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#!/Users/user/anaconda/envs/cv/bin/python
import os
import sys
sys.path.insert(0, os.path.join(os.environ['HOME'], "neural/pix2pix"))
sys.path.insert(0, os.path.join(os.environ['HOME'], "code/pix2pix"))
from options.dataset_options import DatasetOptions
from shutil import move, copyfile
from PIL import Image, ImageOps
from shutil import copyfile, rmtree
import glob
import numpy as np
import subprocess
import cv2
opt = DatasetOptions().parse()
in_dir = opt.in_dir
out_dir = opt.out_dir
out_base = os.path.basename(out_dir)
if os.path.exists(out_dir):
rmtree(out_dir)
os.makedirs(out_dir)
if opt.split is True:
os.makedirs(os.path.join(out_dir, "A/"))
os.makedirs(os.path.join(out_dir, "A/train/"))
os.makedirs(os.path.join(out_dir, "A/test/"))
os.makedirs(os.path.join(out_dir, "A/val/"))
if opt.ab is True:
os.makedirs(os.path.join(out_dir, "B/"))
os.makedirs(os.path.join(out_dir, "B/train/"))
os.makedirs(os.path.join(out_dir, "B/test/"))
os.makedirs(os.path.join(out_dir, "B/val/"))
file = open(os.path.join(out_dir, "opt.txt"), "w")
for arg in vars(opt):
file.write("{}: {}\n".format(arg, getattr(opt, arg)))
file.close()
images = sorted(glob.glob(os.path.join(in_dir, '*.*g')))
image_count = len(images)
print("{}, {} images => {}".format(in_dir, image_count, out_base))
for i, fn in enumerate(images):
pil_image = Image.open(fn).convert('RGB')
img = np.array(pil_image)
img = img[:, :, ::-1].copy()
out_file = "frame_{:05d}.png".format(i)
if i > 0 and (i % 100) == 0:
print("{}...".format(i))
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(lab)
if opt.clahe is True:
clahe = cv2.createCLAHE(clipLimit=opt.clip_limit, tileGridSize=(8,8))
l = clahe.apply(l)
if opt.brightness_gradient is True:
l = np.add(l.astype('float64'), ((i / image_count) - 0.5) * opt.brightness_sigma)
np.clip(l, 0, 255, out=l)
l = l.astype('uint8')
if opt.brightness_gradient is True or opt.clahe is True:
limg = cv2.merge((l,a,b))
img = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
if opt.posterize is True:
img = cv2.pyrMeanShiftFiltering(img, opt.spatial_window, opt.color_window)
if opt.grayscale is True:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if opt.blur is True:
img = cv2.GaussianBlur(img, (opt.blur_radius, opt.blur_radius), opt.blur_sigma)
if opt.canny is True:
img = cv2.Canny(img, opt.canny_lo, opt.canny_hi)
if opt.split is True:
if (i % 10) == 3:
wd = "test/"
elif (i % 10) == 6:
wd = "val/"
else:
wd = "train/"
cv2.imwrite(os.path.join(out_dir, "A/", wd, out_file), img)
if opt.ab is True:
copyfile(fn, os.path.join(out_dir, "B/", wd, out_file))
else:
cv2.imwrite(os.path.join(out_dir, out_file), img)
print("{}...".format(image_count))
if opt.mov:
print("ffmpeg...")
mov_file = "{}.mp4".format(out_base)
cmd = ("ffmpeg",
"-loglevel", "quiet",
"-i", os.path.join(out_dir, "frame_%05d.png"),
"-y", "-c:v", "libx264", "-vf", "fps=30", "-pix_fmt", "yuv420p",
mov_file)
process = subprocess.Popen(cmd, stdout=subprocess.PIPE)
output, error = process.communicate()
if opt.scp:
print("scp...")
cmd = ("scp", mov_file, opt.scp)
process = subprocess.Popen(cmd, stdout=subprocess.PIPE)
output, error = process.communicate()
print("https://asdf.us/neural/" + mov_file)
# cmd = ("mplayer", mov_file)
# process = subprocess.Popen(cmd, stdout=subprocess.PIPE)
# output, error = process.communicate()
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