from detection.utils import * def detect(input_file, config): img = cv2.imread(input_file) height, width, _ = img.shape aspect_ratio = config["Aspect Ratio"] height = int(width*aspect_ratio) """ Resize image to limits in config file """ limits = np.array(config['Limits']) transform_mat = cv2.getPerspectiveTransform(np.float32(limits), np.float32( [[0, 0], [width, 0], [width, height], [0, height]])) img = cv2.warpPerspective(img, transform_mat, (width, height)) """ Prepare upper and lower mask board """ upper_mask = np.zeros(img.shape[0:2], np.uint8) lower_mask = np.zeros(img.shape[0:2], np.uint8) upper_mask = cv2.rectangle(upper_mask, (0, 0), (width, int(height/2)), 255, thickness=cv2.FILLED) lower_mask = cv2.rectangle(lower_mask, (0, int(height / 2)), (width, height), 255, thickness=cv2.FILLED) """ Find blob """ sat = saturation(img) # sum = mean_image([satA, satB]) blob_mask = find_blob(sat) blob = cv2.bitwise_and(img, img, mask=blob_mask) """ Print blob information """ print("Blob covering:") print("\t{:.2f}% of the board.".format(mean_percent_value(blob_mask))) print("\t{:.2f}% of the upper board.".format( mean_percent_value(cv2.bitwise_and(blob_mask, blob_mask, mask=upper_mask), img_ratio=0.5))) print("\t{:.2f}% of the lower board.".format( mean_percent_value(cv2.bitwise_and(blob_mask, blob_mask, mask=lower_mask), img_ratio=0.5))) """ Find food """ food_list, food_mask, food_img = find_food(img, config['Min Food Size'], config['Low Food Color'], config['High Food Color']) """ Print food information """ print("Total food discovered: " + str(len(food_list))) # for i, food in enumerate(food_list): # print("\tFood N°" + str(i) + ": " + str(food)) return img, blob_mask, blob, food_mask, food_img def print_results(orig, blob_mask, blob, food_mask, food, discrete, scale=1.0, filename=None, hide=False): padding = 35 nbr_width = 2 nbr_height = 3 font = cv2.FONT_HERSHEY_SIMPLEX fontsize = 0.45 thickness = 1 scaled_height = int(orig.shape[0]*scale) scaled_width = int(orig.shape[1]*scale) pad = np.zeros((scaled_height, padding, orig.shape[2]), dtype=np.uint8) line_pad = np.zeros((padding, (scaled_width + padding) * nbr_width + padding, orig.shape[2]), dtype=np.uint8) print_img = cv2.resize(orig, (scaled_width, scaled_height)) middle = ((0, int(scaled_height/2)), (scaled_width, int(scaled_height/2))) cv2.line(print_img, middle[0], middle[1], (0, 255, 0), thickness=1) cv2.putText(print_img, 'Mid Line', (middle[0][0] + 5, middle[0][1] - 5), font, fontsize, (0, 255, 0), thickness, cv2.LINE_AA) print_blob_mask = cv2.resize(cv2.cvtColor(blob_mask, cv2.COLOR_GRAY2BGR), (scaled_width, scaled_height)) print_blob = cv2.resize(blob, (scaled_width, scaled_height)) print_food_mask = cv2.resize(cv2.cvtColor(food_mask, cv2.COLOR_GRAY2BGR), (scaled_width, scaled_height)) print_food = cv2.resize(food, (scaled_width, scaled_height)) print_discrete = cv2.resize(discrete, (scaled_width, scaled_height)) concat_line1 = np.concatenate((pad, print_img, pad, print_discrete, pad), axis=1) concat_line2 = np.concatenate((pad, print_blob_mask, pad, print_blob, pad), axis=1) concat_line3 = np.concatenate((pad, print_food_mask, pad, print_food, pad), axis=1) aggregate = np.concatenate((line_pad, concat_line1, line_pad, concat_line2, line_pad, concat_line3, line_pad)) cv2.putText(aggregate, 'Original:', (0 * (scaled_width + padding) + padding + 5, 0 * (scaled_height + padding) + padding - 5), font, fontsize, (255, 255, 255), thickness, cv2.LINE_AA) cv2.putText(aggregate, 'Discrete:', (1 * (scaled_width + padding) + padding + 5, 0 * (scaled_height + padding) + padding - 5), font, fontsize, (255, 255, 255), thickness, cv2.LINE_AA) cv2.putText(aggregate, 'Blob Mask:', (0 * (scaled_width + padding) + padding + 5, 1 * (scaled_height + padding) + padding - 5), font, fontsize, (255, 255, 255), thickness, cv2.LINE_AA) cv2.putText(aggregate, 'Blob:', (1 * (scaled_width + padding) + padding + 5, 1 * (scaled_height + padding) + padding - 5), font, fontsize, (255, 255, 255), thickness, cv2.LINE_AA) cv2.putText(aggregate, 'Food Mask:', (0 * (scaled_width + padding) + padding + 5, 2 * (scaled_height + padding) + padding - 5), font, fontsize, (255, 255, 255), thickness, cv2.LINE_AA) cv2.putText(aggregate, 'Food Regions:', (1 * (scaled_width + padding) + padding + 5, 2 * (scaled_height + padding) + padding - 5), font, fontsize, (255, 255, 255), thickness, cv2.LINE_AA) if filename is not None: cv2.imwrite(filename + ".jpg", aggregate) if not hide: cv2.imshow("Results", aggregate) print("\nPress any key...") cv2.waitKey(0) def discretize(blob_img, food_mask, width, height): img_height, img_width, _ = blob_img.shape discrete_blob = cv2.resize(blob_img, (width, height), interpolation=cv2.INTER_NEAREST) discrete_food = cv2.resize(food_mask, (width, height), interpolation=cv2.INTER_NEAREST) discrete_food_list = [] for x in range(height): for y in range(width): if discrete_food[x, y] != 0: discrete_food_list.append((y, x)) height, width, _ = discrete_blob.shape discrete_blob = cv2.cvtColor(discrete_blob, cv2.COLOR_BGR2GRAY) # If discrete blob has to be connected, used this : # contours, hierarchy = cv2.findContours(discrete_blob, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # c = max(contours, key=cv2.contourArea) # mask = np.zeros(discrete_blob.shape, np.uint8) # cv2.drawContours(mask, [c], -1, 255, cv2.FILLED) # discrete_blob = cv2.bitwise_and(discrete_blob, discrete_blob, mask=mask) discrete_blob_bgr = cv2.cvtColor(discrete_blob, cv2.COLOR_GRAY2BGR) discrete_img = cv2.resize(discrete_blob_bgr, (0, 0), fx=10, fy=10, interpolation=cv2.INTER_NEAREST) for (x, y) in discrete_food_list: cv2.rectangle(discrete_img, (x * 10, y * 10), ((x + 1) * 10, (y + 1) * 10), (0, 255, 0), thickness=cv2.FILLED) if discrete_blob[y, x] != 0: cv2.drawMarker(discrete_img, (x * 10 + 5, y * 10 + 5), (255, 255, 255), thickness=2, markerSize=9, markerType=cv2.MARKER_TILTED_CROSS) return discrete_img, discrete_blob, discrete_food_list