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