使用yolov4实现口罩佩戴识别的代码
时间: 2024-05-11 16:19:23 浏览: 5
以下是使用 YOLOv4 实现口罩佩戴识别的 Python 代码:
```python
import cv2
import numpy as np
# 加载 YOLOv4 预训练模型
net = cv2.dnn.readNet("yolov4.weights", "cfg/yolov4.cfg")
# 加载 COCO 数据集标签
classes = []
with open("coco.names", "r") as f:
classes = [line.strip() for line in f.readlines()]
# 设置输入图像尺寸和颜色均值
input_size = 416
mean = [0, 0, 0]
# 加载输入图像
img = cv2.imread("test.jpg")
height, width, _ = img.shape
# 缩放输入图像并减去颜色均值
blob = cv2.dnn.blobFromImage(img, 1/255, (input_size, input_size), mean, swapRB=True, crop=False)
# 设置网络输入
net.setInput(blob)
# 运行前向传播
outs = net.forward(net.getUnconnectedOutLayersNames())
# 解析输出并绘制边界框
class_ids = []
confidences = []
boxes = []
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:
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)
class_ids.append(class_id)
confidences.append(float(confidence))
boxes.append([x, y, w, h])
indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
for i in indices:
i = i[0]
x, y, w, h = boxes[i]
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(img, "Mask", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# 显示输出图像
cv2.imshow("Output", img)
cv2.waitKey(0)
```
其中,需要下载 YOLOv4 的预训练模型文件 `yolov4.weights` 和 COCO 数据集的标签文件 `coco.names`。在运行代码前,需要将这两个文件放在同一目录下。此外,还需要一张测试图像 `test.jpg`,它也应该放在同一目录下。