ONNX Object Detector

import cv2
import numpy as np
import onnxruntime as rt
from skimage.transform import resize

if __name__ == "__main__":
    sess = rt.InferenceSession('model.onnx')
    shape = sess.get_inputs()[0].shape
    output = sess.get_outputs()[0].shape
    shape.pop(0)
    shape = tuple(shape)
    output.pop(0)

    img = cv2.imread('bill.jpg')
    X = np.array(img)
    X = resize(X/255, shape)
    X = X[np.newaxis, :, :, :]
    X = X.astype(np.float32)

    input_name = sess.get_inputs()[0].name
    label_name = sess.get_outputs()[0].name
    pred_onnx = sess.run([label_name], {input_name: X})
    print(pred_onnx)

    while True:
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        for x, y, w, h in pred_onnx:
            cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
            roiGray = gray[y:y+h, x:x+w]
            roiColor = img[y:y+h, x:x+w]

        cv2.imshow("Detect", cv2.resize(img, (500, 500)))
        cv2.waitKey(0)