274 lines
9.8 KiB
Python
274 lines
9.8 KiB
Python
import cv2
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import numpy as np
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import onnxruntime as ort
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class YOLOv8ONNX:
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def __init__(self, model_path, conf_threshold=0.5, iou_threshold=0.4):
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"""
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初始化YOLOv8 ONNX模型
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Args:
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model_path: ONNX模型文件路径
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conf_threshold: 置信度阈值
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iou_threshold: NMS IoU阈值
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"""
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self.conf_threshold = conf_threshold
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self.iou_threshold = iou_threshold
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# 创建ONNX Runtime会话
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self.session = ort.InferenceSession(model_path)
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# 获取模型输入输出信息
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self.input_name = self.session.get_inputs()[0].name
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self.output_name = self.session.get_outputs()[0].name
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# 获取输入尺寸
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input_shape = self.session.get_inputs()[0].shape
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self.input_height = input_shape[2]
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self.input_width = input_shape[3]
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def preprocess(self, image):
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"""
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预处理图像
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Args:
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image: 输入图像 (BGR格式)
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Returns:
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preprocessed_image: 预处理后的图像
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scale_ratio: 缩放比例
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pad_info: 填充信息 (pad_x, pad_y)
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"""
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# 获取原图尺寸
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h, w = image.shape[:2]
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# 计算缩放比例
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scale = min(self.input_height / h, self.input_width / w)
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new_h, new_w = int(h * scale), int(w * scale)
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# 等比例缩放
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resized_image = cv2.resize(image, (new_w, new_h))
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# 计算填充
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pad_x = (self.input_width - new_w) // 2
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pad_y = (self.input_height - new_h) // 2
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# 创建填充后的图像
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padded_image = np.full((self.input_height, self.input_width, 3), 114, dtype=np.uint8)
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padded_image[pad_y:pad_y + new_h, pad_x:pad_x + new_w] = resized_image
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# 转换为模型输入格式: BGR -> RGB, HWC -> CHW, 归一化
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input_image = padded_image[:, :, ::-1].transpose(2, 0, 1).astype(np.float32) / 255.0
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input_image = np.expand_dims(input_image, axis=0) # 添加batch维度
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return input_image, scale, (pad_x, pad_y)
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def postprocess(self, outputs, scale, pad_info, original_shape):
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"""
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后处理模型输出 - 针对YOLOv8格式优化
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Args:
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outputs: 模型原始输出
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scale: 图像缩放比例
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pad_info: 填充信息
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original_shape: 原图尺寸
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Returns:
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boxes: 检测框 [[x1, y1, x2, y2], ...]
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scores: 置信度分数
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class_ids: 类别ID
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"""
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predictions = outputs[0] # 形状通常是: [1, 6, 8400] 或 [1, num_classes+4, num_boxes]
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# YOLOv8输出格式: [batch, 4+num_classes, num_boxes]
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# 需要转置为 [batch, num_boxes, 4+num_classes]
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if len(predictions.shape) == 3:
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predictions = predictions.transpose(0, 2, 1) # [1, num_boxes, 4+num_classes]
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predictions = predictions[0] # 移除batch维度: [num_boxes, 4+num_classes]
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# 打印调试信息
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# print(f"预测输出形状: {predictions.shape}")
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# print(f"前几个预测值: {predictions[:5]}")
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# 分离坐标和分类信息
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boxes = predictions[:, :4] # [x_center, y_center, width, height]
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scores = predictions[:, 4:] # 类别置信度 [num_boxes, num_classes]
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# print(f"检测框形状: {boxes.shape}")
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# print(f"分数形状: {scores.shape}")
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# 获取最高置信度和对应类别
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class_ids = np.argmax(scores, axis=1)
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confidences = np.max(scores, axis=1)
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# print(f"置信度范围: {confidences.min():.4f} - {confidences.max():.4f}")
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# print(f"检测到的类别: {np.unique(class_ids)}")
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# 过滤低置信度检测
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valid_indices = confidences > self.conf_threshold
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valid_boxes = boxes[valid_indices]
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valid_confidences = confidences[valid_indices]
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valid_class_ids = class_ids[valid_indices]
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# print(f"过滤后检测数量: {len(valid_boxes)}")
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if len(valid_boxes) == 0:
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return [], [], []
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# 转换为 [x1, y1, x2, y2] 格式
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x_center, y_center, width, height = valid_boxes[:, 0], valid_boxes[:, 1], valid_boxes[:, 2], valid_boxes[:, 3]
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x1 = x_center - width / 2
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y1 = y_center - height / 2
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x2 = x_center + width / 2
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y2 = y_center + height / 2
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converted_boxes = np.stack([x1, y1, x2, y2], axis=1)
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# 坐标反变换到原图
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pad_x, pad_y = pad_info
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converted_boxes[:, [0, 2]] = (converted_boxes[:, [0, 2]] - pad_x) / scale
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converted_boxes[:, [1, 3]] = (converted_boxes[:, [1, 3]] - pad_y) / scale
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# 限制坐标范围
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h, w = original_shape[:2]
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converted_boxes[:, [0, 2]] = np.clip(converted_boxes[:, [0, 2]], 0, w)
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converted_boxes[:, [1, 3]] = np.clip(converted_boxes[:, [1, 3]], 0, h)
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# 非极大值抑制 (NMS)
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indices = cv2.dnn.NMSBoxes(
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converted_boxes.tolist(),
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valid_confidences.tolist(),
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self.conf_threshold,
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self.iou_threshold
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)
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if len(indices) > 0:
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indices = indices.flatten()
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return converted_boxes[indices], valid_confidences[indices], valid_class_ids[indices]
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return [], [], []
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def detect(self, image):
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"""
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对图像进行目标检测
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Args:
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image: 输入图像 (BGR格式)
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Returns:
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boxes: 检测框列表
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scores: 置信度分数列表
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class_ids: 类别ID列表
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"""
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# 预处理
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input_image, scale, pad_info = self.preprocess(image)
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# 推理
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outputs = self.session.run([self.output_name], {self.input_name: input_image})
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# 后处理
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boxes, scores, class_ids = self.postprocess(outputs, scale, pad_info, image.shape)
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return boxes, scores, class_ids
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def draw_detections(image, boxes, scores, class_ids, class_names=None):
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"""
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在图像上绘制检测结果
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Args:
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image: 输入图像
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boxes: 检测框
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scores: 置信度分数
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class_ids: 类别ID
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class_names: 类别名称列表（可选）
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Returns:
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绘制了检测结果的图像
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"""
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result_image = image.copy()
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for i, (box, score, class_id) in enumerate(zip(boxes, scores, class_ids)):
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x1, y1, x2, y2 = map(int, box)
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# 绘制边界框
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cv2.rectangle(result_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
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# 准备标签文本
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if class_names and class_id < len(class_names):
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label = f"{class_names[class_id]}: {score:.2f}"
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else:
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label = f"Class {class_id}: {score:.2f}"
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# 绘制标签背景
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label_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)[0]
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cv2.rectangle(result_image, (x1, y1 - label_size[1] - 10),
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(x1 + label_size[0], y1), (0, 255, 0), -1)
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# 绘制标签文本
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cv2.putText(result_image, label, (x1, y1 - 5),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
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return result_image
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# # 使用示例
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# if __name__ == "__main__":
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# # 1. 初始化检测器 (降低置信度阈值进行测试)
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# detector = YOLOv8ONNX("path/to/your/model.onnx", conf_threshold=0.25, iou_threshold=0.45)
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# # 2. 加载图像
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# image = cv2.imread("path/to/your/image.jpg")
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# # 3. 进行检测
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# boxes, scores, class_ids = detector.detect(image)
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# # 4. 定义你的两个类别名称 (根据训练时的类别顺序调整)
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# class_names = ["class_0", "class_1"] # 替换为你的实际类别名称
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# # 5. 打印检测结果
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# print(f"检测到 {len(boxes)} 个目标:")
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# for i, (box, score, class_id) in enumerate(zip(boxes, scores, class_ids)):
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# class_name = class_names[class_id] if class_id < len(class_names) else f"unknown_{class_id}"
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# print(f" {i+1}: {class_name} - 置信度: {score:.3f} - 位置: {box}")
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# # 6. 绘制结果
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# result_image = draw_detections(image, boxes, scores, class_ids, class_names)
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# # 7. 显示或保存结果
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# cv2.imshow("Detection Result", result_image)
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# cv2.waitKey(0)
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# cv2.destroyAllWindows()
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# # 保存结果
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# # cv2.imwrite("result.jpg", result_image)
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# # 使用示例
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if __name__ == "__main__":
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# 1. 初始化检测器
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detector = YOLOv8ONNX("/home/admin-root/haotian/康达瑞贝斯机器狗/yolov8_20250820.onnx", conf_threshold=0.25, iou_threshold=0.45)
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# 2. 加载图像
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image = cv2.imread("/home/admin-root/haotian/康达瑞贝斯机器狗/YoloV8Obj/dataset_20250819/train/images/1e4c75b76e531606e2adc491a8f09ae8_frame_000140.jpg")
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# 3. 进行检测
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boxes, scores, class_ids = detector.detect(image)
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print(boxes, scores, class_ids)
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# 4. 可选：定义类别名称
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class_names = ["extinguish", "other"] # 根据你的模型调整
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# 5. 打印检测结果
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print(f"检测到 {len(boxes)} 个目标:")
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for i, (box, score, class_id) in enumerate(zip(boxes, scores, class_ids)):
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class_name = class_names[class_id] if class_id < len(class_names) else f"unknown_{class_id}"
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print(f" {i+1}: {class_name} - 置信度: {score:.3f} - 位置: {box}")
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# 5. 绘制结果
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result_image = draw_detections(image, boxes, scores, class_ids, class_names)
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# 6. 显示或保存结果
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# cv2.imshow("Detection Result", result_image)
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# cv2.waitKey(0)
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# cv2.destroyAllWindows()
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# 保存结果
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cv2.imwrite("result_yolov8.jpg", result_image) |