TensorFlow2.x Object Detection API 的安装与配置可参考前面的两篇文章：
TensorFlow2.x GPU版安装与CUDA版本选择指南
TensorFlow2.x 目标检测API安装配置步骤详细教程
安装配置完成后，可以使用代码测试了。
一、在Model Zoo下载需要测试的模型，这里选择的SSD MobileNet V2 FPNLite 320x320
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md
下载后解压，可以看到有如些这些文件(这里后放到D:TensorFlowTestmodel文件夹下)
二、在Object Detection API安装目录找到pbtxt配置文件，D:TensorFlowmodelsresearchobject_detectiondata
将mscoco_label_map.pbtxt拷贝到指定文件夹，这里放到model文件夹内 与saved_model文件夹同目录
三、使用测试图像，加载模型测试，如果缺cv2模块则pip install opencv-python，单张图片测试代码如下：
代码语言：javascript复制#!/usr/bin/env python
# coding: utf-8
"""
Object Detection From TF2 Saved Model
=====================================
"""
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1)
import pathlib
import tensorflow as tf
import time
import cv2
import numpy as np
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2)
# Enable GPU dynamic memory allocation
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
model_dir = "./model"
label_path = './model/mscoco_label_map.pbtxt'
path_saved_model = model_dir "/saved_model"
# Load saved model and build the detection function
detect_fn = tf.saved_model.load(path_saved_model)
category_index = label_map_util.create_category_index_from_labelmap(label_path,use_display_name=True)
import warnings
warnings.filterwarnings('ignore') # Suppress Matplotlib warnings
#----------------read image and test--------------------#
image_path = "./images/6.jpg"
image_np = cv2.imread(image_path)
input_tensor = tf.convert_to_tensor(image_np)
input_tensor = input_tensor[tf.newaxis, ...]
detections = detect_fn(input_tensor)
num_detections = int(detections.pop('num_detections'))
detections = {key: value[0, :num_detections].numpy()
for key, value in detections.items()}
detections['num_detections'] = num_detections
# detection_classes should be ints.
detections['detection_classes'] = detections['detection_classes'].astype(np.int64)
image_np_with_detections = image_np.copy()
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_detections,
detections['detection_boxes'],
detections['detection_classes'],
detections['detection_scores'],
category_index,
use_normalized_coordinates=True,
max_boxes_to_draw=200,
min_score_thresh=0.30,
agnostic_mode=False)
cv2.imshow("object_detection_demo", image_np_with_detections)
cv2.waitKey()
cv2.destroyAllWindows()
print('Done')测试图像：
运行结果：
换成EfficientDet D0 512x512的测试效果如下：
