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yolov34-cpp-opencv-dnn's Introduction

4种YOLO目标检测的C++和Python两种版本的实现

本程序包含了经典的YOLOv3,YOLOv4,Yolo-Fastest和YOLObile这4种YOLO目标检测的实现, 这4种yolo的.cfg和.weights文件,从百度云盘里下载

链接:https://pan.baidu.com/s/1Kcw-VhuDTRzCtVkaNEDOBg 提取码:imgu

下载完成后把下载得到的4个文件夹拷贝到和main_yolo.cpp同一目录下, 只要安装了opencv4.4.0及其以上版本的,就可以在windows和linux系统编译并运行main_yolo.cpp

此外,在Net_config配置参数项里,可以添加一个参数swapRB,控制输入图像是否交换RGB通道的, 之所以要添加这个参数,是因为我看到有的YOLO模型的输入图像并没有做交换通道到RGB的处理。

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yolov34-cpp-opencv-dnn's Issues

成功了!感谢!

但是要用opencv4.4以上哟!而且coco.names还有几个文件夹都要和cpp文件放在一个目录里。我是个菜鸟。
但是这个opencv程序有用gpu加速么?

parse netparameter file error

terminate called after throwing an instance of 'cv::Exception'
  what():  OpenCV(4.3.0) /root/opencv-4.3.0/modules/dnn/src/darknet/darknet_importer.cpp:207: error: (-212:Parsing error) Failed to parse NetParameter file: yolov3/yolov3.cfg in function 'readNetFromDarknet'

python上使用新函数应该更快

classes, scores, boxes = model.detect(frames, conf_thd, nms_thd)

其中model这个是来自:
net = cv2.dnn.readNetFromDarknet(CONFIG_FILE, WEIGHT_FILE) # 读取权重与配置文件

net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
model = cv2.dnn_DetectionModel(net)
model.setInputParams(size=(side_width, side_height), scale=1/255, swapRB=True)

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