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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import os.path | |
| from collections import defaultdict | |
| from typing import Dict, List, Optional, Sequence | |
| import mmcv | |
| import numpy as np | |
| from mmengine import mkdir_or_exist | |
| from mmengine.dist import barrier | |
| from mmdet.registry import METRICS | |
| from mmdet.evaluation.metrics.base_video_metric import BaseVideoMetric | |
| PALETTE = { | |
| 'davis': b'\x00\x00\x00\x80\x00\x00\x00\x80\x00\x80\x80\x00\x00\x00\x80\x80\x00\x80\x00\x80\x80\x80\x80\x80@\x00\x00\xc0\x00\x00@\x80\x00\xc0\x80\x00@\x00\x80\xc0\x00\x80@\x80\x80\xc0\x80\x80\x00@\x00\x80@\x00\x00\xc0\x00\x80\xc0\x00\x00@\x80\x80@\x80\x00\xc0\x80\x80\xc0\x80@@\x00\xc0@\x00@\xc0\x00\xc0\xc0\x00@@\x80\xc0@\x80@\xc0\x80\xc0\xc0\x80\x00\x00@\x80\x00@\x00\x80@\x80\x80@\x00\x00\xc0\x80\x00\xc0\x00\x80\xc0\x80\x80\xc0@\x00@\xc0\x00@@\x80@\xc0\x80@@\x00\xc0\xc0\x00\xc0@\x80\xc0\xc0\x80\xc0\x00@@\x80@@\x00\xc0@\x80\xc0@\x00@\xc0\x80@\xc0\x00\xc0\xc0\x80\xc0\xc0@@@\xc0@@@\xc0@\xc0\xc0@@@\xc0\xc0@\xc0@\xc0\xc0\xc0\xc0\xc0 \x00\x00\xa0\x00\x00 \x80\x00\xa0\x80\x00 \x00\x80\xa0\x00\x80 \x80\x80\xa0\x80\x80`\x00\x00\xe0\x00\x00`\x80\x00\xe0\x80\x00`\x00\x80\xe0\x00\x80`\x80\x80\xe0\x80\x80 @\x00\xa0@\x00 \xc0\x00\xa0\xc0\x00 @\x80\xa0@\x80 \xc0\x80\xa0\xc0\x80`@\x00\xe0@\x00`\xc0\x00\xe0\xc0\x00`@\x80\xe0@\x80`\xc0\x80\xe0\xc0\x80 \x00@\xa0\x00@ \x80@\xa0\x80@ \x00\xc0\xa0\x00\xc0 \x80\xc0\xa0\x80\xc0`\x00@\xe0\x00@`\x80@\xe0\x80@`\x00\xc0\xe0\x00\xc0`\x80\xc0\xe0\x80\xc0 @@\xa0@@ \xc0@\xa0\xc0@ @\xc0\xa0@\xc0 \xc0\xc0\xa0\xc0\xc0`@@\xe0@@`\xc0@\xe0\xc0@`@\xc0\xe0@\xc0`\xc0\xc0\xe0\xc0\xc0\x00 \x00\x80 \x00\x00\xa0\x00\x80\xa0\x00\x00 \x80\x80 \x80\x00\xa0\x80\x80\xa0\x80@ \x00\xc0 \x00@\xa0\x00\xc0\xa0\x00@ \x80\xc0 \x80@\xa0\x80\xc0\xa0\x80\x00`\x00\x80`\x00\x00\xe0\x00\x80\xe0\x00\x00`\x80\x80`\x80\x00\xe0\x80\x80\xe0\x80@`\x00\xc0`\x00@\xe0\x00\xc0\xe0\x00@`\x80\xc0`\x80@\xe0\x80\xc0\xe0\x80\x00 @\x80 @\x00\xa0@\x80\xa0@\x00 \xc0\x80 \xc0\x00\xa0\xc0\x80\xa0\xc0@ @\xc0 @@\xa0@\xc0\xa0@@ \xc0\xc0 \xc0@\xa0\xc0\xc0\xa0\xc0\x00`@\x80`@\x00\xe0@\x80\xe0@\x00`\xc0\x80`\xc0\x00\xe0\xc0\x80\xe0\xc0@`@\xc0`@@\xe0@\xc0\xe0@@`\xc0\xc0`\xc0@\xe0\xc0\xc0\xe0\xc0 \x00\xa0 \x00 \xa0\x00\xa0\xa0\x00 \x80\xa0 \x80 \xa0\x80\xa0\xa0\x80` \x00\xe0 \x00`\xa0\x00\xe0\xa0\x00` \x80\xe0 \x80`\xa0\x80\xe0\xa0\x80 `\x00\xa0`\x00 \xe0\x00\xa0\xe0\x00 `\x80\xa0`\x80 \xe0\x80\xa0\xe0\x80``\x00\xe0`\x00`\xe0\x00\xe0\xe0\x00``\x80\xe0`\x80`\xe0\x80\xe0\xe0\x80 @\xa0 @ \xa0@\xa0\xa0@ \xc0\xa0 \xc0 \xa0\xc0\xa0\xa0\xc0` @\xe0 @`\xa0@\xe0\xa0@` \xc0\xe0 \xc0`\xa0\xc0\xe0\xa0\xc0 `@\xa0`@ \xe0@\xa0\xe0@ `\xc0\xa0`\xc0 \xe0\xc0\xa0\xe0\xc0``@\xe0`@`\xe0@\xe0\xe0@``\xc0\xe0`\xc0`\xe0\xc0\xe0\xe0\xc0', | |
| 'mose': b'\x00\x00\x00\xe4\x1a\x1c7~\xb8M\xafJ\x98N\xa3\xff\x7f\x00\xff\xff3\xa6V(\xf7\x81\xbf\x99\x99\x99f\xc2\xa5\xfc\x8db\x8d\xa0\xcb\xe7\x8a\xc3\xa6\xd8T\xff\xd9/\xe5\xc4\x94\xb3\xb3\xb3\x8d\xd3\xc7\xff\xff\xb3\xbe\xba\xda\xfb\x80r\x80\xb1\xd3\xfd\xb4b\xb3\xdei\xfc\xcd\xe5\xd9\xd9\xd9\xbc\x80\xbd\xcc\xeb\xc5\xff\xedo', | |
| } | |
| class VOSMetric(BaseVideoMetric): | |
| """mAP evaluation metrics for the VIS task. | |
| Args: | |
| metric (str | list[str]): Metrics to be evaluated. | |
| Default value is `youtube_vis_ap`.. | |
| outfile_prefix (str | None): The prefix of json files. It includes | |
| the file path and the prefix of filename, e.g., "a/b/prefix". | |
| If not specified, a temp file will be created. Defaults to None. | |
| collect_device (str): Device name used for collecting results from | |
| different ranks during distributed training. Must be 'cpu' or | |
| 'gpu'. Defaults to 'cpu'. | |
| prefix (str, optional): The prefix that will be added in the metric | |
| names to disambiguate homonyms metrics of different evaluators. | |
| If prefix is not provided in the argument, self.default_prefix | |
| will be used instead. Default: None | |
| format_only (bool): If True, only formatting the results to the | |
| official format and not performing evaluation. Defaults to False. | |
| """ | |
| default_prefix: Optional[str] = 'vip_seg' | |
| def __init__(self, | |
| collect_device: str = 'cpu', | |
| prefix: Optional[str] = None, | |
| format_only: bool = False, | |
| palette: Optional[str] = None, | |
| results_path: str = 'DAVIS' | |
| ) -> None: | |
| super().__init__(collect_device=collect_device, prefix=prefix) | |
| self.format_only = format_only | |
| if palette is not None: | |
| self.palette = PALETTE[palette] | |
| else: | |
| self.palette = None | |
| self.results_path = results_path | |
| self.per_video_res = [] | |
| self.categories = {} | |
| self._vis_meta_info = defaultdict(list) # record video and image infos | |
| def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: | |
| for track_data_sample in data_samples: | |
| video_data_samples = track_data_sample['video_data_samples'] | |
| if 'pred_track_proposal' not in video_data_samples[0]: | |
| continue | |
| ori_video_len = video_data_samples[0].ori_video_length | |
| if ori_video_len == len(video_data_samples): | |
| # video process | |
| self.process_video(video_data_samples) | |
| else: | |
| # image process | |
| raise NotImplementedError | |
| def process_video(self, data_samples): | |
| video_length = len(data_samples) | |
| mkdir_or_exist(self.results_path) | |
| for frame_id in range(video_length): | |
| img_data_sample = data_samples[frame_id].to_dict() | |
| pred = img_data_sample['pred_track_proposal'] | |
| h, w = pred.shape | |
| pred_map = np.zeros((h, w, 3), dtype=np.uint8) | |
| for ins_id in np.unique(pred): | |
| if ins_id == 0: | |
| continue | |
| r = ins_id // 1000000 | |
| g = (ins_id % 1000000) // 1000 | |
| b = ins_id % 1000 | |
| pred_map[pred == ins_id] = np.array([r, g, b], dtype=np.uint8) | |
| ori_img_path = data_samples[frame_id].img_path | |
| folder_name = os.path.basename(os.path.dirname(ori_img_path)) | |
| file_name = os.path.basename(ori_img_path) | |
| file_name = file_name.replace('.jpg', '.png') | |
| if self.palette is not None: | |
| from PIL import Image | |
| pred_map = mmcv.bgr2rgb(pred_map) | |
| pil_image = Image.fromarray(pred_map) | |
| pil_image = pil_image.convert('P', palette=self.palette) | |
| out_path = os.path.join(self.results_path, folder_name, file_name) | |
| mkdir_or_exist(os.path.dirname(out_path)) | |
| pil_image.save(out_path) | |
| else: | |
| mmcv.imwrite(pred_map, os.path.join(self.results_path, folder_name, file_name)) | |
| def compute_metrics(self, results: List) -> Dict[str, float]: | |
| return {} | |
| def evaluate(self, size: int) -> dict: | |
| """Evaluate the model performance of the whole dataset after processing | |
| all batches. | |
| Args: | |
| size (int): Length of the entire validation dataset. | |
| Returns: | |
| dict: Evaluation metrics dict on the val dataset. The keys are the | |
| names of the metrics, and the values are corresponding results. | |
| """ | |
| # wait for all processes to complete prediction. | |
| barrier() | |
| metrics = self.compute_metrics([]) | |
| return metrics | |