import os
import glob
import argparse
from shutil import rmtree
from PIL import Image
from multiprocessing import Pool, cpu_count
from dotenv import load_dotenv, find_dotenv
import subprocess
load_dotenv(find_dotenv())

# This script generates crops with a specific aspect ratio from a 360 video.
# It creates three sequences (identified by "--label")

parser = argparse.ArgumentParser()
parser.add_argument('--folder', default="./uprez/venice_360_equi_1024/a/")
parser.add_argument('--out_dir', default="./uprez_results/")
parser.add_argument('--label', required=True)
parser.add_argument('--margin', type=int, default=16)
parser.add_argument('--no_clobber', action='store_false')
opt = parser.parse_args()

src_width = 1024
src_height = 512

dst_width = int(src_width / opt.count)
dst_height = int(dst_width / opt.aspect)
dst_size = (dst_width, dst_height,)
y0 = opt.vertical_offset - dst_height / 2 - margin

out_path = os.path.join(opt.out_dir, opt.label)

if not opt.no_clobber:
  if os.path.exists(out_path):
    rmtree(out_path)

for i in range(opt.count):
  x = int(i / opt.count * dst_width) - margin
  if x < 0:
    x += dst_width
  crops.append((x, y0, x + dst_width + margin + margin, y0 + dst_height + margin + margin,))
  path = os.path.join(out_path, chr(97 + i))
  os.makedirs(path)
  paths.append(path)

max_i = opt.max
dataset = []
for i, fn in enumerate(sorted(glob.glob(os.path.join(opt.folder, '*.png')))):
  if i >= max_i:
    break
  out_fn = "frame_{:05d}.png".format(i + 1)
  if opt.no_clobber and os.path.exists(os.path.join(paths[0], out_fn)):
    continue
  dataset.append((i, fn,))

def build_thumbnail(i, fn):
  out_fn = "frame_{:05d}.png".format(i + 1)
  if (i % 100) == 0:
    print("{}...".format(i))

  canvas = Image.new('RGB', (int(src_width + dst_width + margin), src_height,))
  image = Image.open(fn)
  canvas.paste(image, (0, 0))
  canvas.paste(image, (src_width, 0))

  for n, path in enumerate(paths):
    image.crop(crops[n]).save(os.path.join(path, out_fn))

chunksize = 3
with Pool(processes=cpu_count()) as pool:
  pool.starmap(build_thumbnail, dataset, chunksize)