def interchange_params_and_make_batch(flm_1, flm_2):
# import ipdb; ipdb.set_trace()
flame_batch = torch.zeros((4, flm_2.shape[1]),
dtype=torch.float32,
device=flm_1.device)
# shape
flame_batch[0, :] = flm_1
flame_batch[0, 0:100] = flm_2[0, 0:100]
# shape + exp
flame_batch[1, :] = flm_1
flame_batch[1, 0:150] = flm_2[0, 0:150]
# flame_batch[1, 153:156] = flm_2[0, 153:156]
# shape + exp + jaw + pose
flame_batch[2, :] = flm_1
flame_batch[2, 0:159] = flm_2[0, 0:159]
# shape + exp + jaw + pose + texture
flame_batch[3, :] = flm_1
flame_batch[3, 0:159] = flm_2[0, 0:159]
flame_batch[3, 159:209] = flm_2[0, 159:209]
# import ipdb; ipdb.set_trace()
flame_batch = position_to_given_location(flame_decoder, flame_batch)
return flame_batch
f'{cnst.output_root}checkpoint/{run_idx}/{model_idx}.model')
generator_1.load_state_dict(ckpt1['generator_running'])
generator_1 = generator_1.eval()
# images = np.zeros((num_smpl_to_eval_on, 3, resolution, resolution)).astype('float32')
pbar = tqdm.tqdm(range(0, num_smpl_to_eval_on, batch_size))
pbar.set_description('Generating_images')
flame_mesh_imgs = None
mdl_id = 'mdl2_'
if settings_for_runs[run_idx]['name'] == 'full_model':
mdl_id = 'mdl1_'
for batch_idx in pbar:
flm_batch = flm_params[batch_idx:batch_idx + batch_size, :]
flm_batch = torch.from_numpy(flm_batch).cuda()
flm_batch = position_to_given_location(flame_decoder, flm_batch)
batch_size_true = flm_batch.shape[0]
if settings_for_runs[run_idx]['normal_maps_as_cond'] or \
settings_for_runs[run_idx]['rendered_flame_as_condition']:
cam = flm_batch[:, constants.DECA_IDX['cam'][0]:constants.
DECA_IDX['cam'][1]:]
shape = flm_batch[:, constants.INDICES['SHAPE'][0]:constants.
INDICES['SHAPE'][1]]
exp = flm_batch[:, constants.INDICES['EXP'][0]:constants.
INDICES['EXP'][1]]
pose = flm_batch[:, constants.INDICES['POSE'][0]:constants.
INDICES['POSE'][1]]
# import ipdb; ipdb.set_trace()
light_code = \
flm_batch[:, constants.DECA_IDX['lit'][0]:constants.DECA_IDX['lit'][1]:].view((batch_size_true, 9, 3))
normalized_eye_cntr_R = []
for indx in range(50):
# i = int(random.randint(0, 60_000))
i = indx
fig, ax1 = plt.subplots(1, 1)
img, flm_rndr, flm_lbl, index = dataset.__getitem__(i, bypass_valid_indexing=False)
img = img[None, ...]
img = fast_image_reshape(img, height_out=show_img_at_res, width_out=show_img_at_res)
img = img[0]
ax1.imshow((img.numpy().transpose((1, 2, 0)) + 1)/2)
flame_batch = torch.from_numpy(flm_lbl[0][None, ...]).cuda()
flame_batch = position_to_given_location(deca_flame, flame_batch)
# import ipdb; ipdb.set_trace()
shape, expression, pose = (flame_batch[:, 0:100, ], flame_batch[:, 100:150], flame_batch[:, 150:156])
vertices, l_m2d, _ = deca_flame(shape_params=shape, expression_params=expression, pose_params=pose)
# l_m2d[:, :, 1] *= -1
# vertices[:, :, 1] *= -1
eye_left_3d = vertices[:, 4051]
eye_right_3d = vertices[:, 4597]
eye_R_2d = (eye_left_3d[:, :2] + flame_batch[:, 157:159]) * flame_batch[:, 156]
eye_L_2d = (eye_right_3d[:, :2] + flame_batch[:, 157:159]) * flame_batch[:, 156]
eye_R_2d[:, 1] *= -1
eye_L_2d[:, 1] *= -1
0.02707223, 0.07585122, 0.05772701, 0.1662246, 0.20778911, 0.24815214,
0.22855483, 0.32920238, 0.52000016,]])
highest_variance_cmp_idx = 2
highest_variance = 0.9143507
mean_plu_3_sigma_light = mean_lighting
mean_plu_3_sigma_light[0, highest_variance_cmp_idx] += 2*highest_variance
mean_plu_3_sigma_light = torch.from_numpy(mean_plu_3_sigma_light.astype('float32')).cuda()
# flame_lighting_plus_3_sigma = np.concatenate((np.zeros((1, flm_val.shape[1]-len(mean_lighting))), mean_plu_3_sigma_light),
# axis=1)
mean_minus_3_sigma_light = mean_lighting
mean_minus_3_sigma_light[0, highest_variance_cmp_idx] -= 2*highest_variance
mean_minus_3_sigma_light = torch.from_numpy(mean_minus_3_sigma_light.astype('float32')).cuda()
flm_params = torch.cat(flm_params, dim=0)
flame_decoder = overlay_visualizer.deca.flame.eval()
flm_params = position_to_given_location(flame_decoder, flm_params)
cam = flm_params[:, constants.DECA_IDX['cam'][0]:constants.DECA_IDX['cam'][1]:]
shape = flm_params[:, constants.INDICES['SHAPE'][0]:constants.INDICES['SHAPE'][1]]
exp = flm_params[:, constants.INDICES['EXP'][0]:constants.INDICES['EXP'][1]]
pose = flm_params[:, constants.INDICES['POSE'][0]:constants.INDICES['POSE'][1]]
# import ipdb; ipdb.set_trace()
light_code = torch.from_numpy(light_code).cuda()[None, ...].repeat(exp.shape[0]-2, 1, 1)
# import ipdb; ipdb.set_trace()
light_code = torch.cat((light_code, mean_minus_3_sigma_light.view(1,9, 3), mean_plu_3_sigma_light.view(1,9, 3)), dim=0)
texture_code = torch.from_numpy(texture_code).cuda()[None, ...].repeat(exp.shape[0]-4, 1)
texture_code_neg_3_sigma = texture_code[0:1, :] * 0
texture_code_neg_3_sigma[0, 0] -= 3
texture_code_pos_3_sigma = texture_code[0:1, :] * 0
texture_code_pos_3_sigma[0, 0] += 3