Implementation of KNN using OpenCV Last Updated : 12 Jul, 2025 Comments Improve Suggest changes Like Article Like Report KNN is one of the most widely used classification algorithms that is used in machine learning. To know more about the KNN algorithm read here KNN algorithm Today we are going to see how we can implement this algorithm in OpenCV and how we can visualize the results in 2D plane showing different features of classes we have in our training data. Let's consider two classes for our code. We generate 20 random data points belonging to the 2 classes using a random generator. The training points will be either of the 'magenta' class or 'yellow' class. The magenta is drawn as square and the label for magenta is 1 similarly yellow is drawn as a circle and is labelled as 0. Code: Python3 # Import necessary libraries import cv2 as cv import numpy as np import matplotlib.pyplot as plt # Create 20 data points randomly on the 2-D plane. # Data_points are random points located on the 2D plane. Data_points = np.random.randint(0, 50, (20, 2)).astype(np.float32) # Label the data points with their class labels. labels = np.random.randint(0, 2, (20, 1)).astype(np.float32) # labels are the classes assigned to data points. # Take the yellow class for 0 label and magenta class for 1 label yellow = Data_points[labels.ravel()== 0] magenta = Data_points[labels.ravel()== 1] # Plot the classes on the 2-D plane # o for circle plt.scatter(yellow[:, 0], yellow[:, 1], 80, 'y', 'o') # s for square plt.scatter(magenta[:, 0], magenta[:, 1], 80, 'm', 's') plt.show() Output: Now consider an unknown new data point, our KNN classifier will label that data point either 0 or 1 depending on its features and the number of neighbours that are defined by us. Code: Python3 # generate a random data point # unknown is a random data point for which we will perform prediction. unknown = np.random.randint(0, 50, (1, 2)).astype(np.float32) # create the knn classifier knn = cv.ml.KNearest_create() # we use cv.ml.ROW_SAMPLE to occupy a row of samples from the samples. knn.train(Data_points, cv.ml.ROW_SAMPLE, labels) # get the labelled result, the numbers, the distance of each data point. # find nearest finds the specified number of neighbours and predicts responses. ret, res, neighbours, distance = knn.findNearest(unknown, 5) # For each row of samples the method finds the k nearest neighbours. # For regression problems, the predicted result is a mean of all the neighbours. # For classification, the class is determined by the majority. # plot the data point with other data points plt.scatter(unknown[:, 0], unknown[:, 1], 80, 'g', '^') # show the result. plt.show() # print the results obtained print( "Label of the unknown data - ", res ) print( "Nearest neighbors - ", neighbours ) print( "Distance of each neighbor - ", distance ) Output: Label of the unknown data - [[1.]] Nearest neighbors - [[1. 1. 0. 1. 1.]] Distance of each neighbor - [[ 1. 65. 130. 173. 245.]] Comment K KaranGupta5 Follow Improve K KaranGupta5 Follow Improve Article Tags : Machine Learning OpenCV python Python-OpenCV Explore Machine Learning BasicsIntroduction to Machine Learning8 min readTypes of Machine Learning13 min readWhat is Machine Learning Pipeline?7 min readApplications of Machine Learning3 min readPython for Machine LearningMachine Learning with Python Tutorial5 min readNumPy Tutorial - Python Library3 min readPandas Tutorial6 min readData Preprocessing in Python4 min readEDA - Exploratory Data Analysis in Python6 min readFeature EngineeringWhat is Feature Engineering?5 min readIntroduction to Dimensionality Reduction4 min readFeature Selection Techniques in Machine Learning6 min readSupervised LearningSupervised Machine Learning7 min readLinear Regression in Machine learning15+ min readLogistic Regression in Machine Learning11 min readDecision Tree in Machine Learning9 min readRandom Forest Algorithm in Machine Learning5 min readK-Nearest Neighbor(KNN) Algorithm8 min readSupport Vector Machine (SVM) Algorithm9 min readNaive Bayes Classifiers7 min readUnsupervised LearningWhat is Unsupervised Learning5 min readK means Clustering – Introduction6 min readHierarchical Clustering in Machine Learning6 min readDBSCAN Clustering in ML - Density based clustering6 min readApriori Algorithm6 min readFrequent Pattern Growth Algorithm5 min readECLAT Algorithm - ML5 min readPrincipal Component Analysis(PCA)7 min readModel Evaluation and TuningEvaluation Metrics in Machine Learning9 min readRegularization in Machine Learning5 min readCross Validation in Machine Learning5 min readHyperparameter Tuning7 min readML | Underfitting and Overfitting5 min readBias and Variance in Machine Learning10 min readAdvanced TechniquesReinforcement Learning8 min readSemi-Supervised Learning in ML5 min readSelf-Supervised Learning (SSL)6 min readEnsemble Learning8 min readMachine Learning PracticeMachine Learning Interview Questions and Answers15+ min read100+ Machine Learning Projects with Source Code [2025]6 min read Like