tensorflow根据pb多bitch size去推导物体

        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(imageSerialized, axis=0)
                image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
                # Each box represents a part of the image where a particular object was detected.
                boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')
                # Each score represent how level of confidence for each of the objects.
                # Score is shown on the result image, together with the class label.
                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})
                boxesList.append([boxes,xmin,ymin])
                scoresList.append(scores)
                classesList.append(classes)
                # extractBox.extractBoxMessage(
                #     RecognizeInfoList,
                #     boxMessageList,
                #     classNameList,
                #     RecognizeInfo,
                #     incisePictureWidth,
                #     incisePictureHeight,
                #     inciseXmin,
                #     inciseYmin,
                #     np.squeeze(boxes),
                #     np.squeeze(classes).astype(np.int32),
                #     np.squeeze(scores),
                #     min_score_thresh=0.5
                # )

　　以及高效率不多次生成和关闭sess:

    def _detector(self,imageSerializedList,boxesList,scoresList,classesList):
        incisePictureWidth=self.beCheckedImageWidth
        incisePictureHeight=self.beCheckedImageHeight
        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_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
                # Each box represents a part of the image where a particular object was detected.
                boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')
                # Each score represent how level of confidence for each of the objects.
                # Score is shown on the result image, together with the class label.
                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.
                for imageSerialized in imageSerializedList:
                    image_np_expanded = np.expand_dims(imageSerialized[0], axis=0)
                    (box, score, cla, num_detection) = sess.run(
                        [boxes, scores, classes, num_detections],
                         feed_dict={image_tensor: image_np_expanded})
                    boxesList.append([box,imageSerialized[1],imageSerialized[2]])
                    scoresList.append(score)
                    classesList.append(cla)