def __init__(self, max_images_in_batch):
texture_data = np.load(cnst.flame_texture_space_dat_file,
allow_pickle=True,
encoding='latin1').item()
self.flm_tex_dec = FlameTextureSpace(texture_data,
data_un_normalizer=None).cuda()
self.flame_visualizer = OverLayViz()
self.face_region_only_mask = np.array(
Image.open(cnst.face_region_mask_file))[:, :, 0:1].transpose(
(2, 0, 1))
self.face_region_only_mask = (
torch.from_numpy(self.face_region_only_mask.astype('float32')) /
255.0)[None, ...]
print('read face region mask')
flength = 5000
cam_t = np.array([0., 0., 0])
self.camera_params = camera_ringnetpp((512, 512),
trans=cam_t,
focal=flength)
self.max_num = max_images_in_batch - 1
# self.max_num = 5
self.pairs = []
for i in range(self.max_num):
for j in range(i + 1, self.max_num):
self.pairs.append((i, j))
self.pairs = np.array(self.pairs)
def save_samples(self, i, model, step, alpha, resolution, fid, run_id):
images = []
# camera_params = camera_dynamic((resolution, resolution), self.cam_t)
flength = 5000
cam_t = np.array([0., 0., 0])
camera_params = camera_ringnetpp((512, 512), trans=cam_t, focal=flength)
with torch.no_grad():
for img_idx in range(self.gen_i):
flame_param_this_batch = self.sampling_flame_labels[img_idx * self.gen_j:(img_idx + 1) * self.gen_j]
if self.pose is not None:
pose_this_batch = self.pose[img_idx * self.gen_j:(img_idx + 1) * self.gen_j]
else:
pose_this_batch = None
idx_this_batch = self.input_indices[img_idx * self.gen_j:(img_idx + 1) * self.gen_j]
img_tensor = model(flame_param_this_batch.clone(), pose_this_batch, step=step, alpha=alpha,
input_indices=idx_this_batch)[-1]
img_tensor = self.overlay_visualizer.range_normalize_images(
dataset_loaders.fast_image_reshape(img_tensor, height_out=256, width_out=256,
non_diff_allowed=True))
images.append(img_tensor.data.cpu())
torchvision.utils.save_image(
torch.cat(images, 0),
f'{cnst.output_root}sample/{str(run_id)}/{str(i + 1).zfill(6)}_res{resolution}x{resolution}_fid_{fid:.2f}.png',
nrow=self.gen_i,
normalize=True,
range=(0, 1))
# General settings
save_images = True
code_size = 236
use_inst_norm = True
core_tensor_res = 4
resolution = 256
alpha = 1
step_max = int(np.log2(resolution) - 2)
root_out_dir = f'{cnst.output_root}sample/'
num_smpl_to_eval_on = 1000
use_styled_conv_stylegan2 = True
flength = 5000
cam_t = np.array([0., 0., 0])
camera_params = camera_ringnetpp((512, 512), trans=cam_t, focal=flength)
run_ids_1 = [
29,
] # with sqrt(2)
# run_ids_1 = [7, 24, 8, 3]
# run_ids_1 = [7, 8, 3]
settings_for_runs = \
{24: {'name': 'vector_cond', 'model_idx': '216000_1', 'normal_maps_as_cond': False,
'rendered_flame_as_condition': False, 'apply_sqrt2_fac_in_eq_lin': False},
29: {'name': 'full_model', 'model_idx': '026000_1', 'normal_maps_as_cond': True,
'rendered_flame_as_condition': True, 'apply_sqrt2_fac_in_eq_lin': True},
7: {'name': 'flm_rndr_tex_interp', 'model_idx': '488000_1', 'normal_maps_as_cond': False,
'rendered_flame_as_condition': True, 'apply_sqrt2_fac_in_eq_lin': False},
3: {'name': 'norm_mp_tex_interp', 'model_idx': '040000_1', 'normal_maps_as_cond': True,
import tqdm
from my_utils.flm_dynamic_fit_overlay import camera_ringnetpp
import numpy as np
from my_utils.visualize_flame_overlay import OverLayViz
from my_utils.ringnet_overlay.util import tensor_vis_landmarks
from PIL import Image
import torch
import matplotlib.pyplot as plt
resolution = 512
flength = 5000
cam_t = np.array([0., 0., 0])
camera_params = camera_ringnetpp((resolution, resolution),
trans=cam_t,
focal=flength)
overlay_visualizer = OverLayViz(full_neck=False,
add_random_noise_to_background=False,
background_img=None,
texture_pattern_name='CHKR_BRD_FLT_TEETH',
flame_version='FLAME_2020_revisited',
image_size=resolution)
dest_dir = '/is/cluster/work/pghosh/gif1.0/fitting_viz/flame_2020/'
# dest_dir = '/is/cluster/work/pghosh/gif1.0/fitting_viz/flame_old/'
for i in tqdm.tqdm(range(0, 300)):
img_file = '/is/cluster/scratch/partha/face_gan_data/FFHQ/images1024x1024/' + str(
i).zfill(5) + '.png'
img_original = Image.open(img_file).resize((resolution, resolution))
images = torch.from_numpy(