### YOLOv9 ONNX 文件使用示例 对于YOLOv9的ONNX文件使用，可以基于`onnxruntime`库来实现推理过程。下面是一个简单的Python脚本用于加载并运行YOLOv9的ONNX模型[^1]。 ```python import onnxruntime as ort import numpy as np from PIL import Image import cv2 def load_model(model_path): providers = ['CPUExecutionProvider'] # 如果安装了GPU版本的onnxruntime并且有可用的CUDA设备，则优先使用GPU加速 if 'CUDAExecutionProvider' in ort.get_available_providers(): providers.insert(0, 'CUDAExecutionProvider') session = ort.InferenceSession(model_path, providers=providers) return session def preprocess_image(image_path, input_shape=(640, 640)): orig_img = Image.open(image_path).convert('RGB') resized_img = orig_img.resize(input_shape) img_array = np.array(resized_img).astype(np.float32) / 255. img_tensor = np.transpose(img_array, (2, 0, 1)) img_tensor = np.expand_dims(img_tensor, axis=0) return img_tensor, orig_img.size def postprocess(output_data, original_size, conf_threshold=0.5): boxes, scores, class_ids = [], [], [] for detection in output_data: confidence = float(detection[4]) if confidence >= conf_threshold: box = detection[:4].tolist() # 这里的转换是为了适应原始图像尺寸的比例调整 width_ratio = original_size[0]/640 height_ratio = original_size[1]/640 box[0] *= width_ratio box[1] *= height_ratio box[2] *= width_ratio box[3] *= height_ratio classes_score = detection[5:] class_id = int(classes_score.argmax()) score = float(classes_score[class_id]) boxes.append(box) scores.append(score) class_ids.append(class_id) indices = cv2.dnn.NMSBoxes(boxes, scores, conf_threshold, nms_threshold=0.4) filtered_boxes, filtered_scores, filtered_class_ids = [], [], [] for i in indices: filtered_boxes.append(boxes[i]) filtered_scores.append(scores[i]) filtered_class_ids.append(class_ids[i]) return filtered_boxes, filtered_scores, filtered_class_ids if __name__ == "__main__": model_path = "path/to/your/yolov9.onnx" image_path = "path/to/test/image.jpg" session = load_model(model_path) input_name = session.get_inputs()[0].name processed_input, original_size = preprocess_image(image_path) outputs = session.run(None, {input_name: processed_input}) final_output = outputs[0][0] # 假设输出的第一个张量包含了所有的检测框信息 bboxes, confidences, labels = postprocess(final_output, original_size) print(f"Detected objects with their bounding boxes and confidence levels:") for bbox, label, confidence in zip(bboxes, labels, confidences): print(f"Label={label}, Confidence={confidence:.2f}, BBox={bbox}") ``` 此代码片段展示了如何利用`onnxruntime`加载YOLOv9的ONNX模型，并对其进行基本的操作，包括但不限于： - 加载指定路径下的ONNX模型； - 对输入图片做必要的预处理操作以便于送入网络中计算； - 执行前向传播得到预测结果； - 解析输出数据获得最终的目标边界框及其置信度得分； 值得注意的是，在实际应用当中可能还需要进一步优化上述流程中的某些环节以满足特定需求，比如更复杂的后处理逻辑或是针对不同框架间差异性的适配工作等。