kitti-object-eval-python's Introduction

kitti-object-eval-python

Fast kitti object detection eval in python(finish eval in less than 10 second), support 2d/bev/3d/aos. , support coco-style AP. If you use command line interface, numba need some time to compile jit functions.

WARNING: The "coco" isn't official metrics. Only "AP(Average Precision)" is.

Dependencies

Only support python 3.6+, need numpy, skimage, numba, fire, scipy. If you have Anaconda, just install cudatoolkit in anaconda. Otherwise, please reference to this page to set up llvm and cuda for numba.

Install by conda:

conda install -c numba cudatoolkit=x.x  (8.0, 9.0, 10.0, depend on your environment)

Usage

commandline interface:

python evaluate.py evaluate --label_path=/path/to/your_gt_label_folder --result_path=/path/to/your_result_folder --label_split_file=/path/to/val.txt --current_class=0 --coco=False

python interface:

import kitti_common as kitti
from eval import get_official_eval_result, get_coco_eval_result
def _read_imageset_file(path):
    with open(path, 'r') as f:
        lines = f.readlines()
    return [int(line) for line in lines]
det_path = "/path/to/your_result_folder"
dt_annos = kitti.get_label_annos(det_path)
gt_path = "/path/to/your_gt_label_folder"
gt_split_file = "/path/to/val.txt" # from https://xiaozhichen.github.io/files/mv3d/imagesets.tar.gz
val_image_ids = _read_imageset_file(gt_split_file)
gt_annos = kitti.get_label_annos(gt_path, val_image_ids)
print(get_official_eval_result(gt_annos, dt_annos, 0)) # 6s in my computer
print(get_coco_eval_result(gt_annos, dt_annos, 0)) # 18s in my computer

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