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Zero
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import traceback
from tqdm import tqdm
import os
import torch
import numpy as np
from PIL import Image
from omegaconf import OmegaConf
from time import time
from pixel3dmm.utils.uv import uv_pred_to_mesh
from pixel3dmm.lightning.p3dmm_system import system as p3dmm_system
#from pixel3dmm.lightning.system_flame_params_legacy import system as system_flame_params_legacy
from pixel3dmm import env_paths
def pad_to_3_channels(img):
if img.shape[-1] == 3:
return img
elif img.shape[-1] == 1:
return np.concatenate([img, np.zeros_like(img[..., :1]), np.zeros_like(img[..., :1])], axis=-1)
elif img.shape[-1] == 2:
return np.concatenate([img, np.zeros_like(img[..., :1])], axis=-1)
else:
raise ValueError('too many dimensions in prediction type!')
def gaussian_fn(M, std):
n = torch.arange(0, M) - (M - 1.0) / 2.0
sig2 = 2 * std * std
w = torch.exp(-n ** 2 / sig2)
return w
def gkern(kernlen=256, std=128):
"""Returns a 2D Gaussian kernel array."""
gkern1d_x = gaussian_fn(kernlen, std=std * 5)
gkern1d_y = gaussian_fn(kernlen, std=std)
gkern2d = torch.outer(gkern1d_y, gkern1d_x)
return gkern2d
valid_verts = np.load(f'{env_paths.VALID_VERTICES_WIDE_REGION}')
def main(cfg):
if cfg.model.prediction_type == 'flame_params':
cfg.data.mirror_aug = False
# data loader
if cfg.model.feature_map_type == 'DINO':
feature_map_size = 32
elif cfg.model.feature_map_type == 'sapiens':
feature_map_size = 64
batch_size = 1 #cfg.inference_batch_size
checkpoints = {
'uv_map': f"{env_paths.CKPT_UV_PRED}",
'normals': f"{env_paths.CKPT_N_PRED}",
}
model_checkpoint = checkpoints[cfg.model.prediction_type]
model = None
prediction_types = cfg.model.prediction_type.split(',')
conv = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=11, bias=False, padding='same')
g_weights = gkern(11, 2)
g_weights /= torch.sum(g_weights)
conv.weight = torch.nn.Parameter(g_weights.unsqueeze(0).unsqueeze(0))
OUT_NAMES = str(cfg.video_name).split(',')
print(f'''
<<<<<<<< STARTING PIXEL3DMM INFERENCE for {cfg.video_name} in {prediction_types} MODE >>>>>>>>
''')
for OUT_NAME in OUT_NAMES:
folder = f'{env_paths.PREPROCESSED_DATA}/{OUT_NAME}/'
IMAGE_FOLDER = f'{folder}/cropped'
SEGEMNTATION_FOLDER = f'{folder}/seg_og/'
out_folders = {}
out_folders_wGT = {}
out_folders_viz = {}
for prediction_type in prediction_types:
out_folders[prediction_type] = f'{env_paths.PREPROCESSED_DATA}/{OUT_NAME}/p3dmm/{prediction_type}/'
out_folders_wGT[prediction_type] = f'{env_paths.PREPROCESSED_DATA}/{OUT_NAME}/p3dmm_wGT/{prediction_type}/'
os.makedirs(out_folders[prediction_type], exist_ok=True)
os.makedirs(out_folders_wGT[prediction_type], exist_ok=True)
out_folders_viz[prediction_type] = f'{env_paths.PREPROCESSED_DATA}/{OUT_NAME}/p3dmm_extraViz/{prediction_type}/'
os.makedirs(out_folders_viz[prediction_type], exist_ok=True)
image_names = os.listdir(f'{IMAGE_FOLDER}')
image_names.sort()
if os.path.exists(out_folders[prediction_type]):
if len(os.listdir(out_folders[prediction_type])) == len(image_names):
return
if model is None:
model = p3dmm_system.load_from_checkpoint(model_checkpoint, strict=False)
# TODO: disable randomness, dropout, etc...
# model.eval()
model = model.cuda()
for i in tqdm(range(len(image_names))):
#if not int(image_names[i].split('_')[0]) in [17, 175, 226, 279]:
# continue
try:
for i_batch in range(batch_size):
img = np.array(Image.open(f'{IMAGE_FOLDER}/{image_names[i]}').resize((512, 512))) / 255 # need 512,512 images as input; normalize to [0, 1] range
img = torch.from_numpy(img)[None, None].float().cuda() # 1,1,512,512,3
img_seg = np.array(Image.open(f'{SEGEMNTATION_FOLDER}/{image_names[i][:-4]}.png').resize((512, 512), Image.NEAREST))
if len(img_seg.shape) == 3:
img_seg = img_seg[..., 0]
#img_seg = np.array(Image.open(f'{SEGEMNTATION_FOLDER}/{int(image_names[i][:-4])*3:05d}.png').resize((512, 512), Image.NEAREST))
mask = ((img_seg == 2) | ((img_seg > 3) & (img_seg < 14)) ) & ~(img_seg==11)
mask = torch.from_numpy(mask).long().cuda()[None, None] # 1, 1, 512, 512
#mask = torch.ones_like(img[..., 0]).cuda().bool()
batch = {
'tar_msk': mask,
'tar_rgb': img,
}
batch_mirrored = {
'tar_rgb': torch.flip(batch['tar_rgb'], dims=[3]).cuda(),
'tar_msk': torch.flip(batch['tar_msk'], dims=[3]).cuda(),
}
# execute model twice, once with original image, once with mirrored original image,
# and then average results after undoing the mirroring operation on the prediction
with torch.no_grad():
output, conf = model.net(batch)
output_mirrored, conf = model.net(batch_mirrored)
if 'uv_map' in output:
fliped_uv_pred = torch.flip(output_mirrored['uv_map'], dims=[4])
fliped_uv_pred[:, :, 0, :, :] *= -1
fliped_uv_pred[:, :, 0, :, :] += 2*0.0075
output['uv_map'] = (output['uv_map'] + fliped_uv_pred)/2
if 'normals' in output:
fliped_uv_pred = torch.flip(output_mirrored['normals'], dims=[4])
fliped_uv_pred[:, :, 0, :, :] *= -1
output['normals'] = (output['normals'] + fliped_uv_pred)/2
if 'disps' in output:
fliped_uv_pred = torch.flip(output_mirrored['disps'], dims=[4])
fliped_uv_pred[:, :, 0, :, :] *= -1
output['disps'] = (output['disps'] + fliped_uv_pred)/2
for prediction_type in prediction_types:
for i_batch in range(batch_size):
i_view = 0
gt_rgb = batch['tar_rgb']
# normalize to [0,1] range
if prediction_type == 'uv_map':
tmp_output = torch.clamp((output[prediction_type][i_batch, i_view] + 1) / 2, 0, 1)
elif prediction_type == 'disps':
tmp_output = torch.clamp((output[prediction_type][i_batch, i_view] + 50) / 100, 0, 1)
elif prediction_type in ['normals', 'normals_can']:
tmp_output = output[prediction_type][i_batch, i_view]
tmp_output = tmp_output / torch.norm(tmp_output, dim=0).unsqueeze(0)
tmp_output = torch.clamp((tmp_output + 1) / 2, 0, 1)
# undo "weird" convention of normals that I used for preprocessing
tmp_output = torch.stack(
[tmp_output[0, ...], 1 - tmp_output[2, ...], 1 - tmp_output[1, ...]],
dim=0)
content = [
gt_rgb[i_batch, i_view].detach().cpu().numpy(),
pad_to_3_channels(tmp_output.permute(1, 2, 0).detach().cpu().float().numpy()),
]
catted = (np.concatenate(content, axis=1) * 255).astype(np.uint8)
Image.fromarray(catted).save(f'{out_folders_wGT[prediction_type]}/{image_names[i]}')
Image.fromarray(
pad_to_3_channels(
tmp_output.permute(1, 2, 0).detach().cpu().float().numpy() * 255).astype(
np.uint8)).save(
f'{out_folders[prediction_type]}/{image_names[i][:-4]}.png')
# this visulization is quite slow, therefore disable it per default
if prediction_type == 'uv_map' and cfg.viz_uv_mesh:
to_show_non_mirr = uv_pred_to_mesh(
output[prediction_type][i_batch:i_batch + 1, ...],
batch['tar_msk'][i_batch:i_batch + 1, ...],
batch['tar_rgb'][i_batch:i_batch + 1, ...],
right_ear = [537, 1334, 857, 554, 941],
left_ear = [541, 476, 237, 502, 286],
)
Image.fromarray(to_show_non_mirr).save(f'{out_folders_viz[prediction_type]}/{image_names[i]}')
except Exception as exx:
traceback.print_exc()
pass
print(f'''
<<<<<<<< FINISHED PIXEL3DMM INFERENCE for {cfg.video_name} in {prediction_types} MODE >>>>>>>>
''')
if __name__ == '__main__':
base_conf = OmegaConf.load(f'{env_paths.CODE_BASE}/configs/base.yaml')
cli_conf = OmegaConf.from_cli()
cfg = OmegaConf.merge(base_conf, cli_conf)
main(cfg) |