jshilong
commented
1 year ago Because I was busy with other projects, I didn't organize this part of the code. This is an early version based on 2.0. If you need it, you can organize it into the current codebase
import copy
import json
import random
import numpy as np
from mmdet.datasets import DATASETS, CustomDataset
from .crowdhumantools import Database
@DATASETS.register_module()
class CrowdHumanDataset(CustomDataset):
def __init__(self, *args, debug=False, **kwargs):
self.debug = debug
super(CrowdHumanDataset, self).__init__(*args, **kwargs)
def load_annotations(self, json_file):
with open(json_file, 'r') as file:
gt_records = file.readlines()
dataset_dicts = []
ann_keys = ['tag', 'hbox', 'vbox', 'head_attr', 'extra']
for i, anno_str in enumerate(gt_records):
if i > 10 and self.debug:
break
anno_dict = json.loads(anno_str)
record = {}
record['file_name'] = '{}.jpg'.format(anno_dict['ID'])
record['image_id'] = anno_dict['ID']
anns = dict()
bbox_list = []
label_list = []
for anno in anno_dict['gtboxes']:
obj = {key: anno[key] for key in ann_keys if key in anno}
if obj['tag'] == 'mask':
continue
x, y, w, h = anno['fbox']
bbox_list.append([x, y, x + w, y + h])
label_list.append(0)
if len(bbox_list):
anns['bboxes'] = np.array(bbox_list, dtype=np.float32)
anns['labels'] = np.array(label_list, dtype=np.int64)
else:
anns['bboxes'] = np.zeros((0, 4), dtype=np.float32)
anns['labels'] = np.array([], dtype=np.int64)
record['anns'] = anns
# import mmcv
# mmcv.imshow_bboxes(record["file_name"], anns["bboxes"])
dataset_dicts.append(record)
return dataset_dicts
def _set_group_flag(self):
self.flag = np.zeros(len(self), dtype=np.uint8)
def _filter_imgs(self, min_size=16):
valid_inds = []
for i, record in enumerate(self.data_infos):
if not len(record['anns']['bboxes']):
continue
valid_inds.append(i)
return valid_inds
def prepare_train_img(self, idx):
img_info = dict()
ann_info = dict(bboxes=None,
labels=None,
bboxes_ignore=None,
masks=None,
seg_map=None)
img_info['filename'] = copy.deepcopy(self.data_infos[idx]['file_name'])
ann_info['bboxes'] = copy.deepcopy(
self.data_infos[idx]['anns']['bboxes'])
ann_info['labels'] = copy.deepcopy(
self.data_infos[idx]['anns']['labels'])
ann_info['bboxes_ignore'] = np.zeros((0, 4), dtype=np.float32)
results = dict(img_info=img_info, ann_info=ann_info)
self.pre_pipeline(results)
return self.pipeline(results)
def prepare_test_img(self, idx):
img_info = dict()
img_info['filename'] = copy.deepcopy(self.data_infos[idx]['file_name'])
results = dict(img_info=img_info)
self.pre_pipeline(results)
return self.pipeline(results)
def evaluate(self,
results,
metric='mAP',
logger=None,
proposal_nums=(100, 300, 1000),
iou_thr=0.5,
scale_ranges=None):
f = open(__file__.replace('crowdhuman.py', 'id_wh.json'))
ID_hw = json.load(f)
f.close()
filename = str(random.random()).split('.')[-1]
with open(f'./{filename}_crowd_eval.json', 'w') as f:
for i, single_results in enumerate(results):
dump_dict = dict()
dump_dict['ID'] = self.data_infos[i]['image_id']
dump_dict['height'] = ID_hw[dump_dict['ID']][0]
dump_dict['width'] = ID_hw[dump_dict['ID']][1]
dtboxes = []
bboxes = single_results[0].tolist()
for bbox_id, single_bbox in enumerate(bboxes):
temp_dict = dict()
x1, y1, x2, y2, score = single_bbox
temp_dict['box'] = [x1, y1, x2 - x1, y2 - y1]
temp_dict['score'] = single_bbox[-1]
temp_dict['tag'] = 1
dtboxes.append(temp_dict)
dump_dict['dtboxes'] = dtboxes
f.write(json.dumps(dump_dict) + '\n')
database = Database(self.ann_file, f'./{filename}_crowd_eval.json',
'box', None, 0)
database.compare()
AP, recall, _ = database.eval_AP()
mMR, _ = database.eval_MR()
import os
os.popen(f'rm {filename}_crowd_eval.json')
return dict(bbox_mAP=AP, mMR=mMR, recall=recall)
def __repr__(self):
"""Print the number of instance number."""
pass
def __len__(self):
return len(self.data_infos)&
#!/usr/bin/python3
# Copyright (C) 2019-2021 Megvii Inc. All rights reserved.
import json
import os
import numpy as np
PERSON_CLASSES = ['background', 'person']
class Image(object):
def __init__(self, mode):
self.ID = None
self._width = None
self._height = None
self.dtboxes = None
self.gtboxes = None
self.eval_mode = mode
self._ignNum = None
self._gtNum = None
self._dtNum = None
def load(self, record, body_key, head_key, class_names, gtflag):
"""
:meth: read the object from a dict
"""
if 'ID' in record and self.ID is None:
self.ID = record['ID']
if 'width' in record and self._width is None:
self._width = record['width']
if 'height' in record and self._height is None:
self._height = record['height']
if gtflag:
self._gtNum = len(record['gtboxes'])
body_bbox, head_bbox = self.load_gt_boxes(record, 'gtboxes',
class_names)
if self.eval_mode == 0:
self.gtboxes = body_bbox
self._ignNum = (body_bbox[:, -1] == -1).sum()
elif self.eval_mode == 1:
self.gtboxes = head_bbox
self._ignNum = (head_bbox[:, -1] == -1).sum()
elif self.eval_mode == 2:
gt_tag = np.array([
body_bbox[i, -1] != -1 and head_bbox[i, -1] != -1
for i in range(len(body_bbox))
])
self._ignNum = (gt_tag == 0).sum()
self.gtboxes = np.hstack((body_bbox[:, :-1], head_bbox[:, :-1],
gt_tag.reshape(-1, 1)))
else:
raise Exception('Unknown evaluation mode!')
if not gtflag:
self._dtNum = len(record['dtboxes'])
if self.eval_mode == 0:
self.dtboxes = self.load_det_boxes(record, 'dtboxes', body_key,
'score')
elif self.eval_mode == 1:
self.dtboxes = self.load_det_boxes(record, 'dtboxes', head_key,
'score')
elif self.eval_mode == 2:
body_dtboxes = self.load_det_boxes(record, 'dtboxes', body_key)
head_dtboxes = self.load_det_boxes(record, 'dtboxes', head_key,
'score')
self.dtboxes = np.hstack((body_dtboxes, head_dtboxes))
else:
raise Exception('Unknown evaluation mode!')
def compare_caltech(self, thres):
"""
:meth: match the detection results with the
groundtruth by Caltech matching strategy
:param thres: iou threshold
:type thres: float
:return: a list of tuples (dtbox, imageID),
in the descending sort of dtbox.score
"""
if self.dtboxes is None or self.gtboxes is None:
return list()
dtboxes = self.dtboxes if self.dtboxes is not None else list()
gtboxes = self.gtboxes if self.gtboxes is not None else list()
dt_matched = np.zeros(dtboxes.shape[0])
gt_matched = np.zeros(gtboxes.shape[0])
dtboxes = np.array(sorted(dtboxes, key=lambda x: x[-1], reverse=True))
gtboxes = np.array(sorted(gtboxes, key=lambda x: x[-1], reverse=True))
if len(dtboxes):
overlap_iou = self.box_overlap_opr(dtboxes, gtboxes, True)
overlap_ioa = self.box_overlap_opr(dtboxes, gtboxes, False)
else:
return list()
scorelist = list()
for i, dt in enumerate(dtboxes):
maxpos = -1
maxiou = thres
for j, gt in enumerate(gtboxes):
if gt_matched[j] == 1:
continue
if gt[-1] > 0:
overlap = overlap_iou[i][j]
if overlap > maxiou:
maxiou = overlap
maxpos = j
else:
if maxpos >= 0:
break
else:
overlap = overlap_ioa[i][j]
if overlap > thres:
maxiou = overlap
maxpos = j
if maxpos >= 0:
if gtboxes[maxpos, -1] > 0:
gt_matched[maxpos] = 1
dt_matched[i] = 1
scorelist.append((dt, 1, self.ID))
else:
dt_matched[i] = -1
else:
dt_matched[i] = 0
scorelist.append((dt, 0, self.ID))
return scorelist
def compare_caltech_union(self, thres):
"""
:meth: match the detection results with the groundtruth
by Caltech matching strategy
:param thres: iou threshold
:type thres: float
:return: a list of tuples (dtbox, imageID), in the
descending sort of dtbox.score
"""
dtboxes = self.dtboxes if self.dtboxes is not None else list()
gtboxes = self.gtboxes if self.gtboxes is not None else list()
if len(dtboxes) == 0:
return list()
dt_matched = np.zeros(dtboxes.shape[0])
gt_matched = np.zeros(gtboxes.shape[0])
dtboxes = np.array(sorted(dtboxes, key=lambda x: x[-1], reverse=True))
gtboxes = np.array(sorted(gtboxes, key=lambda x: x[-1], reverse=True))
dt_body_boxes = np.hstack((dtboxes[:, :4], dtboxes[:, -1][:, None]))
dt_head_boxes = dtboxes[:, 4:8]
gt_body_boxes = np.hstack((gtboxes[:, :4], gtboxes[:, -1][:, None]))
gt_head_boxes = gtboxes[:, 4:8]
overlap_iou = self.box_overlap_opr(dt_body_boxes, gt_body_boxes, True)
overlap_head = self.box_overlap_opr(dt_head_boxes, gt_head_boxes, True)
overlap_ioa = self.box_overlap_opr(dt_body_boxes, gt_body_boxes, False)
scorelist = list()
for i, dt in enumerate(dtboxes):
maxpos = -1
maxiou = thres
for j, gt in enumerate(gtboxes):
if gt_matched[j] == 1:
continue
if gt[-1] > 0:
o_body = overlap_iou[i][j]
o_head = overlap_head[i][j]
if o_body > maxiou and o_head > maxiou:
maxiou = o_body
maxpos = j
else:
if maxpos >= 0:
break
else:
o_body = overlap_ioa[i][j]
if o_body > thres:
maxiou = o_body
maxpos = j
if maxpos >= 0:
if gtboxes[maxpos, -1] > 0:
gt_matched[maxpos] = 1
dt_matched[i] = 1
scorelist.append((dt, 1, self.ID))
else:
dt_matched[i] = -1
else:
dt_matched[i] = 0
scorelist.append((dt, 0, self.ID))
return scorelist
def box_overlap_opr(self, dboxes: np.ndarray, gboxes: np.ndarray, if_iou):
eps = 1e-6
assert dboxes.shape[-1] >= 4 and gboxes.shape[-1] >= 4
N, K = dboxes.shape[0], gboxes.shape[0]
dtboxes = np.tile(np.expand_dims(dboxes, axis=1), (1, K, 1))
gtboxes = np.tile(np.expand_dims(gboxes, axis=0), (N, 1, 1))
iw = (np.minimum(dtboxes[:, :, 2], gtboxes[:, :, 2]) -
np.maximum(dtboxes[:, :, 0], gtboxes[:, :, 0]))
ih = (np.minimum(dtboxes[:, :, 3], gtboxes[:, :, 3]) -
np.maximum(dtboxes[:, :, 1], gtboxes[:, :, 1]))
inter = np.maximum(0, iw) * np.maximum(0, ih)
dtarea = (dtboxes[:, :, 2] - dtboxes[:, :, 0]) * (dtboxes[:, :, 3] -
dtboxes[:, :, 1])
if if_iou:
gtarea = (gtboxes[:, :, 2] -
gtboxes[:, :, 0]) * (gtboxes[:, :, 3] - gtboxes[:, :, 1])
ious = inter / (dtarea + gtarea - inter + eps)
else:
ious = inter / (dtarea + eps)
return ious
def clip_all_boader(self):
def _clip_boundary(boxes, height, width):
assert boxes.shape[-1] >= 4
boxes[:, 0] = np.minimum(np.maximum(boxes[:, 0], 0), width - 1)
boxes[:, 1] = np.minimum(np.maximum(boxes[:, 1], 0), height - 1)
boxes[:, 2] = np.maximum(np.minimum(boxes[:, 2], width), 0)
boxes[:, 3] = np.maximum(np.minimum(boxes[:, 3], height), 0)
return boxes
assert self.dtboxes.shape[-1] >= 4
assert self.gtboxes.shape[-1] >= 4
assert self._width is not None and self._height is not None
if self.eval_mode == 2:
self.dtboxes[:, :4] = _clip_boundary(self.dtboxes[:, :4],
self._height, self._width)
self.gtboxes[:, :4] = _clip_boundary(self.gtboxes[:, :4],
self._height, self._width)
self.dtboxes[:, 4:8] = _clip_boundary(self.dtboxes[:, 4:8],
self._height, self._width)
self.gtboxes[:, 4:8] = _clip_boundary(self.gtboxes[:, 4:8],
self._height, self._width)
else:
self.dtboxes = _clip_boundary(self.dtboxes, self._height,
self._width)
self.gtboxes = _clip_boundary(self.gtboxes, self._height,
self._width)
def load_gt_boxes(self, dict_input, key_name, class_names):
assert key_name in dict_input
if len(dict_input[key_name]) < 1:
return np.empty([0, 5])
head_bbox = []
body_bbox = []
for rb in dict_input[key_name]:
if rb['tag'] in class_names:
body_tag = class_names.index(rb['tag'])
head_tag = 1
else:
body_tag = -1
head_tag = -1
if 'extra' in rb:
if 'ignore' in rb['extra']:
if rb['extra']['ignore'] != 0:
body_tag = -1
head_tag = -1
if 'head_attr' in rb:
if 'ignore' in rb['head_attr']:
if rb['head_attr']['ignore'] != 0:
head_tag = -1
head_bbox.append(np.hstack((rb['hbox'], head_tag)))
body_bbox.append(np.hstack((rb['fbox'], body_tag)))
head_bbox = np.array(head_bbox)
head_bbox[:, 2:4] += head_bbox[:, :2]
body_bbox = np.array(body_bbox)
body_bbox[:, 2:4] += body_bbox[:, :2]
return body_bbox, head_bbox
def load_det_boxes(self,
dict_input,
key_name,
key_box,
key_score=None,
key_tag=None):
assert key_name in dict_input
if len(dict_input[key_name]) < 1:
return np.empty([0, 5])
else:
assert key_box in dict_input[key_name][0]
if key_score:
assert key_score in dict_input[key_name][0]
if key_tag:
assert key_tag in dict_input[key_name][0]
if key_score:
if key_tag:
bboxes = np.vstack([
np.hstack((rb[key_box], rb[key_score], rb[key_tag]))
for rb in dict_input[key_name]
])
else:
bboxes = np.vstack([
np.hstack((rb[key_box], rb[key_score]))
for rb in dict_input[key_name]
])
else:
if key_tag:
bboxes = np.vstack([
np.hstack((rb[key_box], rb[key_tag]))
for rb in dict_input[key_name]
])
else:
bboxes = np.vstack(
[rb[key_box] for rb in dict_input[key_name]])
bboxes[:, 2:4] += bboxes[:, :2]
return bboxes
def compare_voc(self, thres):
"""
:meth: match the detection results with the
groundtruth by VOC matching strategy
:param thres: iou threshold
:type thres: float
:return: a list of tuples (dtbox, imageID),
in the descending sort of dtbox.score
"""
if self.dtboxes is None:
return list()
dtboxes = self.dtboxes
gtboxes = self.gtboxes if self.gtboxes is not None else list()
dtboxes.sort(key=lambda x: x.score, reverse=True)
gtboxes.sort(key=lambda x: x.ign)
scorelist = list()
for i, dt in enumerate(dtboxes):
maxpos = -1
maxiou = thres
for j, gt in enumerate(gtboxes):
overlap = dt.iou(gt)
if overlap > maxiou:
maxiou = overlap
maxpos = j
if maxpos >= 0:
if gtboxes[maxpos].ign == 0:
gtboxes[maxpos].matched = 1
dtboxes[i].matched = 1
scorelist.append((dt, self.ID))
else:
dtboxes[i].matched = -1
else:
dtboxes[i].matched = 0
scorelist.append((dt, self.ID))
return scorelist
class Database(object):
def __init__(self,
gtpath=None,
dtpath=None,
body_key=None,
head_key=None,
mode=0):
"""
mode=0: only body; mode=1: only head
"""
self.images = dict()
self.eval_mode = mode
self.loadData(gtpath, body_key, head_key, if_gt=True)
self.loadData(dtpath, body_key, head_key, if_gt=False)
self._ignNum = sum([self.images[i]._ignNum for i in self.images])
self._gtNum = sum([self.images[i]._gtNum for i in self.images])
self._imageNum = len(self.images)
self.scorelist = None
def loadData(self, fpath, body_key=None, head_key=None, if_gt=True):
assert os.path.isfile(fpath), fpath + ' does not exist!'
with open(fpath, 'r') as f:
lines = f.readlines()
records = [json.loads(line.strip('\n')) for line in lines]
if if_gt:
for record in records:
self.images[record['ID']] = Image(self.eval_mode)
self.images[record['ID']].load(record, body_key, head_key,
PERSON_CLASSES, True)
else:
for record in records:
self.images[record['ID']].load(record, body_key, head_key,
PERSON_CLASSES, False)
self.images[record['ID']].clip_all_boader()
def compare(self, thres=0.5, matching=None):
"""match the detection results with the groundtruth in the whole
database."""
assert matching is None or matching == 'VOC', matching
scorelist = list()
for ID in self.images:
if matching == 'VOC':
result = self.images[ID].compare_voc(thres)
else:
result = self.images[ID].compare_caltech(thres)
scorelist.extend(result)
# In the descending sort of dtbox score.
scorelist.sort(key=lambda x: x[0][-1], reverse=True)
self.scorelist = scorelist
def eval_MR(self, ref='CALTECH_-2'):
"""evaluate by Caltech-style log-average miss rate.
ref: str - "CALTECH_-2"/"CALTECH_-4"
"""
# find greater_than
def _find_gt(lst, target):
for idx, item in enumerate(lst):
if item >= target:
return idx
return len(lst) - 1
assert ref == 'CALTECH_-2' or ref == 'CALTECH_-4', ref
if ref == 'CALTECH_-2':
# CALTECH_MRREF_2: anchor points
# (from 10^-2 to 1) as in P.Dollar's paper
ref = [
0.0100, 0.0178, 0.03160, 0.0562, 0.1000, 0.1778, 0.3162,
0.5623, 1.000
]
else:
# CALTECH_MRREF_4: anchor points
# (from 10^-4 to 1) as in S.Zhang's paper
ref = [
0.0001, 0.0003, 0.00100, 0.0032, 0.0100, 0.0316, 0.1000,
0.3162, 1.000
]
if self.scorelist is None:
self.compare()
tp, fp = 0.0, 0.0
fppiX, fppiY = list(), list()
for i, item in enumerate(self.scorelist):
if item[1] == 1:
tp += 1.0
elif item[1] == 0:
fp += 1.0
fn = (self._gtNum - self._ignNum) - tp
recall = tp / (tp + fn)
missrate = 1.0 - recall
fppi = fp / self._imageNum
fppiX.append(fppi)
fppiY.append(missrate)
score = list()
for pos in ref:
argmin = _find_gt(fppiX, pos)
if argmin >= 0:
score.append(fppiY[argmin])
score = np.array(score)
MR = np.exp(np.log(score).mean())
return MR, (fppiX, fppiY)
def eval_AP(self):
"""
:meth: evaluate by average precision
"""
# calculate general ap score
def _calculate_map(recall, precision):
assert len(recall) == len(precision)
area = 0
for i in range(1, len(recall)):
delta_h = (precision[i - 1] + precision[i]) / 2
delta_w = recall[i] - recall[i - 1]
area += delta_w * delta_h
return area
tp, fp = 0.0, 0.0
rpX, rpY = list(), list()
total_gt = self._gtNum - self._ignNum
total_images = self._imageNum
fpn = []
recalln = []
thr = []
fppi = []
for i, item in enumerate(self.scorelist):
if item[1] == 1:
tp += 1.0
elif item[1] == 0:
fp += 1.0
fn = total_gt - tp
recall = tp / (tp + fn)
precision = tp / (tp + fp)
rpX.append(recall)
rpY.append(precision)
fpn.append(fp)
recalln.append(tp)
thr.append(item[0][-1])
fppi.append(fp / total_images)
AP = _calculate_map(rpX, rpY)
return AP, recall, (rpX, rpY, thr, fpn, recalln, fppi)
tyhnu
Hi, thanks for sharing this nice work.
I am very interested in your research and my study interest is pedestrian detection. The proposed DDQ framework works well on dense detection and also achieves a SOTA performance on the CrowdHuman dataset. Is it available for sharing the related code of experiments on the CrowdHuman dataset? Thanks.