如何在tensorflow中提取图像的精确坐标矢量？

import tensorflow as tf
import numpy as np
import cv2

# 加载目标检测模型
model = tf.saved_model.load('path/to/saved_model')

# 加载图像
image = cv2.imread('path/to/image.jpg')

# 图像预处理
input_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
input_image = tf.expand_dims(input_image, 0)
input_image = tf.cast(input_image, tf.float32) / 255.0

# 模型推理
detections = model(input_image)

# 提取边界框坐标
boxes = detections['detection_boxes'][0].numpy()
scores = detections['detection_scores'][0].numpy()
classes = detections['detection_classes'][0].numpy().astype(np.int32)

# 过滤低置信度的目标
threshold = 0.5
filtered_boxes = boxes[scores >= threshold]
filtered_classes = classes[scores >= threshold]

# 将边界框坐标转换为图像上的像素坐标
height, width, _ = image.shape
pixels_boxes = filtered_boxes * np.array([height, width, height, width])

# 打印精确坐标矢量
for box in pixels_boxes:
    ymin, xmin, ymax, xmax = box
    print(f'目标坐标：({xmin}, {ymin}), ({xmax}, {ymax})')