import json
import cv2
import numpy as np
import os
import random
from glob import glob

from torch.utils.data import Dataset
from torchvision import transforms
from PIL import Image, ImageDraw
import torch
import albumentations as A


class ZalandoDataset(Dataset):
    def __init__(self, transform, root="/tmp/zalando/train/", width = 512, height = 512):
        self.root = root
        self.transform = transform
        self.width = width
        self.height = height
        self.image_paths = sorted(glob(f'{self.root}image/*.jpg'))
        self.ref_paths = sorted(glob(f'{self.root}cloth/*.jpg'))
        self.parse_paths = sorted(glob(f"{self.root}image-parse-v3/*.png"))
        self.prompts = ["", "a professional, detailed, high-quality image", "shirt"]
        self.labels = {
          0:  ['background',  [0, 10]],
          1:  ['hair',        [1, 2]],
          2:  ['face',        [4, 13]],
          3:  ['upper',       [5, 6, 7]],
          4:  ['bottom',      [9, 12]],
          5:  ['left_arm',    [14]],
          6:  ['right_arm',   [15]],
          7:  ['left_leg',    [16]],
          8:  ['right_leg',   [17]],
          9:  ['left_shoe',   [18]],
          10: ['right_shoe',  [19]],
          11: ['socks',       [8]],
          12: ['noise',       [3, 11]]
      }
        self.random_trans=A.Compose([
            A.HorizontalFlip(p=0.5),
            A.Rotate(limit=20),
            A.Blur(p=0.3),
            #A.ElasticTransform(p=0.3)
            ])
        
    
    def img_segment(self,parse_img, wanted_label = 3):
        size = parse_img.width
        im_parse_pil = transforms.Resize((size,size), interpolation=0)(parse_img) # transform
        parse = torch.from_numpy(np.array(im_parse_pil)[None]).long() # None is equivalent np.expand_dims  long() is equivalent to self.to(torch.int64)
        parse_map = torch.FloatTensor(20, size, size).zero_()
        parse_map = parse_map.scatter_(0, parse, 1.0)
        new_parse_map = torch.FloatTensor(13, size, size).zero_()
        for i in range(len(self.labels)):
            for label in self.labels[i][1]:
                new_parse_map[i] += parse_map[label]

        shirt_mask = new_parse_map[wanted_label].numpy()
        return shirt_mask.astype(dtype="uint8") * 255

    def add_noise(self, image):
        image = image.astype(np.uint8)

        # Find contours
        contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        # Choose a random contour
        if contours:
            random_contour = contours[np.random.randint(len(contours))]

            # Create a blank canvas
            canvas = np.zeros_like(image)

            # Draw the contour on the canvas
            cv2.drawContours(canvas, [random_contour], 0, 255, thickness=10)

            # Dilate the canvas to add more thickness to the white paint
            kernel = np.ones((15,15), np.uint8)
            canvas = cv2.dilate(canvas, kernel, iterations=1)

            # Subtract the original contour from the dilated contour to obtain only the boundary
            boundary = cv2.absdiff(canvas, image)

            # Generate random points on the boundary
            points_on_boundary = []
            for i in range(len(random_contour)):
                x, y = random_contour[i][0]
                points_on_boundary.append((x, y))
            points_on_boundary = np.array(points_on_boundary)

            # Draw random thick lines at each point
            for point in points_on_boundary:
                # thickness = np.random.randint(5,30)
                thickness = 30
                # length = np.random.randint(10,30)
                length = 0.1
                angle = np.random.randint(0,360)
                endpoint = (int(point[0] + length * np.cos(angle * np.pi / 180)),
                            int(point[1] + length * np.sin(angle * np.pi / 180)))
                cv2.line(boundary, tuple(point), endpoint, 255, thickness)

            # Add the canvas with the white water paint on the edges of the contour region to the original image
            image = cv2.bitwise_or(image, boundary)
        return image

    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, idx):
        target_filename = self.image_paths[idx]
        parse_filename = self.parse_paths[idx]
        
        target = cv2.imread(target_filename)
        target_clip = cv2.resize(target, (224,224))
        target = cv2.resize(target, (self.width,self.height))
        
        parse = Image.open(parse_filename)
        parse_clip = parse.resize((224,224))
        parse = parse.resize((self.width,self.height))
        
        mask = self.img_segment(parse,3)
        mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
        
        mask_clip = self.img_segment(parse_clip,3)
        mask_clip = self.add_noise(mask_clip)
        mask_clip = cv2.cvtColor(mask_clip, cv2.COLOR_GRAY2BGR)
        
        # Do not forget that OpenCV read images in BGR order.
        target_clip = cv2.cvtColor(target_clip, cv2.COLOR_BGR2RGB)
        target = cv2.cvtColor(target, cv2.COLOR_BGR2RGB)
        masked_shirt = target * (mask > 0)
        
        # Normalize source images to [0, 1]. source = reference image (orig control)
        mask = mask.astype(np.float32) / 255.0
        target_clip = target_clip.astype(np.float32) / 255.0
        masked_image = target_clip * (mask_clip < 0.5)
        masked_shirt = masked_shirt.astype(np.float32) / 255.0

        # Normalize target images to [-1, 1].
        target_normalized = (target.astype(np.float32) / 127.5) - 1.0
        
        # generate masked image
        return dict(jpg=target_normalized, txt="", hint=masked_image, mask = mask, masked_image = masked_shirt, path=target_filename)