YOLOV4实现口罩佩戴识别检测完整代码
时间: 2023-11-22 08:05:41 浏览: 48
以下是使用YOLOv4实现口罩佩戴识别检测的完整代码:
```python
# 导入必要的库
import cv2
import numpy as np
import time
# 加载YOLOv4模型
net = cv2.dnn.readNetFromDarknet("yolov4.cfg", "yolov4.weights")
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA_FP16)
# 加载类别标签
classes = []
with open("coco.names", "r") as f:
classes = [line.strip() for line in f.readlines()]
# 定义输出层名称
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# 定义颜色列表
colors = np.random.uniform(0, 255, size=(len(classes), 3))
# 读取测试图像
img = cv2.imread("test.jpg")
# 对图像进行预处理
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), swapRB=True, crop=False)
# 将预处理后的图像输入到网络中
net.setInput(blob)
# 运行前向传递,获取网络输出
outs = net.forward(output_layers)
# 初始化检测结果列表
class_ids = []
confidences = []
boxes = []
# 遍历每个输出层,获取检测结果
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5 and class_id == 0:
# 计算边界框坐标
center_x = int(detection[0] * img.shape[1])
center_y = int(detection[1] * img.shape[0])
w = int(detection[2] * img.shape[1])
h = int(detection[3] * img.shape[0])
x = int(center_x - w / 2)
y = int(center_y - h / 2)
# 将检测结果保存到列表中
class_ids.append(class_id)
confidences.append(float(confidence))
boxes.append([x, y, w, h])
# 非最大抑制,去除重叠的边界框
indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
# 显示检测结果
for i in indices:
i = i[0]
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
color = colors[class_ids[i]]
cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
# 显示检测结果图像
cv2.imshow("Detection result", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
注:以上代码仅为演示用途,实际应用中需要根据数据集和应用场景进行适当调整。
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