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="./sequences/venice_360/equi")
parser.add_argument('--label', required=True)
parser.add_argument('--vertical_offset', type=int, default=983)
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 = 4096
src_height = 2048
src_one_half_width = int(src_width * 3/2)

count = 3
output_aspect = 3

overall_aspect = count * output_aspect
overlapped_aspect = overall_aspect - count * opt.overlap
crop_width = src_width * output_aspect / overlapped_aspect
crop_height = crop_width / output_aspect

# c0 = 7/6 * src_width
c1 = 3/6 * src_width
# c2 = 5/6 * src_width

# x0 = c0 - crop_width / 2
x1 = c1 - crop_width / 2
# x2 = c2 - crop_width / 2

y0 = opt.vertical_offset - crop_height / 2

# p0 = (x0, y0, x0 + crop_width, y0 + crop_height,)
# p1 = (x1, y0, x1 + crop_width, y0 + crop_height,)
# p2 = (x2, y0, x2 + crop_width, y0 + crop_height,)

# label_0 = opt.label + '_a'
label_1 = opt.label
# label_2 = opt.label + '_c'

# labels = [label_0, label_1, label_2]
labels = [label_1]

# path_0 = os.path.join("sequences", label_0)
path_1 = os.path.join("sequences", label_1)
# path_2 = os.path.join("sequences", label_2)

if opt.clobber:
  # if os.path.exists(path_0):
  #   rmtree(path_0)
  if os.path.exists(path_1):
    rmtree(path_1)
  # if os.path.exists(path_2):
  #   rmtree(path_2)

# os.makedirs(path_0)
os.makedirs(path_1)
# os.makedirs(path_2)

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(path_1, 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', (src_one_half_width, src_height,))
  image = Image.open(fn)
  canvas.paste(image, (0, 0))
  canvas.paste(image, (src_width, 0))

  ii = i % src_width
  p1 = (x1 + ii, y0, x1 + crop_width + ii, y0 + crop_height,)

  # canvas.crop(p0).resize((1024, 512,), Image.ANTIALIAS).save(os.path.join(path_0, out_fn))
  canvas.crop(p1).resize((1024, 512,), Image.ANTIALIAS).save(os.path.join(path_1, out_fn))
  # canvas.crop(p2).resize((1024, 512,), Image.ANTIALIAS).save(os.path.join(path_2, 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
    ])