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inference.py
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inference.py
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from dataset.video_data import get_deca_tform
from PIL import Image
from models.cvthead import CVTHead
import face_alignment
from skimage.transform import estimate_transform, warp, resize, rescale
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import MultiStepLR
import numpy as np
import os
import argparse
import yaml
import pickle
import logging
def preprocess_image(img_pth, fa, device="cuda"):
img = Image.open(img_pth)
img = img.resize((256, 256))
img_npy = np.array(img) # (H=256,W=256,3), val:0-255
img_npy = img_npy[:, :, :3]
landmark = fa.get_landmarks(img_npy)[0]
tform = get_deca_tform(landmark) # (3,3)
img_npy = img_npy / 255. # (H,W,3), val: 0-1
crop_image = warp(img_npy, tform.inverse, output_shape=(224, 224)) # (224, 224, 3), val:[0, 1]
img_tensor = torch.from_numpy(img_npy).float() # tensor, (H, W, 3), val: [0, 1]
img_tensor = img_tensor.permute(2, 0, 1) # (H, W, 3) --> (3, H, W)
img_tensor = (img_tensor - 0.5) / 0.5 # (3,H,W), [-1, 1]
crop_image = torch.tensor(np.asarray(crop_image)).float() # (224, 224, 3), val:0-1
crop_image = crop_image.permute(2, 0, 1) # (3, 224, 224), val:0-1
tform = torch.tensor(np.asarray(tform)).float() # (3,3)
img_tensor = img_tensor.unsqueeze(0).to(device) # (1,3,256,256)
crop_image = crop_image.unsqueeze(0).to(device) # (1,3,224,224)
tform = tform.unsqueeze(0).to(device) # (1,3,3)
return img_tensor, crop_image, tform
def driven_by_face(model, src_pth, drv_pth, out_pth, device, softmask=True):
# face landmark detector
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, device=device)
src_img, src_img_crop, src_tform = preprocess_image(src_pth, fa, device)
drv_img, drv_img_crop, drv_tform = preprocess_image(drv_pth, fa, device)
with torch.no_grad():
outputs = model(src_img_crop, drv_img_crop, src_img, drv_img, src_tform, drv_tform, is_train=False, is_cross_id=True)
predict_img = outputs["pred_drv_img"] # (1,3,256,256), tensor, val:[-1,1]
predict_mask = outputs["pred_drv_mask"] # (1,256,256), tensor, val:[0, 1], soft mask
# visualize
predict_img = 0.5 * (predict_img + 1)
predict_img = predict_img[0].permute(1,2,0).cpu().numpy() # (256,256,3), npy
predict_mask = predict_mask[0].permute(1,2,0).cpu().numpy() # (256,256,1), npy
if not softmask:
predict_mask = (predict_mask > 0.6).float() # (256,256,1), npy
# apply mask
predict_img = predict_img * predict_mask + (1 - predict_mask) # apply mask to predicted image, val:[0, 1], npy
predict_img = (predict_img * 255).astype(np.uint8)
predict_img = Image.fromarray(predict_img)
predict_img.save(out_pth)
def driven_by_flame_coefs(model, src_pth, out_pth, device, softmask=False):
# face landmark detector
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, device=device)
src_img, src_img_crop, src_tform = preprocess_image(src_pth, fa, device)
with torch.no_grad():
pose = torch.zeros(1, 6).to(src_img.device) # (1, 6) rotation (3) + jaw pose (3)
# ######################### shape ###############################
frames = []
for i in range(10):
shape = torch.zeros(1, 100).to(src_img.device) # (1, 100)
shape[0, 0] = 2 * i / 10
outputs = model.flame_coef_generation(src_img_crop, src_img, src_tform, shape=shape, pose=pose)
predict_img = outputs["pred_drv_img"] # (1,3,256,256), tensor, val:[-1,1]
predict_mask = outputs["pred_drv_mask"] # (1,256,256), tensor, val:[0, 1], soft mask
# visualize
predict_img = 0.5 * (predict_img + 1)
predict_img = predict_img[0].permute(1,2,0).cpu().numpy() # (256,256,3), npy
predict_mask = predict_mask[0].permute(1,2,0).cpu().numpy() # (256,256,1), npy
if not softmask:
predict_mask = (predict_mask > 0.6) + 0.0 # (256,256,1), npy
# apply mask
predict_img = predict_img * predict_mask + (1 - predict_mask) # apply mask to predicted image, val:[0, 1], npy
predict_img = (predict_img * 255).astype(np.uint8)
predict_img = Image.fromarray(predict_img)
frames.append(predict_img)
# predict_img.save("examples/shape_{}.png".format(i))
frame_one = frames[0]
out_name = os.path.join(out_pth, "shape.gif")
frame_one.save(out_name, format="GIF", append_images=frames, save_all=True, duration=200, loop=0)
# ######################### exp ###############################
frames = []
for i in range(10):
exp = torch.zeros(1, 100).to(src_img.device) # (1, 100)
exp[0, 0] = 2 * i / 10
outputs = model.flame_coef_generation(src_img_crop, src_img, src_tform, exp=exp, pose=pose)
predict_img = outputs["pred_drv_img"] # (1,3,256,256), tensor, val:[-1,1]
predict_mask = outputs["pred_drv_mask"] # (1,256,256), tensor, val:[0, 1], soft mask
# visualize
predict_img = 0.5 * (predict_img + 1)
predict_img = predict_img[0].permute(1,2,0).cpu().numpy() # (256,256,3), npy
predict_mask = predict_mask[0].permute(1,2,0).cpu().numpy() # (256,256,1), npy
if not softmask:
predict_mask = (predict_mask > 0.6) + 0.0 # (256,256,1), npy
# apply mask
predict_img = predict_img * predict_mask + (1 - predict_mask) # apply mask to predicted image, val:[0, 1], npy
predict_img = (predict_img * 255).astype(np.uint8)
predict_img = Image.fromarray(predict_img)
frames.append(predict_img)
# predict_img.save("examples/exp_{}.png".format(i))
frame_one = frames[0]
out_name = os.path.join(out_pth, "exp.gif")
frame_one.save(out_name, format="GIF", append_images=frames, save_all=True, duration=200, loop=0)
# ######################### view ###############################
frames = []
for i in range(12):
pose = torch.zeros(1, 6).to(src_img.device) # (1, 100)
pose[0, 1] = - np.pi / 4 + i * np.pi / 24
outputs = model.flame_coef_generation(src_img_crop, src_img, src_tform, pose=pose)
predict_img = outputs["pred_drv_img"] # (1,3,256,256), tensor, val:[-1,1]
predict_mask = outputs["pred_drv_mask"] # (1,256,256), tensor, val:[0, 1], soft mask
# visualize
predict_img = 0.5 * (predict_img + 1)
predict_img = predict_img[0].permute(1,2,0).cpu().numpy() # (256,256,3), npy
predict_mask = predict_mask[0].permute(1,2,0).cpu().numpy() # (256,256,1), npy
if not softmask:
predict_mask = (predict_mask > 0.6) + 0.0 # (256,256,1), npy
# apply mask
predict_img = predict_img * predict_mask + (1 - predict_mask) # apply mask to predicted image, val:[0, 1], npy
predict_img = (predict_img * 255).astype(np.uint8)
predict_img = Image.fromarray(predict_img)
frames.append(predict_img)
frame_one = frames[0]
out_name = os.path.join(out_pth, "pose.gif")
frame_one.save(out_name, format="GIF", append_images=frames, save_all=True, duration=200, loop=0)
# ######################### Jaw pose ###############################
frames = []
for i in range(12):
pose = torch.zeros(1, 6).to(src_img.device) # (1, 100)
pose[0, 3] = 0.5 * i / 12
outputs = model.flame_coef_generation(src_img_crop, src_img, src_tform, pose=pose)
predict_img = outputs["pred_drv_img"] # (1,3,256,256), tensor, val:[-1,1]
predict_mask = outputs["pred_drv_mask"] # (1,256,256), tensor, val:[0, 1], soft mask
# visualize
predict_img = 0.5 * (predict_img + 1)
predict_img = predict_img[0].permute(1,2,0).cpu().numpy() # (256,256,3), npy
predict_mask = predict_mask[0].permute(1,2,0).cpu().numpy() # (256,256,1), npy
if not softmask:
predict_mask = (predict_mask > 0.6) + 0.0 # (256,256,1), npy
# apply mask
predict_img = predict_img * predict_mask + (1 - predict_mask) # apply mask to predicted image, val:[0, 1], npy
predict_img = (predict_img * 255).astype(np.uint8)
predict_img = Image.fromarray(predict_img)
frames.append(predict_img)
frame_one = frames[0]
out_name = os.path.join(out_pth, "jaw.gif")
frame_one.save(out_name, format="GIF", append_images=frames, save_all=True, duration=200, loop=0)
def main(args):
device = "cuda"
# >>>>>>>>>>>>>>>>> Model >>>>>>>>>>>>>>>>>
model = CVTHead() # cpu model
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = model.to(device) # gpu model
# load pre-trained weights
ckpt = torch.load(args.ckpt_pth, map_location="cpu")["model"]
model.load_state_dict(ckpt, strict=False)
print(f'-- Number of parameters (G): {sum(p.numel() for p in model.parameters())/1e6} M\n')
if args.flame:
driven_by_flame_coefs(model, args.src_pth, args.out_pth, device, softmask=True)
else:
driven_by_face(model, args.src_pth, args.drv_pth, args.out_pth, device, softmask=True)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='CVTHead Inference')
parser.add_argument('--src_pth', type=str, default="examples/1.png")
parser.add_argument('--drv_pth', type=str, default="examples/2.png")
parser.add_argument('--out_pth', type=str, default="examples/output.png")
parser.add_argument('--ckpt_pth', type=str, default="data/cvthead.pt")
parser.add_argument('--flame', action='store_true')
args = parser.parse_args()
main(args)