2017 年 6 月， Google 公司開放了 TensorFlow Object Detection API 。 這 個項目使用 TensorFlow 實現了大多數深度學習目標檢測框架，真中就包括Faster R-CNN。
本系列文章將
（1）先介紹如何安裝 TensorFlow Object Detection API；
（2）再介紹如何使用已經訓練好的模型進行物體檢測 ；
（3）最後介紹如何訓練自己的 模型；
之前已經完成了安裝篇的講解（Tensorflow Object Detection API安裝）安裝環境如果是win10 CPU的話請參考（win10 CPU Tensorflow Object Detection API安裝與測試）
本文講基於已有的訓練模型做目標檢測。
TensorFlow Object Detection API 默認提供了 5 個預訓練模型，都是使用 coco 數據集訓練完成的，結構分別爲
SSD+MobileNet、 （想了解網絡結構可參考Mobilenet的模型結構和MobileNet-SSD的模型結構）
SSD+Inception、
R-FCN+ResNet10I 、
Faster RCNN+ResNetl0l 、
Faster RCNN+Inception_ResNet
官方給了一個檢測的例子，在object_detection 文件夾下 有個 object_detection_tutorial.ipynb 文件，運行方式是：打開anaconda prompt(類似cmd，假設你前提已經安裝了jupyter notebook)，將工作路徑切換到object_detection目錄，輸入jupyter notebook; 然後就出現網頁交互式的界面。輸入指令如下：
使用“Shift+Enter”組合鍵對.ipynb文件一一執行。該文件的源碼和執行結果如下:
#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的網絡結構爲例子，你可可以將MODEL_NAME的內容替換成以下內容。
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'
模型的下載地址爲 Tensorflow預訓練模型下載
可自行對比各個模型結構的檢測性能。