tensorflow利用预训练模型进行目标检测（三）：将检测结果存入mysql数据库

mysql版本：5.7 ； 数据库：rdshare；表captain_america3_sd用来记录某帧是否被检测。表captain_america3_d用来记录检测到的数据。

python模块，包部分内容参考http://www.runoob.com/python/python-modules.html  https://www.cnblogs.com/ningskyer/articles/6025964.html

 

一、连接数据库

参考：

# 将视频插入数据库
def video_insert(filename,couse_id):
    conn =MySQLdb.connect(user='root',passwd='****',host='sh-cdb-myegtz7i.sql.tencentcdb.com',port=63619,db='bitbear',charset='utf8')
    cursor = conn.cursor()

    # 查找课程报告表中courseh_id等于解析得到的course_id的记录，得到courser_id
    # courseh_id是课程记录表中的course_id；courser_id是课程报告表中的主键；course_id是本程序中
    sql="SELECT courser_id FROM course_report WHERE courseh_id ='%s' "% (couse_id);
    cursor.execute(sql)
    results = cursor.fetchall()
    if(results):
        print(results)
        courser_id=results[0][0]
        print(results[0][0])

        # 获取该文件的路径
        #rarpath = os.getcwd();
        rarpath =filename
        print(rarpath)

        # 将记录插入
        #try:
        sql="UPDATE course_report SET json = '%s' WHERE courser_id = '%s' " % (rarpath,courser_id)
        cursor.execute(sql)
        cursor.rowcount
        conn.commit()
        cursor.close()

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首先需要安装mysql驱动  sudo apt-get install python-mysqldb 

安装完成之后可以在Python解释器中测试一下

输入 import MySQLdb #注意大小写

如果不报错，就证明安装成功了。

简单测试版本

# 将detection的结果存入mysql数据库
def detection_to_database(object_name):
    conn =MySQLdb.connect(user='root',passwd='****',host='localhost',port=3306,db='rdshare',charset='utf8')
    cursor = conn.cursor()


    #sql="SELECT person FROM captain_america3_d WHERE id =1 ";
    #cursor.execute(sql)
    #results = cursor.fetchall()
    #if(results):
    #    print(results)

    sql="INSERT INTO captain_america3_sd (is_detected) VALUES (1)"
    cursor.execute(sql)
    cursor.rowcount
    conn.commit()
    cursor.close()

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 二、修改文件结构

在同一目录下新建detection_control.py文件，相当于main文件，控制detection的流程，读入参数

#!usr/bin/python
# -*- coding: utf-8 -*-

import datetime
import os
import time
import argparse
import detection as mod_detection
import sys
reload(sys)
sys.setdefaultencoding('utf8')

os.environ['TF_CPP_MIN_LOG_LEVEL']='3'


def parse_args():
    '''parse args'''
    parser = argparse.ArgumentParser()
    parser.add_argument('--image_path', default='/home/yanjieliu/rdshare/dataset/ca36000_36100/')
    parser.add_argument('--image_start_num', default='36000')
    parser.add_argument('--image_end_num', default='36002')
    parser.add_argument('--model_name',
                        default='ssd_inception_v2_coco_2018_01_28')
    return parser.parse_args()

if __name__ == '__main__':
# 运行
    args=parse_args()
    for frame_num in range(int(args.image_start_num),int(args.image_end_num)):
        print(frame_num)
        #调用detection.py文件中的Detection函数，并向其传递参数
        mod_detection.Detection(args, frame_num)

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调用detection.py中的Detection函数，进行识别

detection.py文件内容如下

#!usr/bin/python
# -*- coding: utf-8 -*-

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot 
from matplotlib import pyplot as plt
import os
import tensorflow as tf
from PIL import Image
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

import datetime
# 关闭tensorflow警告
import time
import MySQLdb
import argparse
import sys
reload(sys)
sys.setdefaultencoding('utf8')

os.environ['TF_CPP_MIN_LOG_LEVEL']='3'

detection_graph = tf.Graph()


# 将detection的结果存入mysql数据库
def detection_to_database(object_name, frame_num):
    conn =MySQLdb.connect(user='root',passwd='****',host='localhost',port=3306,db='rdshare',charset='utf8')
    cursor = conn.cursor()

    #查询目标检测状态表，查看frame_num是否已经被检测过，若是，则更新，若否，则插入
    sql="SELECT is_detected FROM captain_america3_sd WHERE frame_num ='%s' "% (frame_num);
    cursor.execute(sql)
    results = cursor.fetchall()
    if(results):
        print(results)
        sql="UPDATE captain_america3_sd SET is_detected=1";
    else:
        print('null')
        sql="INSERT INTO captain_america3_sd (is_detected, frame_num) VALUES (1,'%s')"%(frame_num);

    cursor.execute(sql)


    cursor.rowcount
    conn.commit()
    cursor.close()


# 加载模型数据-------------------------------------------------------------------------------------------------------
def loading(model_name):

    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        PATH_TO_CKPT = '/home/yanjieliu/models/models/research/object_detection/pretrained_models/'+model_name + '/frozen_inference_graph.pb'
        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='')
    return detection_graph



# Detection检测-------------------------------------------------------------------------------------------------------
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)
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('/home/yanjieliu/models/models/research/object_detection/data', 'mscoco_label_map.pbtxt')
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=90, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

def Detection(args, frame_num):
    image_path=args.image_path
    loading(args.model_name)
    #start = time.time()
    with detection_graph.as_default():
        with tf.Session(graph=detection_graph) as sess:
            # for image_path in TEST_IMAGE_PATHS:
            image = Image.open('%simage-%s.jpeg'%(image_path, frame_num))

            # 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.将识别结果标记在图片上
            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)
            # output result输出
            for i in range(3):
                if classes[0][i] in category_index.keys():
                    class_name = category_index[classes[0][i]]['name']
                    detection_to_database(class_name, frame_num)
                else:
                    class_name = 'N/A'
                print("object：%s gailv：%s" % (class_name, scores[0][i]))
                
            # matplotlib输出图片
            # Size, in inches, of the output images.
            IMAGE_SIZE = (20, 12)
            plt.figure(figsize=IMAGE_SIZE)
            plt.imshow(image_np)
            plt.show()

def parse_args():
    '''parse args'''
    parser = argparse.ArgumentParser()
    parser.add_argument('--image_path', default='/home/yanjieliu/rdshare/dataset/ca36000_36100/')
    parser.add_argument('--image_start_num', default='36000')
    parser.add_argument('--image_end_num', default='36002')
    parser.add_argument('--model_name',
                        default='ssd_inception_v2_coco_2018_01_28')
    return parser.parse_args()



if __name__ == '__main__':
# 运行
    args=parse_args()
    start = time.time()
    Detection(args, frame_num)
    end = time.time()
    print('time:\n')
    print str(end-start)




#将时间写入到文件，方便统计
#    with open('./outputs/1to10test_outputs.txt', 'a') as f:
#        f.write('\n')
#        f.write(str(end-start))

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转载于:https://www.cnblogs.com/vactor/p/10031414.html