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('[{:.3f}'.format(aps[k]) + ']' + 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 + '/{}.png'.format(figure_tile))
plt.close(fig)
def analyze_individual_category(k, cocoDt, cocoGt, catId, iou_type):
nm = cocoGt.loadCats(catId)[0]
print('--------------analyzing {}-{}---------------'.format(
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('-------------create {}-----------------'.format(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(
'-------------create {}-----------------'.format(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('--------------saving {}-{}---------------'.format(
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()