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import argparse
import os
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MultipleLocator
from mmcv.ops import nms
from mmdet.evaluation import bbox_overlaps
from mmdet.utils import replace_cfg_vals, update_data_root
from mmengine import Config, DictAction
from mmengine.fileio import load
from mmengine.registry import init_default_scope
from mmengine.utils import ProgressBar
from mmyolo.registry import DATASETS
def parse_args():
parser = argparse.ArgumentParser(
description='Generate confusion matrix from detection results')
parser.add_argument('config', help='test config file path')
parser.add_argument(
'prediction_path', help='prediction path where test .pkl result')
parser.add_argument(
'save_dir', help='directory where confusion matrix will be saved')
parser.add_argument(
'--show', action='store_true', help='show confusion matrix')
parser.add_argument(
'--color-theme',
default='plasma',
help='theme of the matrix color map')
parser.add_argument(
'--score-thr',
type=float,
default=0.3,
help='score threshold to filter detection bboxes')
parser.add_argument(
'--tp-iou-thr',
type=float,
default=0.5,
help='IoU threshold to be considered as matched')
parser.add_argument(
'--nms-iou-thr',
type=float,
default=None,
help='nms IoU threshold, only applied when users want to change the'
'nms IoU threshold.')
parser.add_argument(
'--cfg-options',
nargs='+',
action=DictAction,
help='override some settings in the used config, the key-value pair '
'in xxx=yyy format will be merged into config file. If the value to '
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
'Note that the quotation marks are necessary and that no white space '
'is allowed.')
args = parser.parse_args()
return args
def calculate_confusion_matrix(dataset,
results,
score_thr=0,
nms_iou_thr=None,
tp_iou_thr=0.5):
"""Calculate the confusion matrix.
Args:
dataset (Dataset): Test or val dataset.
results (list[ndarray]): A list of detection results in each image.
score_thr (float|optional): Score threshold to filter bboxes.
Default: 0.
nms_iou_thr (float|optional): nms IoU threshold, the detection results
have done nms in the detector, only applied when users want to
change the nms IoU threshold. Default: None.
tp_iou_thr (float|optional): IoU threshold to be considered as matched.
Default: 0.5.
"""
num_classes = len(dataset.metainfo['classes'])
confusion_matrix = np.zeros(shape=[num_classes + 1, num_classes + 1])
assert len(dataset) == len(results)
prog_bar = ProgressBar(len(results))
for idx, per_img_res in enumerate(results):
res_bboxes = per_img_res['pred_instances']
gts = dataset.get_data_info(idx)['instances']
analyze_per_img_dets(confusion_matrix, gts, res_bboxes, score_thr,
tp_iou_thr, nms_iou_thr)
prog_bar.update()
return confusion_matrix
def analyze_per_img_dets(confusion_matrix,
gts,
result,
score_thr=0,
tp_iou_thr=0.5,
nms_iou_thr=None):
"""Analyze detection results on each image.
Args:
confusion_matrix (ndarray): The confusion matrix,
has shape (num_classes + 1, num_classes + 1).
gt_bboxes (ndarray): Ground truth bboxes, has shape (num_gt, 4).
gt_labels (ndarray): Ground truth labels, has shape (num_gt).
result (ndarray): Detection results, has shape
(num_classes, num_bboxes, 5).
score_thr (float): Score threshold to filter bboxes.
Default: 0.
tp_iou_thr (float): IoU threshold to be considered as matched.
Default: 0.5.
nms_iou_thr (float|optional): nms IoU threshold, the detection results
have done nms in the detector, only applied when users want to
change the nms IoU threshold. Default: None.
"""
true_positives = np.zeros(len(gts))
gt_bboxes = []
gt_labels = []
for gt in gts:
gt_bboxes.append(gt['bbox'])
gt_labels.append(gt['bbox_label'])
gt_bboxes = np.array(gt_bboxes)
gt_labels = np.array(gt_labels)
unique_label = np.unique(result['labels'].numpy())
for det_label in unique_label:
mask = (result['labels'] == det_label)
det_bboxes = result['bboxes'][mask].numpy()
det_scores = result['scores'][mask].numpy()
if nms_iou_thr:
det_bboxes, _ = nms(
det_bboxes, det_scores, nms_iou_thr, score_threshold=score_thr)
ious = bbox_overlaps(det_bboxes[:, :4], gt_bboxes)
for i, score in enumerate(det_scores):
det_match = 0
if score >= score_thr:
for j, gt_label in enumerate(gt_labels):
if ious[i, j] >= tp_iou_thr:
det_match += 1
if gt_label == det_label:
true_positives[j] += 1 # TP
confusion_matrix[gt_label, det_label] += 1
if det_match == 0: # BG FP
confusion_matrix[-1, det_label] += 1
for num_tp, gt_label in zip(true_positives, gt_labels):
if num_tp == 0: # FN
confusion_matrix[gt_label, -1] += 1
def plot_confusion_matrix(confusion_matrix,
labels,
save_dir=None,
show=True,
title='Normalized Confusion Matrix',
color_theme='plasma'):
"""Draw confusion matrix with matplotlib.
Args:
confusion_matrix (ndarray): The confusion matrix.
labels (list[str]): List of class names.
save_dir (str|optional): If set, save the confusion matrix plot to the
given path. Default: None.
show (bool): Whether to show the plot. Default: True.
title (str): Title of the plot. Default: `Normalized Confusion Matrix`.
color_theme (str): Theme of the matrix color map. Default: `plasma`.
"""
# normalize the confusion matrix
per_label_sums = confusion_matrix.sum(axis=1)[:, np.newaxis]
confusion_matrix = \
confusion_matrix.astype(np.float32) / per_label_sums * 100
num_classes = len(labels)
fig, ax = plt.subplots(
figsize=(0.5 * num_classes, 0.5 * num_classes * 0.8), dpi=180)
cmap = plt.get_cmap(color_theme)
im = ax.imshow(confusion_matrix, cmap=cmap)
plt.colorbar(mappable=im, ax=ax)
title_font = {'weight': 'bold', 'size': 12}
ax.set_title(title, fontdict=title_font)
label_font = {'size': 10}
plt.ylabel('Ground Truth Label', fontdict=label_font)
plt.xlabel('Prediction Label', fontdict=label_font)
# draw locator
xmajor_locator = MultipleLocator(1)
xminor_locator = MultipleLocator(0.5)
ax.xaxis.set_major_locator(xmajor_locator)
ax.xaxis.set_minor_locator(xminor_locator)
ymajor_locator = MultipleLocator(1)
yminor_locator = MultipleLocator(0.5)
ax.yaxis.set_major_locator(ymajor_locator)
ax.yaxis.set_minor_locator(yminor_locator)
# draw grid
ax.grid(True, which='minor', linestyle='-')
# draw label
ax.set_xticks(np.arange(num_classes))
ax.set_yticks(np.arange(num_classes))
ax.set_xticklabels(labels)
ax.set_yticklabels(labels)
ax.tick_params(
axis='x', bottom=False, top=True, labelbottom=False, labeltop=True)
plt.setp(
ax.get_xticklabels(), rotation=45, ha='left', rotation_mode='anchor')
# draw confution matrix value
for i in range(num_classes):
for j in range(num_classes):
ax.text(
j,
i,
'{}%'.format(
int(confusion_matrix[
i,
j]) if not np.isnan(confusion_matrix[i, j]) else -1),
ha='center',
va='center',
color='w',
size=7)
ax.set_ylim(len(confusion_matrix) - 0.5, -0.5) # matplotlib>3.1.1
fig.tight_layout()
if save_dir is not None:
plt.savefig(
os.path.join(save_dir, 'confusion_matrix.png'), format='png')
if show:
plt.show()
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# replace the ${key} with the value of cfg.key
cfg = replace_cfg_vals(cfg)
# update data root according to MMYOLO_DATASETS
update_data_root(cfg)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
init_default_scope(cfg.get('default_scope', 'mmyolo'))
results = load(args.prediction_path)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
dataset = DATASETS.build(cfg.test_dataloader.dataset)
confusion_matrix = calculate_confusion_matrix(dataset, results,
args.score_thr,
args.nms_iou_thr,
args.tp_iou_thr)
plot_confusion_matrix(
confusion_matrix,
dataset.metainfo['classes'] + ('background', ),
save_dir=args.save_dir,
show=args.show,
color_theme=args.color_theme)
if __name__ == '__main__':
main()
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