#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import glob
import os
from pathlib import Path

import requests
from io import BytesIO
from PIL import Image

import cv2

import numpy as np

import random

from apistar.exceptions import ErrorResponse
from arkindex import ArkindexClient, options_from_env
api_client = ArkindexClient(**options_from_env())

def download_image(url):
    '''
    Download an image and open it with Pillow
    '''
    assert url.startswith('http'), 'Image URL must be HTTP(S)'
    # Download the image
    # Cannot use stream=True as urllib's responses do not support the seek(int) method,
    # which is explicitly required by Image.open on file-like objects
    resp = requests.get(url)
    resp.raise_for_status()

    # Preprocess the image and prepare it for classification
    image = Image.open(BytesIO(resp.content))
    print('Downloaded image {} - size={}x{}'.format(url,
                                                          image.size[0],
                                                          image.size[1]))

    return image

def write_file(file_name, content):
    with open(file_name, 'w') as f:
        f.write(content)

def get_image(image_url, grayscale, out_dir):
    out_full_img_dir = os.path.join(out_dir, 'full', page_id)
    os.makedirs(out_full_img_dir, exist_ok=True)
    out_full_img_path = os.path.join(out_full_img_dir, 'full.jpg')
    if grayscale:
        download_image(image_url).convert('L').save(
                        out_full_img_path, format='jpeg')
        img = cv2.imread(out_full_img_path, cv2.IMREAD_GRAYSCALE)
    else:
        download_image(image_url).save(
                        out_full_img_path, format='jpeg')
        img = cv2.imread(out_full_img_path)
    return img
    
def extract_lines(page_id, grayscale=True, out_dir='/tmp'):
    count = 0
    line_bounding_rects = []
    line_polygons = []
    line_transcriptions = []
    try:
        for res in api_client.paginate('ListTranscriptions', id=page_id, type='line'):
            text = res['text']
            if not text or not text.strip():
                continue
            line_transcriptions.append(text)
            polygon = res['zone']['polygon']
            line_polygons.append(polygon)
            [x, y, w, h] = cv2.boundingRect(np.asarray(polygon))
            line_bounding_rects.append([x, y, w, h])
            count += 1
    except ErrorResponse as e:
        print("ListTranscriptions failed", e.status_code, e.title, e.content, page_id)
        raise e

    full_image_url = res['zone']['image']['s3_url']
    
    img = get_image(full_image_url, grayscale=grayscale, out_dir=out_dir)
    
    out_line_img_dir = os.path.join(out_dir, 'Lines', page_id)
    os.makedirs(out_line_img_dir, exist_ok=True)
    for i, [x, y, w, h] in enumerate(line_bounding_rects):
        croped = img[y:y + h, x:x + w].copy()
#        cv2.imwrite(f'{out_line_img_dir}/{i}.jpg', croped)
        cv2.imwrite(f'{out_line_img_dir}_{i}.jpg', croped)
    
    out_line_text_dir = os.path.join(out_dir, 'Transcriptions', page_id)
    os.makedirs(out_line_text_dir, exist_ok=True)
    for i, text in enumerate(line_transcriptions):
        write_file(f"{out_line_text_dir}_{i}.txt", text)
#        write_file(f"{out_line_text_dir}/{i}.txt", text)


split_train_ratio = 0.8
split_test_ratio = 0.1
split_val_ratio = 1 - split_train_ratio - split_test_ratio


def page_level_split(line_ids):
#    page_ids = list({'_'.join(line_id.split('_')[:-1]) for line_id in line_ids})
    page_ids = list({line_id for line_id in line_ids})
    random.shuffle(page_ids)
    page_count = len(page_ids)
    
    train_page_ids = page_ids[:round(page_count * split_train_ratio)]
    page_ids = page_ids[round(page_count * split_train_ratio):]
    
    test_page_ids = page_ids[:round(page_count * split_test_ratio)]
    page_ids = page_ids[round(page_count * split_test_ratio):]
    
    val_page_ids = page_ids
    
    page_dict = {page_id: TRAIN for page_id in train_page_ids}
    page_dict.update({page_id: TEST for page_id in test_page_ids})
    page_dict.update({page_id: VAL for page_id in val_page_ids})
    return (train_page_ids, val_page_ids, test_page_ids), page_dict

TRAIN,TEST,VAL = 0,1,2
out_file_dict = {0 : 'Train', 1 : 'Test', 2 : 'Validation'}

def create_partitions(line_ids, out_dir):
    (train_page_ids, val_page_ids, test_page_ids), page_dict = page_level_split(line_ids)
    datasets = [[] for i in range(3)]
    for line_id in line_ids:
        page_id = line_id
        split_id = page_dict[page_id]
        datasets[split_id].append(line_id)
    
    partitions_dir = os.path.join(out_dir, 'Partitions')
    os.makedirs(partitions_dir, exist_ok=True)
    for i, dataset in enumerate(datasets):
        file_name = f"{partitions_dir}/{out_file_dict[i]}Lines.lst"
        with open(file_name, 'w') as f:
            f.write('\n'.join(dataset) + '\n')


out_dir_base = '/tmp/foo2'

#page_id = 'bf23cc96-f6b2-4182-923e-6c163db37eba'
page_ids = ['bf23cc96-f6b2-4182-923e-6c163db37eba',
            '7c51e648-370e-43b7-9340-3b1a17c13828',
            '56521074-59f4-4173-bfc1-4b1384ff8139',]

for page_id in page_ids:
    extract_lines(page_id, out_dir=out_dir_base)


lines_path = Path(f'{out_dir_base}/Lines')
line_ids = [str(file.relative_to(lines_path).with_suffix('')) for file in lines_path.glob('**/*.jpg')]

create_partitions(line_ids, out_dir=out_dir_base)