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PR Curve
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| from pycocotools.coco import COCO | |
| from pycocotools.cocoeval import COCOeval | |
| import argparse | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| def coco_eval(args): | |
| cocoGt = COCO(args.gt_json) | |
| cocoDt = cocoGt.loadRes(args.pred_json) | |
| cocoEval = COCOeval(cocoGt, cocoDt, args.eval_type) | |
| cocoEval.evaluate() | |
| cocoEval.accumulate() | |
| cocoEval.summarize() | |
| # pr-curve | |
| all_precision = cocoEval.eval['precision'][0, :, :, 0, 2] | |
| all_recall = cocoEval.params.recThrs | |
| names=['B', 'Y', 'W', 'R', 'AH', 'BH'] | |
| x = np.arange(0, 1.01, 0.01) | |
| if 0 < len(names) < 98: | |
| for i, y in enumerate(all_precision.T): | |
| plt.plot(x, y, linewidth=1, label=f'{names[i]} {all_precision[:,i].mean():.3f}') # plot(recall, precision) | |
| else: | |
| plt.plot(x, all_precision, linewidth=1, color='grey') # plot(recall, precision) | |
| plt.plot(x, all_precision.mean(1), linewidth=3, color='blue', label='all classes %.3f [email protected]' % all_precision.mean()) | |
| plt.title('PR Curve: [email protected] = %.3f' % all_precision.mean()) | |
| plt.ylabel("Precision") | |
| plt.xlabel("Recall") | |
| plt.legend(loc="best") | |
| plt.savefig('prcurve.jpg',dpi=250) | |
| plt.show() | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser(description='Evaluate segm/bbox/keypoints in COCO format.') | |
| parser.add_argument('gt_json', type=str, help="COCO format segmentation/detection/keypoints ground truth json file") | |
| parser.add_argument('pred_json', type=str, help="COCO format segmentation/detection/keypoints prediction json file") | |
| parser.add_argument('eval_type', type=str, choices=['segm', 'bbox', 'keypoints'], help="Evaluation type") | |
| args = parser.parse_args() | |
| coco_eval(args) |
Author
chenghanc
commented
Jul 11, 2025
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