YoLoV4口罩佩戴识别代码
时间: 2023-09-07 16:17:51 浏览: 47
以下是使用Python和OpenCV实现的基于YoLoV4的口罩佩戴识别代码:
```python
import cv2
import numpy as np
# 加载YOLOv4模型及其配置文件
net = cv2.dnn.readNet("yolov4-tiny.weights", "yolov4-tiny.cfg")
# 加载类别标签
classes = []
with open("coco.names", "r") as f:
classes = [line.strip() for line in f.readlines()]
# 设置随机颜色
colors = np.random.uniform(0, 255, size=(len(classes), 3))
# 读取图像
img = cv2.imread("test.jpg")
# 获取输入图像的高度和宽度
height, width, _ = img.shape
# 预处理图像
blob = cv2.dnn.blobFromImage(img, 1/255, (416, 416), swapRB=True, crop=False)
# 设置输入和输出节点名称
net.setInput(blob)
output_layers = net.getUnconnectedOutLayersNames()
# 运行YOLOv4检测器
outputs = net.forward(output_layers)
# 检测结果后处理
class_ids = []
confidences = []
boxes = []
for output in outputs:
for detection in output:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5 and class_id == 0:
# 如果检测到人脸并且置信度大于0.5,则保存检测结果
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)
# 非极大值抑制(NMS)
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
# 绘制检测结果
font = cv2.FONT_HERSHEY_SIMPLEX
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
color = colors[class_ids[i]]
cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
cv2.putText(img, "Mask", (x, y - 5), font, 0.5, color, 2)
# 显示结果
cv2.imshow("Mask Detection", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
其中,`yolov4-tiny.weights`和`yolov4-tiny.cfg`是YoLoV4的模型文件和配置文件,`coco.names`是类别标签文件,`test.jpg`是待检测的图像。可以根据自己的需求替换这些文件。
该代码使用YoLoV4-tiny模型来进行口罩佩戴检测,只检测人脸区域,并且只检测是否佩戴了口罩。如果检测到佩戴了口罩,则在检测框上方显示“Mask”字样。