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")
# The default is a 24 degree overlap (equivalent to 1/6 of the 3:1 output image)
# Setting a higher overlap means you can have taller vertical FOV.

parser = argparse.ArgumentParser()
parser.add_argument('--folder', default="./results/wood/")
parser.add_argument('--out_dir', default="./thirds/")
parser.add_argument('--label', required=True)
parser.add_argument('--vertical_offset', type=int, default=256)
# parser.add_argument('--dst_width', type=int, default=1024)
# parser.add_argument('--dst_height', type=int, default=512)
parser.add_argument('--count', type=int, default=3)
parser.add_argument('--aspect', type=float, default=3.0)
# parser.add_argument('--folder_id', type=int, required=True)
parser.add_argument('--overlap', type=float, default=0.5)
parser.add_argument('--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

crops = []
paths = []

for i in range(opt.count):
  x = int(i / opt.count * dst_width)
  crops.append((x, y0, x + dst_width, y0 + dst_height,))
  path = os.path.join(opt.out_dir, opt.label, chr(97 + i))
  if opt.clobber:
      if os.path.exists(path):
        rmtree(path)
  os.makedirs(path)
  paths.append(path)

dataset = []
for i, fn in enumerate(sorted(glob.glob(os.path.join(opt.folder, '*.png')))):
  out_fn = "frame_{:05d}.png".format(i + 1)
  if not opt.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), src_height,))
  image = Image.open(fn)

  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)

# if opt.folder_id > 0:
#   endpoint = os.getenv('API_REMOTE') + '/api/file/'
#   for label in labels:
#     subprocess.call([
#       "curl",
#       "-X", "POST",
#       "-d", "folder_id={}".format(opt.folder_id),
#       "-d", "module=pix2pixhd",
#       "-d", "name={}.mov".format(label),
#       "-d", "url=https://s3.amazonaws.com/i.asdf.us/cortex/lens/data/{}/{}.mov".format(opt.folder_id, label),
#       "-d", "dataset={}".format(label),
#       "-d", "activity=splice",
#       "-d", "generated=0",
#       "-d", "processed=1",
#       "-d", "datatype=video",
#       endpoint
#     ])