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Human Pose Classification

A human pose classification ML Modal and web interface with a server.

Installing dependencies

In order to install dependencies first create your own virtual environment using following line of code

python3 -m venv #YOUR-ENV-NAME#

Then activate your virtual environment

Then type following command to install dependencies

python3 -m pip install -r requirements.txt


Note: You should add mediapipe heavytask to the "flask-front-end/serverfiles" with the following name "pose_landmarker_heavy.task". You can find the heavy task in the link below. https://developers.google.com/mediapipe/solutions/vision/pose_landmarker/index


Functions Used in the Project

Data Processing

  1. normalize_dataset

    Params:

    dataSubSet -> pandas.DataFrame

    Return:

    pandas.DataFrame

    This function receives a pandas.DataFrame and return a processed pandas.DataFrame. Function normalises the data by dividing all the values into the maximum entry in the parameter DataFrame. It also substracts the minimum value from all the entries in the DataFrame. So all the values would be reframed into values between 0 to 1.

  2. normalize_dataframe

    Params:

    dataAsDF -> pandas.DataFrame (default: False)

    extract -> Boolean

    Return:

    pandas.DataFrame

    This function send several calls to the normalize_dataset function to normalize X, Y and Z correspondingly. If extract is "True", the return values extracted to a file named as "normalized_landmarks.csv".

  3. train_modal

    Params

    processed_df -> pandas.DataFrame

    Return

    sklearn.neural_network.MLPClassifier

    This function trains a MLPClassifier modal with following parameters (max_ites: 500, solver: "lbfgs", hidden_layer_size: 80). Function also saves the confusion matrix to a file "confusion_matrix.png". The test train split of data 0.07.

  4. train_knn_modal

    Params:

    processed_df -> pandas.DataFrame

    Return:

    sklearn.neighbors.KNeighborsClassifier

    This function trains a KNeighborsClassifier modal with three neighbours. Later, function prints the accuracy score and export the results of predictions to file "knn_accuracy.csv". The test train split of data 0.1.

  5. pickle_modal

    Params:

    modal_to_pickle -> MLPClassifier

    Return:

    NaN

    Functions pickles the modal using joblib to a file named "modal.joblib".

mpprocess

mpprocess file contains the class of MPObject. MPObject class has the following methods:

  1. _init_ Params:

    Nan

    Return:

    Nan

    Method initialises the MPObject objects. It sets the MediaPipe pose_landmarker_heavy.task options and loads the pickled MPClassifier modal.

  2. runMLPC

    Params:

    data -> pandas.DataFrame

    prt -> Boolean (default: False)

    Return:

    prediction -> ndarray

    This method returns the prediction class for a single entry of data. ptr is depreciated.

  3. fetchResults

    Params:

    imagelocation -> str

    Return:

    prediction -> ndarray

    This method runs all the necessary functions to classify a pose from an image whose location stated in the parameter, "iamgelocation".

  4. draw_landmarks_on_image

    Params:

    rgb_image -> numpy.ndarray.view

    detection_results -> poseLandmarkerResult

    Return:

    NaN

    This medhod draws the landmarks to the image and shows. It gets the image and landmarks.

  5. retrieveLandmarks

    Params:

    imagelocation -> str

    prt -> Boolean (default: False)

    Return:

    poseLandmarkerResult.pose_world_landmarks

    This methods calls the relevant MediaPipe functions to extract the landmarks and returns the landmarks.

  6. putDataInFrame

    Params:

    poses -> poseLandmarkerResult.pose_world_landmarks

    Return:

    pandas.DataFrame

    This methods receives a MediaPipe pose_world_landmarks object and converts it to pandas.DataFrame.

  7. normalize_dataset

    Params:

    dataList -> pandas.DataFrame

    Return:

    pandas.DataFrame

    This method normalizes the data entered by dividing it to the maximum entry value and also substracts the minimum value from all entries.

  8. normalizeData

    Params:

    dataAsDF -> pandas.DataFrame

    prt -> Boolean (default: False)

    Return:

    pandas.DataFrame

    This method normalizes the data read through MediaPipe functions and other preprocessing functions. If prt is "True", normalized landmarks will be extracted to the file "normalized_landmarks.csv".

Notes

More data can be found in the files given above in the "NNGA Project_ A Pose Classifier.pdf".

Also some relevant links to the project can be found in the "notes.txt" file.