基于ssd的手势识别模型（object detection api方式）

【Tensorflow】Object Detection API-训练自己的手势识别模型

1. 安装tensorflow以及下载object detection api

1.安装tensorflow：

对于CPU版本：pip install tensorflow
对于GPU版本：pip install tensorflow-gpu
升级tensorflow到最新版1.4.0：pip install --upgrade tensorflow-gpu

2.安装必须库：

sudo pip install pillow
sudo pip install lxml
sudo pip install jupyter
sudo pip install matplotlib
3.下载object detection api:
t clone https://github.com/tensorflow/models.git
4.protobuf编译：在tensorflow/models/research/目录下
protoc object_detection/protos/*.proto --python_out=.
5.添加pythonpath,在tensorflow/models/research/目录下
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
6.测试安装：
python object_detection/builders/model_builder_test.py

2.训练数据集准备

1.在model下新建文件夹dataset/VOCdevkit/VOC2007目录，VOC2007目录机构如下图所示：

2.在VOC2007目录下运行generate_txt.py程序，划分数据集，我的训练集和验证集比例为7:3，总量为1557

import os

import random

train_percent = 0.70

xmlfilepath = 'Annotations'

txtsavepath = 'ImageSets\Main'

total_xml = os.listdir(xmlfilepath)

num=len(total_xml)

list=range(num)

tr=int(num*train_percent)

train=random.sample(list,tr)

ftrain = open('ImageSets/Main/train.txt', 'w')

fval = open('ImageSets/Main/val.txt', 'w')

for i  in list:

    name=total_xml[i][:-4]+'\n'

    if i in train:

        ftrain.write(name)

    else:

        fval.write(name)

ftrain.close()

fval.close()

print ("finished")

3.将models/research/object_detection/dataset_tools目录下的create_pascal_tf_record.py文件复制到dataset文件夹下,做如下修改：
（1）修改第85行：img_path = os.path.join(data['folder'], image_subdirectory, data['filename'])
改为：img_path = os.path.join("VOC2007", image_subdirectory, data['filename'])
原因：因为我的数据标注的xml文件中的folder项是"hand_2",但是我本地并没有该目录，所以直接改为"VOC2007"。
（2）修改第163行：examples_path = os.path.join(data_dir, year, 'ImageSets', 'Main','aeroplane_' + FLAGS.set + '.txt')
改为：examples_path = os.path.join(data_dir, year, 'ImageSets', 'Main',FLAGS.set + '.txt')
原因：我的Main中的txt文件中没有aeroplane_前缀
（3）根据自己的标签创建pascal_label_map.pbtxt 文件，内容如下：

（4）运行以下命令，就可以得到用于训练和验证的tf_record文件：
python create_pascal_tf_record.py
--data_dir=./VOCdevkit
--label_map_path=./pascal_label_map.pbtxt
--year=VOC2007
--set=train
--output_path=./pascal_train.record

python create_pascal_tf_record.py
--data_dir=./VOCdevkit
--label_map_path=./pascal_label_map.pbtxt
--year=VOC2007
--set=val
--output_path=./pascal_val.record
此处写的是相对路径，若有需要可改为绝对路径。运行完成后将会在 目录下得到pascal_train.record和pascal_val.record两个文件，训练集和验证集的二进制文件。

3.解压SSDMobilenet模型

tar -xvf ssd_mobilenet_v1_coco_2018_01_28.tar得到如下文件：

将文件夹里面的model.ckpt.*的三个文件copy到dataset文件夹。

4.修改config文件

将文件models/research/object_detection/samples/configs/ssd_mobilenet_v1_pets.config复制到dataset.修改：
（1）num_classes修改为自己的类别数目，我的是6
（2）修改路径。（5处）
　　fine_tune_checkpoint: "./models/dataset/model.ckpt"
　　input_path: "./models/dataset/pascal_train.record"
　　label_map_path: ".models/dataset/pascal_label_map.pbtxt"
　　input_path: "./models/dataset/pascal_val.record"
　　label_map_path: "./models/dataset/pascal_label_map.pbtxt"
此处建议写为绝对路径
保存config文件，重命名为ssd_mobilenet_v1_pascal.config。

5.开始训练

将models/research/object_detection/model_main.py文件复制到dataset路径下：
在model_main.py文件中加三行代码：

import os

os.environ['CUDA_VISIBLE_DEVICES'] = ''     # 修改为当前能用的GPU

tf.logging.set_verbosity(tf.logging.INFO)     # 打印日志

执行训练命令
python model_main.py
--./ssd_mobilenet_v1_pascal.config
--model_dir=./output
--num_train_steps=50000
--sample_1_of_n_eval_examples=1 --alsologtostderr

6.评估模型

暂时还没找到评估模型的文件

7.查看结果

tensorboard --logdir=./models/dataset/output --port=6006
可以在浏览器打开http://服务器IP:6005/ 页面观察训练过程

主要是观察loss和mAP@.50IOU

8.生成可以被调用的模型

将models/research/object_detection目录下的export_inference_graph.py文件复制到dataset路径下
python export_inference_graph.py
--input_type=image_tensor
--pipeline_config_path=./ssd_mobilenet_v1_pascal.config
--trained_checkpoint_prefix=./output/model.ckpt-10000
--output_directory=./savedModelcd

生成的模型如图所示：

9.调用生成的模型

在dataset目录下创建object_detection_test.py,并将其复制到models/research/object_detection目录下，因为要调用该目录下的utils.py文件
可以在dataset下创建你自己的测试文件夹，然后更改object_detection_test.py的相应的路径

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

## 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("..")

from utils import label_map_util

from utils import visualization_utils as vis_util

# What model to download.

#MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'

#MODEL_FILE = MODEL_NAME + '.tar.gz'

#DOWNLOAD_BASE = #'http://download.tensorflow.org/models/object_detection/'

MODEL_NAME = '/home/minelab/chenqingyun/models/dataset/savedModelcd'

# 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 = "/home/minelab/chenqingyun/models/dataset/pascal_label_map.pbtxt"

NUM_CLASSES = 6

#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_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)

# 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 = '/home/minelab/chenqingyun/models/dataset/1'

#TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]

TEST_IMAGE = "2.jpeg"

#print('the test image is:', TEST_IMAGE)

TEST_IMAGE = os.path.join(PATH_TO_TEST_IMAGES_DIR,TEST_IMAGE)

# Size, in inches, of the output images.

IMAGE_SIZE = (224, 224)

IMGES_LIST = os.listdir(PATH_TO_TEST_IMAGES_DIR)

with detection_graph.as_default():

    with tf.Session(graph=detection_graph) as sess:

        for IMG_NAME in IMGES_LIST:

            TEST_IMAGE = os.path.join(PATH_TO_TEST_IMAGES_DIR,IMG_NAME)

            # 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')

            #for image_path in TEST_IMAGE_PATHS:

            image = Image.open(TEST_IMAGE)

            # 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)

            # print(scores)

            # print(classes)

            # print(category_index)

            final_score = np.squeeze(scores)

            count = 0

            for i in range(100):

                if scores is None or final_score[i] > 0.5:

                    count = count + 1

                    print(IMG_NAME,classes[0][i],scores[0][i])

            #print ('the count of objects is: ', count)

            # plt.figure(figsize=IMAGE_SIZE)

            # plt.imshow(image_np)

            # plt.show()