使用Imagenet类的TensorFlow对象检测应用编程接口？

pip install tensorflow

wget http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz
tar -xzf ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz

import tensorflow as tf
import cv2
import numpy as np

# 加载模型
model = tf.saved_model.load('ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/saved_model')

# 加载标签映射
with open('path/to/label_map.pbtxt', 'r') as f:
    label_map = f.read()

# 读取图像
image = cv2.imread('path/to/image.jpg')
image_np = np.array(image)

# 将图像转换为模型输入格式
input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)

# 运行模型
detections = model(input_tensor)

# 解析检测结果
boxes = detections['detection_boxes'][0].numpy()
classes = detections['detection_classes'][0].numpy().astype(np.int32)
scores = detections['detection_scores'][0].numpy()

# 打印检测结果
for i in range(len(boxes)):
    if scores[i] > 0.5:  # 设置置信度阈值
        print(f"Class: {classes[i]}, Score: {scores[i]}, Box: {boxes[i]}")