-
Notifications
You must be signed in to change notification settings - Fork 119
/
Copy pathcoco_error_analysis.py
171 lines (158 loc) · 6.53 KB
/
coco_error_analysis.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
import copy
import os
from argparse import ArgumentParser
from multiprocessing import Pool
import matplotlib.pyplot as plt
import numpy as np
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
def makeplot(rs, ps, outDir, class_name, iou_type):
cs = np.vstack([
np.ones((2, 3)),
np.array([.31, .51, .74]),
np.array([.75, .31, .30]),
np.array([.36, .90, .38]),
np.array([.50, .39, .64]),
np.array([1, .6, 0])
])
areaNames = ['allarea', 'small', 'medium', 'large']
types = ['C75', 'C50', 'Loc', 'Sim', 'Oth', 'BG', 'FN']
for i in range(len(areaNames)):
area_ps = ps[..., i, 0]
figure_tile = iou_type + '-' + class_name + '-' + areaNames[i]
aps = [ps_.mean() for ps_ in area_ps]
ps_curve = [
ps_.mean(axis=1) if ps_.ndim > 1 else ps_ for ps_ in area_ps
]
ps_curve.insert(0, np.zeros(ps_curve[0].shape))
fig = plt.figure()
ax = plt.subplot(111)
for k in range(len(types)):
ax.plot(rs, ps_curve[k + 1], color=[0, 0, 0], linewidth=0.5)
ax.fill_between(
rs,
ps_curve[k],
ps_curve[k + 1],
color=cs[k],
label=str(f'[{aps[k]:.3f}]' + types[k]))
plt.xlabel('recall')
plt.ylabel('precision')
plt.xlim(0, 1.)
plt.ylim(0, 1.)
plt.title(figure_tile)
plt.legend()
# plt.show()
fig.savefig(outDir + f'/{figure_tile}.png')
plt.close(fig)
def analyze_individual_category(k, cocoDt, cocoGt, catId, iou_type):
nm = cocoGt.loadCats(catId)[0]
print(f'--------------analyzing {k + 1}-{nm["name"]}---------------')
ps_ = {}
dt = copy.deepcopy(cocoDt)
nm = cocoGt.loadCats(catId)[0]
imgIds = cocoGt.getImgIds()
dt_anns = dt.dataset['annotations']
select_dt_anns = []
for ann in dt_anns:
if ann['category_id'] == catId:
select_dt_anns.append(ann)
dt.dataset['annotations'] = select_dt_anns
dt.createIndex()
# compute precision but ignore superclass confusion
gt = copy.deepcopy(cocoGt)
child_catIds = gt.getCatIds(supNms=[nm['supercategory']])
for idx, ann in enumerate(gt.dataset['annotations']):
if (ann['category_id'] in child_catIds
and ann['category_id'] != catId):
gt.dataset['annotations'][idx]['ignore'] = 1
gt.dataset['annotations'][idx]['iscrowd'] = 1
gt.dataset['annotations'][idx]['category_id'] = catId
cocoEval = COCOeval(gt, copy.deepcopy(dt), iou_type)
cocoEval.params.imgIds = imgIds
cocoEval.params.maxDets = [100]
cocoEval.params.iouThrs = [.1]
cocoEval.params.useCats = 1
cocoEval.evaluate()
cocoEval.accumulate()
ps_supercategory = cocoEval.eval['precision'][0, :, k, :, :]
ps_['ps_supercategory'] = ps_supercategory
# compute precision but ignore any class confusion
gt = copy.deepcopy(cocoGt)
for idx, ann in enumerate(gt.dataset['annotations']):
if ann['category_id'] != catId:
gt.dataset['annotations'][idx]['ignore'] = 1
gt.dataset['annotations'][idx]['iscrowd'] = 1
gt.dataset['annotations'][idx]['category_id'] = catId
cocoEval = COCOeval(gt, copy.deepcopy(dt), iou_type)
cocoEval.params.imgIds = imgIds
cocoEval.params.maxDets = [100]
cocoEval.params.iouThrs = [.1]
cocoEval.params.useCats = 1
cocoEval.evaluate()
cocoEval.accumulate()
ps_allcategory = cocoEval.eval['precision'][0, :, k, :, :]
ps_['ps_allcategory'] = ps_allcategory
return k, ps_
def analyze_results(res_file, ann_file, res_types, out_dir):
for res_type in res_types:
assert res_type in ['bbox', 'segm']
directory = os.path.dirname(out_dir + '/')
if not os.path.exists(directory):
print(f'-------------create {out_dir}-----------------')
os.makedirs(directory)
cocoGt = COCO(ann_file)
cocoDt = cocoGt.loadRes(res_file)
imgIds = cocoGt.getImgIds()
for res_type in res_types:
res_out_dir = out_dir + '/' + res_type + '/'
res_directory = os.path.dirname(res_out_dir)
if not os.path.exists(res_directory):
print(f'-------------create {res_out_dir}-----------------')
os.makedirs(res_directory)
iou_type = res_type
cocoEval = COCOeval(
copy.deepcopy(cocoGt), copy.deepcopy(cocoDt), iou_type)
cocoEval.params.imgIds = imgIds
cocoEval.params.iouThrs = [.75, .5, .1]
cocoEval.params.maxDets = [100]
cocoEval.evaluate()
cocoEval.accumulate()
ps = cocoEval.eval['precision']
ps = np.vstack([ps, np.zeros((4, *ps.shape[1:]))])
catIds = cocoGt.getCatIds()
recThrs = cocoEval.params.recThrs
with Pool(processes=48) as pool:
args = [(k, cocoDt, cocoGt, catId, iou_type)
for k, catId in enumerate(catIds)]
analyze_results = pool.starmap(analyze_individual_category, args)
for k, catId in enumerate(catIds):
nm = cocoGt.loadCats(catId)[0]
print(f'--------------saving {k + 1}-{nm["name"]}---------------')
analyze_result = analyze_results[k]
assert k == analyze_result[0]
ps_supercategory = analyze_result[1]['ps_supercategory']
ps_allcategory = analyze_result[1]['ps_allcategory']
# compute precision but ignore superclass confusion
ps[3, :, k, :, :] = ps_supercategory
# compute precision but ignore any class confusion
ps[4, :, k, :, :] = ps_allcategory
# fill in background and false negative errors and plot
ps[ps == -1] = 0
ps[5, :, k, :, :] = (ps[4, :, k, :, :] > 0)
ps[6, :, k, :, :] = 1.0
makeplot(recThrs, ps[:, :, k], res_out_dir, nm['name'], iou_type)
makeplot(recThrs, ps, res_out_dir, 'allclass', iou_type)
def main():
parser = ArgumentParser(description='COCO Error Analysis Tool')
parser.add_argument('result', help='result file (json format) path')
parser.add_argument('out_dir', help='dir to save analyze result images')
parser.add_argument(
'--ann',
default='data/coco/annotations/instances_val2017.json',
help='annotation file path')
parser.add_argument(
'--types', type=str, nargs='+', default=['bbox'], help='result types')
args = parser.parse_args()
analyze_results(args.result, args.ann, args.types, out_dir=args.out_dir)
if __name__ == '__main__':
main()