google image recognition api

" --post-file=test.flac -- header = "Content-Type: Audio/X-Flac; rate = 16000" "http://www.google.com/speech-api/v1/recognize? Xjerr = 1 client = chromium lang = ZH-CN maxresults = 1" The test.example voice is converted into a local file and saved in googlespeechapi.txt: { "Status": 0, "ID": "8f9d46231ac2dadee91d8d6ba1b57779-1 ", "Hypotheses ": [ {"Utterance": "That doesn't work.", "Confidence": 0.87555957}] } This is an early experience of usi

Ps: Based on Java 1.8Version control: MavenYou need to get the corresponding project Api_key,secret_key before use, these parameters must be used when using the API, to generate Access_token.How to get these parameters: apply for a "generic word recognition" project at Baidu Developer Center, and then you can get these parameters.The preparation conditions are complete, and now the

This section corresponds to Google Open source TensorFlow object Detection API Object recognition System Quick start Step (i):Quick Start:jupyter notebook for off-the-shelf inferenceThe steps in this section are simple and do the following:1. After installing Jupyter in the first section, enter the Models folder directory at the Ternimal terminal to execute the c

the node matrix or the number of input Samples # Fourth parameter: Fill method, ' same ' means full 0 padding, ' VALID ' means no padding TensorFlow to realize the forward propagation of the average pool layer Pool = Tf.nn.avg_pool (actived_conv,ksize[1,3,3,1],strides=[1,2,2,1],padding= ' same ') # first parameter: Current layer node Matrix # The second parameter: the size of the filter # gives a one-dimensional array of length 4, but the first and last of the array must be 1

are in the same order.*/int index = 0;for (int i=0;i{PData = mask.ptrfor (int j=0;j{if (pdata[j]==0)rst[index++]= ' 0 ';Elserst[index++]= ' 1 ';}}return rst;}void Photofingerprint::insert (Mat src,string val){String strval = HashValue (src);M_hashmap.insert (paircout}void Photofingerprint::find (Mat src){String Strval=hashvalue (SRC);Hash_mapif (It==m_hashmap.end ()){coutElsecout/* Return *it;*/}int Photofingerprint::D istance (String str1,string str2){if ((Str1.size ()!=64) | | (Str2.size ()!=

parseqrcodebitmap (String bi Tmappath) {//Resolution conversion type UTF-8 HashtableAnd then long press recognize QR code called Rgbluminancesource This classpublic class Rgbluminancesource extends Luminancesource {private byte bitmappixels[];p rotected Rgbluminancesource ( Bitmap Bitmap) {super (Bitmap.getwidth (), Bitmap.getheight ());//First, to get the image of the pixel array content int[] data = new Int[bitmap.getwidth () * Bitmap.getheight ()]

Public platform Message Interface Development image recognition-face recognition I. Preface In the past few small applications, it seems that the response is not cool or hot, and everyone is not interested. Today, we will give you a bright eye: face recognition on the public platform. Some time ago, I saw a report on

Image Recognition in various recognition Libraries In-Spirit Eugene zatepyakin open source stuff Http://code.google.com/p/in-spirit/w/list Face Recognition Http://code.google.com/p/vjdetector/ Flash Kinect Http://code.google.com/p/as3openni/ Face-recognition-library-as3

Integrate the google map api with the GOOGLE Search API. I wrote a class in object-oriented mode, passed a latitude and longitude, and automatically obtained nearby information through GOOGLE LOCAL SEARCH. Such as restaurants and scenic spots, which are marked on the map in

Integrate the google map api with the GOOGLE Search API. I wrote a class in object-oriented mode, passed a latitude and longitude, and automatically obtained nearby information through GOOGLE LOCAL SEARCH. Such as restaurants and scenic spots, which are marked on the map in

, deep learning can reach 99.47% recognition rate [8].While the academic community has received extensive attention, deep learning has also had a huge impact in industry. 6 months after Hinton's team won the Imagenet competition, Google and Baidu released new search engines based on image content. They followed the deep learning model used by Hinton in the Imagen

traditional computer vision methods, hand-designed features, and the difference in accuracy between them was no more than 1%. The degree of accuracy of the Hinton research group exceeds 10% in the second place (see table 1). This results in the field of computer vision generated a great shock, triggering the upsurge of deep learning. Another important challenge in the field of computer vision is human face recognition. Studies have shown that the

License plate Recognition The private cloud is the server version of the license plate recognition software, the use of OCR algorithm to identify the car, the difference is by the license plate cloud recognition deployed in the customer's own server or public server.The following focuses on the private cloud license plate rec