Question

我想检测并识别来自视频Feed的交通标志。我使用Tensorflow ML框架来识别符号，并使用haar分类器来检测符号。

以下是代码：

import cv2
import numpy as np
import tensorflow as tf
import os,time
import threading

# constants
IMAGE_SIZE = 200.0
MATCH_THRESHOLD = 3

def SignRecognizer():

    #to neglect all tensorflow compilation warnings
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

    #path to the blob 
    image_path='/root/Desktop/blob.jpg'

    #read the image data
    image_data = tf.gfile.FastGFile(image_path,'rb').read()

    #load label file,strip off carriage return \n
    label_lines= [line.rstrip() for line in tf.gfile.GFile("/root/Desktop/another_model/retrained_labels.txt")]

    #unpersists graph from file
    with tf.gfile.FastGFile("/root/Desktop/another_model/retrained_graph.pb",'rb') as f:
        graph_def=tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _=tf.import_graph_def(graph_def,name='') 

    with tf.Session() as sess: 
        #feed the image_data as input to the graph and get the first prediction
        softmax_tensor=sess.graph.get_tensor_by_name("final_result:0")
        predictions = sess.run(softmax_tensor,\
                      {'DecodeJpeg/contents:0':image_data})
        #sort to show labels of first prediction in order of confidence
        top_k=predictions[0].argsort()[-len(predictions[0]):][::-1]

        for node_id in top_k:
            human_string=label_lines[node_id]
            print("%s"%(human_string))
            break

roundabout_cascade = cv2.CascadeClassifier("/root/Desktop/tsp/haarcascade_roundabout.xml")
videocapture = cv2.VideoCapture(0)
scale_factor=1.3

while 1:
    ret,pic = videocapture.read()

    # do roundabout detection on street image
    gray = cv2.cvtColor(pic,cv2.COLOR_RGB2GRAY)

    signs = roundabout_cascade.detectMultiScale(pic,scaleFactor=1.4,minNeighbors=6)

    # initialize ORB and BFMatcher
    orb = cv2.ORB_create()
    bf = cv2.BFMatcher(cv2.NORM_HAMMING,crossCheck=True)

    # find the keypoints and descriptors for roadsign image
    roadsign = cv2.imread("/root/Desktop/tsp/roundabout.jpg",0)
    kp_r,des_r = orb.detectAndCompute(roadsign,None)

    for (x,y,w,h) in signs:
        #cv2.rectangle(pic,(x,y),(x+w,y+h),(255,0,0),2)

        # obtain object from street image
        obj = gray[y:y+h,x:x+w]
        color_image=pic[y:y+h,x:x+w]
        cv2.imwrite("/root/Desktop/blob.jpg",color_image)
        cv2.imshow('blob', color_image)

        #start a new thread and run SignRecognizer on it
        t=threading.Thread(name="SignRecognizer",target=SignRecognizer) 
        #set the thread as a daemon to prevent blocking of the main program
        t.setDaemon(True)
        t.start()


        ratio = IMAGE_SIZE / obj.shape[1]
        obj = cv2.resize(obj,(int(IMAGE_SIZE),int(obj.shape[0]*ratio)))

        # find the keypoints and descriptors for object
        kp_o, des_o = orb.detectAndCompute(obj,None)
        if len(kp_o) == 0 or des_o == None: 
            continue

        # match descriptors
        matches = bf.match(des_r,des_o)

        # draw object on street image, if threshold met
        if(len(matches) >= MATCH_THRESHOLD):
            cv2.rectangle(pic,(x,y),(x+w,y+h),(255,0,0),2)
        font = cv2.FONT_HERSHEY_SIMPLEX
        cv2.putText(pic,'Roundabout sign',(x,y),font,1,(255,255,255),1,cv2.LINE_AA)


    cv2.imshow('roundabout_signs',pic)
    k = cv2.waitKey(30) & 0xFF
    if k==2:
        break

cv2.waitKey(0)
cv2.destroyAllWindows()

SignRecognizer函数读取blob图像文件并使用我使用tensorflow ML Framework创建的模型识别符号。

我使用VideoCapture（0）启动网络摄像头并模拟实时视频输入。

我还使用OpenCV的ORB（Oriented FAST和旋转Brief）来消除误报。

我使用线程模块在另一个线程上运行SignRecognizer并将其设置为守护进程，以便主pgm。在识别期间没有被阻止。

一切都很好但是使用线程模块似乎有点滞后。有什么方法可以让它免费？

基于神经网络的交通标志检测与识别

0 个答案: