Object Detection API（4）—— Freeze Model模型导出

Object Detection API（4）—— Freeze Model模型导出

博客：https://blog.csdn.net/qq_34106574

简书：https://www.jianshu.com/u/fb86cd4f8bf8

      上一节使用自定义record数据进行模型训练和测试，本节将训练模型导出为pb格式，方便程序调用，后面还会介绍如何使用opencv的c++程序来调用训练好的模型。

1，导出模型：

object_detection目录下还提供了export_inference_graph.py。直接调用执行命令如下：

python ../research/object_detection/export_inference_graph.py --input_type image_tensor --pipeline_config_path train/faster_rcnn_resnet101_coco.config --trained_checkpoint_prefix train/model.ckpt-1738  --output_directory out

导出完成后，在out目录下，会生成frozen_inference_graph.pb、model.ckpt.data-00000-of-00001、model.ckpt.meta、model.ckpt.data文件。如下图：

2，调用接口测试：

 

（1）添加mymodel在object_detection目录，将pb文件放置在data目录，将测试图像放在test_images目录.

（2）修改object_detection_tutorial.ipynb

（3）在object_detection目录执行命令如下：

                jupyter notebookobject_detection_tutorial.ipynb

（4）测试结果如下：

 

3,调用方式2：

    打开pycharm，建立工程，运行以下程序：

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



class TOD(object):

    def __init__(self):

        self.PATH_TO_CKPT = r'XXXX\models-master\object_detection\train\frozen_inference_graph.pb'

        self.PATH_TO_LABELS = r'XXXX\models-master\object_detection\train\my_label_map.pbtxt'

        self.NUM_CLASSES = 1

        self.detection_graph = self._load_model()

        self.category_index = self._load_label_map()



    def _load_model(self):

        detection_graph = tf.Graph()

        with detection_graph.as_default():

            od_graph_def = tf.GraphDef()

            with tf.gfile.GFile(self.PATH_TO_CKPT, 'rb') as fid:

                serialized_graph = fid.read()

                od_graph_def.ParseFromString(serialized_graph)

                tf.import_graph_def(od_graph_def, name='')

        return detection_graph



    def _load_label_map(self):

        label_map = label_map_util.load_labelmap(self.PATH_TO_LABELS)

        categories = label_map_util.convert_label_map_to_categories(label_map,

                                                                    max_num_classes=self.NUM_CLASSES,

                                                                    use_display_name=True)

        category_index = label_map_util.create_category_index(categories)

        return category_index



    def detect(self, image):

        with self.detection_graph.as_default():

            with tf.Session(graph=self.detection_graph) as sess:

                # Expand dimensions since the model expects images to have shape: [1, None, None, 3]

                image_np_expanded = np.expand_dims(image, axis=0)

                image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')

                boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')

                scores = self.detection_graph.get_tensor_by_name('detection_scores:0')

                classes = self.detection_graph.get_tensor_by_name('detection_classes:0')

                num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')

                # Actual detection.

                (boxes, scores, classes, num_detections) = sess.run(

                    [boxes, scores, classes, num_detections],

                    feed_dict={image_tensor: image_np_expanded})

                # Visualization of the results of a detection.

                vis_util.visualize_boxes_and_labels_on_image_array(

                    image,

                    np.squeeze(boxes),

                    np.squeeze(classes).astype(np.int32),

                    np.squeeze(scores),

                    self.category_index,

                    use_normalized_coordinates=True,

                    line_thickness=8)



        cv2.imshow("Resault", image)

        cv2.waitKey(0)

if __name__ == '__main__':

    image = cv2.imread('test.jpg')

    detecotr = TOD()

    detecotr.detect(image)

 

 

参考资料

[1]https://www.cnblogs.com/qcloud1001/p/7677661.html

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