实用指南：【YOLO目标检测】获取COCO指标

文章目录

• 0 注意
• 1 验证的时候开启save_json选项
• 2 数据集的格式转换成COCO格式
• 4 使用get_COCO.py获取指标

0 注意

图片命名纯数字命名，防止各种各样的报错，类别不要有空格。

1 验证的时候开启save_json选项

import warnings
warnings.filterwarnings('ignore')
import os
import numpy as np
from ultralytics import YOLO
if __name__ == '__main__':
model_path = f'runs/best.pt'
model = YOLO(model_path) # 选择训练好的权重路径
result = model.val(data='datasets/data.yaml',
split='val', # split可以选择train、val、test 根据自己的数据集情况来选择.
imgsz=640,
batch=16,
# iou=0.7,
# rect=False,
save_json=True, # 一定要开启此选项
project='runs/val',
)

运行命令

python val.py

2 数据集的格式转换成COCO格式

import os
import cv2
import json
from tqdm import tqdm
from sklearn.model_selection import train_test_split
import argparse
# visdrone2019
classes = ['pedestrain', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor'] #自己的类别
parser = argparse.ArgumentParser()
parser.add_argument('--image_path', default='',type=str, help="path of images")
parser.add_argument('--label_path', default='',type=str, help="path of labels .txt")
parser.add_argument('--save_path', default='data.json', type=str, help="if not split the dataset, give a path to a json file")
arg = parser.parse_args()
def yolo2coco(arg):
print("Loading data from ", arg.image_path, arg.label_path)
assert os.path.exists(arg.image_path)
assert os.path.exists(arg.label_path)
originImagesDir = arg.image_path
originLabelsDir = arg.label_path
# images dir name
indexes = os.listdir(originImagesDir)
dataset = {
'categories': [], 'annotations': [], 'images': []
}
for i, cls in enumerate(classes, 0):
dataset['categories'].append({
'id': i, 'name': cls, 'supercategory': 'mark'
})
# 标注的id
ann_id_cnt = 0
for k, index in enumerate(tqdm(indexes)):
# 支持 png jpg 格式的图片.
txtFile = f'{index[:index.rfind(".")]
}.txt'
stem = index[:index.rfind(".")]
# 读取图像的宽和高
try:
im = cv2.imread(os.path.join(originImagesDir, index))
height, width, _ = im.shape
except Exception as e:
print(f'{os.path.join(originImagesDir, index)
} read error.\nerror:{e
}')
# 添加图像的信息
if not os.path.exists(os.path.join(originLabelsDir, txtFile)):
# 如没标签，跳过，只保留图片信息.
continue
dataset['images'].append({
'file_name': index,
'id': stem,
'width': width,
'height': height
})
with open(os.path.join(originLabelsDir, txtFile), 'r') as fr:
labelList = fr.readlines()
for label in labelList:
label = label.strip().split()
x = float(label[1])
y = float(label[2])
w = float(label[3])
h = float(label[4])
# convert x,y,w,h to x1,y1,x2,y2
H, W, _ = im.shape
x1 = (x - w / 2) * W
y1 = (y - h / 2) * H
x2 = (x + w / 2) * W
y2 = (y + h / 2) * H
# 标签序号从0开始计算, coco2017数据集标号混乱，不管它了。
cls_id = int(label[0])
width = max(0, x2 - x1)
height = max(0, y2 - y1)
dataset['annotations'].append({
'area': width * height,
'bbox': [x1, y1, width, height],
'category_id': cls_id,
'id': ann_id_cnt,
'image_id': stem,
'iscrowd': 0,
# mask, 矩形是从左上角点按顺时针的四个顶点
'segmentation': [[x1, y1, x2, y1, x2, y2, x1, y2]]
})
ann_id_cnt += 1
# 保存结果
with open(arg.save_path, 'w') as f:
json.dump(dataset, f)
print('Save annotation to {}'.format(arg.save_path))
if __name__ == "__main__":
yolo2coco(arg)

运行命令

pip install tidecv

python yolo2coco.py --image_path /hy-tmp/yolov11/datasets/images/val --label_path /hy-tmp/yolov11/datasets/labels/val --save_path /hy-tmp/yolov11/datasets/val_coco.json

4 使用get_COCO.py获取指标

import warnings
warnings.filterwarnings('ignore')
import argparse
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from tidecv import TIDE, datasets
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--anno_json', type=str, default='', help='label coco json path')
parser.add_argument('--pred_json', type=str, default='', help='pred coco json path')
return parser.parse_known_args()[0]
if __name__ == '__main__':
opt = parse_opt()
anno_json = opt.anno_json
pred_json = opt.pred_json
anno = COCO(anno_json) # init annotations api
if 'info' not in anno.dataset:
anno.dataset['info'] = {
} # 添加空的info字段
# print(anno.dataset.keys())
pred = anno.loadRes(pred_json) # init predictions api
eval = COCOeval(anno, pred, 'bbox')
eval.evaluate()
eval.accumulate()
eval.summarize()
tide = TIDE()
tide.evaluate_range(datasets.COCO(anno_json), datasets.COCOResult(pred_json), mode=TIDE.BOX)
tide.summarize()
tide.plot(out_dir='result')

运行命令

python get_COCO_metrice.py --anno_json /hy-tmp/yolov11/datasets/val_coco.json --pred_json /hy-tmp/yolov11/runs/val/test/predictions.json