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import os
from options.test_options import TestOptions
from options.dataset_options import DatasetOptions
from data import CreateRecursiveDataLoader
from models import create_model
from util.visualizer import Visualizer
from util.util import mkdirs, crop_image
from util import html
from shutil import move, copyfile
from PIL import Image, ImageOps
from skimage.transform import resize
from scipy.misc import imresize
from shutil import copyfile, rmtree
import numpy as np
import cv2
from datetime import datetime
import re
import subprocess
from time import sleep
if __name__ == '__main__':
opt = TestOptions().parse()
data_opt = DatasetOptions().parse(opt.unknown)
opt.nThreads = 1 # test code only supports nThreads = 1
opt.batchSize = 1 # test code only supports batchSize = 1
opt.serial_batches = True # no shuffle
opt.no_flip = True # no flip
# opt.experiment = data_opt.experiment # opt.start_img.split("/")[-1].split(".")[0]
d = datetime.now()
tag = "{}_{}_{}".format(
opt.name, opt.experiment,
d.strftime('%Y%m%d%H%M'))
opt.tag = tag # = "pcfade___201805150250"
opt.render_dir = render_dir = opt.results_dir + opt.name + "/" + tag + "/"
A_offset = 0
A_im = None
A_dir = None
print("create render_dir: {}".format(render_dir))
if os.path.exists(render_dir):
rmtree(render_dir)
mkdirs(render_dir)
# cmd = ("convert", opt.start_img, '-canny', '0x1+10%+30%', render_dir + "frame_00000.png")
# process = subprocess.Popen(cmd, stdout=subprocess.PIPE)
# output, error = process.communicate()
def load_first_frame():
start_img_path = os.path.join(render_dir, "frame_00000.png")
if data_opt.just_copy:
copyfile(opt.start_img, start_img_path)
else:
print("preload {}".format(opt.start_img))
A_img = Image.open(opt.start_img).convert('RGB')
A_im = np.asarray(A_img)
A = process_image(A_im)
cv2.imwrite(start_img_path, A)
numz = re.findall(r'\d+', os.path.basename(opt.start_img))
if len(numz) > 0:
A_offset = int(numz[0])
if A_offset:
print(">> starting offset: {}".format(A_offset))
A_dir = opt.start_img.replace(numz[0], "{:05d}")
else:
print("Sequence not found")
def process_image(im):
img = im[:, :, ::-1].copy()
if data_opt.clahe is True:
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(lab)
clahe = cv2.createCLAHE(clipLimit=data_opt.clip_limit, tileGridSize=(8,8))
l = clahe.apply(l)
limg = cv2.merge((l,a,b))
img = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
if data_opt.posterize is True:
img = cv2.pyrMeanShiftFiltering(img, data_opt.spatial_window, data_opt.color_window)
if data_opt.grayscale is True:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if data_opt.blur is True:
img = cv2.GaussianBlur(img, (data_opt.blur_radius, data_opt.blur_radius), data_opt.blur_sigma)
if data_opt.canny is True:
img = cv2.Canny(img, data_opt.canny_lo, data_opt.canny_hi)
return img
load_first_frame()
data_loader = CreateRecursiveDataLoader(opt)
dataset = data_loader.load_data()
ds = dataset.dataset
model = create_model(opt)
visualizer = Visualizer(opt)
# create website
# web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
# webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch))
# test
last_im = None
for i, data in enumerate(data_loader):
if i >= opt.how_many:
break
model.set_input(data)
model.test()
visuals = model.get_current_visuals()
img_path = model.get_image_paths()
if (i % 20) == 0:
print('%04d: process image... %s' % (i, img_path))
# ims = visualizer.save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio)
im = visuals['fake_B']
tmp_path = render_dir + "frame_{:05d}_tmp.png".format(i+1)
edges_path = render_dir + "frame_{:05d}.png".format(i+1)
render_path = render_dir + "ren_{:05d}.png".format(i+1)
if A_dir is not None:
sequence_path = A_dir.format(A_offset+i+1)
# A_offset
# save rendered image
image_pil = Image.fromarray(im, mode='RGB')
image_pil.save(tmp_path)
os.rename(tmp_path, render_path)
if dataset.name() == 'RecursiveDatasetDataLoader':
if data_opt.recursive and last_im is not None:
tmp_im = im.copy()
if data_opt.sequence and A_dir is not None:
A_img = Image.open(sequence_path).convert('RGB')
A_im = np.asarray(A_img)
frac_a = data_opt.recursive_frac
frac_b = data_opt.sequence_frac
frac_c = 1.0 - frac_a - frac_b
array_a = np.multiply(last_im.astype('float64'), frac_a)
array_b = np.multiply(A_im.astype('float64'), frac_b)
array_c = np.multiply(im.astype('float64'), frac_c)
comb_ab = np.add(array_a, array_b)
comb_abc = np.add(array_ab, array_c)
im = comb_abc.astype('uint8')
else:
frac_a = data_opt.recursive_frac
frac_b = 1.0 - frac_a
array_a = np.multiply(last_im.astype('float64'), frac_a)
array_b = np.multiply(im.astype('float64'), frac_b)
im = np.add(array_a, array_b).astype('uint8')
if data_opt.recurse_roll != 0:
last_im = np.roll(tmp_im, data_opt.recurse_roll, axis=data_opt.recurse_roll_axis)
else:
last_im = im.copy().astype('uint8')
else:
last_im = im.copy().astype('uint8')
tmp_im = im.copy().astype('uint8')
#print(im.shape, im.dtype)
# image_pil = Image.fromarray(im, mode='RGB')
# im = np.asarray(image_pil).astype('uint8')
#print(im.shape, im.dtype)
# src_img = im[:, :, ::-1].copy()
img = process_image(im)
cv2.imwrite(tmp_path, img)
os.rename(tmp_path, edges_path)
# webpage.save()
# os.remove(render_dir + "frame_00000.png")
print(opt.render_dir)
video_fn = tag + "_mogrify.mp4"
cmd = ("ffmpeg", "-i", render_dir + "ren_%05d.png", "-y", "-c:v", "libx264", "-vf", "fps=30", "-pix_fmt", "yuv420p", "-s", "456x256", render_dir + video_fn)
process = subprocess.Popen(cmd, stdout=subprocess.PIPE)
output, error = process.communicate()
print("________")
cmd = ("scp", render_dir + video_fn, "jules@asdf.us:asdf/neural/")
process = subprocess.Popen(cmd, stdout=subprocess.PIPE)
output, error = process.communicate()
print("https://asdf.us/neural/" + video_fn)
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