人脸检测之CenterFace

妖狐艹你老母 2023-02-22 12:19 28阅读 0赞

论文：[CenterFace: Joint Face Detection and Alignment Using Face as Point][CenterFace_ Joint Face Detection and Alignment Using Face as Point]

Github：[https://github.com/Star-Clouds/CenterFace][https_github.com_Star-Clouds_CenterFace]

论文基于centerNet进行改进，提出了anchor free形式的人脸检测框架，可以同时实现人脸检测+关键点检测。精度和速度都优于主流的MTCNN，Face Box等框架。

主要贡献：

1.  提出了anchor free的人脸检测设计，将人脸检测问题转化为关键点估计问题。相比之前的检测算法，该方法的的模型输出的下采样率只为4。
2.  基于多任务学习策略，同时学习人脸检测+关键点定位
3.  网络结构采用了FPN结构
4.  大量的实验表明，速度和精度都空前的好

级联检测器的缺点：

1.  推理速度受图片中人脸数目的影响，当人脸数目增多的时候，推理速度也会大大降低。
2.  每一个模型都单独训练，训练过程繁琐。非端到端的训练模式，整体精度有限。

网络结构：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE0ODQ1MTE5_size_16_color_FFFFFF_t_70][]

网络整体结构采用MobileNetV2结构，MobileNetV2进行了5次下采样，在MobileNetV2的最后一层，增加了3个上采样层。最终输出的大小进行了2次下采样，输出维度为原图的1/4。

Loss函数：

**人脸分类loss，**

![20200703145552656.png][]

其中，α = 2 ，β = 4

**人脸框中心点偏移loss，**

![20200703145610371.png][]

![20200703145625923.png][]

**人脸框宽，高的loss，**

![20200703145641700.png][]

**关键点的loss，**

![20200703145709678.png][]

**整体loss，**

![20200703145658130.png][]

实验结果：

**推理速度，**

**![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE0ODQ1MTE5_size_16_color_FFFFFF_t_70 1][]**

**FDDB精度，**

**![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE0ODQ1MTE5_size_16_color_FFFFFF_t_70 2][]**

**WIDER FACE 精度，**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE0ODQ1MTE5_size_16_color_FFFFFF_t_70 3][]

Onnx推理：

Onnx模型格式，可以方便的使用程序进行op的增删改查操作。包括节点的增加，去除，输入输出维度的修改等。同时，基于onnx runtime的推理可以获得比基于pytorch推理略快的速度。缺点就是整个graph已经固定，不支持动态输入大小。

首先使用change\_onnx.py修改作者提供的onnx模型的输入维度，

import onnx
    
    model = onnx.load("../models/onnx/centerface.onnx")
    
    # The model is represented as a protobuf structure and it can be accessed
    # using the standard python-for-protobuf methods
    
    # iterate through inputs of the graph
    for input in model.graph.input:
        print (input.name, end=": ")
        # get type of input tensor
        tensor_type = input.type.tensor_type
        # check if it has a shape:
        if (tensor_type.HasField("shape")):
            # iterate through dimensions of the shape:
            for num,d in enumerate(tensor_type.shape.dim):
                # the dimension may have a definite (integer) value or a symbolic identifier or neither:
                if (d.HasField("dim_value")):
                    if num ==0:
                        d.dim_value = 1
                    if num ==2:
                        d.dim_value = 480
                    if num ==3:
                        d.dim_value = 640
                    print (d.dim_value, end=", ")  # known dimension
                elif (d.HasField("dim_param")):
                    print (d.dim_param, end=", ")  # unknown dimension with symbolic name
                else:
                    print ("?", end=", ")  # unknown dimension with no name
        else:
            print ("unknown rank", end="")
        print()
        break
    onnx.checker.check_model(model)
    onnx.save(model, 'out.onnx')
    
    
    """
    model = onnx.load('models/centerface.onnx')
    model.graph.input[0].type.tensor_type.shape.dim[0].dim_param = '?'
    model.graph.input[0].type.tensor_type.shape.dim[1].dim_param = '3'
    model.graph.input[0].type.tensor_type.shape.dim[2].dim_param = '?'
    model.graph.input[0].type.tensor_type.shape.dim[3].dim_param = '?'
    onnx.save(model, 'dynamic_model.onnx')
    """

模型另存在out.onnx，和原始的centerface.onnx的输入维度进行对比，

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE0ODQ1MTE5_size_16_color_FFFFFF_t_70 4][]

推理代码，centerface.py，

import numpy as np
    import cv2
    import datetime
    import torch
    import onnxruntime
    import onnx
    
    class CenterFace(object):
        def __init__(self, landmarks=True):
            self.landmarks = landmarks
            self.session = onnxruntime.InferenceSession("out.onnx")
            self.inputs = self.session.get_inputs()[0].name
            self.outputs = ["537", "538", "539", '540']
    
    
            self.img_h_new, self.img_w_new, self.scale_h, self.scale_w = 0, 0, 0, 0
    
        def __call__(self, img, height, width, threshold=0.5):
            #self.img_h_new, self.img_w_new, self.scale_h, self.scale_w = self.transform(height, width)
            self.img_h_new, self.img_w_new, self.scale_h, self.scale_w = 480, 640 , 480/height, 640/width
            return self.inference_opencv(img, threshold)
    
        def inference_opencv(self, img, threshold):
    
            begin = datetime.datetime.now()
            image = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
            image =cv2.resize(image,(self.img_w_new, self.img_h_new))
            input_image = np.expand_dims(np.swapaxes(np.swapaxes(image,0,2),1,2),0).astype(np.float32)
    
            heatmap,scale , offset ,lms = self.session.run(None, {self.inputs: input_image})
    
    
    
            end = datetime.datetime.now()
            print("cpu times = ", end - begin)
            return self.postprocess(heatmap, lms, offset, scale, threshold)
    
        def transform(self, h, w):
            img_h_new, img_w_new = int(np.ceil(h / 32) * 32), int(np.ceil(w / 32) * 32)
            scale_h, scale_w = img_h_new / h, img_w_new / w
            return img_h_new, img_w_new, scale_h, scale_w
    
        def postprocess(self, heatmap, lms, offset, scale, threshold):
            if self.landmarks:
                dets, lms = self.decode(heatmap, scale, offset, lms, (self.img_h_new, self.img_w_new), threshold=threshold)
            else:
                dets = self.decode(heatmap, scale, offset, None, (self.img_h_new, self.img_w_new), threshold=threshold)
            if len(dets) > 0:
                dets[:, 0:4:2], dets[:, 1:4:2] = dets[:, 0:4:2] / self.scale_w, dets[:, 1:4:2] / self.scale_h
                if self.landmarks:
                    lms[:, 0:10:2], lms[:, 1:10:2] = lms[:, 0:10:2] / self.scale_w, lms[:, 1:10:2] / self.scale_h
            else:
                dets = np.empty(shape=[0, 5], dtype=np.float32)
                if self.landmarks:
                    lms = np.empty(shape=[0, 10], dtype=np.float32)
            if self.landmarks:
                return dets, lms
            else:
                return dets
    
        def decode(self, heatmap, scale, offset, landmark, size, threshold=0.1):
            heatmap = np.squeeze(heatmap)
            scale0, scale1 = scale[0, 0, :, :], scale[0, 1, :, :]
            offset0, offset1 = offset[0, 0, :, :], offset[0, 1, :, :]
            c0, c1 = np.where(heatmap > threshold)
            if self.landmarks:
                boxes, lms = [], []
            else:
                boxes = []
            if len(c0) > 0:
                for i in range(len(c0)):
                    s0, s1 = np.exp(scale0[c0[i], c1[i]]) * 4, np.exp(scale1[c0[i], c1[i]]) * 4
                    o0, o1 = offset0[c0[i], c1[i]], offset1[c0[i], c1[i]]
                    s = heatmap[c0[i], c1[i]]
                    x1, y1 = max(0, (c1[i] + o1 + 0.5) * 4 - s1 / 2), max(0, (c0[i] + o0 + 0.5) * 4 - s0 / 2)
                    x1, y1 = min(x1, size[1]), min(y1, size[0])
                    boxes.append([x1, y1, min(x1 + s1, size[1]), min(y1 + s0, size[0]), s])
                    if self.landmarks:
                        lm = []
                        for j in range(5):
                            lm.append(landmark[0, j * 2 + 1, c0[i], c1[i]] * s1 + x1)
                            lm.append(landmark[0, j * 2, c0[i], c1[i]] * s0 + y1)
                        lms.append(lm)
                boxes = np.asarray(boxes, dtype=np.float32)
                keep = self.nms(boxes[:, :4], boxes[:, 4], 0.3)
                boxes = boxes[keep, :]
                if self.landmarks:
                    lms = np.asarray(lms, dtype=np.float32)
                    lms = lms[keep, :]
            if self.landmarks:
                return boxes, lms
            else:
                return boxes
    
        def nms(self, boxes, scores, nms_thresh):
            x1 = boxes[:, 0]
            y1 = boxes[:, 1]
            x2 = boxes[:, 2]
            y2 = boxes[:, 3]
            areas = (x2 - x1 + 1) * (y2 - y1 + 1)
            order = np.argsort(scores)[::-1]
            num_detections = boxes.shape[0]
            suppressed = np.zeros((num_detections,), dtype=np.bool)
    
            keep = []
            for _i in range(num_detections):
                i = order[_i]
                if suppressed[i]:
                    continue
                keep.append(i)
    
                ix1 = x1[i]
                iy1 = y1[i]
                ix2 = x2[i]
                iy2 = y2[i]
                iarea = areas[i]
    
                for _j in range(_i + 1, num_detections):
                    j = order[_j]
                    if suppressed[j]:
                        continue
    
                    xx1 = max(ix1, x1[j])
                    yy1 = max(iy1, y1[j])
                    xx2 = min(ix2, x2[j])
                    yy2 = min(iy2, y2[j])
                    w = max(0, xx2 - xx1 + 1)
                    h = max(0, yy2 - yy1 + 1)
    
                    inter = w * h
                    ovr = inter / (iarea + areas[j] - inter)
                    if ovr >= nms_thresh:
                        suppressed[j] = True
    
            return keep

推理代码，demo.py

import cv2
    import scipy.io as sio
    import os
    from centerface import CenterFace
    
    
    def camera():
        cap = cv2.VideoCapture(0)
        ret, frame = cap.read()
        h, w = frame.shape[:2]
        centerface = CenterFace()
        while True:
            ret, frame = cap.read()
            dets, lms = centerface(frame, h, w, threshold=0.35)
            for det in dets:
                boxes, score = det[:4], det[4]
                cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])), (int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
            for lm in lms:
                for i in range(0, 5):
                    cv2.circle(frame, (int(lm[i * 2]), int(lm[i * 2 + 1])), 2, (0, 0, 255), -1)
            cv2.imshow('out', frame)
            # Press Q on keyboard to stop recording
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
        cap.release()
    
    
    def test_image():
        #frame = cv2.imread('000388.jpg')
        frame = cv2.imread('test.jpg')
        h, w = frame.shape[:2]
        landmarks = True
        centerface = CenterFace(landmarks=landmarks)
        if landmarks:
            dets, lms = centerface(frame, h, w, threshold=0.35)
        else:
            dets = centerface(frame, threshold=0.35)
    
        for det in dets:
            boxes, score = det[:4], det[4]
            cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])), (int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
        if landmarks:
            for lm in lms:
                for i in range(0, 5):
                    cv2.circle(frame, (int(lm[i * 2]), int(lm[i * 2 + 1])), 2, (0, 0, 255), -1)
        #cv2.imshow('out', frame)
        cv2.imwrite('out.jpg',frame)
        #cv2.waitKey(0)
    
    
    def test_image_tensorrt():
        frame = cv2.imread('000388.jpg')
        h, w = 480, 640  # must be 480* 640
        landmarks = True
        centerface = CenterFace(landmarks=landmarks, backend="tensorrt")
        if landmarks:
            dets, lms = centerface(frame, h, w, threshold=0.35)
        else:
            dets = centerface(frame, threshold=0.35)
    
        for det in dets:
            boxes, score = det[:4], det[4]
            cv2.rectangle(frame, (int(boxes[0]), int(boxes[1])), (int(boxes[2]), int(boxes[3])), (2, 255, 0), 1)
        if landmarks:
            for lm in lms:
                for i in range(0, 5):
                    cv2.circle(frame, (int(lm[i * 2]), int(lm[i * 2 + 1])), 2, (0, 0, 255), -1)
        cv2.imshow('out', frame)
        cv2.waitKey(0)
    
    
    def test_widerface():
        Path = 'widerface/WIDER_val/images/'
        wider_face_mat = sio.loadmat('widerface/wider_face_split/wider_face_val.mat')
        event_list = wider_face_mat['event_list']
        file_list = wider_face_mat['file_list']
        save_path = 'save_out/'
    
        for index, event in enumerate(event_list):
            file_list_item = file_list[index][0]
            im_dir = event[0][0]
            # print(save_path + im_dir)
            if not os.path.exists(save_path + im_dir):
                os.makedirs(save_path + im_dir)
            landmarks = True
            centerface = CenterFace(landmarks=landmarks)
            for num, file in enumerate(file_list_item):
                im_name = file[0][0]
                zip_name = '%s/%s.jpg' % (im_dir, im_name)
                print(os.path.join(Path, zip_name))
                img = cv2.imread(os.path.join(Path, zip_name))
                h, w = img.shape[:2]
                if landmarks:
                    dets, lms = centerface(img, h, w, threshold=0.05)
                else:
                    dets = centerface(img, threshold=0.05)
                f = open(save_path + im_dir + '/' + im_name + '.txt', 'w')
                f.write('{:s}\n'.format('%s/%s.jpg' % (im_dir, im_name)))
                f.write('{:d}\n'.format(len(dets)))
                for b in dets:
                    x1, y1, x2, y2, s = b
                    f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'.format(x1, y1, (x2 - x1 + 1), (y2 - y1 + 1), s))
                f.close()
                print('event:%d num:%d' % (index + 1, num + 1))
    
    
    if __name__ == '__main__':
        # camera()
        test_image()
        # test_widerface()

最终效果，

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE0ODQ1MTE5_size_16_color_FFFFFF_t_70 5][]

人脸检测小江湖：

自己的测试，时间包括网络推理+后处理时间，face++为服务接口测试，相比不是很公平。

<table style="width:426.1pt;"> 
 <tbody> 
 <tr> 
 <td style="vertical-align:top;width:114pt;"> 方法 </td> 
 <td style="vertical-align:top;width:45pt;"> MTCNN </td> 
 <td style="vertical-align:top;width:52.5pt;"> ultra-face </td> 
 <td style="vertical-align:top;width:62.5pt;"> Retina-Face-mobilenetv1 </td> 
 <td style="vertical-align:top;width:54.35pt;"> Retina-Face-resnet50 </td> 
 <td style="vertical-align:top;width:59.4pt;"> Centerface-mobileNetv2 </td> 
 <td style="vertical-align:top;width:38.35pt;"> face++ </td> 
 </tr> 
 <tr> 
 <td style="vertical-align:top;width:114pt;"> 模型大小 </td> 
 <td style="vertical-align:top;width:45pt;"> 2.9M </td> 
 <td style="vertical-align:top;width:52.5pt;"> 1M </td> 
 <td style="vertical-align:top;width:62.5pt;"> 1.8M </td> 
 <td style="vertical-align:top;width:54.35pt;"> 105M </td> 
 <td style="vertical-align:top;width:59.4pt;"> 7.2M </td> 
 <td style="vertical-align:top;width:38.35pt;"> - </td> 
 </tr> 
 <tr> 
 <td style="vertical-align:top;width:114pt;"> CPU（640*480） Intel(R) Xeon(R) Silver 4216 </td> 
 <td style="vertical-align:top;width:45pt;"> 600ms </td> 
 <td style="vertical-align:top;width:52.5pt;"> 200ms </td> 
 <td style="vertical-align:top;width:62.5pt;"> 140ms </td> 
 <td style="vertical-align:top;width:54.35pt;"> 2000ms </td> 
 <td style="vertical-align:top;width:59.4pt;"> 130ms </td> 
 <td style="vertical-align:top;width:38.35pt;"> 10ms </td> 
 </tr> 
 <tr> 
 <td style="vertical-align:top;width:114pt;"> GPU（640*480） 2080TI&nbsp; </td> 
 <td style="vertical-align:top;width:45pt;"> 110ms </td> 
 <td style="vertical-align:top;width:52.5pt;"> 18ms </td> 
 <td style="vertical-align:top;width:62.5pt;"> 38ms </td> 
 <td style="vertical-align:top;width:54.35pt;"> 50ms </td> 
 <td style="vertical-align:top;width:59.4pt;"> 8ms </td> 
 </tr> 
 <tr> 
 <td style="vertical-align:top;width:114pt;"> CPU（1280*720） Intel(R) Xeon(R) Silver 4216 </td> 
 <td style="vertical-align:top;width:45pt;"> 1000ms </td> 
 <td style="vertical-align:top;width:52.5pt;"> 500ms </td> 
 <td style="vertical-align:top;width:62.5pt;"> 350ms </td> 
 <td style="vertical-align:top;width:54.35pt;"> 3500ms </td> 
 <td style="vertical-align:top;width:59.4pt;"> 300ms </td> 
 <td style="vertical-align:top;width:38.35pt;"> 10ms </td> 
 </tr> 
 <tr> 
 <td style="vertical-align:top;width:114pt;"> GPU（1280*720） 2080TI&nbsp; </td> 
 <td style="vertical-align:top;width:45pt;"> 200ms </td> 
 <td style="vertical-align:top;width:52.5pt;"> 40ms </td> 
 <td style="vertical-align:top;width:62.5pt;"> 100ms </td> 
 <td style="vertical-align:top;width:54.35pt;"> 120ms </td> 
 <td style="vertical-align:top;width:59.4pt;"> 25ms </td> 
 </tr> 
 <tr> 
 <td style="vertical-align:top;width:114pt;"> CPU（1920*1080） Intel(R) Xeon(R) Silver 4216 </td> 
 <td style="vertical-align:top;width:45pt;"> 1600ms </td> 
 <td style="vertical-align:top;width:52.5pt;"> - </td> 
 <td style="vertical-align:top;width:62.5pt;"> 800ms </td> 
 <td style="vertical-align:top;width:54.35pt;"> 8000ms </td> 
 <td style="vertical-align:top;width:59.4pt;"> 750ms </td> 
 <td style="vertical-align:top;width:38.35pt;"> 10ms </td> 
 </tr> 
 <tr> 
 <td style="vertical-align:top;width:114pt;"> GPU（1920*1080） 2080TI&nbsp; </td> 
 <td style="vertical-align:top;width:45pt;"> 330ms </td> 
 <td style="vertical-align:top;width:52.5pt;"> - </td> 
 <td style="vertical-align:top;width:62.5pt;"> 200ms </td> 
 <td style="vertical-align:top;width:54.35pt;"> 250ms </td> 
 <td style="vertical-align:top;width:59.4pt;"> 50ms </td> 
 </tr> 
 <tr> 
 <td style="vertical-align:top;width:114pt;"> 精度 </td> 
 <td style="vertical-align:top;width:45pt;"> 框略好，5点好 </td> 
 <td style="vertical-align:top;width:52.5pt;"> 框检出少 </td> 
 <td style="vertical-align:top;width:62.5pt;"> 框差，5点差 </td> 
 <td style="vertical-align:top;width:54.35pt;"> 框误检大，5点一般 </td> 
 <td style="vertical-align:top;width:59.4pt;"> 框好，5点非常差 </td> 
 <td style="vertical-align:top;width:38.35pt;"> 框好，点好 </td> 
 </tr> 
 </tbody> 
</table>

[CenterFace_ Joint Face Detection and Alignment Using Face as Point]: https://arxiv.org/abs/1911.03599
[https_github.com_Star-Clouds_CenterFace]: https://github.com/Star-Clouds/CenterFace
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE0ODQ1MTE5_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200703145534293.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE0ODQ1MTE5,size_16,color_FFFFFF,t_70
[20200703145552656.png]: https://img-blog.csdnimg.cn/20200703145552656.png
[20200703145610371.png]: https://img-blog.csdnimg.cn/20200703145610371.png
[20200703145625923.png]: https://img-blog.csdnimg.cn/20200703145625923.png
[20200703145641700.png]: https://img-blog.csdnimg.cn/20200703145641700.png
[20200703145709678.png]: https://img-blog.csdnimg.cn/20200703145709678.png
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