import cv2 import numpy as np import time import sys import os CONFIDENCE = 0.5 SCORE_THRESHOLD = 0.5 IOU_THRESHOLD = 0.5 # the neural network configuration config_path = "cfg/yolov3.cfg" # the YOLO net weights file weights_path = "weights/yolov3.weights" # loading all the class labels (objects) labels = open("data/coco.names").read().strip().split("\n") # generating colors for each object for later plotting colors = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8") # load the YOLO network net = cv2.dnn.readNetFromDarknet(config_path, weights_path) # path_name = "images/city_scene.jpg" path_name = sys.argv[1] image = cv2.imread(path_name) file_name = os.path.basename(path_name) filename, ext = file_name.split(".") h, w = image.shape[:2] # create 4D blob blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False) # sets the blob as the input of the network net.setInput(blob) # get all the layer names ln = net.getLayerNames() try: ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] except IndexError: # in case getUnconnectedOutLayers() returns 1D array when CUDA isn't available ln = [ln[i - 1] for i in net.getUnconnectedOutLayers()] # feed forward (inference) and get the network output # measure how much it took in seconds start = time.perf_counter() layer_outputs = net.forward(ln) time_took = time.perf_counter() - start print(f"Time took: {time_took:.2f}s") boxes, confidences, class_ids = [], [], [] # loop over each of the layer outputs for output in layer_outputs: # loop over each of the object detections for detection in output: # extract the class id (label) and confidence (as a probability) of # the current object detection scores = detection[5:] class_id = np.argmax(scores) confidence = scores[class_id] # discard weak predictions by ensuring the detected # probability is greater than the minimum probability if confidence > CONFIDENCE: # scale the bounding box coordinates back relative to the # size of the image, keeping in mind that YOLO actually # returns the center (x, y)-coordinates of the bounding # box followed by the boxes' width and height box = detection[:4] * np.array([w, h, w, h]) (centerX, centerY, width, height) = box.astype("int") # use the center (x, y)-coordinates to derive the top and # and left corner of the bounding box x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) # update our list of bounding box coordinates, confidences, # and class IDs boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) class_ids.append(class_id) # perform the non maximum suppression given the scores defined before idxs = cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD) font_scale = 1 thickness = 1 # ensure at least one detection exists if len(idxs) > 0: # loop over the indexes we are keeping for i in idxs.flatten(): # extract the bounding box coordinates x, y = boxes[i][0], boxes[i][1] w, h = boxes[i][2], boxes[i][3] # draw a bounding box rectangle and label on the image color = [int(c) for c in colors[class_ids[i]]] cv2.rectangle(image, (x, y), (x + w, y + h), color=color, thickness=thickness) text = f"{labels[class_ids[i]]}: {confidences[i]:.2f}" # calculate text width & height to draw the transparent boxes as background of the text (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0] text_offset_x = x text_offset_y = y - 5 box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height)) overlay = image.copy() cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED) # add opacity (transparency to the box) image = cv2.addWeighted(overlay, 0.6, image, 0.4, 0) # now put the text (label: confidence %) cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, color=(0, 0, 0), thickness=thickness) # cv2.imshow("image", image) # if cv2.waitKey(0) == ord("q"): # pass cv2.imwrite(filename + "_yolo3." + ext, image)