yolov8onnx推理代码

### YOLOv8 ONNX 推理代码示例 对于YOLOv8模型，在ONNX Runtime环境下进行推理可以按照如下Python代码实现： ```python import onnxruntime as ort import numpy as np from PIL import Image, ImageDraw import cv2 def preprocess(image_path): image = Image.open(image_path).convert('RGB') image_resized = image.resize((640, 640)) img_array = np.array(image_resized) # Normalize to [0, 1] and HWC to CHW format then add batch dimension. input_tensor = (img_array / 255.0)[np.newaxis, :, :, :].transpose(0, 3, 1, 2).astype(np.float32) return input_tensor def postprocess(outputs, conf_threshold=0.7, iou_threshold=0.5): boxes, scores, class_ids = [], [], [] output = outputs[0][0] for detection in output: score = float(detection[4]) if score >= conf_threshold: classes_scores = detection[5:] class_id = np.argmax(classes_scores) if classes_scores[class_id] > score: box = [ int(detection[0] - detection[2]/2), int(detection[1] - detection[3]/2), int(detection[0] + detection[2]/2), int(detection[1] + detection[3]/2)] boxes.append(box) scores.append(score) class_ids.append(class_id) indices = cv2.dnn.NMSBoxes( bboxes=boxes, scores=scores, score_threshold=conf_threshold, nms_threshold=iou_threshold ) filtered_boxes, filtered_scores, filtered_class_ids = [], [], [] for index in indices: filtered_boxes.append(boxes[index]) filtered_scores.append(scores[index]) filtered_class_ids.append(class_ids[index]) return filtered_boxes, filtered_scores, filtered_class_ids if __name__ == "__main__": session = ort.InferenceSession("yolov8.onnx", providers=['CUDAExecutionProvider']) input_name = session.get_inputs()[0].name test_image_path = "test.jpg" input_data = preprocess(test_image_path) raw_result = session.run(None, {input_name: input_data}) boxes, scores, class_ids = postprocess(raw_result) draw = ImageDraw.Draw(Image.open(test_image_path)) for idx, bbox in enumerate(boxes): label = f"{class_ids[idx]}:{scores[idx]:.2f}" draw.rectangle(bbox, outline="red", width=3) draw.text((bbox[0], bbox[1]), label, fill="white") result_img = Image.open(test_image_path) result_img.show() ``` 上述代码展示了如何加载一个预训练好的YOLOv8 ONNX模型，并通过给定的一张图像执行目标检测任务。此过程涉及到了输入数据的前处理以及输出结果后的后处理操作[^1]。