impo
 
rt os
import json
import PIL.Image
import PIL.ImageDraw
import numpy as np
from labelme2coco.utils import create_dir, list_jsons_recursively
from labelme2coco.image_utils import read_image_shape_as_dict

class Labelme2Coco:
    def __init__(self, labelme_folder='', save_json_path='./new.json'):
        self.save_json_path = save_json_path
        self.images = []
        self.categories = []
        self.annotations = []
        self.label_set = set()
        self.annID = 1
        self.height = 0
        self.width = 0

        save_json_dir = os.path.dirname(save_json_path)
        create_dir(save_json_dir)

        _, labelme_json = list_jsons_recursively(labelme_folder)
        self.labelme_json = labelme_json

        self.save_json()

    def data_transfer(self):
        for num, json_path in enumerate(self.labelme_json):
            with open(json_path, 'r') as fp:
                data = json.load(fp)
                self.images.append(self.image(data, num, json_path))
                for shapes in data['shapes']:
                    label = shapes['label']
                    if label not in self.label_set:
                        self.categories.append(self.category(label))
                        self.label_set.add(label)
                    points = shapes['points']
                    self.annotations.append(self.annotation(points, label, num))
                    self.annID += 1

    def image(self, data, num, json_path):
        image = {}
        _, img_extension = os.path.splitext(data["imagePath"])
        image_path = json_path.replace(".json", img_extension)
        img_shape = read_image_shape_as_dict(image_path)
        height, width = img_shape['height'], img_shape['width']

        image['height'] = height
        image['width'] = width
        image['id'] = int(num + 1)
        image['file_name'] = os.path.basename(image_path)
        print(image_path)
        self.height = height
        self.width = width

        return image

    def category(self, label):
        category = {
            'supercategory': label,
            'id': int(len(self.label_set) + 1),
            'name': label
        }
        return category

    def annotation(self, points, label, num):
        annotation = {
            'iscrowd': 0,
            'image_id': int(num + 1),
            'bbox': list(map(float, self.getbbox(points))),
            'segmentation': [np.asarray(points).flatten().tolist()],
            'category_id': self.getcatid(label),
            'id': int(self.annID),
            'area': self.height * self.width
        }
        return annotation

    def getcatid(self, label):
        for category in self.categories:
            if label == category['name']:
                return category['id']
        return -1

    def getbbox(self, points):
        polygons = points
        mask = self.polygons_to_mask([self.height, self.width], polygons)
        return self.mask2box(mask)

    def mask2box(self, mask):
        index = np.argwhere(mask == 1)
        rows = index[:, 0]
        cols = index[:, 1]

        left_top_row = np.min(rows)
        left_top_col = np.min(cols)

        right_bottom_row = np.max(rows)
        right_bottom_col = np.max(cols)

        return [left_top_col, left_top_row, right_bottom_col - left_top_col,
                right_bottom_row - left_top_row]

    def polygons_to_mask(self, img_shape, polygons):
        mask = np.zeros(img_shape, dtype=np.uint8)
        mask = PIL.Image.fromarray(mask)
        xy = list(map(tuple, polygons))
        PIL.ImageDraw.Draw(mask).polygon(xy=xy, outline=1, fill=1)
        mask = np.array(mask, dtype=bool)
        return mask

    def data2coco(self):
        data_coco = {
            'images': self.images,
            'categories': self.categories,
            'annotations': self.annotations
        }
        return data_coco

    def save_json(self):
        self.data_transfer()
        self.data_coco = self.data2coco()

        json.dump(self.data_coco, open(self.save_json_path, 'w', encoding='utf-8'), indent=4, separators=(',', ': '),
                  cls=MyEncoder)


class MyEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.integer):
            return int(obj)
        elif isinstance(obj, np.floating):
            return float(obj)
        elif isinstance(obj, np.ndarray):
            return obj.tolist()
        else:
            return super(MyEncoder, self).default(obj)


if __name__ == "__main__":
    labelme_folder = "./datasets/images_json/train"
    save_json_path = "./train_coco_format.json"
    labelme2coco(labelme_folder, save_json_path)