218 lines
7.0 KiB
Python
218 lines
7.0 KiB
Python
import json
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import os
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from uuid import uuid4
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from ultralytics import YOLO
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def yolo_to_labelstudio(results, class_names, model_version="yolov8_1.0"):
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"""将 YOLOv8 检测结果转换为 Label Studio 预测格式
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Args:
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results: YOLOv8 Results 对象
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class_names: 类别名称列表
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model_version: 模型版本标识
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Returns:
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Label Studio 兼容的预测字典
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"""
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predictions = []
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# 获取原始图像尺寸 (height, width)
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orig_height = results[0].orig_shape[0]
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orig_width = results[0].orig_shape[1]
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for box in results[0].boxes:
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# 提取检测框信息
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x_min, y_min, x_max, y_max = box.xyxy[0].tolist()
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conf = box.conf.item()
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cls_idx = int(box.cls.item())
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# 计算百分比坐标（保留3位小数）
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x_percent = round((x_min / orig_width) * 100, 3)
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y_percent = round((y_min / orig_height) * 100, 3)
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width_percent = round((x_max - x_min) / orig_width * 100, 3)
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height_percent = round((y_max - y_min) / orig_height * 100, 3)
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# 构建预测项
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prediction = {
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"id": str(uuid4()), # 生成唯一ID
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"type": "rectanglelabels",
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"from_name": "label",
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"to_name": "image",
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"original_width": orig_width,
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"original_height": orig_height,
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"value": {
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"x": x_percent,
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"y": y_percent,
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"width": width_percent,
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"height": height_percent,
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"rotation": 0,
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"rectanglelabels": [class_names[cls_idx]]
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},
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"score": round(conf, 4) # 保留4位小数
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}
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predictions.append(prediction)
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return {
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"model_version": model_version,
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"result": predictions
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}
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def main(model_list, class_name_list, images_path, save_file, pre_root, conf=0.3):
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"""_summary_
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Args:
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model_list (_type_): 模型列表
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class_name_list (_type_): 模型输出类名称
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images_path (_type_): 图像路径
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save_file (_type_): 保存json文件路径
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pre_root (_type_): json中文件路径前缀
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"""
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image_names = os.listdir(images_path)
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all_images = [os.path.join(images_path, t) for t in image_names]
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json_list = list()
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for i in range(len(all_images)):
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predictions = None
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for j in range(len(model_list)):
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results = model_list[j](all_images[i],
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conf = conf, device=0)
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predictions_t = yolo_to_labelstudio(results, class_name_list[j])
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if predictions is None:
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predictions = predictions_t
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else:
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predictions["result"] += predictions_t["result"]
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t_d = dict()
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# "劳保鞋识别/dataset/new_all/train/images/dataset2_-01-15-2-2-2-2-25_jpg.rf.267991c87898d90733d5c7623941a999.jpg"
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# t_d["data"] = {"image": f"/data/local-files/?d=劳保鞋识别/dataset/new_all/train/images/{image_names[i]}"}
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# t_d["data"] = {"image": f"/data/local-files/?d=images_20250624105811/{image_names[i]}"}
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t_d["data"] = {"image": f"/data/local-files/?d={pre_root}/{image_names[i]}"}
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t_d["predictions"] = list()
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t_d["predictions"].append(predictions)
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json_list.append(t_d)
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print(len(json_list))
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# return
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with open(save_file, "w", encoding="utf-8") as f:
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json.dump(json_list,f,
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indent=4, # 4空格缩进
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sort_keys=True, # 按键名字典序排序
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ensure_ascii=False # 支持非ASCII字符
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)
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# 使用示例
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if __name__ == "__main__":
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# 初始化模型
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# model_0 = YOLO('/home/admin-root/haotian/python哈汽锻8安全帽识别/HelmetHeadBAC/trainHardHatsV1/weights/best.pt')
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# 头,安全帽
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model_0 = YOLO('/home/admin-root/haotian/python哈汽锻8安全帽识别/HelmetHeadBAC/trainHardHatsV12/weights/best.pt')
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# 检测鞋的模型
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model_1 = YOLO('/home/admin-root/haotian/劳保鞋识别/jingzhu_245_1112/tensorrtx-master/yolov8/build_1113/best.pt')
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# 定义类别名称 (需与训练时一致)
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CLASS_NAMES_0 = [
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"helmet", "head",
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]
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CLASS_NAMES_1 = [
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"shoe",
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]
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model_2 = YOLO('/home/admin-root/haotian/python哈汽锻8安全帽识别/HelmetHeadBAC/trainHardHatsV1HelmetHeadShoe2/weights/best.pt')
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CLASS_NAMES_2 = [
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"helmet",
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"head",
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"shoe"
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]
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model_list = list()
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model_list.append(model_2)
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class_name_list = list()
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class_name_list.append(CLASS_NAMES_2)
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# images_path = '/home/admin-root/haotian/劳保鞋识别/dataset/new_all/train/images'
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# images_path = '/home/admin-root/haotian/锻8/tensorrtx/yolov8/images_20250624105811'
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images_path = '/home/admin-root/haotian/锻8/tensorrtx/yolov8/images_20250624105822'
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# save_path = '/home/admin-root/haotian/python哈汽锻8安全帽识别/output/outputShoeV2'
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main(model_list, class_name_list, images_path, "duan8_22.json", "images_20250624105822")
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#-----------------------------------------------------------------旧版代码-------------------------------------------------------------------------------
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# image_names = os.listdir(images_path)
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# all_images = [os.path.join(images_path, t) for t in image_names]
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# json_list = list()
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# for i in range(len(all_images)):
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# # conf 置信度在.
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# results_0 = model_0(all_images[i],
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# conf = 0.5, device=0)
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# predictions_0 = yolo_to_labelstudio(results_0, CLASS_NAMES_0)
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# results_1 = model_1(all_images[i],
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# conf=0.5, device=0)
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# predictions_1 = yolo_to_labelstudio(results_1, CLASS_NAMES_1)
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# predictions_0["result"] += predictions_1["result"]
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# t_d = dict()
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# # "劳保鞋识别/dataset/new_all/train/images/dataset2_-01-15-2-2-2-2-25_jpg.rf.267991c87898d90733d5c7623941a999.jpg"
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# # t_d["data"] = {"image": f"/data/local-files/?d=劳保鞋识别/dataset/new_all/train/images/{image_names[i]}"}
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# t_d["data"] = {"image": f"/data/local-files/?d=images_20250624105811/{image_names[i]}"}
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# t_d["predictions"] = list()
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# t_d["predictions"].append(predictions_0)
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# json_list.append(t_d)
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# # if i == 10:
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# # break
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# with open("label_studio_new_all.json", "w", encoding="utf-8") as f:
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# json.dump(json_list,f,
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# indent=4, # 4空格缩进
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# sort_keys=True, # 按键名字典序排序
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# ensure_ascii=False # 支持非ASCII字符
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# )
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