, param2=c2) if not len(circles): return None return circles[0][0]
def __findCircles(mask): """ Finds circles from mask and returns HoughCircles """ circles = cv2.HoughCircles(mask, cv2.HOUGH_GRADIENT, 1, 100, param1=30, param2=50) return circles
def find_hough_circles(img): circles = cv2.HoughCircles(pupil_img,cv2.HOUGH_GRADIENT,1,20, param1=50,param2=30,minRadius=0,maxRadius=80) if circles is not None: circles = np.uint16(np.around(circles)) for i in circles[0,:]: # draw the outer circle cv2.circle(img,(i[0],i[1]),i[2],(0,255,0),2) # draw the center of the circle cv2.circle(img,(i[0],i[1]),2,(0,0,255),3)
def capture_white_circles(self): self.prep_for_white_circles() img = cv2.cvtColor(self.white_query, cv2.COLOR_BGR2GRAY) cimg = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 40, param1=50, param2=30, minRadius=5, maxRadius=60) if circles is None: return circles = np.uint16(np.around(circles)) for i in circles[0, :]: self.circlePoints.append((i[0], i[1])) if self._debug: self.draw_circles(circles, cimg)
def findCircles(hue, intensity): houghCirclesMask = np.zeros(hue.shape, dtype=np.uint8) blurred_hue = cv2.GaussianBlur(hue, (9, 9), 2) blurred_intensity = cv2.GaussianBlur(intensity, (9, 9), 2) hue_circles = cv2.HoughCircles(blurred_hue, cv2.cv.CV_HOUGH_GRADIENT, 0.5, hue.shape[0] / 8, param1=10, param2=25, maxRadius=100) intensity_circles = cv2.HoughCircles(blurred_intensity, cv2.cv.CV_HOUGH_GRADIENT, 0.5, hue.shape[0] / 8, param1=185, param2=20, maxRadius=100) circles = np.vstack((hue_circles[0] if hue_circles is not None else np.empty((0, 3)), intensity_circles[0] if intensity_circles is not None else np.empty((0, 3)))) for (x, y, r) in circles: cv2.circle(houghCirclesMask, (int(round(x)), int(round(y))), int(round(r)), 255, -1) return houghCirclesMask
def get_center_scale(self, calibration_image): """ :param calibration_image: The HSV-image to use for calculation :return: Position of center point in image (tuple), ratio px per cm (reproduction scale) """ gray = cv2.cvtColor(calibration_image, cv2.COLOR_HSV2BGR) gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY) gray = cv2.GaussianBlur(gray, (5, 5), 1) circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 100, param1=50, param2=30, minRadius=50, maxRadius=300) center_circle = (0, 0, 0) min_dist = 0xFFFFFFFFFFF for circle in circles[0]: dist_x = abs(circle[0] - calibration_image.shape[1] / 2) dist_y = abs(circle[1] - calibration_image.shape[0] / 2) if(dist_x + dist_y) < min_dist: min_dist = dist_x + dist_y center_circle = circle rgb = cv2.cvtColor(calibration_image, cv2.COLOR_HSV2RGB) cv2.circle(rgb, (center_circle[0], center_circle[1]), center_circle[2], (0, 255, 0), 1) center = center_circle[0], center_circle[1] radius = center_circle[2] ratio_pxcm = radius / 10.25 self.center = center self.ratio_pxcm = ratio_pxcm return [center, ratio_pxcm]
def findCircles(): # read image - 0 is greyscale, 1 - color table_img = cv2.imread('training_sets/tables/extable6.png', 1) table_img_col = table_img.copy() table_img_grey = cv2.cvtColor(table_img, cv2.COLOR_BGR2GRAY) table_orig = table_img_grey.copy() # smooth table_img_grey = cv2.blur(table_img_grey, (3,3)) # perform canny edge detection table_canny = cv2.Canny(table_img_grey, 15, 30) t_c_copy = table_canny.copy() # Perform Hough circle transform circles = cv2.HoughCircles(table_canny, cv2.HOUGH_GRADIENT, 1, 25, param1=90, param2=30, maxRadius=50, minRadius=14) avgObjRadius = 0 stripes = [] solids = [] cueBall = (0,0) pockets = [] if circles is not None: print("Found circles") circles = np.round(circles[0, :]).astype("int") totAvgRadius = sum(i[2] for i in circles) // len(circles) objBallCounter = 0 for x, y, r in circles: if r <= totAvgRadius: objBallCounter += 1 avgObjRadius += r avgObjRadius = avgObjRadius // objBallCounter for x, y, r in circles: if r > 30: pockets.append([x, y, r]) cv2.circle(table_img, (x, y), r, (0, 210, 30), 3) else: # store pixels within circle below ball = isolateBall(x, y, avgObjRadius, table_img) ballType = classifyBall(ball) if ballType == "stripe": stripes.append((x, y)) elif ballType == "solid": solids.append((x, y)) elif ballType == "cue": cueBall = (x, y) else: raise Exception("Ball can not be classified. X= " + x + " Y= " + y) cv2.circle(table_img, (x, y), avgObjRadius, (150, 100, 255), 4) #concatenate before+after images img = np.concatenate((table_img_col, cv2.cvtColor(t_c_copy, cv2.COLOR_GRAY2BGR), table_img), axis=0) filename = 'img.png' cv2.imwrite(filename, img) return filename
def find_eye_radius(self, eye): self.log.info("measuring eye radius") # gray = cv2.cvtColor(eye, cv2.COLOR_BGR2GRAY) # gray = cv2.GaussianBlur(gray, (5, 5), 0) # param_1 = 200: Upper threshold for the internal Canny edge detector # param_2 = 100*: Threshold for center detection. # circles = cv2.HoughCircles(gray, cv2.cv.CV_HOUGH_GRADIENT, 1, 10, # param1=200, param2=100, minRadius=0, maxRadius=0) # if circles is not None: # circles = np.uint16(np.around(circles)) # pick smallest circle works half time # pick centralish circle # minimum radus of 7 pixels # for i in circles[0, :]: # if i[2] < self.radius: # x = i[0] # y = i[1] # self.radius = i[2] # draw # cv2.circle(eye, (x, y), self.radius, (0, 255, 0), 2) # draw a dot on the circle (pupil) # cv2.circle(eye, (x, y), 2, (0, 0, 255), 3) # k = cv2.waitKey(0) # print self.radius # cv2.imshow("eye", eye) return self.radius
def find_blob() : radius = 0 # Load input image _, bgr_image = img.read() orig_image = bgr_image bgr_image = cv2.medianBlur(bgr_image, 3) # Convert input image to HSV hsv_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2HSV) # Threshold the HSV image, keep only the red pixels lower_red_hue_range = cv2.inRange(hsv_image, (0, 100, 100), (10, 255, 255)) upper_red_hue_range = cv2.inRange(hsv_image, (160, 100, 100), (179, 255, 255)) # Combine the above two images red_hue_image = cv2.addWeighted(lower_red_hue_range, 1.0, upper_red_hue_range, 1.0, 0.0) red_hue_image = cv2.GaussianBlur(red_hue_image, (9, 9), 2, 2) # Use the Hough transform to detect circles in the combined threshold image circles = cv2.HoughCircles(red_hue_image, cv.CV_HOUGH_GRADIENT, 1, 120, 100, 20, 10, 0); # Loop over all detected circles and outline them on the original image all_r = np.array([]) if circles is not None: for i in circles[0]: all_r = np.append(all_r, int(round(i[2]))) closest_ball = all_r.argmax() center=(int(round(circles[0][closest_ball][0])), int(round(circles[0][closest_ball][1]))) radius=int(round(circles[0][closest_ball][2])) if draw_circle_enable: cv2.circle(orig_image, center, radius, (0, 255, 0), 5); # Show images if show_image_enable: cv2.namedWindow("Threshold lower image", cv2.WINDOW_AUTOSIZE) cv2.imshow("Threshold lower image", lower_red_hue_range) cv2.namedWindow("Threshold upper image", cv2.WINDOW_AUTOSIZE) cv2.imshow("Threshold upper image", upper_red_hue_range) cv2.namedWindow("Combined threshold images", cv2.WINDOW_AUTOSIZE) cv2.imshow("Combined threshold images", red_hue_image) cv2.namedWindow("Detected red circles on the input image", cv2.WINDOW_AUTOSIZE) cv2.imshow("Detected red circles on the input image", orig_image) k = cv2.waitKey(5) & 0xFF if k == 27: return (0, 0), 0 if radius > 3: return center, radius; else: return (0, 0), 0
