yolov8旋转目标检测部署教程（附代码c++/python）

为了编写一个详细的YOLOv8旋转目标检测ONNX部署教程，我们需要考虑几个关键点：模型转换为ONNX格式、ONNX模型的部署以及后处理逻辑。由于YOLOv8本身还未发布，我们将基于现有的知识和技术来进行推断。

以下是部署YOLOv8旋转目标检测模型到ONNX的步骤，包括代码示例。请注意，这只是一个假设性的教程，因为YOLOv8的具体细节尚未公开。
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1. 准备环境

确保安装了以下依赖：

Python 3.7+
PyTorch 1.10+
torchvision
OpenCV
numpy
onnx
onnxruntime
tqdm

安装所需的库：

pip install torch torchvision opencv-python numpy onnx onnxruntime tqdm

2. 模型转换为ONNX格式

假设你已经有了一个经过训练的YOLOv8旋转目标检测模型，接下来将其转换为ONNX格式。

导入库

import torch
import onnx
from onnxsim import simplify

转换为ONNX

def convert_to_onnx(model, input_size=(640, 640), output_file="yolov8.onnx"):dummy_input = torch.randn(1, 3, *input_size)  # 1 batch, 3 channels, input sizeinput_names = ["input"]output_names = ["output"]torch.onnx.export(model,dummy_input,output_file,export_params=True,opset_version=11,do_constant_folding=True,input_names=input_names,output_names=output_names,dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}})print(f"Model has been converted to ONNX format and saved to {output_file}")# Simplify the ONNX modelonnx_model = onnx.load(output_file)model_simplified, check = simplify(onnx_model)assert check, "Simplified ONNX model could not be validated"onnx.save(model_simplified, output_file)print(f"Simplified ONNX model saved to {output_file}")

3. ONNX模型部署

接下来，我们将使用ONNX Runtime来加载和运行ONNX模型。

导入库

import cv2
import numpy as np
import onnxruntime

加载ONNX模型

def load_onnx_model(model_path):sess = onnxruntime.InferenceSession(model_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])input_name = sess.get_inputs()[0].nameoutput_name = sess.get_outputs()[0].namereturn sess, input_name, output_name

预处理

def preprocess_image(image_path, input_size=(640, 640)):img = cv2.imread(image_path)img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)img = cv2.resize(img, input_size)img = img.astype(np.float32)img /= 255.0img = np.transpose(img, (2, 0, 1))  # HWC -> CHWimg = np.expand_dims(img, axis=0)  # Add batch dimensionreturn img

后处理

def postprocess(output, image_shape, input_size=(640, 640)):# 假设输出包含旋转框的坐标和角度detections = output[0]boxes = detections[:, :5]  # x, y, width, height, anglescores = detections[:, 5]labels = detections[:, 6]# 调整检测框到原始图像尺寸scale_x = image_shape[1] / input_size[1]scale_y = image_shape[0] / input_size[0]boxes[:, 0] *= scale_xboxes[:, 1] *= scale_yboxes[:, 2] *= scale_xboxes[:, 3] *= scale_yreturn boxes, scores, labels

推理过程

def detect_rotated_boxes(image_path, sess, input_name, output_name, input_size=(640, 640)):img = preprocess_image(image_path, input_size)outputs = sess.run([output_name], {input_name: img})boxes, scores, labels = postprocess(outputs[0], cv2.imread(image_path).shape, input_size)return boxes, scores, labels

可视化结果

def visualize(image_path, boxes, scores, labels):img = cv2.imread(image_path)for box, score, label in zip(boxes, scores, labels):x, y, w, h, angle = box# 使用OpenCV绘制旋转矩形box_points = cv2.boxPoints(((x, y), (w, h), angle))box_points = np.int0(box_points)cv2.drawContours(img, [box_points], 0, (0, 0, 255), 2)cv2.putText(img, f"{label} {score:.2f}", (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)cv2.imshow("Rotated Object Detection", img)cv2.waitKey(0)cv2.destroyAllWindows()

4. 运行检测

编译和运行
1）编译cd examples/rknn_yolov8_obb_demobash build-linux_RK3588.sh2）运行cd install/rknn_yolov8obb_demo_Linux./rknn_yolov8obb_demo

结果展示

类别：

CLASSES = ['plane', 'ship', 'storage tank', 'baseball diamond', 'tennis court', 'basketball court','ground track field', 'harbor', 'bridge', 'large vehicle', 'small vehicle', 'helicopter', 'roundabout','soccer ball field', 'swimming pool']

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