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)