TensorFlow目标检测环境---准备好了吗？-CFANZ编程社区

TensorFlow目标检测环境搭建与测试步骤：

第一步：安装配置环境，参考：

https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md

第二步：下载模型、配置文件与代码，下载地址：

链接：https://pan.baidu.com/s/1cbcHRyrN8SvtyvzpQsUV-w

提取码：v5r5

第三步：修改pb模型和pbtxt配置文件路径进行测试，代码如下：

test_object_detection_config.py(上述链接中包含)

import cv2
import tensorflow as tf
import numpy as np
from utils import label_map_util
from utils import visualization_utils as vis_util


#import os
#os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'


# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_FROZEN_GRAPH = './model/frozen_inference_graph_faster_rcnn.pb'


# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = ('./model/mscoco_label_map.pbtxt')


NUM_CLASSES = 90


detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')


label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categorys = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,
                                                           use_display_name=True)
categorys_index = label_map_util.create_category_index(categorys)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True


with detection_graph.as_default():
    with tf.Session(graph=detection_graph,config=config) as sess:
        image = cv2.imread("./many_cars.jpg")
        image_np_expanded = np.expand_dims(image, axis=0)
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
        scores = detection_graph.get_tensor_by_name('detection_scores:0')
        classes = detection_graph.get_tensor_by_name('detection_classes:0')
        num_detections = detection_graph.get_tensor_by_name('num_detections:0')
        (boxes, scores, classes, num_detections) = sess.run([boxes, scores, classes, \
                                                             num_detections],
                                                            feed_dict={image_tensor: image_np_expanded})
        vis_util.visualize_boxes_and_labels_on_image_array(
            image,
            np.squeeze(boxes),
            np.squeeze(classes).astype(np.int32),
            np.squeeze(scores),
            categorys_index,
            min_score_thresh=0.5,
            use_normalized_coordinates=True,
            line_thickness=4
        )
        cv2.imshow("object_detection", image)
        cv2.imwrite("result.jpg", image)
        cv2.waitKey(0)