TensorFlow Object Detection API教程——利用自己制作的数据集进行训练预测和测试

TensorFlow Object Detection API教程——利用自己制作的数据集进行训练预测和测试

感想

如果制作数据集不清楚的，这里我介绍一下，怎样利用自己的数据集进行训练啦。我用的是python3，ubuntu16.04来跑模型的

我建议最好把官网的demo跑通以后，熟悉一下流程，然后进行下面的操作，不然很容易走进误区。

训练

首先git下载tensorflow models模块：

git clone PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slimexport PYTHONPATH="${PYTHONPATH}:/home/whsyxt/Downloads/gaoshengwu/models/research:/home/whsyxt/Downloads/gaoshengwu/models/research/slim/"

这样每次登录都会生效，不添加环境变量也行，只是用起来不怎么方便，官网给的方法也行：

# From tensorflow/models/research/protoc object_detection/protos/*.proto --python_out=.# From tensorflow/models/research/export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim

进入research目录，运行：

sudo python3 setup.py install

其它的安装参考这个网址里面的就行了。

然后，把生成好的tfrecords文件放在./models/research/object_detection/data

在./models/research/object_detection目录下创建training文件夹，里面再创建文件夹ssd_inception_v2_whsyxt文件夹，然后创建label map文件，我的label map文件为whsyxt_label_map.pbtxt，内容为：

item { id: 2 name: 'person'}item { id: 1 name: 'car'}

然后把ssd_inception_v2_coco.config文件放在ssd_inception_v2_whsyxt文件夹下，修改里面的配置，我的配置修改如下：

num_classes: 2

然后修改路径：

train_input_reader: { tf_record_input_reader { input_path: "data/whsyxt_train.tfrecord" } label_map_path: "training/ssd_inception_v2_whsyxt/whsyxt_label_map.pbtxt"}

eval_input_reader: { tf_record_input_reader { input_path: "data/whsyxt_validation.tfrecord" } label_map_path: "training/ssd_inception_v2_whsyxt/whsyxt_label_map.pbtxt" shuffle: false num_readers: 1 num_epochs: 1}

按照我的样子照葫芦画瓢就行了，其实就是改一下类别数目和路径

# SSD with Inception v2 configuration for MSCOCO Dataset.# Users should configure the fine_tune_checkpoint field in the train config as# well as the label_map_path and input_path fields in the train_input_reader and# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that# should be configured.model { ssd { num_classes: 2 box_coder { faster_rcnn_box_coder { y_scale: 10.0 x_scale: 10.0 height_scale: 5.0 width_scale: 5.0 } } matcher { argmax_matcher { matched_threshold: 0.5 unmatched_threshold: 0.5 ignore_thresholds: false negatives_lower_than_unmatched: true force_match_for_each_row: true } } similarity_calculator { iou_similarity { } } anchor_generator { ssd_anchor_generator { num_layers: 6 min_scale: 0.2 max_scale: 0.95 aspect_ratios: 1.0 aspect_ratios: 2.0 aspect_ratios: 0.5 aspect_ratios: 3.0 aspect_ratios: 0.3333 reduce_boxes_in_lowest_layer: true } } image_resizer { fixed_shape_resizer { height: 300 width: 300 } } box_predictor { convolutional_box_predictor { min_depth: 0 max_depth: 0 num_layers_before_predictor: 0 use_dropout: false dropout_keep_probability: 0.8 kernel_size: 3 box_code_size: 4 apply_sigmoid_to_scores: false conv_hyperparams { activation: RELU_6, regularizer { l2_regularizer { weight: 0.00004 } } initializer { truncated_normal_initializer { stddev: 0.03 mean: 0.0 } } } } } feature_extractor { type: 'ssd_inception_v2' min_depth: 16 depth_multiplier: 1.0 conv_hyperparams { activation: RELU_6, regularizer { l2_regularizer { weight: 0.00004 } } initializer { truncated_normal_initializer { stddev: 0.03 mean: 0.0 } } batch_norm { train: true, scale: true, center: true, decay: 0.9997, epsilon: 0.001, } } } loss { classification_loss { weighted_sigmoid { anchorwise_output: true } } localization_loss { weighted_smooth_l1 { anchorwise_output: true } } hard_example_miner { num_hard_examples: 3000 iou_threshold: 0.99 loss_type: CLASSIFICATION max_negatives_per_positive: 3 min_negatives_per_image: 0 } classification_weight: 1.0 localization_weight: 1.0 } normalize_loss_by_num_matches: true post_processing { batch_non_max_suppression { score_threshold: 1e-8 iou_threshold: 0.6 max_detections_per_class: 100 max_total_detections: 100 } score_converter: SIGMOID } }}train_config: { batch_size: 24 optimizer { rms_prop_optimizer: { learning_rate: { exponential_decay_learning_rate { initial_learning_rate: 0.004 decay_steps: 800720 decay_factor: 0.95 } } momentum_optimizer_value: 0.9 decay: 0.9 epsilon: 1.0 } } fine_tune_checkpoint: "ssd_inception_v2_coco_2017_11_17/model.ckpt" from_detection_checkpoint: true # Note: The below line limits the training process to 200K steps, which we # empirically found to be sufficient enough to train the pets dataset. This # effectively bypasses the learning rate schedule (the learning rate will # never decay). Remove the below line to train indefinitely. num_steps: 200000 data_augmentation_options { random_horizontal_flip { } } data_augmentation_options { ssd_random_crop { } }}train_input_reader: { tf_record_input_reader { input_path: "data/whsyxt_train.tfrecord" } label_map_path: "training/ssd_inception_v2_whsyxt/whsyxt_label_map.pbtxt"}eval_config: { num_examples: 8000 # Note: The below line limits the evaluation process to 10 evaluations. # Remove the below line to evaluate indefinitely. max_evals: 10}eval_input_reader: { tf_record_input_reader { input_path: "data/whsyxt_validation.tfrecord" } label_map_path: "training/ssd_inception_v2_whsyxt/whsyxt_label_map.pbtxt" shuffle: false num_readers: 1 num_epochs: 1}

然后回退到object detection目录，我的训练命令为：

python3 train.py \--logtostderr \--train_dir=training/ssd_inception_v2_whsyxt \--pipeline_config_path=training/ssd_inception_v2_whsyxt/ssd_inception_v2_coco.config

训练完毕后，会在training/ssd_inception_v2_whsyxt目录下产生很多的ckpt文件。

我们用训练好的模型做预测时，这里在object detection目录下创建inference_graph/ssd_whsyxt_inference_graph目录，用于存放模型的预测文件，我的运行命令为：

python3 export_inference_graph.py \ --input_type image_tensor \ --pipeline_config_path training/ssd_inception_v2_whsyxt/ssd_inception_v2_coco.config \ --trained_checkpoint_prefix training/ssd_inception_v2_whsyxt/model.ckpt-146589 \ --output_directory inference_graph/ssd_whsyxt_inference_graph

这时输出的文件，我们就可以拿来做预测了，预测的代码我也仿照官方的代码写了一个，我的是测试视频或者打开摄像头的，文件名为object_detection_tutorial.py,这里贴出来给大家参考：

# coding: utf-8# # 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.# # Importsimport numpy as npimport osimport sysimport tarfileimport tensorflow as tfimport zipfilefrom collections import defaultdictfrom io import StringIOfrom matplotlib import pyplot as pltfrom PIL import Imageimport cv2cap = cv2.VideoCapture(0) #打开摄像头# cap = cv2.VideoCapture("car.mp4")# cap = cv2.VideoCapture("DJI_0004.MOV")import time#if tf.__version__ != '1.4.0': # raise ImportError('Please upgrade your tensorflow installation to v1.4.0!')# ## Env setup# This is needed to display the images.# get_ipython().magic(u'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_utilfrom 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](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_2017_11_17'# MODEL_FILE = MODEL_NAME + '.tar.gz'MODEL_NAME = 'inference_graph/ssd_whsyxt_inference_graph'# DOWNLOAD_BASE = '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')PATH_TO_LABELS = os.path.join('training/ssd_inception_v2_whsyxt', 'whsyxt_label_map.pbtxt')NUM_CLASSES = 2# ## 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 finelabel_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 codedef 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'# PATH_TO_TEST_IMAGES_DIR = 'demo_2017117'images=os.listdir(PATH_TO_TEST_IMAGES_DIR)#TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]TEST_IMAGE_PATHS=[]for image_name in images: if(str(image_name.split(".")[-1])=="jpg"): TEST_IMAGE_PATHS.append(os.path.join(PATH_TO_TEST_IMAGES_DIR,image_name))# TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR,images) for i in range(1, 3) ]'''# Size, in inches, of the output images.IMAGE_SIZE = (12, 8)with detection_graph.as_default(): with tf.Session(graph=detection_graph) as sess: while True: ret, image_np = cap.read() # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) # Definite input and output Tensors for detection_graph 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. detection_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. detection_scores = detection_graph.get_tensor_by_name('detection_scores:0') detection_classes = detection_graph.get_tensor_by_name('detection_classes:0') num_detections = detection_graph.get_tensor_by_name('num_detections:0') start = time.time() # Actual detection. (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) end = time.time() # Time elapsed seconds = end - start print( "Time taken : {0} seconds".format(seconds)) # Calculate frames per second fps = 1 / seconds; print( "Estimated frames per second : {0}".format(fps)); # 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) cv2.imshow('object detection', cv2.resize(image_np, (800,600))) if cv2.waitKey(25) & 0xFF == ord('q'): cv2.destroyAllWindows() break

参考文献

[1].Tensorflow Object Detection API.https://github.com/tensorflow/models/tree/master/research/object_detection

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