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%matplotlib inline
from __future__ import print_function
from ipywidgets import interact_manual, fixed

import os.path

from math import ceil

import numpy as np
import random

from skimage import io
from skimage.transform import rotate, rescale, resize

import matplotlib.pyplot as plt

import json

import warnings


    

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def rescale_coords(coords, factor_h, factor_v, h_limits, v_limits):
    rescale_cs = []
    for coord in coords:
        if(coord[0] > h_limits[0] and coord[2] < h_limits[1] and
           coord[1] > v_limits[0] and coord[3] < h_limits[1]):
            r_coord = []
            r_coord.append(coord[0] * factor_h)
            r_coord.append(coord[1] * factor_v)
            r_coord.append(coord[2] * factor_h)
            r_coord.append(coord[3] * factor_v)
            rescale_cs.append(r_coord)
    return rescale_cs


    

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# Contamos el número de imágenes que tenemos en
# el directorio de las imágenes etiquetadas
import os, os.path
import re

path="../../rsc/img/Default"
dest_path = "../../rsc/img/rescaled/"

pattern = re.compile("^.*\.jpg$", re.IGNORECASE)

def list_images(path='../../rsc/img/Default'):
    images_list = []
    for name in os.listdir(path):
        json_name = name.split(".")[0] + ".json"
        if pattern.match(name) \
        and os.path.exists(path + "/" + json_name):
            images_list.append(path + "/" + name)
    return images_list


    

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def read_coords_conversion(coords_dict):
    for k, coords in coords_dict.items():
        coords_mod = np.array(coords)
        coords_mod = coords_mod[:,[2,0,3,1]]
        coords_mod = coords_mod.tolist()
        coords_dict[k] = coords_mod
    return coords_dict


    

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def save_coords_conversion(coords_dict):
    for k, coords in coords_dict.items():
        if len(coords) == 0:
            continue
        coords_mod = np.array(coords)
        coords_mod = coords_mod[:,[1,3,0,2]]
        coords_mod = coords_mod.tolist()
        coords_dict[k] = coords_mod
    return coords_dict


    

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import copy

images_names_list = list_images(path)

if len(images_names_list) == 0:
    raise ValueError("Number of images must be greater than 0")

for image_path in images_names_list:
    
    warnings.filterwarnings('ignore')
    original_image = io.imread(image_path)

    json_path = "../.." + image_path.split(".")[-2] + ".json"
    image_name = os.path.split(image_path)[1].split(".")[0]
    image_with_format = image_name + ".jpg"

    # Cargamos coordenadas, si existen,
    # y si no existe fichero de coordenadas
    # pasamos a la siguiente imagen
    if os.path.exists(json_path):
        with open(json_path) as jsonfile:
            coords_dict = json.load(jsonfile)
            coords_dict = coords_dict[image_with_format]
            coords_dict = read_coords_conversion(coords_dict)
    else:
        continue

    # Cambios de tamaño en la imagen
    mod_image = original_image[0:1920,0:1920]
    mod_image = resize(mod_image,(480,480),mode='constant')
    
    mod_coords = dict()
    for k, coords in coords_dict.items():
            mod_coords[k] = rescale_coords(coords, 
                                           480/1920, 480/1920, (0,1920), (0,1920)) 
    
    # Guardamos imagen
    io.imsave(dest_path + image_name + ".jpg", mod_image, quality=30)

    with open(dest_path + image_name + ".json", "w") as jsonfile2:
        mod_coords_dict = dict()
        mod_coords_dict[image_name +".jpg"] = save_coords_conversion(mod_coords)
        json.dump(mod_coords_dict, jsonfile2)