def Reputation_threshold(image1, image2, image3, image4, image5, crowd_density_array): input_image1 = cv2.imread(str(image1)) input_image2 = cv2.imread(str(image2)) input_image3 = cv2.imread(str(image3)) input_image4 = cv2.imread(str(image4)) input_image5 = cv2.imread(str(image5)) input_image_1 = fix_image_size(input_image1) blur_map_1, score_1, blurry_1 = estimate_blur(input_image_1) input_image_2 = fix_image_size(input_image2) blur_map_2, score_2, blurry_2 = estimate_blur(input_image_2) input_image_3 = fix_image_size(input_image3) blur_map_3, score_3, blurry_3 = estimate_blur(input_image_3) input_image_4 = fix_image_size(input_image4) blur_map_4, score_4, blurry_4 = estimate_blur(input_image_4) input_image_5 = fix_image_size(input_image5) blur_map_5, score_5, blurry_5 = estimate_blur(input_image_5) # print("Quality_score1: {0}, blurry1: {1}".format(score_1, blurry_1)) # print("Quality_score2: {0}, blurry2: {1}".format(score_2, blurry_2)) # print("Quality_score3: {0}, blurry3: {1}".format(score_3, blurry_3)) # print("Quality_score4: {0}, blurry4: {1}".format(score_4, blurry_4)) # print("Quality_score5: {0}, blurry5: {1}".format(score_5, blurry_5)) """ if args.display: cv2.imshow("input", input_image1) cv2.imshow("result", pretty_blur_map(blur_map)) cv2.waitKey(0)""" img_gray = cv2.cvtColor(input_image1, cv2.COLOR_BGR2GRAY) noise_value_img_1 = estimate_noise(img_gray) img_gray = cv2.cvtColor(input_image2, cv2.COLOR_BGR2GRAY) noise_value_img_2 = estimate_noise(img_gray) img_gray = cv2.cvtColor(input_image3, cv2.COLOR_BGR2GRAY) noise_value_img_3 = estimate_noise(img_gray) img_gray = cv2.cvtColor(input_image4, cv2.COLOR_BGR2GRAY) noise_value_img_4 = estimate_noise(img_gray) img_gray = cv2.cvtColor(input_image5, cv2.COLOR_BGR2GRAY) noise_value_img_5 = estimate_noise(img_gray) # print("Noise of camera 1 is ", noise_value_img_1) # print("Noise of camera 2 is ", noise_value_img_2) # print("Noise of camera 3 is ", noise_value_img_3) # print("Noise of camera 4 is ", noise_value_img_4) # print("Noise of camera 5 is ", noise_value_img_5) global overlapping_array view_7 = cameras(0, overlapping_array) view_1 = cameras(1, [0.3, 1, 0.3, 0.5, 0.4]) view_5 = cameras(2, [0.5, 0.3, 1, 0.4, 0.4]) view_6 = cameras(3, [0.4, 0.5, 0.4, 1, 0.4]) view_8 = cameras(4, [0.2, 0.5, 0.3, 0.4, 1]) # crowd density will be calculated from a separate code.These are dummy values #scalar = StandardScaler(with_mean=False) #crowd_density_array = [[10], [5], [7]] crowd_density_array = [crowd_density_array] #scalar.fit(crowd_density_array) #crowd_density_array = scalar.transform(crowd_density_array) #[crowd_density_array] = np.array(crowd_density_array).reshape((1, 3)) [crowd_density_array] = normalize(crowd_density_array, norm='l2') view_7.crowd_density([view_7.cid, crowd_density_array[view_7.cid]]) view_7.crowd_density([view_1.cid, crowd_density_array[view_1.cid]]) view_7.crowd_density([view_5.cid, crowd_density_array[view_5.cid]]) view_7.crowd_density([view_6.cid, crowd_density_array[view_6.cid]]) view_7.crowd_density([view_8.cid, crowd_density_array[view_8.cid]]) view_1.crowd_density([view_1.cid, crowd_density_array[view_1.cid]]) view_1.crowd_density([view_7.cid, crowd_density_array[view_7.cid]]) view_1.crowd_density([view_5.cid, crowd_density_array[view_5.cid]]) view_1.crowd_density([view_6.cid, crowd_density_array[view_6.cid]]) view_1.crowd_density([view_8.cid, crowd_density_array[view_8.cid]]) view_5.crowd_density([view_5.cid, crowd_density_array[view_5.cid]]) view_5.crowd_density([view_7.cid, crowd_density_array[view_7.cid]]) view_5.crowd_density([view_1.cid, crowd_density_array[view_1.cid]]) view_5.crowd_density([view_6.cid, crowd_density_array[view_6.cid]]) view_5.crowd_density([view_8.cid, crowd_density_array[view_8.cid]]) #scalar = StandardScaler(with_mean=False) #noise_array = [[noise_value_img_1], [noise_value_img_2], [noise_value_img_3]] noise_array = [[ noise_value_img_1, noise_value_img_2, noise_value_img_3, noise_value_img_4, noise_value_img_5 ]] #scalar.fit(noise_array) #noise_array = scalar.transform(noise_array) #[noise_array] = np.array(noise_array).reshape((1, 3)) [noise_array] = normalize(noise_array, norm='l2') view_7.noise([view_7.cid, noise_array[view_7.cid]]) view_7.noise([view_1.cid, noise_array[view_1.cid]]) view_7.noise([view_5.cid, noise_array[view_5.cid]]) view_7.noise([view_6.cid, noise_array[view_6.cid]]) view_7.noise([view_8.cid, noise_array[view_8.cid]]) view_1.noise([view_7.cid, noise_array[view_7.cid]]) view_1.noise([view_1.cid, noise_array[view_1.cid]]) view_1.noise([view_5.cid, noise_array[view_5.cid]]) view_1.noise([view_6.cid, noise_array[view_6.cid]]) view_1.noise([view_8.cid, noise_array[view_8.cid]]) view_5.noise([view_7.cid, noise_array[view_7.cid]]) view_5.noise([view_1.cid, noise_array[view_1.cid]]) view_5.noise([view_5.cid, noise_array[view_5.cid]]) view_5.noise([view_6.cid, noise_array[view_6.cid]]) view_5.noise([view_8.cid, noise_array[view_8.cid]]) #scalar = StandardScaler(with_mean=False) #blur_array = [[score_1], [score_2], [score_3]] blur_array = [[score_1, score_2, score_3, score_4, score_5]] #scalar.fit(blur_array) #blur_array = scalar.transform(blur_array) #[blur_array] = np.array(blur_array).reshape((1, 3)) [blur_array] = normalize(blur_array, norm='l2') view_7.blur([view_7.cid, blur_array[view_7.cid]]) view_7.blur([view_1.cid, blur_array[view_1.cid]]) view_7.blur([view_5.cid, blur_array[view_5.cid]]) view_7.blur([view_6.cid, blur_array[view_6.cid]]) view_7.blur([view_8.cid, blur_array[view_8.cid]]) view_1.blur([view_1.cid, blur_array[view_1.cid]]) view_1.blur([view_7.cid, blur_array[view_7.cid]]) view_1.blur([view_5.cid, blur_array[view_5.cid]]) view_1.blur([view_6.cid, blur_array[view_6.cid]]) view_1.blur([view_8.cid, blur_array[view_8.cid]]) view_5.blur([view_5.cid, blur_array[view_5.cid]]) view_5.blur([view_7.cid, blur_array[view_7.cid]]) view_5.blur([view_1.cid, blur_array[view_1.cid]]) view_5.blur([view_6.cid, blur_array[view_6.cid]]) view_5.blur([view_8.cid, blur_array[view_8.cid]]) thresh_view_7 = list(view_7.threshold_calc()) #thresh_view_1 = view_1.threshold_calc() #thresh_view_5 = view_5.threshold_calc() # print("Threshold of cameras w.r.t to View7 is ", thresh_view_7) return thresh_view_7
help='set logging level to debug', action="store_true") parser.add_argument("-d", "--display", dest='display', help='display images', action="store_true") args = parser.parse_args() if args.verbose: logging.basicConfig(level=logging.DEBUG) else: logging.basicConfig(level=logging.INFO) assert os.path.exists(args.input_image) input_image = cv2.imread(args.input_image) if args.fix_size: input_image = fix_image_size(input_image) blur_map, score, blurry = estimate_blur(input_image) logging.info("score: {0}, blurry: {1}".format(score, blurry)) if args.display: cv2.imshow("input", input_image) cv2.imshow("result", pretty_blur_map(blur_map)) cv2.waitKey(0)
for i in self.noises: self.threshold[count] += weightage[3] * i count += 1 [self.threshold] = normalize([self.threshold], norm='l2') return self.threshold print("Enter the input images name for the three cameras") s1, s2, s3 = raw_input().split() input_image1 = cv2.imread(s1) input_image2 = cv2.imread(s2) input_image3 = cv2.imread(s3) input_image_1 = fix_image_size(input_image1) blur_map_1, score_1, blurry_1 = estimate_blur(input_image_1) input_image_2 = fix_image_size(input_image2) blur_map_2, score_2, blurry_2 = estimate_blur(input_image_2) input_image_3 = fix_image_size(input_image3) blur_map_3, score_3, blurry_3 = estimate_blur(input_image_3) print("Quality_score1: {0}, blurry1: {1}".format(score_1, blurry_1)) print("Quality_score2: {0}, blurry2: {1}".format(score_2, blurry_2)) print("Quality_score3: {0}, blurry3: {1}".format(score_3, blurry_3)) """ if args.display: cv2.imshow("input", input_image1) cv2.imshow("result", pretty_blur_map(blur_map)) cv2.waitKey(0)"""
else: save_path = None results = [] for image_path in find_images(args.images): image = cv2.imread(str(image_path)) if image is None: logging.warning( f'warning! failed to read image from {image_path}; skipping!') continue logging.info(f'processing {image_path}') if args.fix_size: image = fix_image_size(image) else: logging.warning( 'not normalizing image size for consistent scoring!') blur_map, score, blurry = estimate_blur(image) logging.info( f'image_path: {image_path} score: {score} blurry: {blurry}') results.append({ 'input_path': str(image_path), 'score': score, 'blurry': blurry }) if args.display:
else: save_path = None results = [] for image_path in find_images(args.images): image = cv2.imread(str(image_path)) if image is None: logging.warning( f'warning! failed to read image from {image_path}; skipping!') continue logging.info(f'processing {image_path}') if fix_size: image = fix_image_size(image) # convert to 2E6 else: logging.warning( 'not normalizing image size for consistent scoring!') blur_map, score, blurry = estimate_blur(image, threshold=args.threshold) print(blur_map[50:330, 399:402].sum()) if (blurry is False and blur_map[50:330, 399:402].sum() > 8000): blurry = True logging.info( f'image_path: {image_path} score: {score} blurry: {blurry}') # if(score < 45): logging.info(f'score: {score}') results.append({ 'input_path': str(image_path),