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  • 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)
    

      

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  • 原文地址:https://www.cnblogs.com/tangmiao/p/9111434.html
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