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| import logging | |
| import numpy as np | |
| from torchvision.transforms import ToTensor, ToPILImage | |
| import torch | |
| import torch.nn.functional as F | |
| import cv2 | |
| from . import util | |
| from torch.nn import Conv2d, Module, ReLU, MaxPool2d, init | |
| class FaceNet(Module): | |
| """Model the cascading heatmaps. """ | |
| def __init__(self): | |
| super(FaceNet, self).__init__() | |
| # cnn to make feature map | |
| self.relu = ReLU() | |
| self.max_pooling_2d = MaxPool2d(kernel_size=2, stride=2) | |
| self.conv1_1 = Conv2d(in_channels=3, out_channels=64, | |
| kernel_size=3, stride=1, padding=1) | |
| self.conv1_2 = Conv2d( | |
| in_channels=64, out_channels=64, kernel_size=3, stride=1, | |
| padding=1) | |
| self.conv2_1 = Conv2d( | |
| in_channels=64, out_channels=128, kernel_size=3, stride=1, | |
| padding=1) | |
| self.conv2_2 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=3, stride=1, | |
| padding=1) | |
| self.conv3_1 = Conv2d( | |
| in_channels=128, out_channels=256, kernel_size=3, stride=1, | |
| padding=1) | |
| self.conv3_2 = Conv2d( | |
| in_channels=256, out_channels=256, kernel_size=3, stride=1, | |
| padding=1) | |
| self.conv3_3 = Conv2d( | |
| in_channels=256, out_channels=256, kernel_size=3, stride=1, | |
| padding=1) | |
| self.conv3_4 = Conv2d( | |
| in_channels=256, out_channels=256, kernel_size=3, stride=1, | |
| padding=1) | |
| self.conv4_1 = Conv2d( | |
| in_channels=256, out_channels=512, kernel_size=3, stride=1, | |
| padding=1) | |
| self.conv4_2 = Conv2d( | |
| in_channels=512, out_channels=512, kernel_size=3, stride=1, | |
| padding=1) | |
| self.conv4_3 = Conv2d( | |
| in_channels=512, out_channels=512, kernel_size=3, stride=1, | |
| padding=1) | |
| self.conv4_4 = Conv2d( | |
| in_channels=512, out_channels=512, kernel_size=3, stride=1, | |
| padding=1) | |
| self.conv5_1 = Conv2d( | |
| in_channels=512, out_channels=512, kernel_size=3, stride=1, | |
| padding=1) | |
| self.conv5_2 = Conv2d( | |
| in_channels=512, out_channels=512, kernel_size=3, stride=1, | |
| padding=1) | |
| self.conv5_3_CPM = Conv2d( | |
| in_channels=512, out_channels=128, kernel_size=3, stride=1, | |
| padding=1) | |
| # stage1 | |
| self.conv6_1_CPM = Conv2d( | |
| in_channels=128, out_channels=512, kernel_size=1, stride=1, | |
| padding=0) | |
| self.conv6_2_CPM = Conv2d( | |
| in_channels=512, out_channels=71, kernel_size=1, stride=1, | |
| padding=0) | |
| # stage2 | |
| self.Mconv1_stage2 = Conv2d( | |
| in_channels=199, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv2_stage2 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv3_stage2 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv4_stage2 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv5_stage2 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv6_stage2 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=1, stride=1, | |
| padding=0) | |
| self.Mconv7_stage2 = Conv2d( | |
| in_channels=128, out_channels=71, kernel_size=1, stride=1, | |
| padding=0) | |
| # stage3 | |
| self.Mconv1_stage3 = Conv2d( | |
| in_channels=199, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv2_stage3 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv3_stage3 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv4_stage3 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv5_stage3 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv6_stage3 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=1, stride=1, | |
| padding=0) | |
| self.Mconv7_stage3 = Conv2d( | |
| in_channels=128, out_channels=71, kernel_size=1, stride=1, | |
| padding=0) | |
| # stage4 | |
| self.Mconv1_stage4 = Conv2d( | |
| in_channels=199, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv2_stage4 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv3_stage4 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv4_stage4 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv5_stage4 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv6_stage4 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=1, stride=1, | |
| padding=0) | |
| self.Mconv7_stage4 = Conv2d( | |
| in_channels=128, out_channels=71, kernel_size=1, stride=1, | |
| padding=0) | |
| # stage5 | |
| self.Mconv1_stage5 = Conv2d( | |
| in_channels=199, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv2_stage5 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv3_stage5 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv4_stage5 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv5_stage5 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv6_stage5 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=1, stride=1, | |
| padding=0) | |
| self.Mconv7_stage5 = Conv2d( | |
| in_channels=128, out_channels=71, kernel_size=1, stride=1, | |
| padding=0) | |
| # stage6 | |
| self.Mconv1_stage6 = Conv2d( | |
| in_channels=199, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv2_stage6 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv3_stage6 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv4_stage6 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv5_stage6 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=7, stride=1, | |
| padding=3) | |
| self.Mconv6_stage6 = Conv2d( | |
| in_channels=128, out_channels=128, kernel_size=1, stride=1, | |
| padding=0) | |
| self.Mconv7_stage6 = Conv2d( | |
| in_channels=128, out_channels=71, kernel_size=1, stride=1, | |
| padding=0) | |
| for m in self.modules(): | |
| if isinstance(m, Conv2d): | |
| init.constant_(m.bias, 0) | |
| def forward(self, x): | |
| """Return a list of heatmaps.""" | |
| heatmaps = [] | |
| h = self.relu(self.conv1_1(x)) | |
| h = self.relu(self.conv1_2(h)) | |
| h = self.max_pooling_2d(h) | |
| h = self.relu(self.conv2_1(h)) | |
| h = self.relu(self.conv2_2(h)) | |
| h = self.max_pooling_2d(h) | |
| h = self.relu(self.conv3_1(h)) | |
| h = self.relu(self.conv3_2(h)) | |
| h = self.relu(self.conv3_3(h)) | |
| h = self.relu(self.conv3_4(h)) | |
| h = self.max_pooling_2d(h) | |
| h = self.relu(self.conv4_1(h)) | |
| h = self.relu(self.conv4_2(h)) | |
| h = self.relu(self.conv4_3(h)) | |
| h = self.relu(self.conv4_4(h)) | |
| h = self.relu(self.conv5_1(h)) | |
| h = self.relu(self.conv5_2(h)) | |
| h = self.relu(self.conv5_3_CPM(h)) | |
| feature_map = h | |
| # stage1 | |
| h = self.relu(self.conv6_1_CPM(h)) | |
| h = self.conv6_2_CPM(h) | |
| heatmaps.append(h) | |
| # stage2 | |
| h = torch.cat([h, feature_map], dim=1) # channel concat | |
| h = self.relu(self.Mconv1_stage2(h)) | |
| h = self.relu(self.Mconv2_stage2(h)) | |
| h = self.relu(self.Mconv3_stage2(h)) | |
| h = self.relu(self.Mconv4_stage2(h)) | |
| h = self.relu(self.Mconv5_stage2(h)) | |
| h = self.relu(self.Mconv6_stage2(h)) | |
| h = self.Mconv7_stage2(h) | |
| heatmaps.append(h) | |
| # stage3 | |
| h = torch.cat([h, feature_map], dim=1) # channel concat | |
| h = self.relu(self.Mconv1_stage3(h)) | |
| h = self.relu(self.Mconv2_stage3(h)) | |
| h = self.relu(self.Mconv3_stage3(h)) | |
| h = self.relu(self.Mconv4_stage3(h)) | |
| h = self.relu(self.Mconv5_stage3(h)) | |
| h = self.relu(self.Mconv6_stage3(h)) | |
| h = self.Mconv7_stage3(h) | |
| heatmaps.append(h) | |
| # stage4 | |
| h = torch.cat([h, feature_map], dim=1) # channel concat | |
| h = self.relu(self.Mconv1_stage4(h)) | |
| h = self.relu(self.Mconv2_stage4(h)) | |
| h = self.relu(self.Mconv3_stage4(h)) | |
| h = self.relu(self.Mconv4_stage4(h)) | |
| h = self.relu(self.Mconv5_stage4(h)) | |
| h = self.relu(self.Mconv6_stage4(h)) | |
| h = self.Mconv7_stage4(h) | |
| heatmaps.append(h) | |
| # stage5 | |
| h = torch.cat([h, feature_map], dim=1) # channel concat | |
| h = self.relu(self.Mconv1_stage5(h)) | |
| h = self.relu(self.Mconv2_stage5(h)) | |
| h = self.relu(self.Mconv3_stage5(h)) | |
| h = self.relu(self.Mconv4_stage5(h)) | |
| h = self.relu(self.Mconv5_stage5(h)) | |
| h = self.relu(self.Mconv6_stage5(h)) | |
| h = self.Mconv7_stage5(h) | |
| heatmaps.append(h) | |
| # stage6 | |
| h = torch.cat([h, feature_map], dim=1) # channel concat | |
| h = self.relu(self.Mconv1_stage6(h)) | |
| h = self.relu(self.Mconv2_stage6(h)) | |
| h = self.relu(self.Mconv3_stage6(h)) | |
| h = self.relu(self.Mconv4_stage6(h)) | |
| h = self.relu(self.Mconv5_stage6(h)) | |
| h = self.relu(self.Mconv6_stage6(h)) | |
| h = self.Mconv7_stage6(h) | |
| heatmaps.append(h) | |
| return heatmaps | |
| LOG = logging.getLogger(__name__) | |
| TOTEN = ToTensor() | |
| TOPIL = ToPILImage() | |
| params = { | |
| 'gaussian_sigma': 2.5, | |
| 'inference_img_size': 736, # 368, 736, 1312 | |
| 'heatmap_peak_thresh': 0.1, | |
| 'crop_scale': 1.5, | |
| 'line_indices': [ | |
| [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], | |
| [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 12], [12, 13], | |
| [13, 14], [14, 15], [15, 16], | |
| [17, 18], [18, 19], [19, 20], [20, 21], | |
| [22, 23], [23, 24], [24, 25], [25, 26], | |
| [27, 28], [28, 29], [29, 30], | |
| [31, 32], [32, 33], [33, 34], [34, 35], | |
| [36, 37], [37, 38], [38, 39], [39, 40], [40, 41], [41, 36], | |
| [42, 43], [43, 44], [44, 45], [45, 46], [46, 47], [47, 42], | |
| [48, 49], [49, 50], [50, 51], [51, 52], [52, 53], [53, 54], | |
| [54, 55], [55, 56], [56, 57], [57, 58], [58, 59], [59, 48], | |
| [60, 61], [61, 62], [62, 63], [63, 64], [64, 65], [65, 66], | |
| [66, 67], [67, 60] | |
| ], | |
| } | |
| class Face(object): | |
| """ | |
| The OpenPose face landmark detector model. | |
| Args: | |
| inference_size: set the size of the inference image size, suggested: | |
| 368, 736, 1312, default 736 | |
| gaussian_sigma: blur the heatmaps, default 2.5 | |
| heatmap_peak_thresh: return landmark if over threshold, default 0.1 | |
| """ | |
| def __init__(self, face_model_path, | |
| inference_size=None, | |
| gaussian_sigma=None, | |
| heatmap_peak_thresh=None): | |
| self.inference_size = inference_size or params["inference_img_size"] | |
| self.sigma = gaussian_sigma or params['gaussian_sigma'] | |
| self.threshold = heatmap_peak_thresh or params["heatmap_peak_thresh"] | |
| self.model = FaceNet() | |
| self.model.load_state_dict(torch.load(face_model_path)) | |
| # if torch.cuda.is_available(): | |
| # self.model = self.model.cuda() | |
| # print('cuda') | |
| self.model.eval() | |
| def __call__(self, face_img): | |
| H, W, C = face_img.shape | |
| w_size = 384 | |
| x_data = torch.from_numpy(util.smart_resize(face_img, (w_size, w_size))).permute([2, 0, 1]) / 256.0 - 0.5 | |
| x_data = x_data.to(self.cn_device) | |
| with torch.no_grad(): | |
| hs = self.model(x_data[None, ...]) | |
| heatmaps = F.interpolate( | |
| hs[-1], | |
| (H, W), | |
| mode='bilinear', align_corners=True).cpu().numpy()[0] | |
| return heatmaps | |
| def compute_peaks_from_heatmaps(self, heatmaps): | |
| all_peaks = [] | |
| for part in range(heatmaps.shape[0]): | |
| map_ori = heatmaps[part].copy() | |
| binary = np.ascontiguousarray(map_ori > 0.05, dtype=np.uint8) | |
| if np.sum(binary) == 0: | |
| continue | |
| positions = np.where(binary > 0.5) | |
| intensities = map_ori[positions] | |
| mi = np.argmax(intensities) | |
| y, x = positions[0][mi], positions[1][mi] | |
| all_peaks.append([x, y]) | |
| return np.array(all_peaks) |