def METHOD_NAME(jsonfile, out_img, eval_size, small_stride):
    coco = COCO(annotation_file=jsonfile)
    allannjson = json.load(open(jsonfile, 'r'))
    be_im_id = allannjson['annotations'][0]['image_id']
    be_im_w = []
    be_im_h = []
    ratio_w = []
    ratio_h = []
    im_wid, im_hei = [], []
    for ann in tqdm(allannjson['annotations']):
        if ann['iscrowd']:
            continue
        x0, y0, w, h = ann['bbox'][:]
        if be_im_id == ann['image_id']:
            be_im_w.append(w)
            be_im_h.append(h)
        else:
            im_w = coco.imgs[be_im_id]['width']
            im_h = coco.imgs[be_im_id]['height']
            im_wid.append(im_w)
            im_hei.append(im_h)
            im_m_w = np.mean(be_im_w)
            im_m_h = np.mean(be_im_h)
            dis_w = im_m_w / im_w
            dis_h = im_m_h / im_h
            ratio_w.append(dis_w)
            ratio_h.append(dis_h)
            be_im_id = ann['image_id']
            be_im_w = [w]
            be_im_h = [h]
    im_w = coco.imgs[be_im_id]['width']
    im_h = coco.imgs[be_im_id]['height']
    im_wid.append(im_w)
    im_hei.append(im_h)
    all_im_m_w = np.mean(im_wid)
    all_im_m_h = np.mean(im_hei)
    im_m_w = np.mean(be_im_w)
    im_m_h = np.mean(be_im_h)
    dis_w = im_m_w / im_w
    dis_h = im_m_h / im_h
    ratio_w.append(dis_w)
    ratio_h.append(dis_h)
    mid_w = median(ratio_w)
    mid_h = median(ratio_h)
    reg_ratio = []
    ratio_all = ratio_h + ratio_w
    for r in ratio_all:
        if r < 0.2:
            reg_ratio.append(r)
        elif r < 0.4:
            reg_ratio.append(r / 2)
        else:
            reg_ratio.append(r / 4)
    reg_ratio = sorted(reg_ratio)
    max_ratio = reg_ratio[int(0.95 * len(reg_ratio))]
    reg_max = round(max_ratio * eval_size / small_stride)
    ratio_w = [i * 1000 for i in ratio_w]
    ratio_h = [i * 1000 for i in ratio_h]
    print(f'Suggested reg_range[1] is {reg_max+1}')
    print(f'Mean of all img_w is {all_im_m_w}')
    print(f'Mean of all img_h is {all_im_m_h}')
    print(f'Median of ratio_w is {mid_w}')
    print(f'Median of ratio_h is {mid_h}')
    print('all_img with box: ', len(ratio_h))
    print('all_ann: ', len(allannjson['annotations']))
    draw_distribution(ratio_w, ratio_h, out_img)