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test_image.py
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import torch
import cv2
import numpy as np
from skimage.transform import estimate_transform, warp
from src.smirk_encoder import SmirkEncoder
from src.FLAME.FLAME import FLAME
from src.renderer.renderer import Renderer
import argparse
import os
import src.utils.masking as masking_utils
from utils.mediapipe_utils import run_mediapipe
from datasets.base_dataset import create_mask
import torch.nn.functional as F
from tqdm import tqdm
import pickle
mediapipe_indices = [276, 282, 283, 285, 293, 295, 296, 300, 334, 336, 46, 52, 53,
55, 63, 65, 66, 70, 105, 107, 249, 263, 362, 373, 374, 380,
381, 382, 384, 385, 386, 387, 388, 390, 398, 466, 7, 33, 133,
144, 145, 153, 154, 155, 157, 158, 159, 160, 161, 163, 173, 246,
168, 6, 197, 195, 5, 4, 129, 98, 97, 2, 326, 327, 358,
0, 13, 14, 17, 37, 39, 40, 61, 78, 80, 81, 82, 84,
87, 88, 91, 95, 146, 178, 181, 185, 191, 267, 269, 270, 291,
308, 310, 311, 312, 314, 317, 318, 321, 324, 375, 402, 405, 409,
415]
def batch_orth_proj(X, camera):
''' orthgraphic projection
X: 3d vertices, [bz, n_point, 3]
camera: scale and translation, [bz, 3], [scale, tx, ty]
'''
# print('-------------')
# print(camera.shape)
camera = camera.clone().view(-1, 1, 3)
X_trans = X[:, :, :2] + camera[:, :, 1:]
X_trans = torch.cat([X_trans, X[:,:,2:]], 2)
Xn = (camera[:, :, 0:1] * X_trans)
return Xn
def batch_draw_keypoints(image, landmarks, color=(255, 255, 255), radius=1):
if isinstance(landmarks, torch.Tensor):
landmarks = landmarks.cpu().numpy()
if isinstance(image, torch.Tensor):
image = image.squeeze(0)
image = image.cpu().numpy().transpose(1,2,0)
image = (image * 255).astype('uint8')
image = np.ascontiguousarray(image[..., ::-1])
for point in landmarks:
image = cv2.circle(image, (int(point[0]), int(point[1])), radius, color, -1)
return image
def crop_face(frame, landmarks, scale=1.0, image_size=224):
left = np.min(landmarks[:, 0])
right = np.max(landmarks[:, 0])
top = np.min(landmarks[:, 1])
bottom = np.max(landmarks[:, 1])
h, w, _ = frame.shape
old_size = (right - left + bottom - top) / 2
center = np.array([right - (right - left) / 2.0, bottom - (bottom - top) / 2.0])
size = int(old_size * scale)
# crop image
src_pts = np.array([[center[0] - size / 2, center[1] - size / 2], [center[0] - size / 2, center[1] + size / 2],
[center[0] + size / 2, center[1] - size / 2]])
DST_PTS = np.array([[0, 0], [0, image_size - 1], [image_size - 1, 0]])
tform = estimate_transform('similarity', src_pts, DST_PTS)
return tform
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--input_path', type=str, default='', help='Path to the input image')
parser.add_argument('--device', type=str, default='cuda', help='Device to run the model on')
parser.add_argument('--checkpoint', type=str, default='', help='Path to the checkpoint')
parser.add_argument('--crop', action='store_true', help='Crop the face using mediapipe')
parser.add_argument('--out_path', type=str, default='', help='Path to save the output (will be created if not exists)')
parser.add_argument('--use_smirk_generator', action='store_true', help='Use SMIRK neural image to image translator to reconstruct the image')
parser.add_argument('--render_orig', action='store_true', help='Present the result w.r.t. the original image/video size without cropping')
args = parser.parse_args()
image_size = 224
# ----------------------- initialize configuration ----------------------- #
smirk_encoder = SmirkEncoder().to(args.device)
checkpoint = torch.load(args.checkpoint)
checkpoint_encoder = {k.replace('smirk_encoder.', ''): v for k, v in checkpoint.items() if 'smirk_encoder' in k} # checkpoint includes both smirk_encoder and smirk_generator
smirk_encoder.load_state_dict(checkpoint_encoder)
smirk_encoder.eval()
if args.use_smirk_generator:
from src.smirk_generator import SmirkGenerator
smirk_generator = SmirkGenerator(in_channels=6, out_channels=3, init_features=32, res_blocks=5).to(args.device)
checkpoint_generator = {k.replace('smirk_generator.', ''): v for k, v in checkpoint.items() if 'smirk_generator' in k} # checkpoint includes both smirk_encoder and smirk_generator
smirk_generator.load_state_dict(checkpoint_generator)
smirk_generator.eval()
# ---- visualize the results ---- #
flame = FLAME().to(args.device)
renderer = Renderer().to(args.device)
with torch.no_grad():
image = cv2.imread(args.input_path)
orig_image_height, orig_image_width, _ = image.shape
kpt_mediapipe = run_mediapipe(image)
# crop face if needed
if args.crop:
if (kpt_mediapipe is None):
# print(f'Could not find landmarks for the image {i} using mediapipe and cannot crop the face. Exiting...')
exit()
kpt_mediapipe = kpt_mediapipe[..., :2]
tform = crop_face(image,kpt_mediapipe,scale=1.4,image_size=image_size)
cropped_image = warp(image, tform.inverse, output_shape=(224, 224), preserve_range=True).astype(np.uint8)
cropped_kpt_mediapipe = np.dot(tform.params, np.hstack([kpt_mediapipe, np.ones([kpt_mediapipe.shape[0],1])]).T).T
cropped_kpt_mediapipe = cropped_kpt_mediapipe[:,:2]
else:
cropped_image = image
cropped_kpt_mediapipe = kpt_mediapipe
cropped_image = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2RGB)
cropped_image = cv2.resize(cropped_image, (224,224))
cropped_image = torch.tensor(cropped_image).permute(2,0,1).unsqueeze(0).float()/255.0
cropped_image = cropped_image.to(args.device)
outputs = smirk_encoder(cropped_image)
flame_output = flame.forward(outputs)
renderer_output = renderer.forward(flame_output['vertices'], outputs['cam'],
landmarks_fan=flame_output['landmarks_fan'], landmarks_mp=flame_output['landmarks_mp'])
rendered_img = renderer_output['rendered_img']
if args.render_orig:
if args.crop:
rendered_img_numpy = (rendered_img.squeeze(0).permute(1,2,0).detach().cpu().numpy()*255.0).astype(np.uint8)
rendered_img_orig = warp(rendered_img_numpy, tform, output_shape=(orig_image_height, orig_image_width), preserve_range=True).astype(np.uint8)
# back to pytorch to concatenate with full_image
rendered_img_orig = torch.Tensor(rendered_img_orig).permute(2,0,1).unsqueeze(0).float()/255.0
else:
rendered_img_orig = F.interpolate(rendered_img, (orig_image_height, orig_image_width), mode='bilinear').cpu()
full_image = torch.Tensor(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)).permute(2,0,1).unsqueeze(0).float()/255.0
grid = torch.cat([full_image, rendered_img_orig], dim=3)
else:
grid = torch.cat([cropped_image, rendered_img, (cropped_image + rendered_img) / 2], dim=3)
# ---- create the neural renderer reconstructed img ---- #
if args.use_smirk_generator:
if (kpt_mediapipe is None):
print('Could not find landmarks for the image using mediapipe and cannot create the hull mask for the smirk generator. Exiting...')
exit()
mask_ratio_mul = 5
mask_ratio = 0.01
mask_dilation_radius = 10
hull_mask = create_mask(cropped_kpt_mediapipe, (224, 224))
rendered_mask = 1 - (rendered_img == 0).all(dim=1, keepdim=True).float()
hull_mask = torch.from_numpy(hull_mask).type(dtype = torch.float32).unsqueeze(0).to(args.device)
masked_img = masking_utils.masking_face(cropped_image, hull_mask, mask_dilation_radius, rendered_mask=rendered_mask)
smirk_generator_input = torch.cat([rendered_img, masked_img], dim=1)
#token
reconstructed_img = smirk_generator(smirk_generator_input, outputs['token'])
if args.render_orig:
if args.crop:
reconstructed_img_numpy = (reconstructed_img.squeeze(0).permute(1,2,0).detach().cpu().numpy()*255.0).astype(np.uint8)
reconstructed_img_orig = warp(reconstructed_img_numpy, tform, output_shape=(orig_image_height, orig_image_width), preserve_range=True).astype(np.uint8)
# back to pytorch to concatenate with full_image
reconstructed_img_orig = torch.Tensor(reconstructed_img_orig).permute(2,0,1).unsqueeze(0).float()/255.0
else:
reconstructed_img_orig = F.interpolate(reconstructed_img, (orig_image_height, orig_image_width), mode='bilinear').cpu()
grid = torch.cat([grid, reconstructed_img_orig], dim=3)
else:
grid = torch.cat([grid, reconstructed_img], dim=3)
grid_numpy = grid.squeeze(0).permute(1,2,0).detach().cpu().numpy()*255.0
grid_numpy = grid_numpy.astype(np.uint8)
grid_numpy = cv2.cvtColor(grid_numpy, cv2.COLOR_BGR2RGB)
if not os.path.exists(args.out_path):
os.makedirs(args.out_path)
image_name = 'reconstruct_' + args.input_path.split('/')[-1]
cv2.imwrite(f"{args.out_path}/{image_name}", grid_numpy)