Skip to main content

Posts

Image Identify using machine learning in Python.

#pip install imageai #pip install Tensorflow #pip install keras from  imageai.Prediction  import  ImagePrediction import  os from  keras.models  import  load_model Givenimage= "picc.jpg" execution_path=os.getcwd() #print(execution_path) prediction = ImagePrediction() prediction.setModelTypeAsSqueezeNet() prediction.setModelPath(os.path.join(execution_path,  "squeezenet_weights_tf_dim_ordering_tf_kernels.h5" )) prediction.loadModel() predictions, probabilities = prediction.predictImage(os.path.join(execution_path, Givenimage), result_count= 3  ) for  eachPrediction, eachProbability  in   zip (predictions, probabilities):      print (eachPrediction ,  " : "  , eachProbability)   Result: WARNING:tensorflow:11 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f941e211598> tr...
Recent posts

Read The Hacker News in efficient way using Python Programming.

import  requests from  bs4  import  BeautifulSoup  import  pprint url= 'https://news.ycombinator.com/' url2= 'https://news.ycombinator.com/news?p=2' res=requests.get(url) res2=requests.get(url2) soup=BeautifulSoup(res.text, 'html.parser' ) soup2=BeautifulSoup(res2.text, 'html.parser' ) links=soup.select( '.storylink' ) subtext=soup.select( '.subtext' ) links2=soup2.select( '.storylink' ) subtext2=soup2.select( '.subtext' ) def   sorting_votes ( hnlist ):    return   sorted (hnlist,key= lambda  k:k[ 'votes' ],reverse= True ) megalinks=links + links2 megasubtext=subtext+subtext2 def   custom_hn ( links , subtext ):   hn=[]    for  idx,item  in   enumerate (links):     title=links[idx].getText()     href=links[idx].get( 'href' , None )     vote=subtext[idx].select( '.score' )           if...

Bamboo chair using cad.

Make a Translator using python?

CODE : pip install translate create a new script file eg. english in the same directory. from translate import Translator translator=Translator( to_lang = "hi" ) with open ( './english.txt' , 'r' ) as file2: text=file2.read() translation=translator.translate(text) print(translation) or another way  jj= str ( input ( 'enter th english' )) from translate import Translator translator=Translator( to_lang = "hi" ) with open ( './english.txt' , 'w' ) as file2: file2.write( f'-- { jj } ' ) with open ( './english.txt' , 'r' ) as file2: text = file2.read() translation=translator.translate(text) print (translation) DEMO:

Join Telegram group Link of qrioxity!

                                                                       join    TELEGRAM

How to control Google chrome jumping Dinosaur game by using Arduino?

Equipment; 1) An Arduino Uno board 2) A Photo resistive sensor(LDR) [analoge or digital] 3) Jumper wires. 4) A Servo motor 9g. 5) Servo motors attachments. 6) Breadboard. How it works; LDR is attached on the screen by which it can read the difference between the black spot(Cactus in the game) and bright spot(no obstacle). For identifying the black spots precisely we have a little bit set the sensitivity of the LDR sensor and brightness of the screen accordingly. This LDR data take as a digital input and control the servo motor as a digital output. Arduino Programming; #include <Servo.h>   Servo myservo;  // create servo object to control a servo // twelve servo objects can be created on most boards   int pos = 0; // variable to store the servo position const  int ldr=7;  const int mot=9;  //--Serial.begin(9600);  void setup() {  myservo.attach(9); // attaches the servo on pin 9 to the servo obj...

Views of Pallithura Beach, Trivandrum, Kerala

These are some views of Pallithura Beach in Kerala. This beach is not so much famous so there is less crowd as you can see in the pictures. But the beach was great.

why viscous fluids is run out with the solid surface or stick on the surface when inclined the glasses?

Unofficial answers are also welcome. Let's leave your discussion on comment section.

Separate negative and positive value from the array using structure function in LabVIEW