/
visualizer.py
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/
visualizer.py
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import os
import subprocess
import argparse
import chainer
import chainer.cuda
import chainer.functions as F
import numpy as np
from chainer import Variable
from PIL import Image
def out_generated_movie(fsgen, vgen, vdis, rows, cols, seed, dst):
@chainer.training.make_extension()
def make_image(trainer):
np.random.seed(seed)
xp = fsgen.xp
with chainer.using_config('train', False) and \
chainer.no_backprop_mode():
z_slow = xp.random.uniform(
-1, 1, (rows, fsgen.z_slow_dim)).astype('f')
z_slow = Variable(z_slow)
z_fast = fsgen(z_slow)
B, n_z_fast, n_frames = z_fast.shape
z_fast = F.reshape(F.transpose(
z_fast, [0, 2, 1]), (B * n_frames, n_z_fast))
B, n_z_slow = z_slow.shape
z_slow = F.reshape(F.broadcast_to(F.reshape(
z_slow, (B, 1, n_z_slow)), (B, n_frames, n_z_slow)),
(B * n_frames, n_z_slow))
img_fake = vgen(z_slow, z_fast)
x = chainer.cuda.to_cpu(img_fake.data)
def save_image(x, name, mode=None):
_, C, H, W = x.shape
x = x.reshape((rows, cols, C, H, W))
x = x.transpose(0, 3, 1, 4, 2)
if C == 1:
x = x.reshape((rows * H, cols * W))
else:
x = x.reshape((rows * H, cols * W, C))
preview_dir = '{}/preview'.format(dst)
preview_path = preview_dir +\
'/image_{}_{:0>8}.png'.format(name, trainer.updater.iteration)
if not os.path.exists(preview_dir):
os.makedirs(preview_dir)
Image.fromarray(x, mode=mode).convert('RGB').save(preview_path)
x = np.asarray(x * 127 + 127, dtype=np.uint8)
save_image(x, "img")
_, C, H, W = x.shape
x = x.transpose(0, 2, 3, 1) # N, H, W, C
save_mov(
x, rows, cols, H, W, C,
'{}/preview/video_{:0>8}.avi'.format(dst, trainer.updater.iteration))
return make_image
def save_mov(img, rows, cols, H, W, C, fn):
img = img.reshape(rows, cols, H, W, C)
img = img.transpose(1, 0, 2, 3, 4)
img = img.reshape(cols,
int(np.sqrt(rows)), int(np.sqrt(rows)),
H, W, C)
img = img.transpose(0, 1, 3, 2, 4, 5)
img = img.reshape(cols, H * int(np.sqrt(rows)),
W * int(np.sqrt(rows)), C)
out_dir = os.path.splitext(fn)[0]
if not os.path.exists(out_dir):
os.makedirs(out_dir)
for frame_i, frame in enumerate(img):
if frame.ndim == 2 or frame.shape[2] == 1:
frame = np.broadcast_to(frame, (frame.shape[0], frame.shape[1], 3))
frame = frame.astype(np.uint8)
Image.fromarray(frame).save(
'{}/{}.png'.format(out_dir, frame_i))
subprocess.call([
'ffmpeg', '-r', '10', '-i',
'{}/%d.png'.format(out_dir),
'-qscale', '0', '-s', '640x640', fn])
subprocess.call([
'ffmpeg', '-i', '{}.avi'.format(os.path.splitext(fn)[0]),
fn.replace('.avi', '.mov')])
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--preview_png', type=str)
parser.add_argument('--H', type=int, default=64)
parser.add_argument('--W', type=int, default=64)
parser.add_argument('--C', type=int, default=3)
parser.add_argument('--rows', type=int, default=100)
parser.add_argument('--cols', type=int, default=16)
args = parser.parse_args()
out_dir = os.path.splitext(args.preview_png)[0]
if not os.path.exists(out_dir):
os.makedirs(out_dir)
img = np.asarray(Image.open(args.preview_png))
img = img.reshape(args.rows, args.H, args.cols, args.W, args.C)
img = img.transpose(2, 0, 1, 3, 4) # cols, rows, H, W, C
img = img.reshape(args.cols,
int(np.sqrt(args.rows)), int(np.sqrt(args.rows)),
args.H, args.W, args.C)
img = img.transpose(0, 1, 3, 2, 4, 5)
img = img.reshape(args.cols, args.H * int(np.sqrt(args.rows)),
args.W * int(np.sqrt(args.rows)), args.C)
for frame_i, frame in enumerate(img):
Image.fromarray(
frame.astype(np.uint8)).save(
'{}/{}.png'.format(out_dir, frame_i))
subprocess.call([
'ffmpeg', '-r', '10', '-i',
'{}/%d.png'.format(out_dir),
'-qscale', '0', '-s', '640x640', '{}/out.avi'.format(out_dir)])
subprocess.call([
'ffmpeg', '-i', '{}/out.avi'.format(out_dir),
'{}/out.mov'.format(out_dir)])