Currency Detection By Image In Python

Currency Detection By Image In Python


In this article, I will tell you guys how you will create a Currency Detection By Image In Python programming language.This program will capture a image and read it,after that detect Currency is real or fake. 

ABSTRACTION 

This program uses some proposal technic, there it uses a picture process technique to extract money details and see it is fake or not. 

Automatic detection and recognition of World currency note’s have uses many of the analysis to complete the progress significantly in the large potential applications/Projects in the Python.

INTRODUCTION


Today Technology is growing up,so have to create some technic which is different. Now today the banking sector is additionally obtaining modern day-by-day.This brings a deep would like of automatic fraud currency detection in the machine and automatic product merchant machine. several researchers are inspired to develop strong and economical automatic currency detection machine. 

An automatic machine which might notice banknotes are currently widely employed in dispensers of a contemporary product like candies, soft drinks bottle to bus or railway tickets. The technology of currency recognition essentially aims for distinctive and extracting visible and invisible options of currency notes. Until now, several techniques are planned to spot the currency note. however, the most effective approach is to use the visible options of the note. for instance, color and size. however, this manner isn't useful if the note is dirty or torn. 

If our concept is bad it affect many places on the program. therefore it's vital that however, we tend to extract the options of the image of the currency note and apply the correct algorithmic rule to enhance accuracy to acknowledge the note. 

we tend to apply here a straightforward algorithmic rule that works properly. The image of the currency note is captured through a camera. The hidden options of the note are highlighted within the actinic radiation. currently, process on the image is completed thereon non-inheritable image exploitation ideas like image segmentation, edge data of image and characteristics feature extraction. 

Python is used to create it withit we use matplotlib,now we follow some procedure works, and analysis. Feature extraction of pictures is a difficult task in the digital image process. It involves abstraction of airy and beheld options of World bill notes. This access consists of assorted accomplish like angel acquisition, bend detection, blah calibration conversion, affection extraction, angel analysis, and chief. 

Acquisition of image is a method of making digital pictures, from a physical scene. Here, the image is captured by a straightforward camera specified all the options are highlighted. Image is then hold on for the additional process. 

ADVANTAGES 


  • This program is accurate,means very compatible to use on any  system. 
  • This program have less distortion rate.


Program



from utils import * 
from matplotlib import pyplot as plt 

import subprocess from gtts 
import gTTS 

max_val = 8 
max_pt = -1 
max_kp = 0 

orb = cv2.ORB_create() 
image = read_img('image/pic.jpg') 

size= resize_img(image, 0.4) display('original', size) 
(kp1, des1) = orb.detectAndCompute(image, None) 

images= ['images/img1.jpg', 'images/img2.jpg', 'images/img3.jpg', 'img4/500.jpg'] 

for i in range(0, len(images)): 
          training = cv2.imread(images[i]) 
         (kp2, des2) = orb.detectAndCompute(image, None) 
         bf = cv2.BFMatcher() 
        match= bf.knnMatch(des1, des2, k=2) 
        good = [] 

for (m, n) in match: 
       if m.distance < 0.789 * n.distance: 
              good.append([m]) 
      if len(good) > max_val: 
             max_val = len(good) 
             max_pt = i 
             max_kp = kp2 

print(i, '', training_set[i], '', len(good)) 

    if max_val != 8: 
            print(training[max_pt]) print('good matches ', max_val) 
           train_image = cv2.imread(training[max_pt]) 
           image3 = cv2.drawMatchesKnn(image, kp1, train_imgage,       max_kp, good, 4) 
          note = str(training[max_pt])[6:-4] 
          print('\nDetected denomination: Rs. ', note) (plt.imshow(imgage3), plt.show()) 

    else: 
          print('No Matches') 



CONCLUSION 


The conferred approach offers An economical technique of pretend currency detection supported python programming language. three necessary security measures explored for fraud currency detection are the protection thread, run brand, and identification mark. 

Image process algorithms are applied to extract the options. To mix the multiple options, call a score of all the options were consolidated. The effectiveness of the projected approach is tried by 100% recognition fraud detection accuracy and therefore the low price of mean sq. error. 

The future perspective of the approach is to sight alternative world currencies and to infuse the conferred technique into the python softwares, in order that its proving to be a larger use. the appliance areas which will be helpful through the projected approach embody fraud. 

our currency detection program could be used on the some fields like electronic currency exchange and cash deposit victimization ATM etc.


                                           Other Links 

>>> Python Basics                                      >>> Data Structure in Python

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                                         >>> Pandas Tutorials
                                                        
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