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import copy
import os
from argparse import ArgumentParser
from multiprocessing import Pool
import matplotlib.pyplot as plt
import numpy as np
from numpy.core.defchararray import index
import pandas as pd
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
def add_sub(df_q, fig, index=1, all=4, col_name='dif_x1'):
# 1 of 4
ax1 = fig.add_subplot((all+3)//4, 4, index) # 1 row x 4 col, set 1
ax1.set_ylabel('frequency')
ax1.set_title(col_name)
ax1.grid(axis='y', color='gray', lw=0.5)
n, bins, _ = plt.hist(df_q[col_name], bins=20)
xs = (bins[:-1] + bins[1:])/2 # 各柱の端が返るのでずらす
ys = n
for x, y in zip(xs, ys):
if y > 0:
plt.text(x, y, str(int(y)), horizontalalignment="center")
return
def add_sub_hv_stack(df_q, fig, index=1, all=4, col_name='dif_x1'):
# 1 of 4
ax1 = fig.add_subplot((all+3)//4, min(all, 4), index) # 1 row x 4 col, set 1
ax1.set_ylabel('frequency')
ax1.set_title(col_name)
ax1.grid(axis='y', color='gray', lw=0.5)
n, bins, _ = plt.hist(df_q[col_name], bins=20, color='C0', label='all')
n_h, _, _ = plt.hist(df_q.query('gt_w > gt_h')[col_name],
histtype='stepfilled', color='C1', bins=bins, label='hori')
ax1.legend()
xs = (bins[:-1] + bins[1:])/2 # 各柱の端が返るのでずらす
ys = n
for x, y in zip(xs, ys):
if y > 0:
plt.text(x, y, str(int(y)), horizontalalignment="center")
ys = n_h
for x, y in zip(xs, ys):
if y > 0:
plt.text(x, y, str(int(y)), horizontalalignment="center", color='C1')
return
def save_hist(df, query, col_names, save_dir='hist_png', fname_head='', hv_stack=False):
df_q = df.query(query)
fig = plt.figure(figsize=(8.0*min(len(col_names),4), 6.0*((len(col_names)+3)//4)), facecolor="azure", edgecolor="coral")
fig.suptitle(query)
if hv_stack:
for i in range(len(col_names)):
add_sub_hv_stack(df_q, fig, i+1, len(col_names), col_names[i])
else:
for i in range(len(col_names)):
add_sub(df_q, fig, i+1, len(col_names), col_names[i])
savepng_name = '{}.png'.format(fname_head)
print('Save: {}'.format(savepng_name))
os.makedirs(save_dir, exist_ok=True)
plt.savefig(os.path.join(save_dir, savepng_name), bbox_inches='tight')
plt.clf()
plt.close()
return
def analyze_individual_category(k,
cocoDt,
cocoGt,
catId,
iou_type,
areas=None):
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)
if nm.get('supercategory'):
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 = [0.1]
cocoEval.params.useCats = 1
if areas:
cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]],
[areas[0], areas[1]], [areas[1], areas[2]]]
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 = [0.1]
cocoEval.params.useCats = 1
if areas:
cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]],
[areas[0], areas[1]], [areas[1], areas[2]]]
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,
out_csv,
hv = 'SUM',
histplots=None,
areas=None):
for res_type in res_types:
assert res_type in ['bbox', 'segm']
if areas:
assert len(areas) == 3, '3 integers should be specified as areas, \
representing 3 area regions'
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:
iou_type = res_type
cocoEval = COCOeval(
copy.deepcopy(cocoGt), copy.deepcopy(cocoDt), iou_type)
cocoEval.params.imgIds = imgIds
cocoEval.params.iouThrs = [0.75, 0.5, 0.1]
cocoEval.params.maxDets = [100]
if areas:
cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]],
[areas[0], areas[1]],
[areas[1], areas[2]]]
cocoEval.evaluate()
cocoEval.accumulate() # ここまでで解析実行完了
print("=========================")
cols = ['file_name', 'image_id', 'gt_id', 'dt_id', 'category_id', 'iou', 'score',
'gt_x', 'gt_y', 'gt_w', 'gt_h', # GT
'dt_x', 'dt_y', 'dt_w', 'dt_h', # Detected bbox of max iou
'dif_x1', 'dif_y1', 'dif_x2', 'dif_y2', 'dif_w', 'dif_h', # dt - gt
'rat_x1', 'rat_y1', 'rat_x2', 'rat_y2', 'rat_w', 'rat_h' # (dt - gt) / gt_w(or gt_h)
]
df_pos = pd.DataFrame(index=[], columns=cols)
for imgId in cocoEval.params.imgIds:
img_file_name = cocoGt.imgs[imgId]['file_name']
print("processing {}: {}/{}".format(img_file_name, imgId+1, len(cocoEval.params.imgIds)))
# img {'file_name': '1883229_R0000227_contents_L.jpg', 'width': 2602, 'height': 3329, 'id': 0}
for catId in cocoEval.params.catIds:
for dt_idx, iou_arr in enumerate(cocoEval.ious[(imgId, catId)]):
# print("idx: {}, iou_list:{}".format(dt_idx, iou_arr))
arg_max_idx =np.argmax(iou_arr)
gt_idx = cocoEval._gts[imgId, catId][arg_max_idx]['id']
# _gts[ImgId, CatId]で、ImgId中のCatIdのannがとれる
score = cocoEval._dts[imgId, catId][dt_idx]['score']
gt_bbox = cocoEval._gts[imgId, catId][arg_max_idx]['bbox'] # [x, y, w, h]
dt_bbox = cocoEval._dts[imgId, catId][dt_idx]['bbox']
dif_x1 = dt_bbox[0] - gt_bbox[0]
dif_y1 = dt_bbox[1] - gt_bbox[1]
dif_w = dt_bbox[2] - gt_bbox[2]
dif_h = dt_bbox[3] - gt_bbox[3]
dif_x2 = dif_x1 + dif_w # right
dif_y2 = dif_y1 + dif_h # bottom
rat_x1 = dif_x1 / gt_bbox[2] # dif_x / gt_w
rat_y1 = dif_y1 / gt_bbox[3] # dif_y / gt_h
rat_x2 = dif_x2 / gt_bbox[2] # dif_x / gt_w
rat_y2 = dif_y2 / gt_bbox[3] # dif_y / gt_h
rat_w = dif_w / gt_bbox[2] # dif_w / gt_w
rat_h = dif_h / gt_bbox[3] # idf_h / gt_h
record = pd.Series(
np.concatenate([
[img_file_name, imgId, gt_idx, dt_idx, catId, iou_arr[arg_max_idx], score],
gt_bbox,
dt_bbox,
[dif_x1, dif_y1, dif_x2, dif_y2, dif_w, dif_h,
rat_x1, rat_y1, rat_x2, rat_y2, rat_w, rat_h]],
axis=0) # concat
, index=df_pos.columns)
df_pos = df_pos.append(record, ignore_index=True)
df_pos.to_csv(os.path.join(out_dir, out_csv))
# create histograms
df = pd.read_csv(os.path.join(out_dir, out_csv), index_col=0, header=0)
if histplots:
q_iou_list=['0.50<=iou',
'0.50<=iou<0.75',
'0.75<=iou<0.90',
'0.90<=iou']
classes = ['line_main' , 'line_inote', 'line_hnote', 'line_caption',
'block_fig', 'block_table', 'block_pillar', 'block_folio',
'block_rubi', 'block_chart', 'block_eqn', 'block_cfm',
'block_eng']
for cid, cname in enumerate(classes):
for q_iou in q_iou_list:
if hv=='SEPARATE':
# horizontally bbox
query = 'gt_w > gt_h & category_id=={} & {}'.format(cid, q_iou)
head = '{}_{}_hori'.format(cname, q_iou)
col_names = ['dif_x1', 'dif_x2', 'dif_y1', 'dif_y2',
'rat_x1', 'rat_x2', 'rat_y1', 'rat_y2',
'dif_h', 'dif_w', 'rat_h', 'rat_w']
hist_save_dir = os.path.join(out_dir, 'hist_png')
save_hist(df, query, col_names, save_dir=hist_save_dir, fname_head=head)
# vertically bbox
query = 'gt_w < gt_h & category_id=={} & {}'.format(cid, q_iou)
head = '{}_{}_vert'.format(cname, q_iou)
save_hist(df, query, col_names, save_dir=hist_save_dir, fname_head=head)
elif hv=='STACK':
query = 'category_id=={} & {}'.format(cid, q_iou)
head = '{}_{}'.format(cname, q_iou)
col_names = ['dif_x1', 'dif_x2', 'dif_y1', 'dif_y2',
'rat_x1', 'rat_x2', 'rat_y1', 'rat_y2',
'dif_h', 'dif_w', 'rat_h', 'rat_w']
hist_save_dir = os.path.join(out_dir, 'hist_png')
save_hist(df, query, col_names, save_dir=hist_save_dir, fname_head=head, hv_stack=True)
else:
query = 'category_id=={} & {}'.format(cid, q_iou)
head = '{}_{}'.format(cname, q_iou)
col_names = ['dif_x1', 'dif_x2', 'dif_y1', 'dif_y2',
'rat_x1', 'rat_x2', 'rat_y1', 'rat_y2',
'dif_h', 'dif_w', 'rat_h', 'rat_w']
hist_save_dir = os.path.join(out_dir, 'hist_png')
save_hist(df, query, col_names, save_dir=hist_save_dir, fname_head=head)
return
def main():
parser = ArgumentParser(description='COCO Error Analysis Tool')
parser.add_argument('result', help='result file (json format) path')
parser.add_argument('ann', help='annotation file (json format) path')
parser.add_argument(
'--out_dir',
default='res_pos_analysis',
help='output dir')
parser.add_argument(
'--out_csv',
default='df_pos.csv',
help='file to save analyze result csv')
parser.add_argument(
'--types', type=str, nargs='+', default=['bbox'], help='result types')
parser.add_argument(
'--hv',
default='SUM',
help='Create histograms with/without distinction between vertically and horizontally written documents.'
'SUM(default): wihout distinction'
'SEPARATE : with distinction creating different bar charts'
'STACK : with distinction using one stacked bar charts'
)
parser.add_argument(
'--histplots',
action='store_false',
help='export histogram plots (default is true')
parser.add_argument(
'--areas',
type=int,
nargs='+',
default=[1024, 9216, 10000000000],
help='area regions')
args = parser.parse_args()
analyze_results(
args.result,
args.ann,
args.types,
out_dir=args.out_dir,
out_csv=args.out_csv,
hv =args.hv,
histplots=args.histplots,
areas=args.areas)
if __name__ == '__main__':
main()
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