how to make preprocess for images in c++

[fatejzz]

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Question

At present, I am trying to deploy my Yolo model in ros. And I use c++ for my Yolo model to make predictions. However, it doesn't work well. Given the same image, when I use detect.py it can make correct predictions. But when I use my code it can't predict well for the existing wrong box. I think it is because of the image preprocessing process, I imitate the letterbox in detect.py, but it will meet a problem.

File "code/torch/models/yolo.py", line 71, in forward _35 = (_20).forward(_34, ) _36 = (_22).forward((_21).forward(_35, ), _29, ) _37 = (_24).forward(_33, _35, (_23).forward(_36, ), ) ~~~~~~~~~~~~ <--- HERE return (_37,) class Detect(Module): File "code/torch/models/yolo.py", line 101, in forward _19 = torch.split_with_sizes(torch.sigmoid(_18), [2, 2, 6], 4) xy, wh, conf, = _19 _20 = torch.add(torch.mul(xy, CONSTANTS.c0), CONSTANTS.c1) ~~~~~~~~~ <--- HERE xy0 = torch.mul(_20, torch.select(CONSTANTS.c2, 0, 0)) _21 = torch.pow(torch.mul(wh, CONSTANTS.c0), 2)

Traceback of TorchScript, original code (most recent call last): /home/jzz/yolov5/models/yolo.py(71): forward /data/jzz/envs/jzz/lib/python3.7/site-packages/torch/nn/modules/module.py(1098): _slow_forward /data/jzz/envs/jzz/lib/python3.7/site-packages/torch/nn/modules/module.py(1110): _call_impl /home/jzz/yolov5/models/yolo.py(158): _forward_once /home/jzz/yolov5/models/yolo.py(135): forward /data/jzz/envs/jzz/lib/python3.7/site-packages/torch/nn/modules/module.py(1098): _slow_forward /data/jzz/envs/jzz/lib/python3.7/site-packages/torch/nn/modules/module.py(1110): _call_impl /data/jzz/envs/jzz/lib/python3.7/site-packages/torch/jit/_trace.py(965): trace_module /data/jzz/envs/jzz/lib/python3.7/site-packages/torch/jit/_trace.py(750): trace export.py(98): export_torchscript export.py(520): run /data/jzz/envs/jzz/lib/python3.7/site-packages/torch/autograd/grad_mode.py(27): decorate_context export.py(602): main export.py(607): RuntimeError: The size of tensor a (48) must match the size of tensor b (80) at non-singleton dimension 2

cv::Mat resized_frame = letterbox(frame); cvtColor(resized_frame,resized_frame,CV_BGR2RGB); torch::Tensor in_tensor = torch::from_blob(resized_frame.data, {resized_frame.rows,resized_frame.cols, 3}, torch::kByte); but if the code in the third line wrote as
torch::Tensor in_tensor = torch::from_blob(resized_frame.data, {640,640, 3}, torch::kByte); it could work but there are some problems. On the one hand, it will predict wrong box which doesn't occur in python detect.py. On the other hand, sometimes it will predict different boxes given the same image.

Additional

here is my code of letter box cv::Mat letterbox(const cv::Mat& src) { int in_w = src.cols; int in_h = src.rows; int tar_w = kIMAGE_W_; int tar_h = kIMAGE_H_; float r = min(float(tar_h) / in_h, float(tar_w) / in_w); r=min(r,float(1)); int inside_w = round(in_w * r); int inside_h = round(in_h * r); int padd_w = tar_w - inside_w; int padd_h = tar_h - inside_h; padd_w=padd_w%64;
padd_h=padd_h%64;
cv::Mat resize_img; cv::resize(src, resize_img, cv::Size(inside_w, inside_h)); //cvtColor(resize_img, resize_img, COLOR_BGR2RGB);

    padd_w = padd_w / 2;
    padd_h = padd_h / 2;
    // // std::cout<<"padd_w"<<padd_w<<"padd_h"<<padd_h<<std::endl;
    int top = int(round(padd_h - 0.1));
    int bottom = int(round(padd_h + 0.1));
    int left = int(round(padd_w - 0.1));
    int right = int(round(padd_w + 0.1));
    cv::copyMakeBorder(resize_img, resize_img, top, bottom, left, right, cv::BORDER_CONSTANT, cv::Scalar(114, 114, 114));
    return resize_img;
}

[github-actions[bot]]

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[glenn-jocher]

@fatejzz I'm sorry, we don't have resources to review custom code, but we have a few YOLOv5 C++ Inference examples on ONNX and OpenVINO exported models here:

C++ Inference

YOLOv5 OpenCV DNN C++ inference on exported ONNX model examples:

• https://github.com/Hexmagic/ONNX-yolov5/blob/master/src/test.cpp
• https://github.com/doleron/yolov5-opencv-cpp-python

YOLOv5 OpenVINO C++ inference examples:

• https://github.com/dacquaviva/yolov5-openvino-cpp-python

See Export tutorial for details:

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Good luck 🍀 and let us know if you have any other questions!

[zhiqwang]

Hi @fatejzz , you can also refer following implementation, we rewrite it with OpenCV's C++ API .

https://github.com/zhiqwang/yolov5-rt-stack/blob/293b378fa2c7d1bc76fac75309b62a951680ac35/deployment/tensorrt/main.cpp#L80-L123

float letterbox(
    const cv::Mat& image,
    cv::Mat& out_image,
    const cv::Size& new_shape = cv::Size(640, 640),
    int stride = 32,
    const cv::Scalar& color = cv::Scalar(114, 114, 114),
    bool fixed_shape = false,
    bool scale_up = true) {
  cv::Size shape = image.size();
  float r = std::min(
      (float)new_shape.height / (float)shape.height, (float)new_shape.width / (float)shape.width);
  if (!scale_up) {
    r = std::min(r, 1.0f);
  }

  int newUnpad[2]{
      (int)std::round((float)shape.width * r), (int)std::round((float)shape.height * r)};

  cv::Mat tmp;
  if (shape.width != newUnpad[0] || shape.height != newUnpad[1]) {
    cv::resize(image, tmp, cv::Size(newUnpad[0], newUnpad[1]));
  } else {
    tmp = image.clone();
  }

  float dw = new_shape.width - newUnpad[0];
  float dh = new_shape.height - newUnpad[1];

  if (!fixed_shape) {
    dw = (float)((int)dw % stride);
    dh = (float)((int)dh % stride);
  }

  dw /= 2.0f;
  dh /= 2.0f;

  int top = int(std::round(dh - 0.1f));
  int bottom = int(std::round(dh + 0.1f));
  int left = int(std::round(dw - 0.1f));
  int right = int(std::round(dw + 0.1f));
  cv::copyMakeBorder(tmp, out_image, top, bottom, left, right, cv::BORDER_CONSTANT, color);

  return 1.0f / r;
}

[fatejzz]

@zhiqwang @glenn-jocher when I convert the input image into the tensor according to the image shape and input it into the model, there will be some errors like the above. But when I created the 640x640x3 tensor, the model could run.

[fatejzz]

I just quote the two codes, and it can work well until now. but the 'letterbox' has some differences in mechanisms from others. padd_w=padd_w%64; padd_h=padd_h%64;

[fatejzz]

@glenn-jocher After discussing it with others, I am wondering whether it is because when I use torchscript to export the model, the model's input size has been limited like [640,640].

[glenn-jocher]

@fatejzz yes, most exports require fixed input sizes. I think only PyTorch and ONNX --dynamic support dynamic input sizes.

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