1 files changed, 119 insertions, 0 deletions
diff --git a/scripts/builders/mogrify-dir.py b/scripts/builders/mogrify-dir.py
new file mode 100755
index 0000000..b7370f5
--- /dev/null
+++ b/scripts/builders/mogrify-dir.py
@@ -0,0 +1,119 @@
+#!/Users/user/anaconda/envs/cv/bin/python
+
+import os
+
+import sys
+sys.path.insert(0, '/Users/user/neural/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(out_dir + "A/")
+  os.makedirs(out_dir + "A/train/")
+  os.makedirs(out_dir + "A/test/")
+  os.makedirs(out_dir + "A/val/")
+  if opt.ab is True:
+    os.makedirs(out_dir + "B/")
+    os.makedirs(out_dir + "B/train/")
+    os.makedirs(out_dir + "B/test/")
+    os.makedirs(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 != 0:
+    img = cv2.GaussianBlur(img, (opt.blur, opt.blur), 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(out_dir + "A/" + wd + out_file, img)
+    if opt.ab is True:
+      copyfile(fn, 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()