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PaddleRS/paddlers/tasks/utils/det_metrics/coco_utils.py

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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import copy
import os
import os.path as osp
import numpy as np
import itertools
from paddlers.models.ppdet.metrics.map_utils import draw_pr_curve
from paddlers.models.ppdet.metrics.json_results import get_det_res, get_det_poly_res, get_seg_res, get_solov2_segm_res
import paddlers.utils.logging as logging
def get_infer_results(outs, catid, bias=0):
"""
Get result at the stage of inference.
The output format is dictionary containing bbox or mask result.
For example, bbox result is a list and each element contains
image_id, category_id, bbox and score.
"""
if outs is None or len(outs) == 0:
raise ValueError(
'The number of valid detection result if zero. Please use reasonable model and check input data.'
)
im_id = outs['im_id']
infer_res = {}
if 'bbox' in outs:
if len(outs['bbox']) > 0 and len(outs['bbox'][0]) > 6:
infer_res['bbox'] = get_det_poly_res(
outs['bbox'], outs['bbox_num'], im_id, catid, bias=bias)
else:
infer_res['bbox'] = get_det_res(
outs['bbox'], outs['bbox_num'], im_id, catid, bias=bias)
if 'mask' in outs:
# mask post process
infer_res['mask'] = get_seg_res(outs['mask'], outs['bbox'],
outs['bbox_num'], im_id, catid)
if 'segm' in outs:
infer_res['segm'] = get_solov2_segm_res(outs, im_id, catid)
return infer_res
def cocoapi_eval(anns,
style,
coco_gt=None,
anno_file=None,
max_dets=(100, 300, 1000),
classwise=False):
"""
Args:
anns: Evaluation result.
style (str): COCOeval style, can be `bbox` , `segm` and `proposal`.
coco_gt (str): Whether to load COCOAPI through anno_file,
eg: coco_gt = COCO(anno_file)
anno_file (str): COCO annotations file.
max_dets (tuple): COCO evaluation maxDets.
classwise (bool): Whether per-category AP and draw P-R Curve or not.
"""
assert coco_gt is not None or anno_file is not None
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
if coco_gt is None:
coco_gt = COCO(anno_file)
logging.info("Start evaluate...")
coco_dt = loadRes(coco_gt, anns)
if style == 'proposal':
coco_eval = COCOeval(coco_gt, coco_dt, 'bbox')
coco_eval.params.useCats = 0
coco_eval.params.maxDets = list(max_dets)
else:
coco_eval = COCOeval(coco_gt, coco_dt, style)
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
if classwise:
# Compute per-category AP and PR curve
try:
from terminaltables import AsciiTable
except Exception as e:
logging.error(
'terminaltables not found, plaese install terminaltables. '
'for example: `pip install terminaltables`.')
raise e
precisions = coco_eval.eval['precision']
cat_ids = coco_gt.getCatIds()
# precision: (iou, recall, cls, area range, max dets)
assert len(cat_ids) == precisions.shape[2]
results_per_category = []
for idx, catId in enumerate(cat_ids):
# area range index 0: all area ranges
# max dets index -1: typically 100 per image
nm = coco_gt.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(
(str(nm["name"]), '{:0.3f}'.format(float(ap))))
pr_array = precisions[0, :, idx, 0, 2]
recall_array = np.arange(0.0, 1.01, 0.01)
draw_pr_curve(
pr_array,
recall_array,
out_dir=style + '_pr_curve',
file_name='{}_precision_recall_curve.jpg'.format(nm["name"]))
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)
logging.info('Per-category of {} AP: \n{}'.format(style, table.table))
logging.info("per-category PR curve has output to {} folder.".format(
style + '_pr_curve'))
# flush coco evaluation result
sys.stdout.flush()
return coco_eval.stats
def loadRes(coco_obj, anns):
"""
Load result file and return a result api object.
:param resFile (str) : file name of result file
:return: res (obj) : result api object
"""
# This function has the same functionality as pycocotools.COCO.loadRes,
# except that the input anns is list of results rather than a json file.
# Refer to
# https://github.com/cocodataset/cocoapi/blob/8c9bcc3cf640524c4c20a9c40e89cb6a2f2fa0e9/PythonAPI/pycocotools/coco.py#L305,
# matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
# or matplotlib.backends is imported for the first time
# pycocotools import matplotlib
import matplotlib
matplotlib.use('Agg')
from pycocotools.coco import COCO
import pycocotools.mask as maskUtils
import time
res = COCO()
res.dataset['images'] = [img for img in coco_obj.dataset['images']]
tic = time.time()
assert type(anns) == list, 'results in not an array of objects'
annsImgIds = [ann['image_id'] for ann in anns]
assert set(annsImgIds) == (set(annsImgIds) & set(coco_obj.getImgIds())), \
'Results do not correspond to current coco set'
if 'caption' in anns[0]:
imgIds = set([img['id'] for img in res.dataset['images']]) & set(
[ann['image_id'] for ann in anns])
res.dataset['images'] = [
img for img in res.dataset['images'] if img['id'] in imgIds
]
for id, ann in enumerate(anns):
ann['id'] = id + 1
elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
res.dataset['categories'] = copy.deepcopy(coco_obj.dataset[
'categories'])
for id, ann in enumerate(anns):
bb = ann['bbox']
x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]
if not 'segmentation' in ann:
ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
ann['area'] = bb[2] * bb[3]
ann['id'] = id + 1
ann['iscrowd'] = 0
elif 'segmentation' in anns[0]:
res.dataset['categories'] = copy.deepcopy(coco_obj.dataset[
'categories'])
for id, ann in enumerate(anns):
# now only support compressed RLE format as segmentation results
ann['area'] = maskUtils.area(ann['segmentation'])
if not 'bbox' in ann:
ann['bbox'] = maskUtils.toBbox(ann['segmentation'])
ann['id'] = id + 1
ann['iscrowd'] = 0
elif 'keypoints' in anns[0]:
res.dataset['categories'] = copy.deepcopy(coco_obj.dataset[
'categories'])
for id, ann in enumerate(anns):
s = ann['keypoints']
x = s[0::3]
y = s[1::3]
x0, x1, y0, y1 = np.min(x), np.max(x), np.min(y), np.max(y)
ann['area'] = (x1 - x0) * (y1 - y0)
ann['id'] = id + 1
ann['bbox'] = [x0, y0, x1 - x0, y1 - y0]
res.dataset['annotations'] = anns
res.createIndex()
return res
def makeplot(rs, ps, outDir, class_name, iou_type):
"""
针对某个特定类别，绘制不同评估要求下的准确率和召回率。
绘制结果说明参考COCODataset官网给出分析工具说明https://cocodataset.org/#detection-eval。
Refer to https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/coco_error_analysis.py#L13
Args:
rs (np.array): 在不同置信度阈值下计算得到的召回率。
ps (np.array): 在不同置信度阈值下计算得到的准确率。ps与rs相同位置下的数值为同一个置信度阈值
计算得到的准确率与召回率。
outDir (str): 图表保存的路径。
class_name (str): 类别名。
iou_type (str): iou计算方式，若为检测框，则设置为'bbox'，若为像素级分割结果，则设置为'segm'。
"""
import matplotlib.pyplot as plt
cs = np.vstack([
np.ones((2, 3)),
np.array([0.31, 0.51, 0.74]),
np.array([0.75, 0.31, 0.30]),
np.array([0.36, 0.90, 0.38]),
np.array([0.50, 0.39, 0.64]),
np.array([1, 0.6, 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_title = 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, 1.0)
plt.ylim(0, 1.0)
plt.title(figure_title)
plt.legend()
# plt.show()
fig.savefig(osp.join(outDir, f'{figure_title}.png'))
plt.close(fig)
def analyze_individual_category(k, cocoDt, cocoGt, catId, iou_type, areas=None):
"""
针对某个特定类别，分析忽略亚类混淆和类别混淆时的准确率。
Refer to https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/coco_error_analysis.py#L174
Args:
k (int): 待分析类别的序号。
cocoDt (pycocotols.coco.COCO): 按COCO类存放的预测结果。
cocoGt (pycocotols.coco.COCO): 按COCO类存放的真值。
catId (int): 待分析类别在数据集中的类别id。
iou_type (str): iou计算方式，若为检测框，则设置为'bbox'，若为像素级分割结果，则设置为'segm'。
Returns:
int:
dict: 有关键字'ps_supercategory'和'ps_allcategory'。关键字'ps_supercategory'的键值是忽略亚类间
混淆时的准确率，关键字'ps_allcategory'的键值是忽略类别间混淆时的准确率。
"""
# matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
# or matplotlib.backends is imported for the first time
# pycocotools import matplotlib
import matplotlib
matplotlib.use('Agg')
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
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 = [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 coco_error_analysis(eval_details_file=None,
gt=None,
pred_bbox=None,
pred_mask=None,
save_dir='./output'):
"""
逐个分析模型预测错误的原因，并将分析结果以图表的形式展示。
分析结果说明参考COCODataset官网给出分析工具说明https://cocodataset.org/#detection-eval。
Refer to https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/coco_error_analysis.py
Args:
eval_details_file (str): 模型评估结果的保存路径，包含真值信息和预测结果。
gt (list): 数据集的真值信息。默认值为None。
pred_bbox (list): 模型在数据集上的预测框。默认值为None。
pred_mask (list): 模型在数据集上的预测mask。默认值为None。
save_dir (str): 可视化结果保存路径。默认值为'./output'。
Note:
eval_details_file的优先级更高，只要eval_details_file不为None，
就会从eval_details_file提取真值信息和预测结果做分析。
当eval_details_file为None时，则用gt、pred_mask、pred_mask做分析。
"""
import multiprocessing as mp
# matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
# or matplotlib.backends is imported for the first time
# pycocotools import matplotlib
import matplotlib
matplotlib.use('Agg')
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
if eval_details_file is not None:
import json
with open(eval_details_file, 'r') as f:
eval_details = json.load(f)
pred_bbox = eval_details['bbox']
if 'mask' in eval_details:
pred_mask = eval_details['mask']
gt = eval_details['gt']
if gt is None or pred_bbox is None:
raise Exception(
"gt/pred_bbox/pred_mask is None now, please set right eval_details_file or gt/pred_bbox/pred_mask."
)
if pred_bbox is not None and len(pred_bbox) == 0:
raise Exception("There is no predicted bbox.")
if pred_mask is not None and len(pred_mask) == 0:
raise Exception("There is no predicted mask.")
def _analyze_results(cocoGt, cocoDt, res_type, out_dir):
"""
Refer to
https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/coco_error_analysis.py#L235
"""
directory = osp.dirname(osp.join(out_dir, ''))
if not osp.exists(directory):
logging.info('-------------create {}-----------------'.format(
out_dir))
os.makedirs(directory)
imgIds = cocoGt.getImgIds()
res_out_dir = osp.join(out_dir, res_type, '')
res_directory = os.path.dirname(res_out_dir)
if not os.path.exists(res_directory):
logging.info('-------------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
thread_num = mp.cpu_count() if mp.cpu_count() < 8 else 8
thread_pool = mp.pool.ThreadPool(thread_num)
args = [(k, cocoDt, cocoGt, catId, iou_type)
for k, catId in enumerate(catIds)]
analyze_results = thread_pool.starmap(analyze_individual_category, args)
for k, catId in enumerate(catIds):
nm = cocoGt.loadCats(catId)[0]
logging.info('--------------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)
coco_gt = COCO()
coco_gt.dataset = gt
coco_gt.createIndex()
if pred_bbox is not None:
coco_dt = loadRes(coco_gt, pred_bbox)
_analyze_results(coco_gt, coco_dt, res_type='bbox', out_dir=save_dir)
if pred_mask is not None:
coco_dt = loadRes(coco_gt, pred_mask)
_analyze_results(coco_gt, coco_dt, res_type='segm', out_dir=save_dir)
logging.info("The analysis figures are saved in {}".format(save_dir))