def evaluate(label_path,
result_path,
save_path,
current_class=['Car', 'Pedestrian', 'Cyclist'],
coco=False,
score_thresh=-1):
class_to_name = {0: 'Car',
1: 'Pedestrian',
2: 'Cyclist',
3: 'DontCare'}
gt_annos = kitti.get_label_annos(label_path)
dt_annos = kitti.get_label_annos(result_path)
# visualize(gt_annos, dt_annos)
print(len(dt_annos))
if score_thresh > 0:
dt_annos = kitti.filter_annos_low_score(dt_annos, score_thresh)
if coco:
print(get_coco_eval_result(dt_annos, dt_annos, current_class))
else:
result_str, _ = get_official_eval_result(gt_annos, dt_annos, current_class, class_to_name)
print(result_str)
with open(save_path, 'w+') as f:
f.write("\n")
f.write(result_str)
def evaluate(label_path,
result_path,
current_class=0,
coco=False,
score_thresh=-1,
eval_dist=None):
dt_annos, image_ids = kitti.get_label_annos(result_path,
return_image_ids=True,
eval_dist=eval_dist)
print('Eval {} images'.format(len(dt_annos)))
if score_thresh > 0:
dt_annos = kitti.filter_annos_low_score(dt_annos, score_thresh)
#val_image_ids = _read_imageset_file(label_split_file)
gt_annos = kitti.get_label_annos(label_path,
image_ids,
eval_dist=eval_dist)
# Playground
# print(gt_annos[48].keys())
#print(gt_annos[48]["name"])
#print(gt_annos[48]["dimensions"])
#print(gt_annos[48]["location"])
## Playground end
if coco:
print(get_coco_eval_result(gt_annos, dt_annos, current_class))
else:
print(get_official_eval_result(gt_annos, dt_annos, current_class))
def show_result(gt_annos, pd_annos, obj_type=[0]):
# obj_type [0] == Car
result_kitti = get_official_eval_result(gt_annos, pd_annos, obj_type)
print(result_kitti)
result_coco = get_coco_eval_result(gt_annos, pd_annos, obj_type)
print(result_coco)
def evaluate(label_path,
result_path,
current_class=0,
coco=False,
score_thresh=-1,
eval_dist=None):
dt_annos, image_ids = kitti.get_label_annos(result_path, return_image_ids=True, eval_dist=eval_dist)
print('Eval {} images'.format(len(dt_annos)))
if score_thresh > 0:
dt_annos = kitti.filter_annos_low_score(dt_annos, score_thresh)
#val_image_ids = _read_imageset_file(label_split_file)
gt_annos = kitti.get_label_annos(label_path, image_ids, eval_dist=eval_dist)
if coco:
print(get_coco_eval_result(gt_annos, dt_annos, current_class))
else:
print(get_official_eval_result(gt_annos, dt_annos, current_class))
def evaluate(label_path,
result_path,
label_split_file,
current_class=0,
coco=False,
score_thresh=-1):
dt_annos = kitti.get_label_annos(result_path)
if score_thresh > 0:
dt_annos = kitti.filter_annos_low_score(dt_annos, score_thresh)
val_image_ids = _read_imageset_file(label_split_file)
val_image_ids = exists(val_image_ids, result_path)
gt_annos = kitti.get_label_annos(label_path, val_image_ids)
dt_annos = kitti.get_label_annos(result_path, val_image_ids)
if coco:
print(get_coco_eval_result(gt_annos, dt_annos, current_class))
else:
print(get_official_eval_result(gt_annos, dt_annos, current_class))
import kitti_common as kitti
import json
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 [line[:-1] for line in lines]
gt_split_file = "/mnt/nfs/scratch1/pmallya/nusc_kitti/val/nusc_val.txt"
val_image_ids = _read_imageset_file(gt_split_file)
# print("Val Image IDs: ", val_image_ids)
det_path = "/mnt/nfs/scratch1/pmallya/nusc_kitti/val/infer_2/"
dt_annos = kitti.get_label_annos(det_path, val_image_ids)
gt_path = "/mnt/nfs/scratch1/pmallya/nusc_kitti/val/label_2/"
gt_annos = kitti.get_label_annos(gt_path, val_image_ids)
# print(dt_annos)
# print(gt_annos)
with open("./outputs/eval_result.json", "w", encoding="utf-8") as f:
json.dump(get_official_eval_result(gt_annos, dt_annos, 0), f, ensure_ascii=False, indent=4)
# f.write("\nCOCO Eval Result:\n")
# print(get_coco_eval_result(gt_annos, dt_annos, 0))