mmdetection绘制PR曲线
# mmdetection 绘制PR曲线
参考：https://github.com/xuhuasheng/mmdetection_plot_pr_curve
import os
import mmcv
import numpy as np
import matplotlib.pyplot as plt
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from mmcv import Config
from mmdet.datasets import build_dataset
def getPRArray(config_file, result_file, metric="bbox"):
"""plot precison-recall curve based on testing results of pkl file.
Args:
config_file (list[list | tuple]): config file path.
result_file (str): pkl file of testing results path.
metric (str): Metrics to be evaluated. Options are
'bbox', 'segm'.
"""
cfg = Config.fromfile(config_file)
# turn on test mode of dataset
if isinstance(cfg.data.test, dict):
cfg.data.test.test_mode = True
elif isinstance(cfg.data.test, list):
for ds_cfg in cfg.data.test:
ds_cfg.test_mode = True
# build dataset
dataset = build_dataset(cfg.data.test)
# load result file in pkl format
pkl_results = mmcv.load(result_file)
# convert pkl file (list[list | tuple | ndarray]) to json
json_results, _ = dataset.format_results(pkl_results)
# initialize COCO instance
coco = COCO(annotation_file=cfg.data.test.ann_file)
coco_gt = coco
coco_dt = coco_gt.loadRes(json_results[metric])
# initialize COCOeval instance
coco_eval = COCOeval(coco_gt, coco_dt, metric)
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
# extract eval data
precisions = coco_eval.eval["precision"]
'''
precisions[T, R, K, A, M]
T: iou thresholds [0.5 : 0.05 : 0.95], idx from 0 to 9
R: recall thresholds [0 : 0.01 : 1], idx from 0 to 100
K: category, idx from 0 to ...
A: area range, (all, small, medium, large), idx from 0 to 3
M: max dets, (1, 10, 100), idx from 0 to 2
'''
return precisions
'''
out为输出的图片名字
'''
def PR(config, result, out):
precisions = getPRArray(config, result)
pr_array1 = precisions[0, :, 0, 0, 2] # IOU = 0.5
pr_array2 = precisions[1, :, 0, 0, 2] # IOU = 0.55
pr_array3 = precisions[2, :, 0, 0, 2] # IOU = 0.6
pr_array4 = precisions[3, :, 0, 0, 2] # IOU = 0.65
pr_array5 = precisions[4, :, 0, 0, 2] # IOU = 0.7
pr_array6 = precisions[5, :, 0, 0, 2] # IOU = 0.75
pr_array7 = precisions[6, :, 0, 0, 2] # IOU = 0.8
pr_array8 = precisions[7, :, 0, 0, 2] # IOU = 0.85
pr_array9 = precisions[8, :, 0, 0, 2] # IOU = 0.9
pr_array10 = precisions[9, :, 0, 0, 2] # IOU = 0.95
x = np.arange(0.0, 1.01, 0.01)
# plot PR curve
plt.plot(x, pr_array1, label="iou=0.5")
plt.plot(x, pr_array2, label="iou=0.55")
plt.plot(x, pr_array3, label="iou=0.6")
plt.plot(x, pr_array4, label="iou=0.65")
plt.plot(x, pr_array5, label="iou=0.7")
plt.plot(x, pr_array6, label="iou=0.75")
plt.plot(x, pr_array7, label="iou=0.8")
plt.plot(x, pr_array8, label="iou=0.85")
plt.plot(x, pr_array9, label="iou=0.9")
plt.plot(x, pr_array10, label="iou=0.95")
plt.xlabel("recall")
plt.ylabel("precison")
plt.xlim(0, 1.0)
plt.ylim(0, 1.01)
plt.grid(True)
plt.legend(loc=2, bbox_to_anchor=(1.05,1.0),borderaxespad = 0.)
plt.savefig(out, bbox_inches="tight")
plt.close()
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import os
import mmcv
import numpy as np
import matplotlib.pyplot as plt
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from mmcv import Config
from mmdet.datasets import build_dataset
def getPRArray(config_file, result_file, metric="bbox"):
"""plot precison-recall curve based on testing results of pkl file.
Args:
config_file (list[list | tuple]): config file path.
result_file (str): pkl file of testing results path.
metric (str): Metrics to be evaluated. Options are
'bbox', 'segm'.
"""
cfg = Config.fromfile(config_file)
# turn on test mode of dataset
if isinstance(cfg.data.test, dict):
cfg.data.test.test_mode = True
elif isinstance(cfg.data.test, list):
for ds_cfg in cfg.data.test:
ds_cfg.test_mode = True
# build dataset
dataset = build_dataset(cfg.data.test)
# load result file in pkl format
pkl_results = mmcv.load(result_file)
# convert pkl file (list[list | tuple | ndarray]) to json
json_results, _ = dataset.format_results(pkl_results)
# initialize COCO instance
coco = COCO(annotation_file=cfg.data.test.ann_file)
coco_gt = coco
coco_dt = coco_gt.loadRes(json_results[metric])
# initialize COCOeval instance
coco_eval = COCOeval(coco_gt, coco_dt, metric)
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
# extract eval data
precisions = coco_eval.eval["precision"]
'''
precisions[T, R, K, A, M]
T: iou thresholds [0.5 : 0.05 : 0.95], idx from 0 to 9
R: recall thresholds [0 : 0.01 : 1], idx from 0 to 100
K: category, idx from 0 to ...
A: area range, (all, small, medium, large), idx from 0 to 3
M: max dets, (1, 10, 100), idx from 0 to 2
'''
return precisions
'''
out为输出的图片名字
'''
def PR(workDir):
outList = os.listdir(workDir)
precisions = {}
for ou in outList:
config = os.path.join(workDir, ou, 'config.py')
result = os.path.join(workDir, ou, 'result.pkl')
precisions.update({ou: getPRArray(config, result)})
x = np.arange(0.0, 1.01, 0.01)
for key, value in precisions.items():
pr_array = value[0, :, 0, 0, 2] # IOU = 0.5
plt.plot(x, pr_array, label=key)
# precisions = getPRArray(config, result)
# pr_array1 = precisions[0, :, 0, 0, 2] # IOU = 0.5
#
# x = np.arange(0.0, 1.01, 0.01)
# for i in range(1, 6):
# pr_array1 += precisions[0, :, i, 0, 2]
# plt.plot(x, pr_array1 / 6, label="iou=0.5")
#
plt.xlabel("recall")
plt.ylabel("precison")
plt.xlim(0, 1.0)
plt.ylim(0, 1.01)
plt.grid(True)
plt.legend(loc=2, bbox_to_anchor=(1.05, 1.0), borderaxespad=0.)
plt.savefig('pr.png', bbox_inches="tight")
plt.close()
PR(r'/home/lxl/mmdetection/lxl')
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上次更新: 2022/09/30, 04:53:04