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