Object Detection API（4）—— Freeze Model模型导出
博客：https://blog.csdn.net/qq_34106574
：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 tffrom object_detection.utils import label_map_utilfrom 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
[if !supportLists][1] [endif]https://www.cnblogs.com/qcloud1001/p/7677661.html
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