labelme转COCO数据集（物体检测）

喜欢ヅ旅行 2022-09-14 15:25 249阅读 0赞

本次采用的数据集是Labelme标注的数据集，地址：链接：https://pan.baidu.com/s/1nxo9-NpNWKK4PwDZqwKxGQ 提取码：kp4e，需要将其转为COCO格式的数据集。转换代码如下：

新建labelme2coco.py

import argparse
    import json
    import matplotlib.pyplot as plt
    import skimage.io as io
    import cv2
    from labelme import utils
    import numpy as np
    import glob
    import PIL.Image
    
    REQUIRE_MASK = False
    labels = { 'aircraft': 1, 'oiltank': 2}
    
    
    class labelme2coco(object):
        def __init__(self, labelme_json=[], save_json_path='./new.json'):
            ''' :param labelme_json: the list of all labelme json file paths :param save_json_path: the path to save new json '''
            self.labelme_json = labelme_json
            self.save_json_path = save_json_path
            self.images = []
            self.categories = []
            self.annotations = []
            # self.data_coco = {}
            self.label = []
            self.annID = 1
            self.height = 0
            self.width = 0
            self.require_mask = REQUIRE_MASK
            self.save_json()
    
        def data_transfer(self):
            for num, json_file in enumerate(self.labelme_json):
                if not json_file == self.save_json_path:
                    with open(json_file, 'r') as fp:
                        data = json.load(fp)
                        self.images.append(self.image(data, num))
                        for shapes in data['shapes']:
                            print("label is ")
                            print(shapes['label'])
                            label = shapes['label']
                            # if label[1] not in self.label:
                            if label not in self.label:
                                print("find new category: ")
                                self.categories.append(self.categorie(label))
                                print(self.categories)
                                # self.label.append(label[1])
                                self.label.append(label)
                            points = shapes['points']
                            self.annotations.append(self.annotation(points, label, num))
                            self.annID += 1
    
        def image(self, data, num):
            image = { }
            img = utils.img_b64_to_arr(data['imageData'])
            height, width = img.shape[:2]
            img = None
            image['height'] = height
            image['width'] = width
            image['id'] = num + 1
            image['file_name'] = data['imagePath'].split('/')[-1]
            self.height = height
            self.width = width
            return image
    
        def categorie(self, label):
            categorie = { }
            categorie['supercategory'] = label
            # categorie['supercategory'] = label
            categorie['id'] = labels[label]  # 0 默认为背景
            categorie['name'] = label
            return categorie
    
        def annotation(self, points, label, num):
            annotation = { }
            print(points)
            x1 = points[0][0]
            y1 = points[0][1]
            x2 = points[1][0]
            y2 = points[1][1]
            contour = np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]])  # points = [[x1, y1], [x2, y2]] for rectangle
            contour = contour.astype(int)
            area = cv2.contourArea(contour)
            print("contour is ", contour, " area = ", area)
            annotation['segmentation'] = [list(np.asarray([[x1, y1], [x2, y1], [x2, y2], [x1, y2]]).flatten())]
            # [list(np.asarray(contour).flatten())]
            annotation['iscrowd'] = 0
            annotation['area'] = area
            annotation['image_id'] = num + 1
    
            if self.require_mask:
                annotation['bbox'] = list(map(float, self.getbbox(points)))
            else:
                x1 = points[0][0]
                y1 = points[0][1]
                width = points[1][0] - x1
                height = points[1][1] - y1
                annotation['bbox'] = list(np.asarray([x1, y1, width, height]).flatten())
    
            annotation['category_id'] = self.getcatid(label)
            annotation['id'] = self.annID
            return annotation
    
        def getcatid(self, label):
            for categorie in self.categories:
                # if label[1]==categorie['name']:
                if label == categorie['name']:
                    return categorie['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):
    
            # np.where(mask==1)
            index = np.argwhere(mask == 1)
            rows = index[:, 0]
            clos = index[:, 1]
    
            left_top_r = np.min(rows)  # y
            left_top_c = np.min(clos)  # x
            right_bottom_r = np.max(rows)
            right_bottom_c = np.max(clos)
            return [left_top_c, left_top_r, right_bottom_c - left_top_c, right_bottom_r - left_top_r]
    
        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 = { }
            data_coco['images'] = self.images
            data_coco['categories'] = self.categories
            data_coco['annotations'] = self.annotations
            return data_coco
    
        def save_json(self):
            print("in save_json")
            self.data_transfer()
            self.data_coco = self.data2coco()
    
            print(self.save_json_path)
            json.dump(self.data_coco, open(self.save_json_path, 'w'), indent=4)
    
    
    labelme_json = glob.glob('LabelmeData/*.json')
    from sklearn.model_selection import train_test_split
    
    trainval_files, test_files = train_test_split(labelme_json, test_size=0.2, random_state=55)
    import os
    
    if not os.path.exists("datasets/coco/annotations"):
        os.makedirs("datasets/coco/annotations/")
    if not os.path.exists("/datasets/coco/train2017"):
        os.makedirs("datasets/coco/train2017")
    if not os.path.exists("datasets/coco/val2017"):
        os.makedirs("datasets/coco/val2017")
    labelme2coco(trainval_files, 'datasets/coco/annotations/instances_train2017.json')
    labelme2coco(test_files, 'datasets/coco/annotations/instances_val2017.json')
    import shutil
    
    for file in trainval_files:
        shutil.copy(os.path.splitext(file)[0] + ".jpg", "datasets/coco/train2017/")
    for file in test_files:
        shutil.copy(os.path.splitext(file)[0] + ".jpg", "datasets/coco/val2017/")