Currency recognition is the process of detecting the patterns of currencies by comparing them to the databases. In an uncontrolled environment, currency recognition is a challenging task. Since it may lead to a lack of image due to constant varying state. In the case of automated currency recognition, the identification of the value of coins and cash is a quite tough task for many applications.
From this page, you can learn complete development details of currency recognition using OpenCV python!!!
Some of the real-time examples of currency recognition are cash counting machines, money classifiers in ATMs, blind person money assistants, etc. These systems majorly are subject to object appearance at certain points. The major steps of currency recognition are listed below. Similarly, we also support you to develop advanced currency recognition applications using the Opencv library in python.
General Currency Recognition Steps
- Step 1: Load the input currency image
- Step 2 Perform preprocessing technique over image
- Step 3: Detect the color of currency image
- Step 4: If the color of the currency image was detected then get the currency type
- Step 5: Perform segmentation based on type
- Step 6: Perform pattern matching with existing patterns
- Step 7: If pattern matched then obtain the currency value
- Step 8: Execute the currency value of input image as output
Overview of Opencv Python for Currency Recognition
Open Source Computer Vision Library is shortly termed as “OpenCV”. This library is mainly intended for image processing, computer vision/graphics, and machine learning fields. For this purpose, it comprises nearly about 2000+ optimized algorithms. Further, it supports various programming languages such as Java, Python, C++, etc. As a matter of fact, it can detect a human face, handwriting, and other objects in images/videos.
By the by, it can process images and videos to identify objects, faces, or even the handwriting of a human. For advanced processing, several packages like NumPy, matplot, etc. van be incorporated. Overall, python enables to process of OpenCV array structure for comprehensive analysis. Further, it also allows working various mathematical operations using vector points on essential image patterns/features.
Overall, currency recognition using OpenCV python has notable demand in the current research community. For your information, here we have given you some operations that are majorly performed in image processing using the OpenCV library.
Major Concepts in Currency Recognition
- Filtering of image foreground based on GrabCut algorithm
- Implementing morphological processes over images
- Implementing thresholding function over image. For instance: adaptive / simple thresholding, etc.
- Image transformation from one color format to other. For instance: BGR to Gray and BGR to HSV, etc.
- Image segmenting / partitioning based on watershed algorithm
- Image smoothing through custom-based filters and blurring operation custom filters to images and blurring of images
- Construction of image pyramids
From the above list of image processing operations, here we have taken three primary operations as data preprocessing, feature extraction, and classification for illustration purposes.
- Basically, data preprocessing is performed for removing unwanted noise / patterns from images.
- As well, feature extraction is performed for filtering key features of image by eliminating unnecessary background details of image.
- Next, classification is performed for categorizing the identified patterns under pre-defined classes / groups.
In the below list, we have given you a few significant techniques/algorithms that are largely employed in these operations. From our experience, we ensure you all these methods yield expected results without affecting image quality. Likewise, we also suggest appropriate advanced techniques for other operations too. .
Data Pre-processing Methods for Currency Recognition
- Color Conversion
- RGB-to-Gray Conversion
- Specific Area Segmentation
- Fuzzy Rule / Logics
- Connected Component Labelling by YIQ space model
- Least Square Method
- Edge Detection
- Image Resolution Minimization
- Proximal / Nearest Neighbor interpolation
- Gray-level Reduction and Noise Elimination
- Gray-Level Reduction
- Weiner Filter Theory
- Low Pass Median Filter
- Contrast Improvement and Brightness Standardization
- Adaptive Histogram Equalization
Feature Extraction Methods for Currency Recognition
- Histogram Details (Kurtosis, Correlation, Skewness, etc.)
- Banknote Features (Length, Size, Region, etc.)
- Corner Details (Sobel Operator, Canny, Prewitt, etc.)
- Color Format Details (HSI, RGB, HSV, etc.)
- Other Features Collected by applying following Algorithms
- Selected ROI
- Similarity Map
- Genetic Algorithm (GA)
- Compressed Sensing
- Difference Map
- Principle Component Analysis (PCA)
- Speeded Up Robust Features (SURF)
- Discrete Wavelet Transform (DWT)
- Local Binary Patterns (LBP)
- Linear Discriminant Analysis (LDA)
- Scale-Invariant Feature Transform (SIFT)
- Gray-Level Co-Occurrence Matrix (GLCM)
Classification Methods for Currency Recognition
- Neural Networks
- Genetic Algorithms
- Support Vector Machines
- Decision Tree Induction
- K-Nearest Neighbor
- Bayesian Classification
- Association-based Classification
Now, we can see the uses of currency recognition using OpenCV python. As mentioned earlier, OpenCV is a library specially introduced for image/video processing. So, it is furnished with enriched pre-defined classes and functions to execute all sorts of complex mathematical and matrix operations over images. Consequently, it makes developers process the image in all possible ways based on customer requirements. For instance: one can detect moving objects, change image color, enhance image quality, detect image outline, etc. For your reference, here we have listed out few basic and important usages of OpenCV in the currency recognition field.
What are the uses of OpenCV for currency recognition?
- Image Edge Detection
- Identification of image edges / corners
- For instance: Canny Edge Detection, etc.
- Video Creation
- Making of video
- For instance: Multi-images
- Geometric Image Conversion
- Implement Geometric alteration over images
- For instance: translation, rotation, etc.
- Hough Circle Transform
- Identification of circles in an image
- Image Transformation
- Transformation of image
- For instance: Cosine Transform, Fourier Transform, etc.
- Hough Line Transform
- Identification of lines in an image
- Image Gradients Detection
- Identification of image edges, gradients, edges etc.
- Image Segmentation
- Segmenting of image
- For instance: Watershed Algorithm
- Shifting Colorspaces
- Trace specific color object in streaming video
- Shift images among various color spaces
- Morphological Transformations
- Perform morphological changes
- For instance: Opening, Dilation, Closing, Erosion, etc.
- Smoothing Images
- Perform smoothing over images
- For instance: Filter the image, Blur the image, customized kernels, etc.
- Interactive Foreground Extraction
- Filtering of image foreground
- For instance: GrabCut Algorithm
- Image Pyramids
- Creation of image pyramids for image blending
- Template Matching
- Detection of specific object in an image using Template Matching
- Image Thresholding
- Transform image into binary images
- For instance: Otsu’s binarization, Global thresholding, Adaptive thresholding, etc.
Generally, the python packages are largely installed by pip (package manager). Similarly, OpenCV can be freely downloaded over the internet and installed using the pip command. This command can be directly typed over the command line in the terminal. For your add-on benefits, here we have given you the installation command of OpenCV python.
When you create a bond with us, we provide you with software installation guidelines with software download links. As well, we support you from the beginning of project development (i.e., software installation) to the project dissertation.
Steps for Installing OpenCV Python
Beginning with the installation
- Collect requirements and download data and software
- Open terminal and Enter the installation commands
- Install required packages
- Complete installation process
Installation Command
- pip install opencv-python
In order to verify the OpenCV is successfully installed or not, type the below commands. Also, check the installed versions of packages.
python
>>>import cv2
>>>print(cv2.__version__)
To read the image, the OpenCv module uses cv2.imread () function. Here, make sure that your input image is stored in the working directory while providing the image path. If not, provide the full path of your input image directory. Further, the image read function also includes the followings,
- IMREAD_UNCHANGED() – Load the image in alpha channel mode
- IMREAD_COLOR() – Load the color image and remove the image transparency. As well, it is a default function
- IMREAD_GRAYSCALE() – Load the grayscale image
Our developers have comprehensive knowledge of handling the OpenCV library to provide you with end-to-end support. Also, we have the best coding skills to solve any kind of complex problem through our smart solutions. In the case of complexity, we tackle the problems by creating our own algorithms and hybrid techniques to implement currency recognition using OpenCV python.
Fake Currency Detection using OpenCV
Now, we can see the steps for reading and viewing input image using OpenCV in python.
- Load the input image from your directory
- Show the loaded image in specific window
- Wait for pressing any key
- Once pressed, terminate all the opened GUI windows
Next, we can see the different versions of OpenCV-python frameworks/libraries. Here, our developing experts have handpicked OpenCV-python 4.5.3.56 and androids studio-opencv 2.4 frameworks for illustration purposes. Since these two frameworks are currently used in different currency recognition systems/applications.
Our developers have developed numerous fake currency detection projects in these frameworks. So, we are adept to work on all the classes and functions of these frameworks to simplify your development phase. In fact, we know all smart points to make your currency recognition process an easier task regardless of complexity. Further, we also support you in other emerging versions of OpenCV to guide you in developing currency recognition applications in your desired frameworks.
Currency Recognition Tools in OpenCV
Opencv-Python 4.5.3.56
- Installation Command
- pip install opencv-python
- When you are working on unoptimized debug build, you can build opencv-python by following below steps.
- At first, install numpy and scikit-build packages through pip commands
- Then, type and execute the command “python setup.py bdist_wheel –build-type=Debug” in the terminal
- At last, install the auto-created wheel file in the dist/ directory using the below pip command
- pip install dist/wheelname.whl
Android Studio – Opencv-2.4 framework
- Work on smart phone platforms like Windows Mobile and Symbian
- Used to develop android-based mobile apps for currency recognition using OpenCV python
- Based on android mobile cell, identify the duplicated currency
- Able to process the captured mobile image input for matching, image processing and storing
- Enable to segment the image using iterative graph cut
- Allow to reduce the image dimension and extract features
On the whole, we guarantee you to provide code execution service in currency recognition using OpenCV python. If you are interested in different real-time applications in currency recognition, then provide you latest project topics based on your research interest.
Once you confirm with your project topic, we take the whole development duty of your project in all aspects like best-fitting framework selection, dataset selection, performance metrics selection, code development, result evaluation, etc. Further, we also extend our support in project dissertation writing too. So, connect with us to create incredible currency recognition using OpenCV python.
