import cv2 import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from tqdm import tqdm from ...utils import log def np_bgr_to_tensor(img_np, dtype): img_rgb = cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB) / 255.0 * 2 - 1 return torch.tensor(img_rgb).permute(2, 0, 1).to(dtype=dtype) def image_preprocess(np_bgr, size, dtype=torch.float32): img_np = cv2.resize(np_bgr, size) return np_bgr_to_tensor(img_np, dtype) def umeyama(src, dst, estimate_scale): """Estimate N-D similarity transformation with or without scaling. Parameters ---------- src : (M, N) array Source coordinates. dst : (M, N) array Destination coordinates. estimate_scale : bool Whether to estimate scaling factor. Returns ------- T : (N + 1, N + 1) The homogeneous similarity transformation matrix. The matrix contains NaN values only if the problem is not well-conditioned. References ---------- .. [1] "Least-squares estimation of transformation parameters between two point patterns", Shinji Umeyama, PAMI 1991, DOI: 10.1109/34.88573 """ num = src.shape[0] dim = src.shape[1] # Compute mean of src and dst. src_mean = src.mean(axis=0) dst_mean = dst.mean(axis=0) # Subtract mean from src and dst. src_demean = src - src_mean dst_demean = dst - dst_mean # Eq. (38). A = np.dot(dst_demean.T, src_demean) / num # Eq. (39). d = np.ones((dim,), dtype=np.double) if np.linalg.det(A) < 0: d[dim - 1] = -1 T = np.eye(dim + 1, dtype=np.double) U, S, V = np.linalg.svd(A) # Eq. (40) and (43). rank = np.linalg.matrix_rank(A) if rank == 0: return np.nan * T elif rank == dim - 1: if np.linalg.det(U) * np.linalg.det(V) > 0: T[:dim, :dim] = np.dot(U, V) else: s = d[dim - 1] d[dim - 1] = -1 T[:dim, :dim] = np.dot(U, np.dot(np.diag(d), V)) d[dim - 1] = s else: T[:dim, :dim] = np.dot(U, np.dot(np.diag(d), V.T)) if estimate_scale: # Eq. (41) and (42). scale = 1.0 / src_demean.var(axis=0).sum() * np.dot(S, d) else: scale = 1.0 T[:dim, dim] = dst_mean - scale * np.dot(T[:dim, :dim], src_mean.T) T[:dim, :dim] *= scale return T def warp_face_pd_fgc(image, landmarks222, save_size=224): pt5_idx = [182, 202, 36, 149, 133] dst_pt5 = ( np.array( [ [0.3843, 0.27], [0.62, 0.2668], [0.503, 0.4185], [0.406, 0.5273], [0.5977, 0.525], ] ) * save_size ) src_pt5 = landmarks222[pt5_idx] M = umeyama(src_pt5, dst_pt5, True)[0:2] warped = cv2.warpAffine(image, M, (save_size, save_size), flags=cv2.INTER_CUBIC) return warped def get_drive_expression_pd_fgc( pd_fpg_motion, images, landmarks, device, dtype=torch.float32 ): emo_list = [] motion_model = pd_fpg_motion.to(device=device) with tqdm(total=len(images)) as pbar: for frame, landmark in zip(images, landmarks): emo_image = warp_face_pd_fgc(frame, landmark, save_size=224) input_tensor = ( image_preprocess(emo_image, (224, 224), dtype) .to(device=device) .unsqueeze(0) ) # headpose_emb, eye_embed, emo_embed, mouth_feat # emo_tensor = motion_model(input_tensor) # emo_list.append(emo_tensor) # headpose_emb [1, 6]; eye_embed [1, 6]; emo_embed [1, 30]; mouth_feat [1, 512] headpose_emb, eye_embed, emo_embed, mouth_feat = motion_model(input_tensor) emotion = { "headpose_emb": headpose_emb.cpu(), "eye_embed": eye_embed.cpu(), "emo_embed": emo_embed.cpu(), "mouth_feat": mouth_feat.cpu(), } emo_list.append(emotion) pbar.set_description("PD_FPG_MOTION") pbar.update() # neg_tensor = motion_model(torch.ones_like(input_tensor)*-1).cpu() # ret_tensor = torch.cat(emo_list, dim=0) # pd_fpg_motion.to(device='cpu') # return dict(pd_fpg=ret_tensor.unsqueeze(0), neg_pd_fpg=neg_tensor.unsqueeze(0)) return emo_list def det_landmarks(face_aligner, frame_list, comfy_pbar): rect_list = [] new_frame_list = [] assert len(frame_list) > 0 face_aligner.reset_track() with tqdm(total=len(frame_list)) as pbar: for i, frame in enumerate(frame_list): faces = face_aligner.forward(frame) if len(faces) > 0: face = sorted( faces, key=lambda x: (x["face_rect"][2] - x["face_rect"][0]) * (x["face_rect"][3] - x["face_rect"][1]), )[-1] rect_list.append(face["face_rect"]) new_frame_list.append(frame) else: log.warning(f"No face detected in the frame {i}, inserting empty frame.") rect_list.append(None) # Add placeholder new_frame_list.append(None) # Add placeholder pbar.set_description("DET stage1") pbar.update() comfy_pbar.update(1) face_aligner.reset_track() save_frame_list = [] save_landmark_list = [] with tqdm(total=len(new_frame_list)) as pbar: for i, (frame, rect) in enumerate(zip(new_frame_list, rect_list)): if frame is None or rect is None: save_frame_list.append(None) save_landmark_list.append(None) log.warning(f"No face detected in the frame {i}, inserting empty landmark.") else: faces = face_aligner.forward(frame, pre_rect=rect) if len(faces) > 0: face = sorted( faces, key=lambda x: (x["face_rect"][2] - x["face_rect"][0]) * (x["face_rect"][3] - x["face_rect"][1]), )[-1] landmarks = face["pre_kpt_222"] save_frame_list.append(frame) save_landmark_list.append(landmarks) else: save_frame_list.append(None) save_landmark_list.append(None) log.warning(f"No face detected in the frame {i}, inserting empty landmark.") pbar.set_description("DET stage2") pbar.update() comfy_pbar.update(1) face_aligner.reset_track() return save_frame_list, save_landmark_list, rect_list def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False): "3x3 convolution with padding" return nn.Conv2d( in_planes, out_planes, kernel_size=3, stride=strd, padding=padding, bias=bias ) class HourGlass(nn.Module): def __init__(self, num_modules, depth, num_features): super(HourGlass, self).__init__() self.num_modules = num_modules self.depth = depth self.features = num_features self.dropout = nn.Dropout(0.5) self._generate_network(self.depth) def _generate_network(self, level): self.add_module("b1_" + str(level), ConvBlock(256, 256)) self.add_module("b2_" + str(level), ConvBlock(256, 256)) if level > 1: self._generate_network(level - 1) else: self.add_module("b2_plus_" + str(level), ConvBlock(256, 256)) self.add_module("b3_" + str(level), ConvBlock(256, 256)) def _forward(self, level, inp): # Upper branch up1 = inp up1 = self._modules["b1_" + str(level)](up1) up1 = self.dropout(up1) # Lower branch low1 = F.max_pool2d(inp, 2, stride=2) low1 = self._modules["b2_" + str(level)](low1) if level > 1: low2 = self._forward(level - 1, low1) else: low2 = low1 low2 = self._modules["b2_plus_" + str(level)](low2) low3 = low2 low3 = self._modules["b3_" + str(level)](low3) up1size = up1.size() rescale_size = (up1size[2], up1size[3]) up2 = F.interpolate(low3, size=rescale_size, mode="bilinear") return up1 + up2 def forward(self, x): return self._forward(self.depth, x) class ConvBlock(nn.Module): def __init__(self, in_planes, out_planes): super(ConvBlock, self).__init__() self.bn1 = nn.BatchNorm2d(in_planes) self.conv1 = conv3x3(in_planes, int(out_planes / 2)) self.bn2 = nn.BatchNorm2d(int(out_planes / 2)) self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4)) self.bn3 = nn.BatchNorm2d(int(out_planes / 4)) self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4)) if in_planes != out_planes: self.downsample = nn.Sequential( nn.BatchNorm2d(in_planes), nn.ReLU(True), nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, bias=False), ) else: self.downsample = None def forward(self, x): residual = x out1 = self.bn1(x) out1 = F.relu(out1, True) out1 = self.conv1(out1) out2 = self.bn2(out1) out2 = F.relu(out2, True) out2 = self.conv2(out2) out3 = self.bn3(out2) out3 = F.relu(out3, True) out3 = self.conv3(out3) out3 = torch.cat((out1, out2, out3), 1) if self.downsample is not None: residual = self.downsample(residual) out3 += residual return out3 class FAN_use(nn.Module): def __init__(self): super(FAN_use, self).__init__() self.num_modules = 1 # Base part self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) self.bn1 = nn.BatchNorm2d(64) self.conv2 = ConvBlock(64, 128) self.conv3 = ConvBlock(128, 128) self.conv4 = ConvBlock(128, 256) # Stacking part hg_module = 0 self.add_module("m" + str(hg_module), HourGlass(1, 4, 256)) self.add_module("top_m_" + str(hg_module), ConvBlock(256, 256)) self.add_module( "conv_last" + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0), ) self.add_module( "l" + str(hg_module), nn.Conv2d(256, 68, kernel_size=1, stride=1, padding=0) ) self.add_module("bn_end" + str(hg_module), nn.BatchNorm2d(256)) if hg_module < self.num_modules - 1: self.add_module( "bl" + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0), ) self.add_module( "al" + str(hg_module), nn.Conv2d(68, 256, kernel_size=1, stride=1, padding=0), ) self.avgpool = nn.MaxPool2d((2, 2), 2) self.conv6 = nn.Conv2d(68, 1, 3, 2, 1) self.fc = nn.Linear(28 * 28, 512) self.bn5 = nn.BatchNorm2d(68) self.relu = nn.ReLU(True) def forward(self, x): x = F.relu(self.bn1(self.conv1(x)), True) x = F.max_pool2d(self.conv2(x), 2) x = self.conv3(x) x = self.conv4(x) previous = x i = 0 hg = self._modules["m" + str(i)](previous) ll = hg ll = self._modules["top_m_" + str(i)](ll) ll = self._modules["bn_end" + str(i)](self._modules["conv_last" + str(i)](ll)) tmp_out = self._modules["l" + str(i)](F.relu(ll)) net = self.relu(self.bn5(tmp_out)) net = self.conv6(net) net = net.view(-1, net.shape[-2] * net.shape[-1]) net = self.relu(net) net = self.fc(net) return net class FanEncoder(nn.Module): def __init__(self, pose_dim=6, eye_dim=6): super(FanEncoder, self).__init__() self.model = FAN_use() self.to_mouth = nn.Sequential( nn.Linear(512, 512), nn.ReLU(), nn.BatchNorm1d(512), nn.Linear(512, 512) ) self.mouth_embed = nn.Sequential( nn.ReLU(), nn.Linear(512, 512 - pose_dim - eye_dim) ) self.to_headpose = nn.Sequential( nn.Linear(512, 512), nn.ReLU(), nn.BatchNorm1d(512), nn.Linear(512, 512) ) self.headpose_embed = nn.Sequential(nn.ReLU(), nn.Linear(512, pose_dim)) self.to_eye = nn.Sequential( nn.Linear(512, 512), nn.ReLU(), nn.BatchNorm1d(512), nn.Linear(512, 512) ) self.eye_embed = nn.Sequential(nn.ReLU(), nn.Linear(512, eye_dim)) self.to_emo = nn.Sequential( nn.Linear(512, 512), nn.ReLU(), nn.BatchNorm1d(512), nn.Linear(512, 512) ) self.emo_embed = nn.Sequential(nn.ReLU(), nn.Linear(512, 30)) def forward_feature(self, x): net = self.model(x) return net def forward(self, x): x = self.model(x) mouth_feat = self.to_mouth(x) headpose_feat = self.to_headpose(x) headpose_emb = self.headpose_embed(headpose_feat) eye_feat = self.to_eye(x) eye_embed = self.eye_embed(eye_feat) emo_feat = self.to_emo(x) emo_embed = self.emo_embed(emo_feat) return headpose_emb, eye_embed, emo_embed, mouth_feat