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This commit is contained in:
Yuwen Xiong
529
detection/mmdet_custom/datasets/crowd_human.py
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529
detection/mmdet_custom/datasets/crowd_human.py
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import itertools
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import logging
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import os.path as osp
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import tempfile
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import warnings
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from collections import OrderedDict
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import mmcv
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import numpy as np
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from mmcv.utils import print_log
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from terminaltables import AsciiTable
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from mmdet.core import eval_recalls
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from mmdet.datasets.api_wrappers import COCO, COCOeval
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from mmdet.datasets.custom import CustomDataset
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from mmdet.datasets.builder import DATASETS
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@DATASETS.register_module()
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class CrowdHumanDataset(CustomDataset):
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CLASSES = ('person', )
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def load_annotations(self, ann_file):
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"""Load annotation from COCO style annotation file.
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Args:
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ann_file (str): Path of annotation file.
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Returns:
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list[dict]: Annotation info from COCO api.
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"""
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self.coco = COCO(ann_file)
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# The order of returned `cat_ids` will not
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# change with the order of the CLASSES
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self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
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self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
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self.img_ids = self.coco.get_img_ids()
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data_infos = []
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total_ann_ids = []
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for i in self.img_ids:
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info = self.coco.load_imgs([i])[0]
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info['filename'] = info['file_name']
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data_infos.append(info)
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ann_ids = self.coco.get_ann_ids(img_ids=[i])
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total_ann_ids.extend(ann_ids)
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assert len(set(total_ann_ids)) == len(
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total_ann_ids), f"Annotation ids in '{ann_file}' are not unique!"
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return data_infos
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def get_ann_info(self, idx):
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"""Get COCO annotation by index.
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Args:
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idx (int): Index of data.
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Returns:
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dict: Annotation info of specified index.
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"""
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img_id = self.data_infos[idx]['id']
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ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
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ann_info = self.coco.load_anns(ann_ids)
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return self._parse_ann_info(self.data_infos[idx], ann_info)
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def get_cat_ids(self, idx):
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"""Get COCO category ids by index.
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Args:
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idx (int): Index of data.
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Returns:
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list[int]: All categories in the image of specified index.
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"""
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img_id = self.data_infos[idx]['id']
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ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
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ann_info = self.coco.load_anns(ann_ids)
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return [ann['category_id'] for ann in ann_info]
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def _filter_imgs(self, min_size=32):
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"""Filter images too small or without ground truths."""
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valid_inds = []
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# obtain images that contain annotation
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ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())
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# obtain images that contain annotations of the required categories
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ids_in_cat = set()
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for i, class_id in enumerate(self.cat_ids):
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ids_in_cat |= set(self.coco.cat_img_map[class_id])
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# merge the image id sets of the two conditions and use the merged set
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# to filter out images if self.filter_empty_gt=True
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ids_in_cat &= ids_with_ann
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valid_img_ids = []
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for i, img_info in enumerate(self.data_infos):
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img_id = self.img_ids[i]
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if self.filter_empty_gt and img_id not in ids_in_cat:
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continue
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if min(img_info['width'], img_info['height']) >= min_size:
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valid_inds.append(i)
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valid_img_ids.append(img_id)
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self.img_ids = valid_img_ids
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return valid_inds
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def _parse_ann_info(self, img_info, ann_info):
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"""Parse bbox and mask annotation.
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Args:
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ann_info (list[dict]): Annotation info of an image.
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with_mask (bool): Whether to parse mask annotations.
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Returns:
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dict: A dict containing the following keys: bboxes, bboxes_ignore,\
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labels, masks, seg_map. "masks" are raw annotations and not \
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decoded into binary masks.
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"""
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gt_bboxes = []
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gt_labels = []
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gt_bboxes_ignore = []
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gt_masks_ann = []
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for i, ann in enumerate(ann_info):
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if ann.get('ignore', False):
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continue
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x1, y1, w, h = ann['bbox']
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inter_w = max(0, min(x1 + w, img_info['width']) - max(x1, 0))
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inter_h = max(0, min(y1 + h, img_info['height']) - max(y1, 0))
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if inter_w * inter_h == 0:
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continue
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if ann['area'] <= 0 or w < 1 or h < 1:
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continue
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if ann['category_id'] not in self.cat_ids:
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continue
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bbox = [x1, y1, x1 + w, y1 + h]
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if ann.get('iscrowd', False):
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gt_bboxes_ignore.append(bbox)
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else:
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gt_bboxes.append(bbox)
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gt_labels.append(self.cat2label[ann['category_id']])
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gt_masks_ann.append(ann.get('segmentation', None))
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if gt_bboxes:
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gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
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gt_labels = np.array(gt_labels, dtype=np.int64)
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else:
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gt_bboxes = np.zeros((0, 4), dtype=np.float32)
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gt_labels = np.array([], dtype=np.int64)
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if gt_bboxes_ignore:
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gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
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else:
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gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
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seg_map = img_info['filename'].replace('jpg', 'png')
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ann = dict(
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bboxes=gt_bboxes,
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labels=gt_labels,
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bboxes_ignore=gt_bboxes_ignore,
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masks=gt_masks_ann,
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seg_map=seg_map)
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return ann
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def xyxy2xywh(self, bbox):
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"""Convert ``xyxy`` style bounding boxes to ``xywh`` style for COCO
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evaluation.
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Args:
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bbox (numpy.ndarray): The bounding boxes, shape (4, ), in
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``xyxy`` order.
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Returns:
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list[float]: The converted bounding boxes, in ``xywh`` order.
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"""
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_bbox = bbox.tolist()
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return [
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_bbox[0],
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_bbox[1],
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_bbox[2] - _bbox[0],
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_bbox[3] - _bbox[1],
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]
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def _proposal2json(self, results):
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"""Convert proposal results to COCO json style."""
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json_results = []
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for idx in range(len(self)):
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img_id = self.img_ids[idx]
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bboxes = results[idx]
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for i in range(bboxes.shape[0]):
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data = dict()
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data['image_id'] = img_id
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data['bbox'] = self.xyxy2xywh(bboxes[i])
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data['score'] = float(bboxes[i][4])
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data['category_id'] = 1
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json_results.append(data)
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return json_results
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def _det2json(self, results):
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"""Convert detection results to COCO json style."""
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json_results = []
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for idx in range(len(self)):
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img_id = self.img_ids[idx]
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result = results[idx]
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for label in range(len(result)):
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bboxes = result[label]
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for i in range(bboxes.shape[0]):
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data = dict()
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data['image_id'] = img_id
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data['bbox'] = self.xyxy2xywh(bboxes[i])
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data['score'] = float(bboxes[i][4])
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data['category_id'] = self.cat_ids[label]
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json_results.append(data)
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return json_results
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def _segm2json(self, results):
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"""Convert instance segmentation results to COCO json style."""
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bbox_json_results = []
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segm_json_results = []
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for idx in range(len(self)):
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img_id = self.img_ids[idx]
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det, seg = results[idx]
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for label in range(len(det)):
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# bbox results
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bboxes = det[label]
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for i in range(bboxes.shape[0]):
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data = dict()
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data['image_id'] = img_id
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data['bbox'] = self.xyxy2xywh(bboxes[i])
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data['score'] = float(bboxes[i][4])
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data['category_id'] = self.cat_ids[label]
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bbox_json_results.append(data)
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# segm results
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# some detectors use different scores for bbox and mask
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if isinstance(seg, tuple):
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segms = seg[0][label]
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mask_score = seg[1][label]
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else:
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segms = seg[label]
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mask_score = [bbox[4] for bbox in bboxes]
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for i in range(bboxes.shape[0]):
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data = dict()
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data['image_id'] = img_id
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data['bbox'] = self.xyxy2xywh(bboxes[i])
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data['score'] = float(mask_score[i])
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data['category_id'] = self.cat_ids[label]
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if isinstance(segms[i]['counts'], bytes):
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segms[i]['counts'] = segms[i]['counts'].decode()
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data['segmentation'] = segms[i]
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segm_json_results.append(data)
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return bbox_json_results, segm_json_results
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def results2json(self, results, outfile_prefix):
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"""Dump the detection results to a COCO style json file.
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There are 3 types of results: proposals, bbox predictions, mask
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predictions, and they have different data types. This method will
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automatically recognize the type, and dump them to json files.
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Args:
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results (list[list | tuple | ndarray]): Testing results of the
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dataset.
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outfile_prefix (str): The filename prefix of the json files. If the
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prefix is "somepath/xxx", the json files will be named
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"somepath/xxx.bbox.json", "somepath/xxx.segm.json",
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"somepath/xxx.proposal.json".
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Returns:
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dict[str: str]: Possible keys are "bbox", "segm", "proposal", and \
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values are corresponding filenames.
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"""
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result_files = dict()
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if isinstance(results[0], list):
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json_results = self._det2json(results)
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result_files['bbox'] = f'{outfile_prefix}.bbox.json'
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result_files['proposal'] = f'{outfile_prefix}.bbox.json'
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mmcv.dump(json_results, result_files['bbox'])
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elif isinstance(results[0], tuple):
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json_results = self._segm2json(results)
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result_files['bbox'] = f'{outfile_prefix}.bbox.json'
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result_files['proposal'] = f'{outfile_prefix}.bbox.json'
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result_files['segm'] = f'{outfile_prefix}.segm.json'
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mmcv.dump(json_results[0], result_files['bbox'])
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mmcv.dump(json_results[1], result_files['segm'])
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elif isinstance(results[0], np.ndarray):
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json_results = self._proposal2json(results)
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result_files['proposal'] = f'{outfile_prefix}.proposal.json'
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mmcv.dump(json_results, result_files['proposal'])
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else:
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raise TypeError('invalid type of results')
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return result_files
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def fast_eval_recall(self, results, proposal_nums, iou_thrs, logger=None):
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gt_bboxes = []
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for i in range(len(self.img_ids)):
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ann_ids = self.coco.get_ann_ids(img_ids=self.img_ids[i])
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ann_info = self.coco.load_anns(ann_ids)
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if len(ann_info) == 0:
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gt_bboxes.append(np.zeros((0, 4)))
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continue
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bboxes = []
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for ann in ann_info:
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if ann.get('ignore', False) or ann['iscrowd']:
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continue
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x1, y1, w, h = ann['bbox']
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bboxes.append([x1, y1, x1 + w, y1 + h])
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bboxes = np.array(bboxes, dtype=np.float32)
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if bboxes.shape[0] == 0:
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bboxes = np.zeros((0, 4))
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gt_bboxes.append(bboxes)
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recalls = eval_recalls(
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gt_bboxes, results, proposal_nums, iou_thrs, logger=logger)
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ar = recalls.mean(axis=1)
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return ar
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def format_results(self, results, jsonfile_prefix=None, **kwargs):
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"""Format the results to json (standard format for COCO evaluation).
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Args:
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results (list[tuple | numpy.ndarray]): Testing results of the
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dataset.
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jsonfile_prefix (str | None): The prefix of json files. It includes
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the file path and the prefix of filename, e.g., "a/b/prefix".
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If not specified, a temp file will be created. Default: None.
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Returns:
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tuple: (result_files, tmp_dir), result_files is a dict containing \
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the json filepaths, tmp_dir is the temporal directory created \
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for saving json files when jsonfile_prefix is not specified.
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"""
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assert isinstance(results, list), 'results must be a list'
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assert len(results) == len(self), (
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'The length of results is not equal to the dataset len: {} != {}'.
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format(len(results), len(self)))
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if jsonfile_prefix is None:
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tmp_dir = tempfile.TemporaryDirectory()
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jsonfile_prefix = osp.join(tmp_dir.name, 'results')
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else:
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tmp_dir = None
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result_files = self.results2json(results, jsonfile_prefix)
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return result_files, tmp_dir
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def evaluate(self,
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results,
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metric='bbox',
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logger=None,
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jsonfile_prefix=None,
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classwise=False,
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proposal_nums=(100, 300, 1000),
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iou_thrs=None,
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metric_items=None):
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"""Evaluation in COCO protocol.
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Args:
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results (list[list | tuple]): Testing results of the dataset.
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metric (str | list[str]): Metrics to be evaluated. Options are
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'bbox', 'segm', 'proposal', 'proposal_fast'.
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logger (logging.Logger | str | None): Logger used for printing
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related information during evaluation. Default: None.
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jsonfile_prefix (str | None): The prefix of json files. It includes
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the file path and the prefix of filename, e.g., "a/b/prefix".
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If not specified, a temp file will be created. Default: None.
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classwise (bool): Whether to evaluating the AP for each class.
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proposal_nums (Sequence[int]): Proposal number used for evaluating
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recalls, such as recall@100, recall@1000.
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Default: (100, 300, 1000).
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iou_thrs (Sequence[float], optional): IoU threshold used for
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evaluating recalls/mAPs. If set to a list, the average of all
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IoUs will also be computed. If not specified, [0.50, 0.55,
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0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95] will be used.
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Default: None.
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metric_items (list[str] | str, optional): Metric items that will
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be returned. If not specified, ``['AR@100', 'AR@300',
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'AR@1000', 'AR_s@1000', 'AR_m@1000', 'AR_l@1000' ]`` will be
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used when ``metric=='proposal'``, ``['mAP', 'mAP_50', 'mAP_75',
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'mAP_s', 'mAP_m', 'mAP_l']`` will be used when
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``metric=='bbox' or metric=='segm'``.
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Returns:
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dict[str, float]: COCO style evaluation metric.
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"""
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metrics = metric if isinstance(metric, list) else [metric]
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allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast']
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for metric in metrics:
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if metric not in allowed_metrics:
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raise KeyError(f'metric {metric} is not supported')
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if iou_thrs is None:
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iou_thrs = np.linspace(
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.5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True)
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if metric_items is not None:
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if not isinstance(metric_items, list):
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metric_items = [metric_items]
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result_files, tmp_dir = self.format_results(results, jsonfile_prefix)
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eval_results = OrderedDict()
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cocoGt = self.coco
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for metric in metrics:
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msg = f'Evaluating {metric}...'
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if logger is None:
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msg = '\n' + msg
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print_log(msg, logger=logger)
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if metric == 'proposal_fast':
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ar = self.fast_eval_recall(
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results, proposal_nums, iou_thrs, logger='silent')
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log_msg = []
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for i, num in enumerate(proposal_nums):
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eval_results[f'AR@{num}'] = ar[i]
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log_msg.append(f'\nAR@{num}\t{ar[i]:.4f}')
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log_msg = ''.join(log_msg)
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print_log(log_msg, logger=logger)
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continue
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iou_type = 'bbox' if metric == 'proposal' else metric
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if metric not in result_files:
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raise KeyError(f'{metric} is not in results')
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try:
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predictions = mmcv.load(result_files[metric])
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if iou_type == 'segm':
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# Refer to https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/coco.py#L331 # noqa
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# When evaluating mask AP, if the results contain bbox,
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# cocoapi will use the box area instead of the mask area
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# for calculating the instance area. Though the overall AP
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# is not affected, this leads to different
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# small/medium/large mask AP results.
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for x in predictions:
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x.pop('bbox')
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warnings.simplefilter('once')
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warnings.warn(
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'The key "bbox" is deleted for more accurate mask AP '
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'of small/medium/large instances since v2.12.0. This '
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'does not change the overall mAP calculation.',
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||||
UserWarning)
|
||||
cocoDt = cocoGt.loadRes(predictions)
|
||||
except IndexError:
|
||||
print_log(
|
||||
'The testing results of the whole dataset is empty.',
|
||||
logger=logger,
|
||||
level=logging.ERROR)
|
||||
break
|
||||
|
||||
cocoEval = COCOeval(cocoGt, cocoDt, iou_type)
|
||||
cocoEval.params.catIds = self.cat_ids
|
||||
cocoEval.params.imgIds = self.img_ids
|
||||
cocoEval.params.maxDets = list(proposal_nums)
|
||||
cocoEval.params.iouThrs = iou_thrs
|
||||
# mapping of cocoEval.stats
|
||||
coco_metric_names = {
|
||||
'mAP': 0,
|
||||
'mAP_50': 1,
|
||||
'mAP_75': 2,
|
||||
'mAP_s': 3,
|
||||
'mAP_m': 4,
|
||||
'mAP_l': 5,
|
||||
'AR@100': 6,
|
||||
'AR@300': 7,
|
||||
'AR@1000': 8,
|
||||
'AR_s@1000': 9,
|
||||
'AR_m@1000': 10,
|
||||
'AR_l@1000': 11
|
||||
}
|
||||
if metric_items is not None:
|
||||
for metric_item in metric_items:
|
||||
if metric_item not in coco_metric_names:
|
||||
raise KeyError(
|
||||
f'metric item {metric_item} is not supported')
|
||||
|
||||
if metric == 'proposal':
|
||||
cocoEval.params.useCats = 0
|
||||
cocoEval.evaluate()
|
||||
cocoEval.accumulate()
|
||||
cocoEval.summarize()
|
||||
if metric_items is None:
|
||||
metric_items = [
|
||||
'AR@100', 'AR@300', 'AR@1000', 'AR_s@1000',
|
||||
'AR_m@1000', 'AR_l@1000'
|
||||
]
|
||||
|
||||
for item in metric_items:
|
||||
val = float(
|
||||
f'{cocoEval.stats[coco_metric_names[item]]:.3f}')
|
||||
eval_results[item] = val
|
||||
else:
|
||||
cocoEval.evaluate()
|
||||
cocoEval.accumulate()
|
||||
cocoEval.summarize()
|
||||
if classwise: # Compute per-category AP
|
||||
# Compute per-category AP
|
||||
# from https://github.com/facebookresearch/detectron2/
|
||||
precisions = cocoEval.eval['precision']
|
||||
# precision: (iou, recall, cls, area range, max dets)
|
||||
assert len(self.cat_ids) == precisions.shape[2]
|
||||
|
||||
results_per_category = []
|
||||
for idx, catId in enumerate(self.cat_ids):
|
||||
# area range index 0: all area ranges
|
||||
# max dets index -1: typically 100 per image
|
||||
nm = self.coco.loadCats(catId)[0]
|
||||
precision = precisions[:, :, idx, 0, -1]
|
||||
precision = precision[precision > -1]
|
||||
if precision.size:
|
||||
ap = np.mean(precision)
|
||||
else:
|
||||
ap = float('nan')
|
||||
results_per_category.append(
|
||||
(f'{nm["name"]}', f'{float(ap):0.3f}'))
|
||||
|
||||
num_columns = min(6, len(results_per_category) * 2)
|
||||
results_flatten = list(
|
||||
itertools.chain(*results_per_category))
|
||||
headers = ['category', 'AP'] * (num_columns // 2)
|
||||
results_2d = itertools.zip_longest(*[
|
||||
results_flatten[i::num_columns]
|
||||
for i in range(num_columns)
|
||||
])
|
||||
table_data = [headers]
|
||||
table_data += [result for result in results_2d]
|
||||
table = AsciiTable(table_data)
|
||||
print_log('\n' + table.table, logger=logger)
|
||||
|
||||
if metric_items is None:
|
||||
metric_items = [
|
||||
'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l'
|
||||
]
|
||||
|
||||
for metric_item in metric_items:
|
||||
key = f'{metric}_{metric_item}'
|
||||
val = float(
|
||||
f'{cocoEval.stats[coco_metric_names[metric_item]]:.3f}'
|
||||
)
|
||||
eval_results[key] = val
|
||||
ap = cocoEval.stats[:6]
|
||||
eval_results[f'{metric}_mAP_copypaste'] = (
|
||||
f'{ap[0]:.3f} {ap[1]:.3f} {ap[2]:.3f} {ap[3]:.3f} '
|
||||
f'{ap[4]:.3f} {ap[5]:.3f}')
|
||||
if tmp_dir is not None:
|
||||
tmp_dir.cleanup()
|
||||
return
|
||||
Reference in New Issue
Block a user