如何在TensorFlow Lite官方detect.py示例中提取class_name值

数据老陈

2026-9-1

问题描述

在树莓派上使用TensorFlow Lite官方示例提供的detect.py脚本开展目标检测开发时，需要在命令行单独打印被检测对象的类别名称：例如模型检测到医用外科口罩时，命令行可直接输出surgical_mask。
目前直接打印detection_result对象可得到如下结构的检测结果：

detections {
  bounding_box {
    origin_x: 135
    origin_y: 14
    width: 478
    height: 457
  }
  classes {
    index: 0
    score: 0.875
    class_name: "surgical_mask"
  }
}

但无法单独提取并打印其中的class_name字段，当前使用的detect.py完整代码如下：

# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Main script to run the object detection routine."""
import argparse
import sys
import time

import cv2
from tflite_support.task import core
from tflite_support.task import processor
from tflite_support.task import vision
import utils


def run(model: str, camera_id: int, width: int, height: int, num_threads: int,
        enable_edgetpu: bool) -> None:
  """Continuously run inference on images acquired from the camera.

  Args:
    model: Name of the TFLite object detection model.
    camera_id: The camera id to be passed to OpenCV.
    width: The width of the frame captured from the camera.
    height: The height of the frame captured from the camera.
    num_threads: The number of CPU threads to run the model.
    enable_edgetpu: True/False whether the model is a EdgeTPU model.
  """

  # Variables to calculate FPS
  counter, fps = 0, 0
  start_time = time.time()

  # Start capturing video input from the camera
  cap = cv2.VideoCapture(camera_id)
  cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
  cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)

  # Visualization parameters
  row_size = 20  # pixels
  left_margin = 24  # pixels
  text_color = (0, 0, 255)  # red
  font_size = 1
  font_thickness = 1
  fps_avg_frame_count = 10

  # Initialize the object detection model
  base_options = core.BaseOptions(
      file_name=model, use_coral=enable_edgetpu, num_threads=num_threads)
  detection_options = processor.DetectionOptions(
      max_results=3, score_threshold=0.3)
  options = vision.ObjectDetectorOptions(
      base_options=base_options, detection_options=detection_options)
  detector = vision.ObjectDetector.create_from_options(options)

  # Continuously capture images from the camera and run inference
  while cap.isOpened():
    success, image = cap.read()
    if not success:
      sys.exit(
          'ERROR: Unable to read from webcam. Please verify your webcam settings.'
      )

    counter += 1
    image = cv2.flip(image, 1)

    # Convert the image from BGR to RGB as required by the TFLite model.
    rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    # Create a TensorImage object from the RGB image.
    input_tensor = vision.TensorImage.create_from_array(rgb_image)

    # Run object detection estimation using the model.
    detection_result = detector.detect(input_tensor)

    # Draw keypoints and edges on input image
    image = utils.visualize(image, detection_result)
    
    
        
    # Calculate the FPS
    if counter % fps_avg_frame_count == 0:
      end_time = time.time()
      fps = fps_avg_frame_count / (end_time - start_time)
      start_time = time.time()

    # Show the FPS
    fps_text = 'FPS = {:.1f}'.format(fps)
    text_location = (left_margin, row_size)
    cv2.putText(image, fps_text, text_location, cv2.FONT_HERSHEY_PLAIN,
                font_size, text_color, font_thickness)
    
    print(detection_result)

    # Stop the program if the ESC key is pressed.
    if cv2.waitKey(1) == 27:
      break
    cv2.imshow('object_detector', image)

  cap.release()
  cv2.destroyAllWindows()


def main():
  parser = argparse.ArgumentParser(
      formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  parser.add_argument(
      '--model',
      help='Path of the object detection model.',
      required=False,
      default='efficientdet_lite0.tflite')
  parser.add_argument(
      '--cameraId', help='Id of camera.', required=False, type=int, default=0)
  parser.add_argument(
      '--frameWidth',
      help='Width of frame to capture from camera.',
      required=False,
      type=int,
      default=640)
  parser.add_argument(
      '--frameHeight',
      help='Height of frame to capture from camera.',
      required=False,
      type=int,
      default=480)
  parser.add_argument(
      '--numThreads',
      help='Number of CPU threads to run the model.',
      required=False,
      type=int,
      default=4)
  parser.add_argument(
      '--enableEdgeTPU',
      help='Whether to run the model on EdgeTPU.',
      action='store_true',
      required=False,
      default=False)
  args = parser.parse_args()

  run(args.model, int(args.cameraId), args.frameWidth, args.frameHeight,
      int(args.numThreads), bool(args.enableEdgeTPU))


if __name__ == '__main__':
  main()

解决方法

detection_result是TFLite Support库定义的结构化Protobuf对象，直接按照对象属性层级访问即可提取class_name字段。
找到代码中print(detection_result)的位置，将其替换为如下代码即可：

# 遍历当前帧所有符合阈值的检测结果
for detection in detection_result.detections:
    # 单个检测结果的classes列表按置信度从高到低排序，取置信度最高的类别
    top_category = detection.classes[0]
    # 提取类别名并打印，可按需同时打印置信度、边界框坐标等信息
    print(top_category.class_name)

补充说明：

初始化检测模型时设置了max_results=3，单帧最多返回3个检测结果，上述循环会打印当前帧内所有检测到的目标类别
如果仅需要打印置信度最高的单个目标类别，可直接使用detection_result.detections[0].classes[0].class_name取值，建议增加空值判断，避免画面中无检测目标时触发索引报错
如果需要筛选特定类别（仅打印口罩类），增加if top_category.class_name == "surgical_mask":判断即可

内容的提问来源于stack exchange，提问作者Carlo Jhon Sario