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generator.py
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import torch
import os
import numpy as np
from dataloader import SceneDataset
import imageio
import data.load_DTU as DTU
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def training_visualization(num_images, cfg, i4d, dataset, epoch, generate_specific_object = True, generate_specific_pose = True):
# Create log dir and copy the config file
basedir = cfg.basedir
expname = cfg.expname
dataset.render_factor = 8
dataloader = dataset.get_loader(num_workers=0)
if generate_specific_object:
iter = cfg.generate_specific_samples
else:
iter = range(num_images)
if generate_specific_pose:
pose_iter = cfg.gen_pose
else:
pose_iter = ['random']
renderings = []
for sample in iter:
for pose in pose_iter:
savedir = os.path.join(basedir, expname, 'training_visualization', f'epoch_{epoch}_{sample}_{pose}')
img_outpath = os.path.join(savedir, f'rendering.png')
if os.path.exists(savedir):
continue
else:
os.makedirs(savedir)
if generate_specific_object:
dataloader.dataset.load_specific_input = sample
print(f'generating object {dataloader.dataset.load_specific_input}')
if generate_specific_pose:
dataloader.dataset.load_specific_rendering_pose = dataset.cam_path[pose]
print(f'generating pose {pose}')
render_data = dataloader.__iter__().__next__()['complete']
rgb = render_and_save(i4d, dataset, render_data, savedir, img_outpath, bool(generate_specific_pose))
renderings.append(rgb)
dataloader.dataset.load_specific_input = None
dataloader.dataset.load_specific_rendering_pose = None
plt.xticks([]), plt.yticks([])
fig = plt.figure()
for i,img in enumerate(renderings):
ax = fig.add_subplot(1, len(renderings), i + 1)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.imshow(img, interpolation='bicubic')
return fig
def render_pose(cfg, i4d, dataset, epoch, specific_obj, pose):
# Create log dir and copy the config file
basedir = cfg.basedir
expname = cfg.expname
dataloader = dataset.get_loader(num_workers=0)
savedir = os.path.join(basedir, expname, 'renderings', f'{specific_obj}_epoch_{epoch}_renderfactor_{cfg.render_factor}_batch_{cfg.fixed_batch}')
os.makedirs(savedir, exist_ok=True)
img_outpath = os.path.join(savedir, f'pose_{pose[0]}.png')
c2w = pose[1]
if os.path.exists(img_outpath):
# Rendering already exists.
return
dataloader.dataset.load_specific_input = specific_obj
dataloader.dataset.load_specific_rendering_pose = c2w
print(f'generating {dataloader.dataset.load_specific_input}, pose: {pose[0]}')
render_data = dataloader.__iter__().__next__()['complete']
render_and_save(i4d, dataset, render_data, savedir, img_outpath, True)
dataloader.dataset.load_specific_input = None
dataloader.dataset.load_specific_rendering_pose = None
def render_and_save(i4d, dataset, render_data, savedir, img_outpath, specific_pose):
# Render image
with torch.no_grad():
if specific_pose:
rgb, ref_images, scan = i4d.render_img(render_data, dataset.render_factor, dataset.H, dataset.W, specific_pose)
else:
rgb, ref_images, target, scan = i4d.render_img(render_data, dataset.render_factor, dataset.H, dataset.W, specific_pose)
filename = os.path.join(savedir, f'target.png')
imageio.imwrite(filename, (target*255).numpy().astype(np.uint8))
# Save rendered image
imageio.imwrite(img_outpath, rgb)
# Copy all reference images into rendering folder
for i, ref_img in enumerate(ref_images):
outpath = os.path.join(savedir, f'ref_img_{i}.png')
if not os.path.exists(outpath):
imageio.imwrite(outpath, (ref_img*255).numpy().astype(np.uint8))
# Put all reference images in a single image and save
outpath = os.path.join(savedir, f'ref_images.png')
if not os.path.exists(outpath):
plt.figure(figsize=(50, 20), dpi=200)
plt.xticks([]), plt.yticks([])
for i in range(10):
ax = plt.subplot(2, 5, i + 1)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.imshow(ref_images[i], interpolation='bicubic')
plt.savefig(outpath)
plt.close()
return rgb
if __name__ == '__main__':
import config_loader
import model
cfg = config_loader.get_config()
cfg.video = True
set = 'test'
dataset = SceneDataset(cfg, set)
i4d = model.Implicit4D(cfg, dataset.proj_pts_to_ref_torch)
i4d.load_model()
if cfg.dataset_type == 'DTU':
for scan in cfg.generate_specific_samples:
print('cfg.gen_pose', cfg.gen_pose)
for pose_idx in cfg.gen_pose:
pose = DTU.load_cam_path()[pose_idx]
render_pose(cfg, i4d, dataset, i4d.start, scan, (pose_idx,pose))