Wed Mar 15, 2023
Recognizing currency from an image using Python can be achieved using various techniques such as image processing, computer vision, and machine learning. Here is a simple approach that uses machine learning with Python's OpenCV library.
import cv2import numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_scorefrom sklearn.neighbors import KNeighborsClassifier# Load the datasetnotes = ["1.png", "2.png", "5.png", "10.png", "20.png", "50.png", "100.png", "200.png"]data = target = for note in notes:img = cv2.imread(note, cv2.IMREAD_GRAYSCALE)img = cv2.resize(img, (100, 100))data.append(img.flatten())target.append(int(note.split(".")))# Split the dataset into training and testing setsX_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)# Train a K-Nearest Neighbors classifierknn = KNeighborsClassifier(n_neighbors=3)knn.fit(X_train, y_train)# Evaluate the modely_pred = knn.predict(X_test)acc = accuracy_score(y_test, y_pred)print("Accuracy:", acc)# Load a new image and predict its currency denominationimg = cv2.imread("test.png", cv2.IMREAD_GRAYSCALE)img = cv2.resize(img, (100, 100))pred = knn.predict([img.flatten()])print("Predicted denomination:", pred)
Note that this is just a simple example, and more sophisticated methods may be required for real-world applications. Additionally, it is important to ensure that the dataset is diverse and representative of the currencies and denominations that the model is intended to recognize.
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