import os
from os import path
import numpy as np
import PIL
from PIL import Image
from packaging import version

import sys
import torch

if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
    PIL_INTERPOLATION = {
        "linear": PIL.Image.Resampling.BILINEAR,
        "bilinear": PIL.Image.Resampling.BILINEAR,
        "bicubic": PIL.Image.Resampling.BICUBIC,
        "lanczos": PIL.Image.Resampling.LANCZOS,
        "nearest": PIL.Image.Resampling.NEAREST,
    }
else:
    PIL_INTERPOLATION = {
        "linear": PIL.Image.LINEAR,
        "bilinear": PIL.Image.BILINEAR,
        "bicubic": PIL.Image.BICUBIC,
        "lanczos": PIL.Image.LANCZOS,
        "nearest": PIL.Image.NEAREST,
    }

ROOT = '/projects/katefgroup/datasets/gs_kubric/InpaintingFormat_Set50'
OUT_ROOT = os.path.join(ROOT)
RESOLUTION = 512

mask_dir = path.join(ROOT, 'Annotations')
image_dir = path.join(ROOT, 'JPEGImages')

videos = []
num_frames = {}
num_objects = {}

vid_names = os.listdir(image_dir)
for _video in vid_names:
    videos.append(_video)
    num_frames[_video] = len(os.listdir(path.join(image_dir, _video)))
    num_objects[_video] = len(os.listdir(path.join(mask_dir, _video)))

for index in range(len(videos)):
    video = videos[index]
    num_objs = num_objects[video]

    os.makedirs(os.path.join(os.path.join(OUT_ROOT, 'MergedAnnotations', video)), exist_ok=True)

    img_file_names = os.listdir(path.join(image_dir, video))

    for f in range(num_frames[video]):
        
        # Load RGBs
        file_name = img_file_names[f]
        img_file = path.join(image_dir, video, file_name)
        img = Image.open(img_file).convert('RGB')
        img = img.resize((RESOLUTION, RESOLUTION))

        binary_masks = np.zeros((2, RESOLUTION, RESOLUTION))
        for i in range(num_objs):
            mask_file = path.join(mask_dir, video, str(i).zfill(3), file_name[:-4]+".png")
            mask = Image.open(mask_file).convert("RGB")
            mask = mask.resize((RESOLUTION, RESOLUTION), PIL_INTERPOLATION["nearest"])
            mask = np.array(mask)[:, :, 0]
            if i != 0:
                binary_masks[1] += (mask > 128)
                binary_masks[1] = np.clip(binary_masks[1], 0, 1)
            else:
                binary_masks[i] = (mask > 128)

        obj_id = 0
        for _ in range(2):
            os.makedirs(os.path.join(os.path.join(OUT_ROOT, 'MergedAnnotations', video, str(obj_id).zfill(3))), exist_ok=True)
            Image.fromarray(binary_masks[obj_id].astype(np.uint8) * 255).save(os.path.join(OUT_ROOT, 'MergedAnnotations', video, str(obj_id).zfill(3), file_name[:-4]+".png"))
            obj_id += 1