(原)人脸姿态识别Fine-Grained Head Pose EstimationWithout Keypoints

转载请注明出处:

https://www.cnblogs.com/darkknightzh/p/12150096.html

论文:

Fine-Grained Head Pose EstimationWithout Keypoints

论文网址:

https://arxiv.org/abs/1710.00925v5

官方pytorch网址:

https://github.com/natanielruiz/deep-head-pose

说明:该代码是pytorch早期的版本。

参考网址:

https://www.cnblogs.com/aoru45/p/11186629.html

1 简介

该论文通过训练多损失的CNN网络来预测人脸的pitch,yaw和roll三个角度值。两个损失分别是分类损失和回归损失。图像通过网络后,得到pitch,yaw和roll这三组特征。

训练阶段:分类损失将特征通过softmax后直接分类,回归损失则是计算归一化后特征的数学期望,并将期望和实际的角度计算MSE loss。

测试阶段:使用归一化后特征的数学期望得到预测的pitch,yaw和roll。

参考网址中指出:分类的loss占比会影响梯度方向,从而会起到一个导向作用,引导回归往一个合适的方向,这是梯度方向上的引导。

2 网络结构

网络结构如下图所示:

输入图像通过Resnet50骨干网络得到特征,而后通过三个fc层,分别作为pitch,yaw和roll的分类输入。代码中CrossEntropyLoss包含了softmax,因而实际上和上图一致。感觉上图右侧按照下面的画法,更易理解吧(只画出了yaw,另外两个同理)。

分类时,起点-99度(包含),终点102度(不包含),间隔3,共67个值,里面共66个间隔,作为离散的类别,对这些使用CrossEntropyLoss计算损失。另一方面,将softmax后归一化的特征作为概率,和对应类别相乘并求和,作为期望,和实际角度计算MSELoss。

损失函数如下:

$L=H(y,\hat{y})+\alpha \centerdot MSE(y,\hat{y})$

其中$y$为预测值,$\hat{y}$为真实值。H代表交叉熵损失。

3 代码

3.1 网络结构

hopenet网络结构代码如下:

 1 class Hopenet(nn.Module):
 2     # Hopenet with 3 output layers for yaw, pitch and roll
 3     # Predicts Euler angles by binning and regression with the expected value
 4     def __init__(self, block, layers, num_bins):
 5         self.inplanes = 64
 6         super(Hopenet, self).__init__()
 7         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
 8         self.bn1 = nn.BatchNorm2d(64)
 9         self.relu = nn.ReLU(inplace=True)
10         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
11         self.layer1 = self._make_layer(block, 64, layers[0])
12         self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
13         self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
14         self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
15         self.avgpool = nn.AvgPool2d(7)   # 至此为resnet的结构。得到特征
16         self.fc_yaw = nn.Linear(512 * block.expansion, num_bins)   # 特征到分类的三个fc层。
17         self.fc_pitch = nn.Linear(512 * block.expansion, num_bins) # 特征到分类的三个fc层。
18         self.fc_roll = nn.Linear(512 * block.expansion, num_bins)  # 特征到分类的三个fc层。
19 
20         self.fc_finetune = nn.Linear(512 * block.expansion + 3, 3)  # Vestigial layer from previous experiments  未使用
21 
22         for m in self.modules():    # 初始化模型参数
23             if isinstance(m, nn.Conv2d):
24                 n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
25                 m.weight.data.normal_(0, math.sqrt(2. / n))
26             elif isinstance(m, nn.BatchNorm2d):
27                 m.weight.data.fill_(1)
28                 m.bias.data.zero_()
29 
30     def _make_layer(self, block, planes, blocks, stride=1):
31         downsample = None
32         if stride != 1 or self.inplanes != planes * block.expansion:
33             downsample = nn.Sequential(
34                 nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
35                 nn.BatchNorm2d(planes * block.expansion),
36             )
37 
38         layers = []
39         layers.append(block(self.inplanes, planes, stride, downsample))
40         self.inplanes = planes * block.expansion
41         for i in range(1, blocks):
42             layers.append(block(self.inplanes, planes))
43 
44         return nn.Sequential(*layers)
45 
46     def forward(self, x):
47         x = self.conv1(x)
48         x = self.bn1(x)
49         x = self.relu(x)
50         x = self.maxpool(x)
51 
52         x = self.layer1(x)
53         x = self.layer2(x)
54         x = self.layer3(x)
55         x = self.layer4(x)
56 
57         x = self.avgpool(x)
58         x = x.view(x.size(0), -1)
59         pre_yaw = self.fc_yaw(x)
60         pre_pitch = self.fc_pitch(x)
61         pre_roll = self.fc_roll(x)
62 
63         return pre_yaw, pre_pitch, pre_roll
View Code
说明:self.fc_finetune未使用

3.2 训练代码

  1 def parse_args():
  2     """Parse input arguments."""
  3     parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
  4     parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]', default=0, type=int)
  5     parser.add_argument('--num_epochs', dest='num_epochs', help='Maximum number of training epochs.', default=5, type=int)
  6     parser.add_argument('--batch_size', dest='batch_size', help='Batch size.', default=16, type=int)
  7     parser.add_argument('--lr', dest='lr', help='Base learning rate.', default=0.001, type=float)
  8     parser.add_argument('--dataset', dest='dataset', help='Dataset type.', default='Pose_300W_LP', type=str)
  9     parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.', default='', type=str)
 10     parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.', default='', type=str)
 11     parser.add_argument('--output_string', dest='output_string', help='String appended to output snapshots.', default = '', type=str)
 12     parser.add_argument('--alpha', dest='alpha', help='Regression loss coefficient.', default=0.001, type=float)
 13     parser.add_argument('--snapshot', dest='snapshot', help='Path of model snapshot.', default='', type=str)
 14 
 15     args = parser.parse_args()
 16     return args
 17 
 18 def get_ignored_params(model):  # Generator function that yields ignored params.
 19     b = [model.conv1, model.bn1, model.fc_finetune]
 20     for i in range(len(b)):
 21         for module_name, module in b[i].named_modules():
 22             if 'bn' in module_name:
 23                 module.eval()
 24             for name, param in module.named_parameters():
 25                 yield param
 26 
 27 def get_non_ignored_params(model):  # Generator function that yields params that will be optimized.
 28     b = [model.layer1, model.layer2, model.layer3, model.layer4]
 29     for i in range(len(b)):
 30         for module_name, module in b[i].named_modules():
 31             if 'bn' in module_name:
 32                 module.eval()
 33             for name, param in module.named_parameters():
 34                 yield param
 35 
 36 def get_fc_params(model):   # Generator function that yields fc layer params.
 37     b = [model.fc_yaw, model.fc_pitch, model.fc_roll]
 38     for i in range(len(b)):
 39         for module_name, module in b[i].named_modules():
 40             for name, param in module.named_parameters():
 41                 yield param
 42 
 43 def load_filtered_state_dict(model, snapshot):
 44     # By user apaszke from discuss.pytorch.org
 45     model_dict = model.state_dict()
 46     snapshot = {k: v for k, v in snapshot.items() if k in model_dict}
 47     model_dict.update(snapshot)
 48     model.load_state_dict(model_dict)
 49 
 50 if __name__ == '__main__':
 51     args = parse_args()
 52 
 53     cudnn.enabled = True
 54     num_epochs = args.num_epochs
 55     batch_size = args.batch_size
 56     gpu = args.gpu_id
 57 
 58     if not os.path.exists('output/snapshots'):
 59         os.makedirs('output/snapshots')
 60 
 61     # ResNet50 structure
 62     #  -99到99,以3为step,共67个bin,这些bin之间共66个位置。因而最后一个参数为66
 63     model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
 64 
 65     if args.snapshot == '':
 66         load_filtered_state_dict(model, model_zoo.load_url('https://download.pytorch.org/models/resnet50-19c8e357.pth'))
 67     else:
 68         saved_state_dict = torch.load(args.snapshot)
 69         model.load_state_dict(saved_state_dict)
 70 
 71     print('Loading data.')
 72 
 73     transformations = transforms.Compose([transforms.Scale(240),
 74             transforms.RandomCrop(224), transforms.ToTensor(),
 75             transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
 76 
 77     if args.dataset == 'Pose_300W_LP':   # 载入数据的dataset
 78         pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
 79     elif args.dataset == 'Pose_300W_LP_random_ds':
 80         pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations)
 81     elif args.dataset == 'Synhead':
 82         pose_dataset = datasets.Synhead(args.data_dir, args.filename_list, transformations)
 83     elif args.dataset == 'AFLW2000':
 84         pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations)
 85     elif args.dataset == 'BIWI':
 86         pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)
 87     elif args.dataset == 'AFLW':
 88         pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations)
 89     elif args.dataset == 'AFLW_aug':
 90         pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations)
 91     elif args.dataset == 'AFW':
 92         pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
 93     else:
 94         print('Error: not a valid dataset name')
 95         sys.exit()
 96 
 97     train_loader = torch.utils.data.DataLoader(dataset=pose_dataset, batch_size=batch_size, shuffle=True, num_workers=2)
 98 
 99     model.cuda(gpu)
100     criterion = nn.CrossEntropyLoss().cuda(gpu)   # 自带softmax
101     reg_criterion = nn.MSELoss().cuda(gpu)
102     alpha = args.alpha   # Regression loss coefficient
103     softmax = nn.Softmax().cuda(gpu)
104     idx_tensor = [idx for idx in range(66)]
105     idx_tensor = Variable(torch.FloatTensor(idx_tensor)).cuda(gpu)
106 
107     optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': 0},
108                                   {'params': get_non_ignored_params(model), 'lr': args.lr},
109                                   {'params': get_fc_params(model), 'lr': args.lr * 5}],
110                                    lr = args.lr)  # 不同层给与不同的学习率
111 
112     print('Ready to train network.')
113     for epoch in range(num_epochs):
114         for i, (images, labels, cont_labels, name) in enumerate(train_loader):  # labels为离散化的bin,cont_labels为实际值
115             images = Variable(images).cuda(gpu)
116 
117             label_yaw = Variable(labels[:,0]).cuda(gpu)   # Binned labels
118             label_pitch = Variable(labels[:,1]).cuda(gpu)
119             label_roll = Variable(labels[:,2]).cuda(gpu)
120 
121             label_yaw_cont = Variable(cont_labels[:,0]).cuda(gpu)   # Continuous labels
122             label_pitch_cont = Variable(cont_labels[:,1]).cuda(gpu)
123             label_roll_cont = Variable(cont_labels[:,2]).cuda(gpu)
124 
125             yaw, pitch, roll = model(images)   # Forward pass
126 
127             loss_yaw = criterion(yaw, label_yaw)       # Cross entropy loss 此处为分类损失,对应类别0-65
128             loss_pitch = criterion(pitch, label_pitch) # label_yaw等为离散化的bin,只有真实的bin为1,其他为0.
129             loss_roll = criterion(roll, label_roll)    # pitch等为特征
130 
131             yaw_predicted = softmax(yaw)   # MSE loss  此处为回归损失
132             pitch_predicted = softmax(pitch)  # yaw_predicted等为特征通过softmax后归一化的特征(即概率)
133             roll_predicted = softmax(roll)
134 
135             # 离散型随机变量的一切可能的取值(idx_tensor)与对应的概率(yaw_predicted)乘积之和称为该离散型随机变量的数学期望
136             yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1) * 3 - 99     # 将归一化的特征对应的实际预测值求和,最终为bz个。
137             pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1) * 3 - 99 # 此处每个概率乘以对应类别,而后求和,得到期望。
138             roll_predicted = torch.sum(roll_predicted * idx_tensor, 1) * 3 - 99 
139 
140             loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw_cont)  # 预测值的总的期望和真实值计算回归损失
141             loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch_cont)
142             loss_reg_roll = reg_criterion(roll_predicted, label_roll_cont)
143 
144             loss_yaw += alpha * loss_reg_yaw   # Total loss 总损失
145             loss_pitch += alpha * loss_reg_pitch
146             loss_roll += alpha * loss_reg_roll
147 
148             loss_seq = [loss_yaw, loss_pitch, loss_roll]
149             grad_seq = [torch.ones(1).cuda(gpu) for _ in range(len(loss_seq))]
150             optimizer.zero_grad()
151             torch.autograd.backward(loss_seq, grad_seq)   # 此处可以认为是不同损失加权(权重均为1)。
152             optimizer.step()
153 
154             if (i+1) % 100 == 0:
155                 print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f'
156                        %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0]))
157 
158         # Save models at numbered epochs.d
159         if epoch % 1 == 0 and epoch < num_epochs:   # 保存模型
160             print('Taking snapshot...')
161             torch.save(model.state_dict(),
162             'output/snapshots/' + args.output_string + '_epoch_'+ str(epoch+1) + '.pkl')
View Code

3.3 dataset代码

 1 class Pose_300W_LP(Dataset):
 2     # Head pose from 300W-LP dataset
 3     def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat', image_mode='RGB'):
 4         self.data_dir = data_dir
 5         self.transform = transform
 6         self.img_ext = img_ext
 7         self.annot_ext = annot_ext
 8 
 9         filename_list = get_list_from_filenames(filename_path)
10 
11         self.X_train = filename_list
12         self.y_train = filename_list
13         self.image_mode = image_mode
14         self.length = len(filename_list)
15 
16     def __getitem__(self, index):
17         img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
18         img = img.convert(self.image_mode)
19         mat_path = os.path.join(self.data_dir, self.y_train[index] + self.annot_ext)
20 
21         # Crop the face loosely
22         pt2d = utils.get_pt2d_from_mat(mat_path)   # 得到2D的landmarks
23         x_min = min(pt2d[0,:])
24         y_min = min(pt2d[1,:])
25         x_max = max(pt2d[0,:])
26         y_max = max(pt2d[1,:])
27 
28         k = np.random.random_sample() * 0.2 + 0.2    # k = 0.2 to 0.40
29         x_min -= 0.6 * k * abs(x_max - x_min)
30         y_min -= 2 * k * abs(y_max - y_min)          # why 2???
31         x_max += 0.6 * k * abs(x_max - x_min)
32         y_max += 0.6 * k * abs(y_max - y_min)
33         img = img.crop((int(x_min), int(y_min), int(x_max), int(y_max)))   # 随机裁减
34 
35         pose = utils.get_ypr_from_mat(mat_path)  # We get the pose in radians,弧度
36         pitch = pose[0] * 180 / np.pi   # 弧度转换为度
37         yaw = pose[1] * 180 / np.pi
38         roll = pose[2] * 180 / np.pi
39 
40         rnd = np.random.random_sample()  # 随机水平旋转
41         if rnd < 0.5:
42             yaw = -yaw    # 摇头需要取反
43             roll = -roll  # 摆头需要取反
44             img = img.transpose(Image.FLIP_LEFT_RIGHT)
45 
46 
47         rnd = np.random.random_sample()  # Blur
48         if rnd < 0.05:
49             img = img.filter(ImageFilter.BLUR)
50 
51         # Bin values
52         bins = np.array(range(-99, 102, 3))   # -99到99,以3为step,共67个bin,这些bin之间共66个位置。
53         # 返回输入在bins中的位置。当输入<bin[0],输出为0。由于此处输入不会<bin[0],因而减1,使得输出范围为0-65
54         binned_pose = np.digitize([yaw, pitch, roll], bins) - 1
55 
56         # Get target tensors
57         labels = binned_pose
58         cont_labels = torch.FloatTensor([yaw, pitch, roll])   # 实际的label
59 
60         if self.transform is not None:
61             img = self.transform(img)
62 
63         return img, labels, cont_labels, self.X_train[index]
64 
65     def __len__(self):
66         # 122,450
67         return self.length
View Code

3.4 测试代码

说明:测试代码进行了修改,只读取一张图像,输入人脸框。

 1 def parse_args():
 2     """Parse input arguments."""
 3     parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
 4     parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]', default=-1, type=int)
 5     parser.add_argument('--snapshot', dest='snapshot', help='Path of model snapshot.', default='hopenet_robust_alpha1.pkl', type=str)
 6     args = parser.parse_args()
 7     return args
 8 
 9 if __name__ == '__main__':
10     args = parse_args()
11 
12     gpu = args.gpu_id
13 
14     if gpu >= 0:
15         cudnn.enabled = True
16 
17     # ResNet50 structure
18     model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)  # 载入模型
19 
20     print('Loading snapshot.')
21     if gpu >= 0:
22         saved_state_dict = torch.load(args.snapshot)
23     else:
24         saved_state_dict = torch.load(args.snapshot, map_location=torch.device('cpu'))  # 无GPU的话,载入模型参数到CPU中
25     model.load_state_dict(saved_state_dict)
26 
27     transformations = transforms.Compose([transforms.Scale(224),
28     transforms.CenterCrop(224), transforms.ToTensor(),
29     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
30 
31     if gpu>=0:
32         model.cuda(gpu)
33 
34     model.eval()  # Change model to 'eval' mode (BN uses moving mean/var).
35     total = 0
36 
37     idx_tensor = [idx for idx in range(66)]
38     idx_tensor = torch.FloatTensor(idx_tensor)
39     if gpu>=0:
40         idx_tensor = idx_tensor.cuda(gpu)
41 
42     img_path = '1.jpg'
43     frame = cv2.imread(img_path, 1)
44     cv2_frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
45 
46     x_min, y_min, x_max, y_max = 439,375,642,596   # 人脸框位置
47 
48     bbox_width = abs(x_max - x_min)   # 人脸框宽
49     bbox_height = abs(y_max - y_min)  # 人脸框高
50     # x_min -= 3 * bbox_width / 4
51     # x_max += 3 * bbox_width / 4
52     # y_min -= 3 * bbox_height / 4
53     # y_max += bbox_height / 4
54     x_min -= 50   # 增大人脸框
55     x_max += 50
56     y_min -= 50
57     y_max += 30
58     x_min = max(x_min, 0)
59     y_min = max(y_min, 0)
60     x_max = min(frame.shape[1], x_max)
61     y_max = min(frame.shape[0], y_max)
62 
63     img = cv2_frame[y_min:y_max,x_min:x_max]    # Crop face loosely。裁减人脸
64     img = Image.fromarray(img)
65 
66     img = transformations(img)   # Transform
67     img_shape = img.size()
68     img = img.view(1, img_shape[0], img_shape[1], img_shape[2])  # NCHW
69     img = torch.tensor(img)
70     if gpu >= 0:
71         img = img.cuda(gpu)
72 
73     yaw, pitch, roll = model(img)  # 得到特征
74 
75     yaw_predicted = F.softmax(yaw)   # 特征归一化
76     pitch_predicted = F.softmax(pitch)
77     roll_predicted = F.softmax(roll)
78 
79     # 得到期望,将期望变换到连续值的度数
80     yaw_predicted = torch.sum(yaw_predicted.data[0] * idx_tensor) * 3 - 99       # Get continuous predictions in degrees.
81     pitch_predicted = torch.sum(pitch_predicted.data[0] * idx_tensor) * 3 - 99
82     roll_predicted = torch.sum(roll_predicted.data[0] * idx_tensor) * 3 - 99
83 
84     frame = utils.draw_axis(frame, yaw_predicted, pitch_predicted, roll_predicted, tdx = (x_min + x_max) / 2, tdy= (y_min + y_max) / 2, size = bbox_height/2)
85     print(yaw_predicted.item(), pitch_predicted.item(), roll_predicted.item())
86     cv2.imwrite('{}.png'.format(os.path.splitext(img_path)[0]), frame)
View Code

其中draw_axis如下:

 1 def draw_axis(img, yaw, pitch, roll, tdx=None, tdy=None, size = 100):
 2     pitch = pitch * np.pi / 180    # 变换到弧度
 3     yaw = -(yaw * np.pi / 180)
 4     roll = roll * np.pi / 180
 5 
 6     if tdx != None and tdy != None:
 7         tdx = tdx
 8         tdy = tdy
 9     else:
10         height, width = img.shape[:2]
11         tdx = width / 2
12         tdy = height / 2
13 
14     # X-Axis pointing to right. drawn in red  # x轴长度,红色指向右侧
15     x1 = size * (cos(yaw) * cos(roll)) + tdx
16     y1 = size * (cos(pitch) * sin(roll) + cos(roll) * sin(pitch) * sin(yaw)) + tdy
17 
18     # Y-Axis | drawn in green    # y轴长度,绿色指向下方
19     #        v
20     x2 = size * (-cos(yaw) * sin(roll)) + tdx
21     y2 = size * (cos(pitch) * cos(roll) - sin(pitch) * sin(yaw) * sin(roll)) + tdy
22 
23     # Z-Axis (out of the screen) drawn in blue   # z轴长度,蓝色,指向屏幕外面
24     x3 = size * (sin(yaw)) + tdx
25     y3 = size * (-cos(yaw) * sin(pitch)) + tdy
26 
27     cv2.line(img, (int(tdx), int(tdy)), (int(x1),int(y1)),(0,0,255),3)
28     cv2.line(img, (int(tdx), int(tdy)), (int(x2),int(y2)),(0,255,0),3)
29     cv2.line(img, (int(tdx), int(tdy)), (int(x3),int(y3)),(255,0,0),2)
30 
31     return img
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posted on 2020-01-04 19:21  darkknightzh  阅读(1174)  评论(0编辑  收藏  举报

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