I have an image of a car and a corresponding bounding box. For example:

(xmin, ymin, xmax, ymax)
(504.8863220214844, 410.2454833984375, 
937.6451416015625, 723.9139404296875)

That's how I draw boxes:

def plot_results(pil_img, prob, boxes):
    plt.figure(figsize=(16,10))
    plt.imshow(pil_img)
    ax = plt.gca()
    for p, (xmin, ymin, xmax, ymax), c in zip(prob, boxes.tolist(), COLORS * 100):
        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
                                   fill=False, color=c, linewidth=3))
        cl = p.argmax()
        text = f'{CLASSES[cl]}: {p[cl]:0.2f}'
        ax.text(xmin, ymin, text, fontsize=15,
                bbox=dict(facecolor='yellow', alpha=0.5))
    plt.axis('off')
    plt.show()
    

I want to measure the distance from car to camera. If the car is nearby, the distance value should be around 0.2-0.4 If the car is far from the camera, the distance value should be around 0.6-0.8.

I also found a solution for my problem:

The main difference between this code and the example you provided is that the bounding box values you've given (504.8863220214844, 410.2454833984375, 937.6451416015625, 723.9139404296875) represent pixels. However, the code in the example has bounding box values that are already normalized between 0 and 1 in relation to the image size. This is why I verbosely defined the image width and height in inches and pixels (also for self explaining code). They are needed to normalize the pixel based widths and positions so they are between 0 and 1 to match the logic in your example, and which you requested. These values can also be helpful when trying to actually measure sizes and distances.

If you are interested in taking this further. I recommend reading about the laws of perspective. Here is an interesting place to start: https://www.handprint.com/HP/WCL/perspect2.html#distance