import cv2
import matplotlib.pyplot as plt
from darknet import Darknet
from utils import detect_objects, load_class_names, plot_boxes, print_objects# Set the location and name of the cfg file
cfg_file = "./cfg/yolov3.cfg"
# Set the location and name of the pre-trained weights file
weight_file = "./weights/yolov3.weights"
# Set the location and name of the COCO object classes file
namesfile = "data/coco.names"
# Load the network architecture
m = Darknet(cfg_file)
# Load the pre-trained weights
m.load_weights(weight_file)
# Load the COCO object classes
class_names = load_class_names(namesfile)m.print_network()layer filters size input output
0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32
1 conv 64 3 x 3 / 2 416 x 416 x 32 -> 208 x 208 x 64
2 conv 32 1 x 1 / 1 208 x 208 x 64 -> 208 x 208 x 32
3 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64
4 shortcut 1
5 conv 128 3 x 3 / 2 208 x 208 x 64 -> 104 x 104 x 128
6 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64
7 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128
8 shortcut 5
9 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64
10 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128
11 shortcut 8
12 conv 256 3 x 3 / 2 104 x 104 x 128 -> 52 x 52 x 256
13 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
14 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
15 shortcut 12
16 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
17 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
18 shortcut 15
19 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
20 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
21 shortcut 18
22 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
23 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
24 shortcut 21
25 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
26 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
27 shortcut 24
28 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
29 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
30 shortcut 27
31 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
32 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
33 shortcut 30
34 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
35 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
36 shortcut 33
37 conv 512 3 x 3 / 2 52 x 52 x 256 -> 26 x 26 x 512
38 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
39 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
40 shortcut 37
41 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
42 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
43 shortcut 40
44 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
45 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
46 shortcut 43
47 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
48 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
49 shortcut 46
50 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
51 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
52 shortcut 49
53 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
54 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
55 shortcut 52
56 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
57 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
58 shortcut 55
59 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
60 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
61 shortcut 58
62 conv 1024 3 x 3 / 2 26 x 26 x 512 -> 13 x 13 x1024
63 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
64 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
65 shortcut 62
66 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
67 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
68 shortcut 65
69 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
70 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
71 shortcut 68
72 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
73 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
74 shortcut 71
75 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
76 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
77 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
78 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
79 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
80 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
81 conv 255 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 255
82 detection
83 route 79
84 conv 256 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 256
85 upsample * 2 13 x 13 x 256 -> 26 x 26 x 256
86 route 85 61
87 conv 256 1 x 1 / 1 26 x 26 x 768 -> 26 x 26 x 256
88 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
89 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
90 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
91 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
92 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
93 conv 255 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 255
94 detection
95 route 91
96 conv 128 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 128
97 upsample * 2 26 x 26 x 128 -> 52 x 52 x 128
98 route 97 36
99 conv 128 1 x 1 / 1 52 x 52 x 384 -> 52 x 52 x 128
100 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
101 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
102 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
103 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
104 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
105 conv 255 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 255
106 detection
Images in the images folder
def pred_images(image_name, iou_thresh=0.4, nms_thresh=0.6):
# first layer of the network is 416 x 416 x 3
# Set the default figure size
plt.rcParams["figure.figsize"] = [24.0, 14.0]
# Load the image
img = cv2.imread(f"./images/{image_name}.jpg")
# Convert the image to RGB
original_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# We resize the image to the input width and height of the first layer of the network.
resized_image = cv2.resize(original_image, (m.width, m.height))
# Display the images
plt.subplot(121)
plt.title("Original Image")
plt.imshow(original_image)
plt.subplot(122)
plt.title("Resized Image")
plt.imshow(resized_image)
plt.show()
# setting the non maximal suppressin threshold to only keep the best bounding box.
# set the NMS threshold
# nms_thresh = 0.9
# set the intersecion over union threshold
# After removing all the predicted bounding boxes that have a low detection
# probability, the second step in NMS, is to select the bounding boxes with
# the highest detection probability and eliminate all the bounding boxes whose
# Intersection Over Union (IOU) value is higher than a given IOU threshold.
# Detect objects in the image
boxes = detect_objects(m, resized_image, iou_thresh, nms_thresh)
# Print the objects found and the confidence level
print_objects(boxes, class_names)
# Plot the image with bounding boxes and corresponding object class labels
plot_boxes(original_image, boxes, class_names, plot_labels=True)pred_images("city_scene", iou_thresh=0.4, nms_thresh=0.6)
It took 1.971 seconds to detect the objects in the image.
Number of Objects Detected: 21
Objects Found and Confidence Level:
1. person: 0.999996
2. person: 1.000000
3. car: 0.707238
4. truck: 0.666981
5. person: 1.000000
6. traffic light: 1.000000
7. truck: 0.856236
8. person: 1.000000
9. person: 1.000000
10. person: 0.999894
11. car: 0.666931
12. person: 0.999990
13. person: 1.000000
14. traffic light: 1.000000
15. traffic light: 1.000000
16. person: 0.999995
17. traffic light: 1.000000
18. traffic light: 0.999999
19. traffic light: 1.000000
20. person: 0.999993
21. person: 0.999996
