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实时人脸关键点检测源码推荐（高效算法）

阅读数 7

还有效果更好的，高精度轻量级关键点网络，参考我的另一篇博文：

轻量级高精度人脸关键点推荐_jacke121的专栏-CSDN博客

带人脸关键点，pc版70多ms，还可以，戴口罩也有效果,侧脸低头偶尔有偏差

GitHub - Jiahao-UTS/SLPT-master

又发现效果比较好的：

https://github.com/OAID/TengineKit

关键点数据集：

WFLW_images.tar.gz

标注:

WFLW_annotations.tar

SLIM

这个好像是1ms的,关键点和姿态都有,效果比较好，比下面pytorch_face_landmark的好，但是不能区分眨眼：

slim网络结构，有训练代码，wingloss：

https://github.com/hommmm/Peppa-Facial-Landmark-PyTorch

测试代码：

import time import cv2import torchimport torch.onnxfrom models.slim import Slimimport onnxruntime as rtimport numpy as np def export(): x = torch.randn(1, 3, 160, 160) model = Slim() model.load_state_dict(torch.load("../pretrained_weights/slim_160_latest.pth", map_location="cpu")) model.eval() torch.onnx.export(model, x, "../pretrained_weights/slim_160_latest.onnx", input_names=["input1"], output_names=['output1']) if __name__ == '__main__': im_size=160 sess = rt.InferenceSession("../pretrained_weights/slim_160_latest.onnx") input_name = sess.get_inputs()[0].name im = cv2.imread(r'd:\qinlan3.jpg') im = cv2.resize(im, (im_size, im_size)) crop_image = (im - 127.0) / 127.0 crop_image = np.array([np.transpose(crop_image, (2, 0, 1))]).astype(np.float32) start = time.time() raw = sess.run(None, {input_name: crop_image})[0][0] end = time.time() print("ONNX Inference Time: {:.6f}".format(end - start)) font = cv2.FONT_HERSHEY_SIMPLEX img_black = np.zeros([800, 800, 3], np.uint8) img_black = cv2.resize(im, (800, 800)) lmks = (np.array(raw[0:136])*160).astype(np.int) lmks=lmks[:136].reshape(-1,2) for point in lmks: im = cv2.circle(im, tuple(point), 2, (0, 255, 0), -1, 1) # frame = cv2.putText(frame, "Pitch: {:.4f}".format(PRY_3d[0]), (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8, # (0, 255, 0), 1, 1) # frame = cv2.putText(frame, "Yaw: {:.4f}".format(PRY_3d[1]), (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.8, # (0, 255, 0), 1, 1) # frame = cv2.putText(frame, "Roll: {:.4f}".format(PRY_3d[2]), (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.8, # (0, 255, 0), 1, 1) color = (200, 160, 75) pred_black = np.round(raw[0:136] * 800).astype(np.int).reshape(-1,2) for i in range(pred_black.shape[0]): p = tuple(pred_black[i]) img_black = cv2.putText(img_black, str(i), tuple(p), font, 0.3, (0, 0, 255), 1) cv2.circle(img_black, p, 1, color, 1, cv2.LINE_AA) cv2.imshow('img_black', img_black[200:, 100:]) cv2.imshow("Peppa ", im) cv2.waitKey(0) 一键获取完整项目代码python运行

PFLD

神力ai出的98个关键点

AI算法详情页

8ms左右，但是飘的很严重，基本不能用。

人脸，人头都能检测，还有人头姿态

https://github.com/jacke121/headpose_final

https://github.com/rafabs97/headpose_final

gpu，头部姿态需要8ms

效果不错，不能区分眨眼

https://github.com/cunjian/pytorch_face_landmark

test_camera_pfld_onnx.py 50ms

ort_session_landmark = onnxruntime.InferenceSession("pfld.onnx")

test_camera_light_onnx.py

onnx_path = "models/onnx/version-RFB-320.onnx"

效果不好：

https://github.com/polarisZhao/Facial-Landmark-Detection

https://github.com/MUST-AI-Lab/HRNet-Facial-Landmark-Detection

https://github.com/emilianavt/OpenSeeFace

https://github.com/KristianMSchmidt/Kaggle-Google-Landmark-Competetition

有数据集：

https://github.com/cunjian/pytorch_face_landmark

tf 1070 64*64 7ms，可能和torch不兼容

https://github.com/songhengyang/face_landmark_factory

无预训练：

https://github.com/srivastava41099/Facial-Keypoint-Detection

https://github.com/yanuartadityan/cnn-facefeature-detection

https://github.com/jacke121/cnn-facefeature-detection

我自己加了一个detect.py

15个关键点：

This is a quick 4-layers single pool CNN for detecting 15 face keypoints.

81个，有误差，不是特别准，特别快，1ms

https://github.com/codeniko/shape_predictor_81_face_landmarks

https://github.com/justusschock/shapenet

效果一般，不准

#!/usr/bin/env python# -*- coding: utf-8 -*-import time import cv2import mathfrom shapenet.networks.feature_extractors import Img224x224Kernel7x7SeparatedDimsfrom shapenet.networks.utils import CustomGroupNormimport torchimport pytestimport numpy as np @pytest.mark.parametrize("num_outputs,num_in_channels,norm_class,p_dropout", [ (16, 1, torch.nn.InstanceNorm2d, 0.1), (16, 1, torch.nn.BatchNorm2d, 0.5), (16, 1, CustomGroupNorm, 0.5), (75, 125, torch.nn.InstanceNorm2d, 0.), (75, 125, torch.nn.BatchNorm2d, 0.), (75, 125, CustomGroupNorm, 0.) ])def test_224_img_size_7_kernel_size_separated_dims(num_outputs, num_in_channels, norm_class, p_dropout):    net = Img224x224Kernel7x7SeparatedDims(num_in_channels, num_outputs,                                           norm_class, p_dropout)     state_dict = torch.load('pretrained_face.ptj')    net.load_state_dict(state_dict)     net.cuda()    net.eval()    input_tensor = torch.rand(1, num_in_channels, 224, 224).cuda()     for i in range(1):        start = time.time()        out= net(input_tensor)#.shape == (16, num_outputs, 1, 1)        print('time', time.time() - start)        print(out) if __name__ == '__main__':    # num_outputs= (16, 1, torch.nn.InstanceNorm2d, 0.1)     # from shapenet.jit import JitHomogeneousShapeLayer    # from shapenet.jit import JitShapeNetwork    #    import torch    import numpy as np     net= torch.jit.load('pretrained_face.ptj')    net.cuda()    net.eval()    vc = cv2.VideoCapture(0)  # 读入视频文件    d = 0    exit_flag = False    c = 0      index = 1    while True:  # 循环读取视频帧        image=cv2.imread('shu.jpg')        c += 1        # if c % 2 == 1:        # continue            # image_origin = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)        images = []        images_origin = []        start=time.time()           image = cv2.resize(image, (224, 224))#,                           # interpolation=cv2.INTER_LINEAR)        image_origin = image        image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)          # image -= np.array((104.00699, 116.66877, 122.67892))        image = cv2.cvtColor(image, cv2.COLOR_BGRA2GRAY)        image = image.astype(np.float32)        image = image/ 255.0         image = image.transpose((1, 0))         images_origin.append(image_origin)  # keep for save result         # image /= 255.0        # image = np.transpose(image, (2, 0, 1))        # image = np.transpose(image, (1, 0))        # image = image.astype(np.float32)        images.append(image)         print("time", (time.time() - start))        images = np.asarray([images])        images_cuda = torch.from_numpy(images).cuda()        out=net(images_cuda)        angle=-45        angle=math.pi/(180/angle)        center_x=112        center_y=112        for i,point in enumerate(out[0]):            x=point[1]            y=point[0]            srx = (x - center_x) * np.cos(angle) + (y - center_y) * np.sin(angle) + center_x             sry = (y - center_y) * np.cos(angle) - (x - center_x) * np.sin(angle) + center_y            cv2.circle(image_origin, (int(srx),int(sry)), 2, (0, 0, 255), -1)            if i>30:                break         cv2.imshow('asdf',image_origin)        cv2.waitKey(1)     # def test_jit_equality():    # layer_kwargs = {"shapes": np.random.rand(26, 68, 2),    # "n_dims": 2,    # "use_cpp": False}    #    # input_tensor = torch.rand(10, 1, 224, 224)    #    # jit_net = JitShapeNetwork(JitHomogeneousShapeLayer, layer_kwargs)    #    # out= torch.jit.trace(jit_net, (torch.rand(1, 1, 224, 224)))    #    # test_224_img_size_7_kernel_size_separated_dims(75, 125, torch.nn.BatchNorm2d, 0)

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