TensorFlow学习——Tensorflow Object Detection API（2.目标检测篇）

2017 年 6 月， Google 公司开放了 TensorFlow Object Detection API 。 这 个项目使用 TensorFlow 实现了大多数深度学习目标检测框架，真中就包括Faster R-CNN。

本系列文章将

（1）先介绍如何安装 TensorFlow Object Detection API；

（2）再介绍如何使用已经训练好的模型进行物体检测 ；

（3）最后介绍如何训练自己的 模型；

之前已经完成了安装篇的讲解（Tensorflow Object Detection API安装）安装环境如果是win10 CPU的话请参考（win10 CPU Tensorflow Object Detection API安装与测试）

本文讲基于已有的训练模型做目标检测。

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TensorFlow Object Detection API 默认提供了 5 个预训练模型，都是使用 coco 数据集训练完成的，结构分别为

SSD+MobileNet、 （想了解网络结构可参考Mobilenet的模型结构和MobileNet-SSD的模型结构）

SSD+Inception、

R-FCN+ResNet10I 、

Faster RCNN+ResNetl0l 、

Faster RCNN+Inception_ResNet

 

官方给了一个检测的例子，在object_detection 文件夹下 有个 object_detection_tutorial.ipynb 文件，运行方式是：打开anaconda prompt(类似cmd，假设你前提已经安装了jupyter notebook)，将工作路径切换到object_detection目录，输入jupyter notebook; 然后就出现网页交互式的界面。输入指令如下：

 

使用“Shift+Enter”组合键对.ipynb文件一一执行。该文件的源码和执行结果如下:

#Object Detection Demo
#Welcome to the object detection inference walkthrough! This notebook will walk you #step by step through the process of using a pre-trained model to detect objects in #an image. Make sure to follow the installation instructions before you start.

#Imports


import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
​
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image

## Env setup
# This is needed to display the images.
%matplotlib inline
​
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")


## Object detection imports
Here are the imports from the object detection module.

from utils import label_map_util
​from utils import visualization_utils as vis_util

# Model preparation 
## Variables
​
#Any model exported using the `export_inference_graph.py` tool can be loaded here #simply by changing `PATH_TO_CKPT` to point to a new .pb file.  
#By default we use an "SSD with Mobilenet" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_mo#del_zoo.md) for a list of other models that can be run out-of-the-box with varying #speeds and accuracies.

# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
​
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
​
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
​
NUM_CLASSES = 90

## Download Model

opener = urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
  file_name = os.path.basename(file.name)
  if 'frozen_inference_graph.pb' in file_name:
    tar_file.extract(file, os.getcwd())


## Load a (frozen) Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')


## Loading label map
#Label maps map indices to category names, so that when our convolution network #predicts `5`, we know that this corresponds to `airplane`.  Here we use internal #utility functions, but anything that returns a dictionary mapping integers to #appropriate string labels would be fine


label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)


## Helper code
def load_image_into_numpy_array(image):
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)


# Detection
# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]
#TEST_IMAGE_PATHS = ['test_images']
# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)


with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    for image_path in TEST_IMAGE_PATHS:
      image = Image.open(image_path)
      # the array based representation of the image will be used later in order to prepare the
      # result image with boxes and labels on it.
      image_np = load_image_into_numpy_array(image)
      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
      image_np_expanded = np.expand_dims(image_np, axis=0)
      image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
      # Each box represents a part of the image where a particular object was detected.
      boxes = 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 = detection_graph.get_tensor_by_name('detection_scores:0')
      classes = detection_graph.get_tensor_by_name('detection_classes:0')
      num_detections = 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,
          np.squeeze(boxes),
          np.squeeze(classes).astype(np.int32),
          np.squeeze(scores),
          category_index,
          use_normalized_coordinates=True,
          line_thickness=8)
      plt.figure(figsize=IMAGE_SIZE)
      plt.imshow(image_np)

 

以上程序是以SSD + MobileNet的网络结构为例子，你可可以将MODEL_NAME的内容替换成以下内容。

MODEL_NAME =’ssd_inception_v2_coco_11_06_2017' 

MODEL NAME = 'rfcn_resnet101_coco_11_06_2017' 

MODEL NAME =’faster_rcnn_resnet10l_coco_11_06_2017 ’ 

MODEL NAME =’ faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017'

模型的下载地址为 Tensorflow预训练模型下载

可自行对比各个模型结构的检测性能。