import json
import argparse
from os.path import join
import copy
import os
from glob import glob
import numpy as np
import cv2
from shapely.geometry import box, Polygon
from shapely.validation import make_valid


def main(args):
    subset_jsons = dict()

    input_id = os.path.basename(args.input).split('.')[0]
    addition_id = input_id + '_addition'

    subset_jsons[input_id] = dict()
    subset_jsons[input_id]['filename'] = args.input
    subset_jsons[input_id]['dataset'] = args.dataset
    subset_jsons[input_id]['subset'] = args.subset

    if args.addition:
        subset_jsons[addition_id] = dict()
        subset_jsons[addition_id]['filename'] = args.addition
        subset_jsons[addition_id]['dataset'] = args.dataset
        subset_jsons[addition_id]['subset'] = args.subset

    image_files = glob(join(args.dataset_folder,'**/*.jpg'), recursive = True)
    if args.dataset in ['cnr', 'plds']:
        image_files = [x for x in image_files if args.subset.lower() in x.lower()]
    print(image_files)
    image_dict = dict()

    for i in range(len(image_files)):
        image_files[i] = image_files[i].replace(args.dataset_folder, '')
        if args.dataset == 'pklot':
            image_files[i] = join(args.subset.upper(), image_files[i])
        else:
            image_files[i] = image_files[i]

        image_id = os.path.split(image_files[i])[1]
        if image_id in image_dict:
            print(f'image {image_id} already exists')
        else:
            image_dict[image_id] = dict()
            image_dict[image_id]['path'] = image_files[i]
            # print(image_id)
            image_dict[image_id]['json'] = False

    print('image_dict' , len(image_dict))

    new_data = generate_new_json(subset_jsons, args, image_dict)

    with open(args.output, 'w') as f:
        json.dump(new_data, f)
        print('new data saved.')


def generate_new_json(subset_jsons, args, image_dict):
    json_data = dict()
    next_ids = dict()
    next_ids['image'] = 1
    next_ids['annotation'] = 1
    next_ids['spot'] = 1
    force_subscribe = False

    rectangle = None

    image_samples = dict()

    for subset_json in subset_jsons.keys():
        filename = subset_jsons[subset_json]['filename']
        print(filename)
        with open(filename) as json_file:
            data = json.load(json_file)
            # init data
            if not json_data:
                json_data['images'] = dict()
                json_data['annotations'] = dict()
                json_data['categories'] = data['categories']
                json_data['parkingSpots'] = dict()
                json_data['statuses'] = data['statuses']
                json_data['climates'] = data['climates']
            else:
                force_subscribe = True

            print('images keys', data['images'][0].keys())
            print('annotations keys', data['annotations'][0].keys())
            if 'parkingSpots' in data:
                print('parkingSpots keys', data['parkingSpots'][0].keys())

            images = data['images']
            annotations = data['annotations']

            if rectangle is None:
                if args.rectangle:
                    rectangle = args.rectangle
                else:
                    rectangle = images[0]['annotationsRectangle']

            print(f'\t rectangle {rectangle}')
            print(f'\t images size {len(images)}')
            print(f'\t annotations size {len(annotations)}')
            if 'parkingSpots' in data:
                parkingSpots = data['parkingSpots']
                print(f'\t parkingSpots size {len(parkingSpots)}')
            else:
                parkingSpots = None

            copy_values(images, annotations, parkingSpots, json_data, next_ids, args.dataset, args.subset, image_dict, rectangle, image_samples, args.check_duplicates, force_subscribe)

            print('...done.')

    json_data['images'] = list(json_data['images'].values())

    if args.add_absent_images:
        for image_id in image_dict.keys():
            if not image_dict[image_id]['json']:
                image_dict[image_id]['json'] = True
                new_image = dict()
                new_image['id'] = next_ids['image']
                new_image['file_name'] = image_dict[image_id]['path']
                new_image['width'] = json_data['images'][0]['width']
                new_image['height'] = json_data['images'][0]['height']
                new_image['annotationsRectangle'] = rectangle

                if any([weather in image_dict[image_id]['path'].lower() for weather in ['cloudy', 'overcast']]):
                    climate = 1
                elif any([weather in image_dict[image_id]['path'].lower() for weather in ['sunny']]):
                    climate = 2
                elif any([weather in image_dict[image_id]['path'].lower() for weather in ['rainy']]):
                    climate = 3
                elif any([weather in image_dict[image_id]['path'].lower() for weather in ['snow']]):
                    climate = 4
                elif any([weather in image_dict[image_id]['path'].lower() for weather in ['normal']]):
                    climate = 5
                else:
                    climate = 6

                if args.dataset.lower() == 'pklot':
                    date = image_dict[image_id]['path'].split('/')[-1]
                    date = date.replace('.jpg', '')
                    time = date.split('_')[1:]
                    date = date.split('_')[0]
                    date = date.split('-')
                elif args.dataset.lower() == 'cnr':
                    date = image_dict[image_id]['path'].split('/')[-1]
                    date = date.replace('.jpg', '')
                    time = date.split('_')[1]
                    time = [time[:2], time[2:], '00']
                    date = date.split('_')[0]
                    date = date.split('-')
                elif args.dataset.lower() == 'plds':
                    date = image_dict[image_id]['path'].split('/')[-2]
                    date = date.split('-')
                    time = None
                else:
                    raise NotImplementedError

                new_image['date'] = date  # "date": [2015, 12, 18]
                new_image['time'] = time  # "time": [10, 17, 05]
                new_image['climate'] = climate

                new_image['dataset'] = args.dataset
                new_image['subset'] = args.subset

                print(f'\t image {image_id} added with path {new_image["file_name"]}')
                print(f'\t{new_image}')
                json_data['images'].append(new_image)
                next_ids['image'] += 1


    json_data['annotations'] = list(json_data['annotations'].values())
    json_data['parkingSpots'] = list(json_data['parkingSpots'].values())

    print(next_ids)
    return json_data

def get_largest_poly_from_self_intersection(coords):
    poly = Polygon(coords)
    if poly.is_valid:
        return poly

    mp = make_valid(poly)

    if type(mp) == Polygon:
        return mp

    largest_poly = max(mp.geoms, key=lambda p: p.area)
    print('largest_poly.area, mp.area', largest_poly.area, mp.area)
    return largest_poly

def copy_values(images, annotations, parkingSpots, json_data, next_ids, dataset, subset, image_dict, rectangle, image_samples, check_duplicates, force_subscribe):
    perc = -1
    for i in range(len(images)):
        image = copy.deepcopy(images[i])
        new_perc = int(i/len(images)*100)
        if new_perc > perc:
            perc = new_perc
            print(f'{perc}% - current image id: {next_ids["image"]}')

        annotations_list = [copy.deepcopy(x) for x in annotations if x['image_id'] == image['id']]

        if parkingSpots:
            spots_list = [copy.deepcopy(x) for x in parkingSpots if x['image_id'] == image['id']]

        if 'path' in image:
            baseline = os.path.split(image_dict[image['file_name']]['path'])[1]
            image['file_name'] = image_dict[baseline]['path']
            image_dict[baseline]['json'] = True
        else:
            baseline = os.path.split(image['file_name'])[1]
            image['file_name'] = image_dict[baseline]['path']
            image_dict[baseline]['json'] = True

        if image['file_name'] in json_data['images']: # if image already exists in json
            image = json_data['images'][image['file_name']]
            if force_subscribe:

                if annotations_list:
                    for j in image_samples[image['id']]['annotation_ids']:
                        json_data['annotations'].pop(j, None)
                    image_samples[image['id']]['annotation_ids'] = list()

                if parkingSpots and spots_list:
                    for j in image_samples[image['id']]['spot_ids']:
                        json_data['parkingSpots'].pop(j, None)
                    image_samples[image['id']]['spot_ids'] = list()

        else: # if image does not exist in json
            image['id'] = next_ids['image']

            image_samples[image['id']] = dict()
            image_samples[image['id']]['annotation_ids'] = list()
            image_samples[image['id']]['spot_ids'] = list()

            next_ids['image'] += 1
            image['dataset'] = dataset
            image['subset'] = subset

            if rectangle:
                image['annotationsRectangle'] = rectangle

            image.pop('dataset_id', None)
            image.pop('category_ids', None)
            image.pop('path', None)
            image.pop('annotated', None)
            image.pop('annotating', None)
            image.pop('num_annotations', None)
            image.pop('metadata', None)
            image.pop('deleted', None)
            image.pop('milliseconds', None)
            image.pop('regenerate_thumbnail', None)

        for annotation in annotations_list:
            annotation.pop('isbbox', None)
            annotation.pop('color', None)
            annotation.pop('metadata', None)

            new_seg = list()
            for ann in annotation['segmentation']:
                coords = np.reshape(ann, (-1, 2))
                coords = np.int32(coords)  # Convert to int32 to fillPoly function

                # remove small polygons
                if cv2.contourArea(coords) < 10:
                    print('poly area', cv2.contourArea(coords), 'removed')
                elif check_duplicates:
                    cur_poly = get_largest_poly_from_self_intersection(coords)
                    no_intersection = True
                    try:
                        # check if current mask intersects with any other polygon
                        for saved_annotation_id in image_samples[image['id']]['annotation_ids']:
                            saved_annotation = json_data['annotations'][saved_annotation_id]
                            for i in range(len(saved_annotation['segmentation'])):
                                other_coords = np.reshape(saved_annotation['segmentation'][i], (-1, 2))
                                other_coords = np.int32(other_coords)
                                other_poly = get_largest_poly_from_self_intersection(other_coords)

                                polygon_intersection = cur_poly.intersection(other_poly).area
                                polygon_union = cur_poly.area + other_poly.area - polygon_intersection
                                iou = polygon_intersection / polygon_union

                                if iou > 0.5:
                                    no_intersection = False

                                    # update mask annotation if intersected one is bigger
                                    if cur_poly.area > other_poly.area:
                                        saved_annotation['segmentation'][i] = ann

                                elif iou > 0.05:
                                    print('iou', iou, 'polygon1', cur_poly.area, 'polygon2', other_poly.area)

                    #TODO calculate intersection and union, and then IOU between polygons inside new_seg

                        for i in range(len(new_seg)):
                            other_coords = np.reshape(new_seg[i], (int(len(new_seg[i])/2), 2))
                            other_coords = np.int32(other_coords)
                            other_poly = get_largest_poly_from_self_intersection(other_coords)

                            polygon_intersection = cur_poly.intersection(other_poly).area
                            polygon_union = cur_poly.area + other_poly.area - polygon_intersection
                            iou = polygon_intersection / polygon_union
                            if iou > 0.5:
                                no_intersection = False
                                if cur_poly.area > other_poly.area:
                                    new_seg[i] = ann
                            elif iou > 0.05:
                                    print('iou', iou, 'polygon1', cur_poly.area, 'polygon2', other_poly.area)

                        if no_intersection:
                            new_seg.append(ann)
                    except Exception as e:
                        print(e)
                else:
                    new_seg.append(ann)
            annotation['segmentation'] = new_seg

            if annotation['segmentation']:
                annotation['id'] = next_ids['annotation']
                annotation['image_id'] = image['id']
                next_ids['annotation'] += 1
                annotation['category_id'] = 1
                json_data['annotations'][annotation['id']] = annotation
                image_samples[image['id']]['annotation_ids'].append(annotation['id'])
                # if print_ann:
                #     print(annotation)

        if parkingSpots:
            for spot in spots_list:
                new_seg = list()
                ann = spot['contour']
                coords = np.reshape(ann, (-1, 2))
                coords = np.int32(coords)  # Convert to int32 to fillPoly function
                if cv2.contourArea(coords) < 20:
                    print('poly area', cv2.contourArea(coords), 'removed')
                elif check_duplicates:
                    cur_poly = get_largest_poly_from_self_intersection(coords)
                    no_intersection = True
                    try:

                        for saved_spot_id in image_samples[image['id']]['spot_ids']:
                            saved_spot = json_data['parkingSpots'][saved_spot_id]

                            other_coords = np.reshape(saved_spot['contour'], (-1, 2))
                            other_coords = np.int32(other_coords)
                            other_poly = get_largest_poly_from_self_intersection(other_coords)

                            polygon_intersection = cur_poly.intersection(other_poly).area
                            polygon_union = cur_poly.area + other_poly.area - polygon_intersection
                            iou = polygon_intersection / polygon_union
                            if iou > 0.5:
                                no_intersection = False
                                # update mask annotation if intersected one is bigger
                                if cur_poly.area > other_poly.area:
                                    saved_spot['contour'] = ann
                            # elif iou > 0.05:
                            #     print('iou spot', iou, 'polygon1', cur_poly.area, 'polygon2', other_poly.area)

                    #TODO calculate intersection and union, and tne IOU between polygons inside new_seg

                        for i in range(len(new_seg)):
                            other_coords = np.reshape(new_seg[i], (int(len(new_seg[i])/2), 2))
                            other_coords = np.int32(other_coords)
                            other_poly = get_largest_poly_from_self_intersection(other_coords)

                            polygon_intersection = cur_poly.intersection(other_poly).area
                            polygon_union = cur_poly.area + other_poly.area - polygon_intersection
                            iou = polygon_intersection / polygon_union
                            if iou > 0.5:
                                no_intersection = False
                                if cur_poly.area > other_poly.area:
                                    new_seg[i] = ann
                            # elif iou > 0.05:
                            #     print('iou spot', iou, 'polygon1', cur_poly.area, 'polygon2', other_poly.area)

                        if no_intersection:
                            new_seg.append(ann)
                    except Exception as e:
                        print(e)
                else:
                    new_seg.append(ann)

                spot['contour'] = new_seg

                if spot['contour']:
                    spot['id'] = next_ids['spot']
                    spot['image_id'] = image['id']
                    next_ids['spot'] += 1
                    annotation['category_id'] = 1
                    json_data['parkingSpots'][spot['id']] = spot
                    image_samples[image['id']]['spot_ids'].append(spot['id'])

        json_data['images'][image['file_name']] = image

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='JSON Merger',
                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('--input', '-i', type=str)
    parser.add_argument('--addition', '-a', type=str, default=None)
    parser.add_argument('--output', '-o', type=str)
    parser.add_argument('--dataset', '-d', type=str)
    parser.add_argument('--dataset_folder', '-df', type=str)
    parser.add_argument('--subset', '-s', type=str)
    parser.add_argument('--check_duplicates', '-cd', dest='check_duplicates', action='store_true')
    parser.set_defaults(check_duplicates=False)
    parser.add_argument('--add_absent_images', '-aai', dest='add_absent_images', action='store_true')
    parser.set_defaults(add_absent_images=False)

    parser.add_argument('--rectangle', '-r', nargs='+', type=int, default=None)

    main(parser.parse_args())