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