添加yolov8rknn实现

机器狗后台服务/app/util/yolov8Obj.py

@@ -1,7 +1,9 @@
 from  ultralytics import YOLO
+# from rknn.api import RKNN
 import cv2
 import numpy as np
 import onnxruntime as ort
+import time 
 
 from app.config.config import yolov8_settings
 
@@ -193,6 +195,240 @@ class YOLOv8ONNX:
         boxes, scores, class_ids = self.postprocess(outputs, scale, pad_info, image.shape)
         
         return boxes, scores, class_ids
+    
+    
+class YOLOv8RKNN:
+    def __init__(self, model_path, input_size=(640, 640)):
+        self.model_path = model_path
+        self.input_size = input_size
+        self.rknn = RKNN()
+        
+        # 类别名称，根据你的2个类别修改
+        self.class_names = ['class1', 'class2']  # 请替换为你实际的类别名称
+        
+        # 初始化模型
+        self.load_model()
+    
+    def load_model(self):
+        """加载RKNN模型"""
+        print("Loading RKNN model...")
+        ret = self.rknn.load_rknn(self.model_path)
+        if ret != 0:
+            print("Load RKNN model failed!")
+            return False
+        
+        # 初始化运行时环境（在RK3588设备上运行）
+        print("Init RKNN runtime...")
+        ret = self.rknn.init_runtime(target='rk3588', device_id=None, perf_debug=False, eval_mem=False)
+        if ret != 0:
+            print("Init RKNN runtime failed!")
+            return False
+        
+        print("RKNN model loaded successfully!")
+        return True
+    
+    def preprocess(self, image):
+        """图像预处理"""
+        # 获取原始图像尺寸
+        self.orig_height, self.orig_width = image.shape[:2]
+        
+        # Resize到模型输入尺寸，保持宽高比
+        scale = min(self.input_size[0]/self.orig_width, self.input_size[1]/self.orig_height)
+        new_width = int(self.orig_width * scale)
+        new_height = int(self.orig_height * scale)
+        
+        # 缩放图像
+        resized = cv2.resize(image, (new_width, new_height))
+        
+        # 创建输入图像（填充到目标尺寸）
+        input_image = np.full((self.input_size[1], self.input_size[0], 3), 114, dtype=np.uint8)
+        
+        # 计算填充位置（居中）
+        y_offset = (self.input_size[1] - new_height) // 2
+        x_offset = (self.input_size[0] - new_width) // 2
+        
+        # 将缩放后的图像放到中心位置
+        input_image[y_offset:y_offset+new_height, x_offset:x_offset+new_width] = resized
+        
+        # 保存缩放参数用于后处理
+        self.scale = scale
+        self.x_offset = x_offset
+        self.y_offset = y_offset
+        
+        return input_image
+    
+    def postprocess(self, outputs, conf_threshold=0.5, nms_threshold=0.4):
+        """后处理：解析YOLO输出并进行NMS"""
+        # YOLOv8输出格式: [batch, 84, 8400] (2个类别: 4+2+80=84，但实际只有6维)
+        # 对于2类别: [x, y, w, h, conf_class1, conf_class2]
+        predictions = outputs[0][0]  # 移除batch维度
+        
+        # 转置为 [8400, 6] 格式
+        predictions = predictions.transpose()
+        
+        boxes = []
+        scores = []
+        class_ids = []
+        
+        for detection in predictions:
+            # 提取坐标和类别置信度
+            x, y, w, h = detection[:4]
+            class_confs = detection[4:6]  # 2个类别的置信度
+            
+            # 找到最大置信度的类别
+            class_id = np.argmax(class_confs)
+            max_conf = class_confs[class_id]
+            
+            if max_conf >= conf_threshold:
+                # 转换坐标格式 (中心点 -> 左上角)
+                x1 = x - w/2
+                y1 = y - h/2
+                x2 = x + w/2
+                y2 = y + h/2
+                
+                # 将坐标映射回原图尺寸
+                x1 = (x1 - self.x_offset) / self.scale
+                y1 = (y1 - self.y_offset) / self.scale
+                x2 = (x2 - self.x_offset) / self.scale
+                y2 = (y2 - self.y_offset) / self.scale
+                
+                # 限制在图像边界内
+                x1 = max(0, min(x1, self.orig_width))
+                y1 = max(0, min(y1, self.orig_height))
+                x2 = max(0, min(x2, self.orig_width))
+                y2 = max(0, min(y2, self.orig_height))
+                
+                boxes.append([x1, y1, x2, y2])
+                scores.append(max_conf)
+                class_ids.append(class_id)
+        
+        # 执行NMS
+        if len(boxes) > 0:
+            boxes = np.array(boxes)
+            scores = np.array(scores)
+            class_ids = np.array(class_ids)
+            
+            # OpenCV NMS
+            indices = cv2.dnn.NMSBoxes(boxes, scores, conf_threshold, nms_threshold)
+            
+            if len(indices) > 0:
+                indices = indices.flatten()
+                return boxes[indices], scores[indices], class_ids[indices]
+        
+        return np.array([]), np.array([]), np.array([])
+    
+    def detect(self, image, conf_threshold=0.5, nms_threshold=0.4):
+        """执行检测"""
+        # 预处理
+        input_image = self.preprocess(image)
+        
+        # 推理
+        start_time = time.time()
+        outputs = self.rknn.inference(inputs=[input_image])
+        inference_time = time.time() - start_time
+        
+        # 后处理
+        boxes, scores, class_ids = self.postprocess(outputs, conf_threshold, nms_threshold)
+        
+        return boxes, scores, class_ids, inference_time
+    
+    def draw_detections(self, image, boxes, scores, class_ids):
+        """在图像上绘制检测结果"""
+        for i in range(len(boxes)):
+            x1, y1, x2, y2 = boxes[i].astype(int)
+            score = scores[i]
+            class_id = int(class_ids[i])
+            
+            # 绘制边界框
+            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            
+            # 绘制标签
+            label = f"{self.class_names[class_id]}: {score:.2f}"
+            label_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)[0]
+            cv2.rectangle(image, (x1, y1-label_size[1]-10), 
+                         (x1+label_size[0], y1), (0, 255, 0), -1)
+            cv2.putText(image, label, (x1, y1-5), 
+                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
+        
+        return image
+    
+    def release(self):
+        """释放资源"""
+        if self.rknn:
+            self.rknn.release()
+
+def main():
+    # 初始化检测器
+    model_path = "/home/orangepi/Desktop/康达机器狗/model_rknn/yolov8_20250820.rknn"
+    detector = YOLOv8RKNN(model_path)
+    
+    # 测试单张图片
+    def test_image(image_path):
+        image = cv2.imread(image_path)
+        if image is None:
+            print(f"Cannot load image: {image_path}")
+            return
+        
+        # 执行检测
+        boxes, scores, class_ids, inference_time = detector.detect(image)
+        
+        print(f"Inference time: {inference_time*1000:.2f}ms")
+        print(f"Detected {len(boxes)} objects")
+        
+        # 绘制结果
+        result_image = detector.draw_detections(image, boxes, scores, class_ids)
+        
+        # 显示结果
+        # cv2.imshow("Detection Result", result_image)
+        # cv2.waitKey(0)
+        # cv2.destroyAllWindows()
+        
+        cv2.imwrite("xxxxxxx.jpg", result_image)
+    
+    # 测试摄像头实时检测
+    def test_camera():
+        cap = cv2.VideoCapture(0)  # 使用默认摄像头
+        if not cap.isOpened():
+            print("Cannot open camera")
+            return
+        
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            
+            # 执行检测
+            boxes, scores, class_ids, inference_time = detector.detect(frame)
+            
+            # 绘制结果
+            result_frame = detector.draw_detections(frame, boxes, scores, class_ids)
+            
+            # 显示FPS
+            fps = 1.0 / inference_time if inference_time > 0 else 0
+            cv2.putText(result_frame, f"FPS: {fps:.1f}", (10, 30), 
+                       cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+            
+            cv2.imshow("Real-time Detection", result_frame)
+            
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+        
+        cap.release()
+        cv2.destroyAllWindows()
+    
+    # 选择测试模式
+    mode = input("选择模式 (1: 图片检测, 2: 摄像头实时检测): ")
+    
+    if mode == "1":
+        image_path = input("输入图片路径: ")
+        test_image(image_path)
+    elif mode == "2":
+        test_camera()
+    else:
+        print("无效选择")
+    
+    # 释放资源
+    detector.release()
 
 def draw_detections(image, boxes, scores, class_ids, class_names=None):
     """