修改了识别逻辑
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@ -2,7 +2,7 @@ import logging
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import shutil
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import zipfile
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from os.path import exists
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from pathlib import Path
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import torch
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import gc
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import os
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@ -385,7 +385,7 @@ class YOLOSegmentationInference:
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return output_image
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def save_results(self, result: InferenceResult, output_dir: str, save_mask: bool = False,
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save_label: bool = False, result_save: [] = None) -> None:
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save_label: bool = False, result_save: List[Dict] = None) -> None:
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"""
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保存推理结果
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@ -508,45 +508,127 @@ class YOLOSegmentationInference:
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plt.show()
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def process_single_image(self, image_path: str, output_dir: Optional[str] = None,
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conf_threshold: float = 0.25, iou_threshold: float = 0.5,
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save_mask: bool = False, save_label: bool = False, show: bool = True,
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result_save: [] = None) -> InferenceResult:
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conf_threshold: float = 0.25, iou_threshold: float = 0.5,
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save_mask: bool = False, save_label: bool = False, show: bool = True,
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result_save: Optional[List] = None):
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"""
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处理单张图片
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处理单张图片（使用YOLO分割模型）
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Args:
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image_path: 图片路径
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output_dir: 输出目录，如果为None则不保存
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image_path: 输入图片路径
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output_dir: 输出目录（如果为None则不保存）
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conf_threshold: 置信度阈值
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iou_threshold: IOU阈值
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save_mask: 是否保存单独的掩码文件
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save_label: 是否保存YOLO格式的标签文件
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show: 是否显示结果
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iou_threshold: NMS IoU阈值
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save_mask: 是否保存单独的掩码图像（PNG格式）
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save_label: 是否保存YOLO格式标签文件（txt，含多边形或边界框）
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show: 是否显示结果图像（按任意键关闭）
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result_save: 可选的外部列表，用于收集本次处理的信息（如图像路径、保存的文件等）
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Returns:
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推理结果
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Ultralytics 的 Results 对象（包含原始图像、边界框、掩码等信息）
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"""
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# 执行推理
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result = self.perform_inference(image_path, conf_threshold, iou_threshold)
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# 1. 确保模型已加载
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if not hasattr(self, 'model') or self.model is None:
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raise ValueError("模型未加载，请先初始化模型（self.model = YOLO('...')）")
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# 绘制结果
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if result.masks is not None and len(result.masks) > 0:
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self.draw_results(result, conf_threshold)
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# 2. 执行推理
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results = self.model.predict(
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source=image_path,
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conf=conf_threshold,
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iou=iou_threshold,
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imgsz=640, # 可根据需要调整，或作为参数传入
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save=False, # 手动保存，不依赖自动保存
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save_txt=False, # 手动处理标签
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retina_masks=True, # 获取与原图同分辨率的高精度掩码
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)
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# 单张图片，取第一个结果
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result = results[0]
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# 保存结果
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if output_dir is not None:
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self.save_results(result, output_dir, save_mask, save_label, result_save)
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# 3. 准备输出目录
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if output_dir:
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output_path = Path(output_dir)
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output_path.mkdir(parents=True, exist_ok=True)
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base_name = Path(image_path).stem
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else:
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output_path = None
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base_name = None
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# # 显示结果
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# 4. 绘制标注图像（使用 result.plot()）
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annotated_img = result.plot() # 返回BGR numpy数组
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# 5. 保存标注图像到输出目录（如果指定）
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if output_path:
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img_save_path = output_path / f"{base_name}_annotated.jpg"
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cv2.imwrite(str(img_save_path), annotated_img)
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else:
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img_save_path = None
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# 6. 保存单独的掩码文件（如果 save_mask 为 True 且有掩码）
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mask_save_paths = []
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if save_mask and result.masks is not None and output_path:
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masks = result.masks.data.cpu().numpy() # (N, H, W) 二值掩码
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for i, mask in enumerate(masks):
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mask_img = (mask * 255).astype(np.uint8) # 转换为0-255图像
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mask_file = output_path / f"{base_name}_mask_{i}.png"
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cv2.imwrite(str(mask_file), mask_img)
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mask_save_paths.append(str(mask_file))
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# 7. 保存YOLO格式标签文件（如果 save_label 为 True）
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label_save_path = None
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if save_label and output_path:
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# 优先保存分割多边形标签（masks.xy）
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if result.masks is not None and hasattr(result.masks, 'xy') and result.masks.xy:
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label_file = output_path / f"{base_name}.txt"
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with open(label_file, 'w') as f:
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for i, poly in enumerate(result.masks.xy):
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cls_id = int(result.boxes.cls[i]) if result.boxes is not None else 0
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# 归一化多边形点坐标（除以图像宽高）
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h, w = result.orig_shape
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normalized = poly / [w, h]
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flat_coords = normalized.flatten().tolist()
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line = f"{cls_id} " + " ".join([f"{x:.6f}" for x in flat_coords])
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f.write(line + "\n")
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label_save_path = str(label_file)
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# 如果没有掩码但有边界框，则保存检测标签（YOLO检测格式）
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elif result.boxes is not None:
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label_file = output_path / f"{base_name}.txt"
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with open(label_file, 'w') as f:
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boxes = result.boxes.xyxyn.cpu().numpy() # 归一化边界框 (xyxy)
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cls_ids = result.boxes.cls.cpu().numpy().astype(int)
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for box, cls_id in zip(boxes, cls_ids):
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# 转换为 YOLO 格式 (x_center, y_center, width, height)
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x1, y1, x2, y2 = box
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x_center = (x1 + x2) / 2
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y_center = (y1 + y2) / 2
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width = x2 - x1
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height = y2 - y1
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line = f"{cls_id} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}"
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f.write(line + "\n")
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label_save_path = str(label_file)
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# # 8. 显示结果（如果 show 为 True）
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# if show:
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# self.show_results(result)
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# cv2.imshow("Segmentation Result", annotated_img)
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# cv2.waitKey(0)
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# cv2.destroyAllWindows()
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# 9. 如果提供了 result_save 列表，将本次结果信息追加进去
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if result_save is not None:
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result_save.append({
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'image_path': image_path,
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'annotated_image_path': str(img_save_path) if img_save_path else None,
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'mask_paths': mask_save_paths,
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'label_path': label_save_path,
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'num_objects': len(result.boxes) if result.boxes else 0,
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})
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# 10. 返回结果（Ultralytics 的 Results 对象，包含所有推理信息）
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return result
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def process_single_image_share_dir(self, image_path, user_name, pwd, output_dir: Optional[str] = None,
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conf_threshold: float = 0.25, iou_threshold: float = 0.5,
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save_mask: bool = False, save_label: bool = False, show: bool = True,
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result_save: [] = None) -> None:
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result_save: List[Dict] = None) -> None:
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"""
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处理单张图片
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@ -585,7 +667,7 @@ class YOLOSegmentationInference:
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def process_image_directory(self, input_dir: str, output_dir: Optional[str] = None,
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conf_threshold: float = 0.25, iou_threshold: float = 0.5,
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save_mask: bool = False, save_label: bool = False, show: bool = False,
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result_save: [] = None) -> List[
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result_save: List[Dict] = None) -> List[
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InferenceResult]:
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"""
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处理目录中的所有图片
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@ -681,7 +763,7 @@ class YOLOSegmentationInference:
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def process_image_directory_share_dir_circle(self, task_id, current_time, input_dir_list, user_name, pwd, output_dir: Optional[str] = None,
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conf_threshold: float = 0.25, iou_threshold: float = 0.5,
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save_mask: bool = False, save_label: bool = False, show: bool = False,
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result_save: [] = None) -> None:
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result_save: List[Dict] = None) -> None:
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for input_dir in input_dir_list :
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self.process_image_directory_share_dir(task_id,current_time,input_dir,user_name,pwd,output_dir,conf_threshold,iou_threshold,save_mask,save_label,show,result_save)
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del_file_shutil(output_dir)
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@ -689,7 +771,7 @@ class YOLOSegmentationInference:
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def process_image_directory_share_dir(self, task_id, current_time, input_dir, user_name, pwd, output_dir: Optional[str] = None,
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conf_threshold: float = 0.25, iou_threshold: float = 0.5,
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save_mask: bool = False, save_label: bool = False, show: bool = False,
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result_save: [] = None) -> None:
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result_save: List[Dict] = None) -> None:
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"""
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处理目录中的所有图片
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