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import numpy as np |
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import json |
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from .AutoDriveDataset import AutoDriveDataset |
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from .convert import convert, id_dict, id_dict_single |
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from tqdm import tqdm |
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single_cls = True |
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class BddDataset(AutoDriveDataset): |
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def __init__(self, cfg, is_train, inputsize, transform=None): |
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super().__init__(cfg, is_train, inputsize, transform) |
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self.db = self._get_db() |
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self.cfg = cfg |
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def _get_db(self): |
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""" |
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get database from the annotation file |
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Inputs: |
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Returns: |
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gt_db: (list)database [a,b,c,...] |
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a: (dictionary){'image':, 'information':, ......} |
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image: image path |
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mask: path of the segmetation label |
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label: [cls_id, center_x//256, center_y//256, w//256, h//256] 256=IMAGE_SIZE |
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""" |
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print('building database...') |
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gt_db = [] |
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height, width = self.shapes |
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for mask in tqdm(list(self.mask_list)): |
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mask_path = str(mask) |
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label_path = mask_path.replace(str(self.mask_root), str(self.label_root)).replace(".png", ".json") |
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image_path = mask_path.replace(str(self.mask_root), str(self.img_root)).replace(".png", ".jpg") |
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lane_path = mask_path.replace(str(self.mask_root), str(self.lane_root)) |
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with open(label_path, 'r') as f: |
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label = json.load(f) |
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data = label['frames'][0]['objects'] |
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data = self.filter_data(data) |
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gt = np.zeros((len(data), 5)) |
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for idx, obj in enumerate(data): |
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category = obj['category'] |
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if category == "traffic light": |
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color = obj['attributes']['trafficLightColor'] |
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category = "tl_" + color |
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if category in id_dict.keys(): |
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x1 = float(obj['box2d']['x1']) |
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y1 = float(obj['box2d']['y1']) |
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x2 = float(obj['box2d']['x2']) |
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y2 = float(obj['box2d']['y2']) |
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cls_id = id_dict[category] |
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if single_cls: |
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cls_id=0 |
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gt[idx][0] = cls_id |
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box = convert((width, height), (x1, x2, y1, y2)) |
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gt[idx][1:] = list(box) |
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rec = [{ |
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'image': image_path, |
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'label': gt, |
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'mask': mask_path, |
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'lane': lane_path |
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}] |
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gt_db += rec |
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print('database build finish') |
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return gt_db |
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def filter_data(self, data): |
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remain = [] |
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for obj in data: |
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if 'box2d' in obj.keys(): |
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if single_cls: |
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if obj['category'] in id_dict_single.keys(): |
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remain.append(obj) |
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else: |
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remain.append(obj) |
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return remain |
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def evaluate(self, cfg, preds, output_dir, *args, **kwargs): |
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""" |
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""" |
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pass |
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