to coco

[JiyueWang]

import os
import json
import glob
import torch

文件夹路径和输出JSON路径

txt_folder = 'runs\detect\exp5\labels' # 读取YOLO格式结果的文件夹路径
output_json = 'runs\detect\exp5\labelscoco_format.json' # 输出COCO格式JSON文件路径

def yolo_to_coco_bbox(bbox, img_width=1, img_height=1):
"""
将 YOLO 坐标转换为 COCO 坐标。
YOLO 格式: (center_x, center_y, width, height) -> COCO 格式: (x_min, y_min, width, height)
"""
x_c, y_c, w, h = bbox
x_min = (x_c - w / 2) * img_width
y_min = (y_c - h / 2) * img_height
width = w * img_width
height = h * img_height
return [x_min, y_min, width, height]

def convert_yolo_to_coco(txt_folder):
"""
读取 YOLO 格式的 txt 文件并转换为 COCO 格式。
"""
coco_results = []
txt_files = glob.glob(os.path.join(txt_folder, "*.txt"))

for txt_file in txt_files:
    image_id = int(os.path.splitext(os.path.basename(txt_file))[0])  # 使用文件名作为 image_id

    with open(txt_file, "r") as f:
        for line in f:
            line = line.strip().split()
            if len(line) < 5:
                continue  # 跳过无效行
            cls, x_center, y_center, width, height = map(float, line[:5])
            conf = float(line[5]) if len(line) > 5 else 1.0  # 默认置信度为1.0（如果没有提供）

            # 转换YOLO边界框坐标为COCO格式
            bbox = yolo_to_coco_bbox([x_center, y_center, width, height])

            # 构建COCO格式的字典
            coco_results.append({
                "image_id": image_id,
                "category_id": int(cls),
                "bbox": bbox,
                "score": conf
            })

return coco_results

转换YOLO结果并保存为COCO格式的JSON

coco_results = convert_yolo_to_coco(txt_folder)
with open(output_json, "w") as f:
json.dump(coco_results, f, indent=4)

print(f"COCO format results saved to {output_json}")

[JiyueWang]

dear junjun

[JiyueWang]

在detect()函数中

output_file = 'results.txt' # 设定单一输出文件路径

在循环外打开文件，确保是追加模式

with open(output_file, 'a') as f:
for path, img, im0s, vid_cap in dataset:
# 检测处理代码...
for *xyxy, conf, cls in reversed(det):
if save_txt: # 将结果写入到单一的文件
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # 归一化坐标
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # 标签格式
f.write(('%g ' * len(line)).rstrip() % line + '\n')

import os

输入文件夹路径（包含多个YOLO格式的结果文件）

input_folder = 'path/to/yolo/results'

输出合并的单一结果文件路径

output_file = 'combined_results.txt'

def combine_yolo_results(input_folder, output_file):
with open(output_file, 'w') as outfile:
# 遍历输入文件夹中的每个文件
for filename in os.listdir(input_folder):
if filename.endswith('.txt'):
# 获取图片ID（从文件名中去掉扩展名）
image_id = os.path.splitext(filename)[0]

            # 读取每个YOLO格式的文件
            with open(os.path.join(input_folder, filename), 'r') as infile:
                for line in infile:
                    # 每行数据：class_id x_center y_center width height
                    # 添加image_id列，并写入到输出文件
                    outfile.write(f"{image_id} {line.strip()}\n")

print(f"合并结果已保存至: {output_file}")

运行合并函数

combine_yolo_results(input_folder, output_file)

[JiyueWang]

junjun 听到请回答

[joe-joe-shen]

dear junjun

1111