执行目标检测 Args: image: 输入图像 Returns: 检测到的边界框列表，每个边界框格式为[x1, y1, x2, y2] Raises: ImageProcessError: 当图像处理失败时 ModelLoadError: 当模型未初始化时
(self, image: Union[bytes, str, Image.Image])
| 47 | raise ModelLoadError(f"检测引擎初始化失败: {str(e)}") from e |
| 48 | |
| 49 | def predict(self, image: Union[bytes, str, Image.Image]) -> List[List[int]]: |
| 50 | """ |
| 51 | 执行目标检测 |
| 52 | |
| 53 | Args: |
| 54 | image: 输入图像 |
| 55 | |
| 56 | Returns: |
| 57 | 检测到的边界框列表，每个边界框格式为[x1, y1, x2, y2] |
| 58 | |
| 59 | Raises: |
| 60 | ImageProcessError: 当图像处理失败时 |
| 61 | ModelLoadError: 当模型未初始化时 |
| 62 | """ |
| 63 | if not self.is_ready(): |
| 64 | raise ModelLoadError("检测引擎未初始化") |
| 65 | |
| 66 | # 验证输入 |
| 67 | validate_image_input(image) |
| 68 | |
| 69 | try: |
| 70 | # 直接使用原始的get_bbox方法 |
| 71 | if isinstance(image, bytes): |
| 72 | return self.get_bbox(image) |
| 73 | elif isinstance(image, Image.Image): |
| 74 | import io |
| 75 | img_bytes = io.BytesIO() |
| 76 | image.save(img_bytes, format='PNG') |
| 77 | return self.get_bbox(img_bytes.getvalue()) |
| 78 | else: |
| 79 | # 其他类型先转换为PIL Image再处理 |
| 80 | pil_image = load_image_from_input(image) |
| 81 | import io |
| 82 | img_bytes = io.BytesIO() |
| 83 | pil_image.save(img_bytes, format='PNG') |
| 84 | return self.get_bbox(img_bytes.getvalue()) |
| 85 | |
| 86 | except Exception as e: |
| 87 | raise ImageProcessError(f"目标检测失败: {str(e)}") from e |
| 88 | |
| 89 | def preproc(self, img, input_size, swap=(2, 0, 1)): |
| 90 | """预处理函数（来自原始代码）""" |
nothing calls this directly
no test coverage detected