1  import os
 2  import cv2
 3  import numpy as np
 4  
 5  # Load YOLO
 6  net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
 7  classes = []
 8  with open("coco.names", "r") as f:
 9      classes = [line.strip() for line in f.readlines()]
10  
11  layer_names = net.getLayerNames()
12  output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
13  
14  # Load dataset
15  dataset_path = r"YOUR_PATH"
16  image_files = [file for file in os.listdir(dataset_path) if file.endswith((".jpg", ".jpeg"))]
17  
18  # Loop through images in the dataset
19  for image_file in image_files:
20      # Read image
21      image = cv2.imread(os.path.join(dataset_path, image_file))
22      if image is None:
23          print(f"Error: Unable to read image file '{image_file}'. Skipping...")
24          continue
25      height, width, channels = image.shape
26  
27      # Detecting objects
28      blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
29      net.setInput(blob)
30      outs = net.forward(output_layers)
31  
32      # Showing information on the screen
33      class_ids = []
34      confidences = []
35      boxes = []
36      for out in outs:
37          for detection in out:
38              scores = detection[5:]
39              class_id = np.argmax(scores)
40              confidence = scores[class_id]
41              if confidence > 0.5:  # Confidence threshold
42                  # Object detected
43                  center_x = int(detection[0] * width)
44                  center_y = int(detection[1] * height)
45                  w = int(detection[2] * width)
46                  h = int(detection[3] * height)
47  
48                  # Rectangle coordinates
49                  x = int(center_x - w / 2)
50                  y = int(center_y - h / 2)
51  
52                  boxes.append([x, y, w, h])
53                  confidences.append(float(confidence))
54                  class_ids.append(class_id)
55  
56      indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)  # Non-maximum suppression
57  
58      font = cv2.FONT_HERSHEY_PLAIN
59      for i in range(len(boxes)):
60          if i in indexes:
61              x, y, w, h = boxes[i]
62              label = str(classes[class_ids[i]])
63              color = (255, 0, 0)
64              cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
65              cv2.putText(image, label, (x, y + 30), font, 3, color, 3)
66              print(f"Object detected: {label} (Confidence: {confidences[i]})")
67  
68      # Show image
69      #cv2.imshow("Image", image)
70      #cv2.waitKey(0)
71      #cv2.destroyAllWindows()