-
Notifications
You must be signed in to change notification settings - Fork 0
/
countPersonTest.py
60 lines (48 loc) · 1.77 KB
/
countPersonTest.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
import cv2
import numpy as np
# 加载YOLOv4配置文件和权重
net = cv2.dnn.readNet("yolov4.cfg", "yolov4.weights")
# 获取YOLOv4输出层的名称
layer_names = net.getLayerNames()
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
# 加载图像
image = cv2.imread("1.jpg")
height, width, channels = image.shape
# 对图像进行预处理
blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
# 将预处理后的图像输入到网络中进行推理
net.setInput(blob)
outs = net.forward(output_layers)
# 解析YOLOv4输出结果
class_ids = []
confidences = []
boxes = []
person_count = 0
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.7 and class_id == 0: # 确认为人的检测结果
person_count += 1
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
x = int(center_x - w / 2)
y = int(center_y - h / 2)
boxes.append([x, y, w, h])
confidences.append(float(confidence))
class_ids.append(class_id)
# 绘制边界框和计数
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
font = cv2.FONT_HERSHEY_SIMPLEX
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(image, 'Person', (x, y - 10), font, 0.5, (0, 255, 0), 2)
cv2.putText(image, f'Person count: {person_count}', (10, 30), font, 0.8, (0, 0, 255), 2)
cv2.imshow("YOLOv4 Output", image)
cv2.waitKey(0)
cv2.destroyAllWindows()