OpenCV-Python也能实现人脸检测了

谁践踏了优雅 2022-09-16 15:28 194阅读 0赞

opencv中也可以实现深度学习中的人脸识别算法了。是怎么一回事呢？就是opencv中的DNN库，更新了好多深度学习的模块或者说是库函数，这样就让我们摆脱了安装庞大繁琐的深度学习框架。我们只需下载相应的权重文件，就可以实现复杂的人脸识别和人脸检测功能了。

## 人脸检测 ##

1、下载权重文件和配置文件

![31d1eec00dc61a5bb27d158e6201cea6.png][]

2、话不多说，直接上代码

# -*-coding:utf-8-*-
    """
    File Name: face_detection.py
    Program IDE: PyCharm
    Date: 2021/10/17
    Create File By Author: Hong
    """
    import cv2 as cv
    
    # 权重文件和配置文件
    model_bin = 'models/opencv_face_detector_uint8.pb'
    config_text = 'models/opencv_face_detector.pbtxt'
    
    
    def face_detection(video_path: str):
        """
        人脸检测，使用DNN中的人脸检测模块
        :param video_path: 传入视频文件
        :return: 没有返回值
        """
        # 加载模型权重和配置文件
        net = cv.dnn.readNetFromTensorflow(model=model_bin, config=config_text)
        cap = cv.VideoCapture(video_path)
    
        while cap.isOpened():
            ret, frame = cap.read()
            # 若读不到视频帧，直接退出
            if not ret:
                break
    
            h, w, c = frame.shape
            # N, C, H, W ——> num_image,channel,height,width
            # 设置输入网络的图像格式
            blob = cv.dnn.blobFromImage(frame, 1.0, (300, 300), (104.0, 177.0, 123.0), False, False)
            net.setInput(blob)
            outs = net.forward()  # 1×1×N×7 分别是批次、图像数量、人脸数、每个人脸7个值（后四个值分别是人脸框的左上角右下角）
            # print(outs.shape)
            for detection in outs[0, 0, :, :]:  # 找出最后7个值
                print(detection)
                score = float(detection[2])  # 第三个值表示该人脸是人脸的概率
                if score > 0.5:  # 大于0.5概率才画框，找出人脸框左上角和右下角坐标
                    left = detection[3] * w
                    top = detection[4] * h
                    right = detection[5] * w
                    bottom = detection[6] * h
                    cv.rectangle(frame, (int(left), int(top)), (int(right), int(bottom)), (0, 0, 255), 2, 8, 0)
                    cv.putText(frame, str(score), (int(left), int(top)), 2, 1, (255, 0, 0))  # 在框的左上角处画出概率
    
            cv.imshow('frame', frame)
    
            c = cv.waitKey(1)
            if c == 27 or 0xFF == ord('q'):
                break
    
        cap.release()
        cv.destroyAllWindows()
    
    
    if __name__ == '__main__':
        path = 'images/video_face.mp4'
        face_detection(path)

结果展示：

![人脸检测][95927cabaf2c2e52d9a16c03ee1031bd.png]

## 人脸检测和人脸关键点检测 ##

1、下载权重文件

![9a0c8b1499e5b68ce748aa2e88fd01c4.png][]

2、项目代码

# -*-coding:utf-8-*-
    """
    File Name: scrfd_face.py
    Program IDE: PyCharm
    Date: 2021/10/17
    Create File By Author: Hong
    """
    import cv2
    import argparse
    import numpy as np
    
    
    class SCRFD():
        def __init__(self, onnxmodel, confThreshold=0.5, nmsThreshold=0.5):
            self.inpWidth = 640
            self.inpHeight = 640
            self.confThreshold = confThreshold
            self.nmsThreshold = nmsThreshold
            self.net = cv2.dnn.readNet(onnxmodel)
            self.keep_ratio = True
            self.fmc = 3
            self._feat_stride_fpn = [8, 16, 32]
            self._num_anchors = 2
    
        def resize_image(self, srcimg):
            padh, padw, newh, neww = 0, 0, self.inpHeight, self.inpWidth
            if self.keep_ratio and srcimg.shape[0] != srcimg.shape[1]:
                hw_scale = srcimg.shape[0] / srcimg.shape[1]
                if hw_scale > 1:
                    newh, neww = self.inpHeight, int(self.inpWidth / hw_scale)
                    img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
                    padw = int((self.inpWidth - neww) * 0.5)
                    img = cv2.copyMakeBorder(img, 0, 0, padw, self.inpWidth - neww - padw, cv2.BORDER_CONSTANT,
                                             value=0)  # add border
                else:
                    newh, neww = int(self.inpHeight * hw_scale) + 1, self.inpWidth
                    img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
                    padh = int((self.inpHeight - newh) * 0.5)
                    img = cv2.copyMakeBorder(img, padh, self.inpHeight - newh - padh, 0, 0, cv2.BORDER_CONSTANT, value=0)
            else:
                img = cv2.resize(srcimg, (self.inpWidth, self.inpHeight), interpolation=cv2.INTER_AREA)
            return img, newh, neww, padh, padw
    
        def distance2bbox(self, points, distance, max_shape=None):
            x1 = points[:, 0] - distance[:, 0]
            y1 = points[:, 1] - distance[:, 1]
            x2 = points[:, 0] + distance[:, 2]
            y2 = points[:, 1] + distance[:, 3]
            if max_shape is not None:
                x1 = x1.clamp(min=0, max=max_shape[1])
                y1 = y1.clamp(min=0, max=max_shape[0])
                x2 = x2.clamp(min=0, max=max_shape[1])
                y2 = y2.clamp(min=0, max=max_shape[0])
            return np.stack([x1, y1, x2, y2], axis=-1)
    
        def distance2kps(self, points, distance, max_shape=None):
            preds = []
            for i in range(0, distance.shape[1], 2):
                px = points[:, i % 2] + distance[:, i]
                py = points[:, i % 2 + 1] + distance[:, i + 1]
                if max_shape is not None:
                    px = px.clamp(min=0, max=max_shape[1])
                    py = py.clamp(min=0, max=max_shape[0])
                preds.append(px)
                preds.append(py)
            return np.stack(preds, axis=-1)
    
        def detect(self, srcimg):
            img, newh, neww, padh, padw = self.resize_image(srcimg)
            blob = cv2.dnn.blobFromImage(img, 1.0 / 128, (self.inpWidth, self.inpHeight), (127.5, 127.5, 127.5),
                                         swapRB=True)
            # Sets the input to the network
            self.net.setInput(blob)
    
            # Runs the forward pass to get output of the output layers
            outs = self.net.forward(self.net.getUnconnectedOutLayersNames())
            # inference output
            scores_list, bboxes_list, kpss_list = [], [], []
            for idx, stride in enumerate(self._feat_stride_fpn):
                scores = outs[idx * self.fmc][0]
                bbox_preds = outs[idx * self.fmc + 1][0] * stride
                kps_preds = outs[idx * self.fmc + 2][0] * stride
                height = blob.shape[2] // stride
                width = blob.shape[3] // stride
                anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32)
                anchor_centers = (anchor_centers * stride).reshape((-1, 2))
                if self._num_anchors > 1:
                    anchor_centers = np.stack([anchor_centers] * self._num_anchors, axis=1).reshape((-1, 2))
    
                pos_inds = np.where(scores >= self.confThreshold)[0]
                bboxes = self.distance2bbox(anchor_centers, bbox_preds)
                pos_scores = scores[pos_inds]
                pos_bboxes = bboxes[pos_inds]
                scores_list.append(pos_scores)
                bboxes_list.append(pos_bboxes)
    
                kpss = self.distance2kps(anchor_centers, kps_preds)
                # kpss = kps_preds
                kpss = kpss.reshape((kpss.shape[0], -1, 2))
                pos_kpss = kpss[pos_inds]
                kpss_list.append(pos_kpss)
    
            scores = np.vstack(scores_list).ravel()
            # bboxes = np.vstack(bboxes_list) / det_scale
            # kpss = np.vstack(kpss_list) / det_scale
            bboxes = np.vstack(bboxes_list)
            kpss = np.vstack(kpss_list)
            bboxes[:, 2:4] = bboxes[:, 2:4] - bboxes[:, 0:2]
            ratioh, ratiow = srcimg.shape[0] / newh, srcimg.shape[1] / neww
            bboxes[:, 0] = (bboxes[:, 0] - padw) * ratiow
            bboxes[:, 1] = (bboxes[:, 1] - padh) * ratioh
            bboxes[:, 2] = bboxes[:, 2] * ratiow
            bboxes[:, 3] = bboxes[:, 3] * ratioh
            kpss[:, :, 0] = (kpss[:, :, 0] - padw) * ratiow
            kpss[:, :, 1] = (kpss[:, :, 1] - padh) * ratioh
            indices = cv2.dnn.NMSBoxes(bboxes.tolist(), scores.tolist(), self.confThreshold, self.nmsThreshold)
            for i in indices:
                i = i[0]
                xmin, ymin, xamx, ymax = int(bboxes[i, 0]), int(bboxes[i, 1]), int(bboxes[i, 0] + bboxes[i, 2]), int(
                    bboxes[i, 1] + bboxes[i, 3])
                cv2.rectangle(srcimg, (xmin, ymin), (xamx, ymax), (0, 0, 255), thickness=2)
                for j in range(5):
                    cv2.circle(srcimg, (int(kpss[i, j, 0]), int(kpss[i, j, 1])), 1, (0, 255, 0), thickness=-1)
                cv2.putText(srcimg, str(round(scores[i], 3)), (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0),
                            thickness=1)
            return srcimg
    
    
    if __name__ == '__main__':
        parser = argparse.ArgumentParser()
        parser.add_argument('--imgpath', type=str, default='images/2.png', help='image path')
        parser.add_argument('--onnxmodel', default='weights/scrfd_500m_kps.onnx', type=str,
                            choices=['weights/scrfd_500m_kps.onnx', 'weights/scrfd_2.5g_kps.onnx',
                                     'weights/scrfd_10g_kps.onnx'], help='onnx model')
        parser.add_argument('--confThreshold', default=0.5, type=float, help='class confidence')
        parser.add_argument('--nmsThreshold', default=0.5, type=float, help='nms iou thresh')
        args = parser.parse_args()
    
        mynet = SCRFD(args.onnxmodel, confThreshold=args.confThreshold, nmsThreshold=args.nmsThreshold)
        srcimg = cv2.imread(args.imgpath)
        outimg = mynet.detect(srcimg)
    
        winName = 'Deep learning object detection in OpenCV'
        cv2.namedWindow(winName, cv2.WINDOW_AUTOSIZE)
        cv2.imshow(winName, outimg)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

结果展示：  
![人脸关键点检测][5352dc5121d6dd0ac117fffecebce407.png]

> 权重文件请关注微信公众号《AI与计算机视觉》，回复 **”人脸检测“** 获取。

[31d1eec00dc61a5bb27d158e6201cea6.png]: /images/20220828/cd168af41d8b468697b2da340890945e.png
[95927cabaf2c2e52d9a16c03ee1031bd.png]: /images/20220828/7efca798dbc445f0ba94ef58cdcc6756.png
[9a0c8b1499e5b68ce748aa2e88fd01c4.png]: /images/20220828/3be101f7f31b4244899dea898e2bddd8.png
[5352dc5121d6dd0ac117fffecebce407.png]: /images/20220828/04b2a6b4b4874711920b680ba74c28bf.png