opencv yolov8 实现人脸检测

首先需要说明的是，YOLOv8 并不是一个现成的模型，这个名称也不常见。可能您想说的是 YOLOv3 或 YOLOv4 。

使用 OpenCV 实现 YOLOv3 或 YOLOv4 进行人脸检测的步骤如下：

1. 下载 YOLOv3 或 YOLOv4 的权重文件和配置文件，例如：

- YOLOv3：[yolov3.cfg](https://github.com/pjreddie/darknet/blob/master/cfg/yolov3.cfg) 和 [yolov3.weights](https://pjreddie.com/media/files/yolov3.weights)
- YOLOv4：[yolov4.cfg](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov4.cfg) 和 [yolov4.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights)

2. 使用 OpenCV 加载模型和配置文件：

   import cv2
   
   # 加载模型和配置文件
   net = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')
   # 或者使用 yolov4
   # net = cv2.dnn.readNetFromDarknet('yolov4.cfg', 'yolov4.weights')

3. 读取图像并创建 blob：

   # 读取图像
   img = cv2.imread('test.jpg')
   
   # 创建 blob
   blob = cv2.dnn.blobFromImage(img, 1/255.0, (416, 416), swapRB=True, crop=False)

4. 将 blob 输入到模型中进行推理：

   # 将 blob 输入到模型中进行推理
   net.setInput(blob)
   outs = net.forward(net.getUnconnectedOutLayersNames())

5. 解析模型的输出，获取检测结果：

   conf_threshold = 0.5  # 置信度阈值
   nms_threshold = 0.4  # 非极大值抑制阈值
   
   # 解析输出，获取检测结果
   boxes = []
   confidences = []
   class_ids = []
   for out in outs:
       for detection in out:
           scores = detection[5:]
           class_id = np.argmax(scores)
           confidence = scores[class_id]
           if confidence > conf_threshold:
               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)
               boxes.append([x, y, w, h])
               confidences.append(float(confidence))
               class_ids.append(class_id)
   
   # 非极大值抑制
   indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)

6. 绘制检测结果：

   # 绘制检测结果
   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)
       text = f'{class_ids[i]}: {confidences[i]:.2f}'
       cv2.putText(img, text, (x, y-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)

完整示例代码如下：

import cv2
import numpy as np

# 加载模型和配置文件
net = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')
# 或者使用 yolov4
# net = cv2.dnn.readNetFromDarknet('yolov4.cfg', 'yolov4.weights')

# 读取图像
img = cv2.imread('test.jpg')

# 创建 blob
blob = cv2.dnn.blobFromImage(img, 1/255.0, (416, 416), swapRB=True, crop=False)

# 将 blob 输入到模型中进行推理
net.setInput(blob)
outs = net.forward(net.getUnconnectedOutLayersNames())

conf_threshold = 0.5  # 置信度阈值
nms_threshold = 0.4  # 非极大值抑制阈值

# 解析输出，获取检测结果
boxes = []
confidences = []
class_ids = []
for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > conf_threshold:
            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)
            boxes.append([x, y, w, h])
            confidences.append(float(confidence))
            class_ids.append(class_id)

# 非极大值抑制
indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)

# 绘制检测结果
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)
    text = f'{class_ids[i]}: {confidences[i]:.2f}'
    cv2.putText(img, text, (x, y-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)

# 显示结果
cv2.imshow('img', img)
cv2.waitKey()
cv2.destroyAllWindows()