import json
import cv2
import numpy as np
import os
from torch.utils.data import Dataset
from PIL import Image
import cv2
from .data_utils import * 
from PIL import Image
from .base import BaseDataset

class MoseDataset(BaseDataset):
    def __init__(self, image_dir, anno):
        self.image_root = image_dir
        self.anno_root = anno

        video_dirs = []
        video_dirs = os.listdir(self.image_root)
        self.data = video_dirs
        self.size = (512,512)
        self.clip_size = (224,224)
        self.dynamic = 2

    def __len__(self):
        return 40000

    def check_region_size(self, image, yyxx, ratio, mode = 'max'):
        pass_flag = True
        H,W = image.shape[0], image.shape[1]
        H,W = H * ratio, W * ratio
        y1,y2,x1,x2 = yyxx
        h,w = y2-y1,x2-x1
        if mode == 'max':
            if h > H or w > W:
                pass_flag = False
        elif mode == 'min':
            if h < H or w < W:
                pass_flag = False
        return pass_flag

    def get_sample(self, idx):
        video_name = self.data[idx]
        video_path = os.path.join(self.image_root, video_name)
        frames = os.listdir(video_path)

        # Sampling frames
        min_interval = len(frames)  // 10
        start_frame_index = np.random.randint(low=0, high=len(frames) - min_interval)
        end_frame_index = start_frame_index + np.random.randint(min_interval,  len(frames) - start_frame_index )
        end_frame_index = min(end_frame_index, len(frames) - 1)

        # Get image path
        ref_image_name = frames[start_frame_index]
        tar_image_name = frames[end_frame_index]
        ref_image_path = os.path.join(self.image_root, video_name, ref_image_name)
        tar_image_path = os.path.join(self.image_root, video_name, tar_image_name)

        ref_mask_path = ref_image_path.replace('JPEGImages','Annotations').replace('.jpg', '.png')
        tar_mask_path = tar_image_path.replace('JPEGImages','Annotations').replace('.jpg', '.png')

        # Read Image and Mask
        ref_image = cv2.imread(ref_image_path)
        ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB)
       
        tar_image = cv2.imread(tar_image_path)
        tar_image = cv2.cvtColor(tar_image, cv2.COLOR_BGR2RGB)

        ref_mask = Image.open(ref_mask_path ).convert('P')
        ref_mask= np.array(ref_mask)

        tar_mask = Image.open(tar_mask_path ).convert('P')
        tar_mask= np.array(tar_mask)
        
        ref_ids = np.unique(ref_mask) 
        tar_ids = np.unique(tar_mask)

        common_ids = list(np.intersect1d(ref_ids, tar_ids))
        common_ids = [ i  for i in common_ids if i != 0 ]
        assert len(common_ids) > 0 
        chosen_id = np.random.choice(common_ids)
        ref_mask = ref_mask == chosen_id 
        tar_mask = tar_mask == chosen_id 
        len_mask = len( self.check_connect( ref_mask.astype(np.uint8) ) )
        assert len_mask == 1
        item_with_collage = self.process_pairs(ref_image, ref_mask, tar_image, tar_mask)
        sampled_time_steps = self.sample_timestep()
        item_with_collage['time_steps'] = sampled_time_steps
        return item_with_collage

    def check_connect(self, mask):
        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        cnt_area = [cv2.contourArea(cnt) for cnt in contours]
        return cnt_area