def draw_boxes_for_image(self, frame, min_score_threshold):
    frame_expanded = np.expand_dims(frame, axis=0)
    (boxes, scores, classes, num) = self.sess.run([self.detection_boxes, self.detection_scores, self.detection_classes, self.num_detections], feed_dict={self.image_tensor: frame_expanded})
    vis_util.visualize_boxes_and_labels_on_image_array(frame, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), self.category_index, use_normalized_coordinates=True, line_thickness=2, min_score_thresh=min_score_threshold)
    '\n        print("Self cateogry index")\n        print(self.category_index)\n        print("Score/Classes")\n        for box, score, cls in zip(np.squeeze(boxes), np.squeeze(scores),np.squeeze(classes).astype(np.int32)):\n            print(score, cls, self.category_index[cls])\n        '
    good_boxes = [box for (box, score, cls) in zip(np.squeeze(boxes), np.squeeze(scores), np.squeeze(classes).astype(np.int32)) if ((score >= min_score_threshold) and ('traffic' not in self.category_index[cls]['name']))]
    return (frame, good_boxes)
def draw_boxes_for_image(self, frame, min_score_threshold):
    frame_expanded = np.expand_dims(frame, axis=0)
    (boxes, scores, classes, num) = self.sess.run([self.detection_boxes, self.detection_scores, self.detection_classes, self.num_detections], feed_dict={self.image_tensor: frame_expanded})
    vis_util.visualize_boxes_and_labels_on_image_array(frame, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), self.category_index, use_normalized_coordinates=True, line_thickness=2, min_score_thresh=min_score_threshold)
    '\n        print("Self cateogry index")\n        print(self.category_index)\n        print("Score/Classes")\n        for box, score, cls in zip(np.squeeze(boxes), np.squeeze(scores),np.squeeze(classes).astype(np.int32)):\n            print(score, cls, self.category_index[cls])\n        '
    good_boxes = [box for (box, score, cls) in zip(np.squeeze(boxes), np.squeeze(scores), np.squeeze(classes).astype(np.int32)) if ((score >= min_score_threshold) and ('traffic' not in self.category_index[cls]['name']))]
    return (frame, good_boxes)