{"id":21461125,"url":"https://github.com/sn1027/hand-gesture-recognition-using-machine-learning-and-mediapipe","last_synced_at":"2026-02-02T23:03:07.154Z","repository":{"id":261473946,"uuid":"884403333","full_name":"SN1027/Hand-Gesture-recognition-using-machine-learning-and-mediapipe","owner":"SN1027","description":"This Hand gesture recognition project using mediapipe is developed to recognize various hand gestures. The user can custom train any number of various hand gestures to train a model. 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By creating an\nintuitive and accessible platform, we seek to empower disabled individuals,\nenabling seamless interaction in everyday situations—whether in educational\nsettings, public services, or personal communication.\n\n**About the repositary files and order of execution of files:-**\n\n**1)Handlandmarker.py**\nThis program is coded to access the camera and display the skeletal coordinates of the detected hands.\nThis is run to check if the camera is accessed and configured properly.\n\n**2)Handlandmarker_model.py**\nThis program is run to custom train various hand gestures that are shown to the camera with custom names as inputted by the user.\nThe gestures that are inputted are processed and made into a CSV file ('Handmarks.csv')and is stored locally for training a model.\n\n**3)Handlandmarktrain.py**\nThis program is run to develop a local trained model.\n\npipelines = {\n    'lr':make_pipeline(StandardScaler() , LogisticRegression()),\n    'rc':make_pipeline(StandardScaler() , RidgeClassifier()),\n    'rf':make_pipeline(StandardScaler() , RandomForestClassifier()),\n    'gb':make_pipeline(StandardScaler() , GradientBoostingClassifier())\n}\n\nThis program develops four model based on the above mentioned algorithm.\n\nThe user can then check the accuracy score of each trained algorithm then choose the best fit algorithm.\nfor algo , model in fit_models.items():\n    pred = model.predict(X_test)\n    print(algo , accuracy_score(y_test , pred))\nwith open('Hand_test_new.pkl' , 'wb') as f:\n    pickle.dump(fit_models['rf'], f)\n\nThe program then creates a pickle file(trained model) which can used to check of gestures in real time.\n\n**4)handmarkeroutput.py**\nThis program runs to detect and recognize various gestures in real-time that are pre-trained in the model as done earlier.\n\n**I Hope this code is useful to developers out there and as well as students drop a like as an appreciation. Thanks in Advance ;)**\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsn1027%2Fhand-gesture-recognition-using-machine-learning-and-mediapipe","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsn1027%2Fhand-gesture-recognition-using-machine-learning-and-mediapipe","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsn1027%2Fhand-gesture-recognition-using-machine-learning-and-mediapipe/lists"}