How to inference a onnx yolov9 model?

[KyriakosChris]

I found how to inference a trt yolov9 model using this detect function:

def detect(self, bgr_img):   
    ## Padded resize
    h, w, _ = bgr_img.shape
    scale = min(self.imgsz[0]/w, self.imgsz[1]/h)
    inp = np.zeros((self.imgsz[1], self.imgsz[0], 3), dtype = np.float32)
    nh = int(scale * h)
    nw = int(scale * w)
    inp[: nh, :nw, :] = cv2.resize(cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB), (nw, nh))
    inp = inp.astype('float32') / 255.0  # 0 - 255 to 0.0 - 1.0
    inp = np.expand_dims(inp.transpose(2, 0, 1), 0)

    ## Inference
    t1 = time.time()
    num_detection, nmsed_bboxes, nmsed_scores, nmsed_classes = self.model.run(inp)
    t2 = time.time()

    ## Apply NMS
    num_detection = num_detection[0][0]
    nmsed_bboxes  = nmsed_bboxes[0]
    nmsed_scores  = nmsed_scores[0]
    nmsed_classes  = nmsed_classes[0]
    print('Detected {} object(s)'.format(num_detection))
    # Rescale boxes from img_size to im0 size
    _, _, height, width = inp.shape
    h, w, _ = bgr_img.shape
    nmsed_bboxes[:, 0] /= scale
    nmsed_bboxes[:, 1] /= scale
    nmsed_bboxes[:, 2] /= scale
    nmsed_bboxes[:, 3] /= scale
    visualize_img = bgr_img.copy()
    for ix in range(num_detection):       # x1, y1, x2, y2 in pixel format
        cls = int(nmsed_classes[ix])
        label = '%s %.2f' % (self.names[cls], nmsed_scores[ix])
        x1, y1, x2, y2 = nmsed_bboxes[ix]

        cv2.rectangle(visualize_img, (int(x1), int(y1)), (int(x2), int(y2)), self.colors[int(cls)], 2)
        cv2.putText(visualize_img, label, (int(x1), int(y1-10)), cv2.FONT_HERSHEY_SIMPLEX, 1, self.colors[int(cls)], 2, cv2.LINE_AA)

    cv2.imwrite('result.jpg', visualize_img)
    return `visualize_img`

But I can't find out how to do the same for an ONNX model. Has someone already made something like this, or should I do it and post it? Thank you.

[jdiaz97]

I have an example in Julia

https://github.com/jdiaz97/yolov9-in-julia/

Applying NMS is needed

[WongKinYiu]

More ONNX examples: https://github.com/WongKinYiu/yolov9?tab=readme-ov-file#useful-links

Added Julia example to readme.

[yxl502]

https://zhuanlan.zhihu.com/p/683979999