{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIAgpK3L7Ztm",
        "outputId": "56f931a2-7e43-4cb1-c755-874bb28c8573"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install necessary packages\n",
        "!pip install mediapipe tensorflow scikit-learn opencv-python-headless\n",
        "\n",
        "# Import libraries\n",
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "import mediapipe as mp\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jInzzU1b-ZIC",
        "outputId": "163cec47-3d6d-435a-9819-fc7755e3ecc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mediapipe in /usr/local/lib/python3.10/dist-packages (0.10.15)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "import mediapipe as mp\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense\n",
        "\n",
        "# Initialize MediaPipe\n",
        "mp_pose = mp.solutions.pose\n",
        "mp_drawing = mp.solutions.drawing_utils\n",
        "\n",
        "def extract_keypoints(video_path):\n",
        "    cap = cv2.VideoCapture(video_path)\n",
        "    pose = mp_pose.Pose()\n",
        "    keypoints = []\n",
        "\n",
        "    while cap.isOpened():\n",
        "        ret, frame = cap.read()\n",
        "        if not ret:\n",
        "            break\n",
        "\n",
        "        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
        "        results = pose.process(frame_rgb)\n",
        "\n",
        "        if results.pose_landmarks:\n",
        "            landmarks = []\n",
        "            for lm in results.pose_landmarks.landmark:\n",
        "                landmarks.extend([lm.x, lm.y, lm.z, lm.visibility])\n",
        "            keypoints.append(landmarks)\n",
        "        else:\n",
        "            keypoints.append([0] * 132)  # 33 landmarks * 4 values (x, y, z, visibility)\n",
        "\n",
        "    cap.release()\n",
        "    return np.array(keypoints)\n"
      ],
      "metadata": {
        "id": "3eMlBHrR-jEC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process_videos_in_batches(parent_dir, batch_size=10):\n",
        "    X = []\n",
        "    y = []\n",
        "\n",
        "    exercise_folders = [f for f in os.listdir(parent_dir) if os.path.isdir(os.path.join(parent_dir, f))]\n",
        "\n",
        "    for exercise_folder in exercise_folders:\n",
        "        exercise_path = os.path.join(parent_dir, exercise_folder)\n",
        "        video_files = [f for f in os.listdir(exercise_path) if f.endswith('.mp4')]\n",
        "\n",
        "        for i, video_file in enumerate(video_files):\n",
        "            video_path = os.path.join(exercise_path, video_file)\n",
        "            keypoints = extract_keypoints(video_path)\n",
        "            # Pad keypoints to ensure consistent length across videos\n",
        "            max_length = 100 # Replace with the expected maximum number of frames\n",
        "            if keypoints.shape[0] < max_length:\n",
        "                padding = np.zeros((max_length - keypoints.shape[0], keypoints.shape[1]))\n",
        "                keypoints = np.concatenate((keypoints, padding), axis=0)\n",
        "            elif keypoints.shape[0] > max_length: # Trim video if it is longer than max length\n",
        "                keypoints = keypoints[:max_length, :]\n",
        "            X.append(keypoints)\n",
        "            y.append(1)  # Assuming all videos are labeled as correct exercise\n",
        "\n",
        "            # If batch size is reached or last video in folder, save batch\n",
        "            if (i + 1) % batch_size == 0 or (i + 1) == len(video_files):\n",
        "                batch_index = i // batch_size\n",
        "                np.save(f'/content/drive/MyDrive/keypoints_batch_{exercise_folder}_{batch_index}.npy', np.array(X))\n",
        "                np.save(f'/content/drive/MyDrive/labels_batch_{exercise_folder}_{batch_index}.npy', np.array(y))\n",
        "                X = []  # Reset lists\n",
        "                y = []\n",
        "\n",
        "    return True\n",
        "\n",
        "# Define the parent directory containing all exercise folders\n",
        "parent_dir = '/content/drive/MyDrive/correct/correct'\n",
        "process_videos_in_batches(parent_dir, batch_size=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4MUyUyh2-ruX",
        "outputId": "9a1bad49-d50d-44b3-e9e6-d1e311cee30e"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/google/protobuf/symbol_database.py:55: UserWarning: SymbolDatabase.GetPrototype() is deprecated. Please use message_factory.GetMessageClass() instead. SymbolDatabase.GetPrototype() will be removed soon.\n",
            "  warnings.warn('SymbolDatabase.GetPrototype() is deprecated. Please '\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import os\n",
        "\n",
        "# Define the directory where your batches are stored\n",
        "batch_dir = '/content/drive/MyDrive/'\n",
        "\n",
        "# Initialize empty lists to store data and labels\n",
        "X = []\n",
        "y = []\n",
        "\n",
        "# Loop through saved batch files\n",
        "for file_name in os.listdir(batch_dir):\n",
        "    if file_name.endswith('.npy'):\n",
        "        if 'keypoints_batch' in file_name:\n",
        "            X.append(np.load(os.path.join(batch_dir, file_name)))\n",
        "        elif 'labels_batch' in file_name:\n",
        "            y.append(np.load(os.path.join(batch_dir, file_name)))\n",
        "\n",
        "# Combine all batches into a single dataset\n",
        "X = np.concatenate(X, axis=0)\n",
        "y = np.concatenate(y, axis=0)\n",
        "\n",
        "print(f'Loaded {X.shape[0]} samples for training.')\n"
      ],
      "metadata": {
        "id": "h79UADwu-9bn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4cbeaae3-f6e4-4467-86d8-a7d098ca78dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded 102 samples for training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense\n",
        "\n",
        "# Split data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Define the LSTM model\n",
        "model = Sequential([\n",
        "    LSTM(64, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])),\n",
        "    LSTM(64),\n",
        "    Dense(32, activation='relu'),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(X_train, y_train, epochs=20, validation_data=(X_test, y_test))\n",
        "\n",
        "# Save the trained model\n",
        "model.save('lstm_model.h5')\n"
      ],
      "metadata": {
        "id": "jyr0HzDTBFWQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ece2fc87-75aa-4b9a-e40b-f1bbb4d6a7c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 307ms/step - accuracy: 0.6601 - loss: 0.6587 - val_accuracy: 1.0000 - val_loss: 0.5224\n",
            "Epoch 2/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 110ms/step - accuracy: 1.0000 - loss: 0.4933 - val_accuracy: 1.0000 - val_loss: 0.3953\n",
            "Epoch 3/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 102ms/step - accuracy: 1.0000 - loss: 0.3809 - val_accuracy: 1.0000 - val_loss: 0.2403\n",
            "Epoch 4/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 106ms/step - accuracy: 1.0000 - loss: 0.2357 - val_accuracy: 1.0000 - val_loss: 0.0733\n",
            "Epoch 5/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - accuracy: 1.0000 - loss: 0.0661 - val_accuracy: 1.0000 - val_loss: 0.0144\n",
            "Epoch 6/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - accuracy: 1.0000 - loss: 0.0128 - val_accuracy: 1.0000 - val_loss: 0.0052\n",
            "Epoch 7/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - accuracy: 1.0000 - loss: 0.0047 - val_accuracy: 1.0000 - val_loss: 0.0024\n",
            "Epoch 8/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - accuracy: 1.0000 - loss: 0.0022 - val_accuracy: 1.0000 - val_loss: 0.0013\n",
            "Epoch 9/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 108ms/step - accuracy: 1.0000 - loss: 0.0012 - val_accuracy: 1.0000 - val_loss: 7.7855e-04\n",
            "Epoch 10/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 101ms/step - accuracy: 1.0000 - loss: 7.1853e-04 - val_accuracy: 1.0000 - val_loss: 5.1570e-04\n",
            "Epoch 11/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 106ms/step - accuracy: 1.0000 - loss: 4.8286e-04 - val_accuracy: 1.0000 - val_loss: 3.7081e-04\n",
            "Epoch 12/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - accuracy: 1.0000 - loss: 3.5438e-04 - val_accuracy: 1.0000 - val_loss: 2.8507e-04\n",
            "Epoch 13/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 103ms/step - accuracy: 1.0000 - loss: 2.7463e-04 - val_accuracy: 1.0000 - val_loss: 2.3120e-04\n",
            "Epoch 14/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 1.0000 - loss: 2.2495e-04 - val_accuracy: 1.0000 - val_loss: 1.9555e-04\n",
            "Epoch 15/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - accuracy: 1.0000 - loss: 1.9260e-04 - val_accuracy: 1.0000 - val_loss: 1.7094e-04\n",
            "Epoch 16/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - accuracy: 1.0000 - loss: 1.6921e-04 - val_accuracy: 1.0000 - val_loss: 1.5331e-04\n",
            "Epoch 17/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 102ms/step - accuracy: 1.0000 - loss: 1.5150e-04 - val_accuracy: 1.0000 - val_loss: 1.4028e-04\n",
            "Epoch 18/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 134ms/step - accuracy: 1.0000 - loss: 1.3873e-04 - val_accuracy: 1.0000 - val_loss: 1.3034e-04\n",
            "Epoch 19/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 163ms/step - accuracy: 1.0000 - loss: 1.2947e-04 - val_accuracy: 1.0000 - val_loss: 1.2256e-04\n",
            "Epoch 20/20\n",
            "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 186ms/step - accuracy: 1.0000 - loss: 1.2323e-04 - val_accuracy: 1.0000 - val_loss: 1.1630e-04\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "import mediapipe as mp\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense\n",
        "\n",
        "# Initialize MediaPipe\n",
        "mp_pose = mp.solutions.pose\n",
        "mp_drawing = mp.solutions.drawing_utils\n",
        "\n",
        "def extract_keypoints(video_path):\n",
        "    cap = cv2.VideoCapture(video_path)\n",
        "    pose = mp_pose.Pose()\n",
        "    keypoints = []\n",
        "\n",
        "    while cap.isOpened():\n",
        "        ret, frame = cap.read()\n",
        "        if not ret:\n",
        "            break\n",
        "\n",
        "        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
        "        results = pose.process(frame_rgb)\n",
        "\n",
        "        if results.pose_landmarks:\n",
        "            landmarks = []\n",
        "            for lm in results.pose_landmarks.landmark:\n",
        "                landmarks.extend([lm.x, lm.y, lm.z, lm.visibility])\n",
        "            keypoints.append(landmarks)\n",
        "        else:\n",
        "            keypoints.append([0] * 132)  # 33 landmarks * 4 values (x, y, z, visibility)\n",
        "\n",
        "    cap.release()\n",
        "    return np.array(keypoints)\n",
        "\n",
        "def predict_exercise(video_path, model):\n",
        "    keypoints = extract_keypoints(video_path) # Now extract_keypoints is available\n",
        "    keypoints = np.expand_dims(keypoints, axis=0)\n",
        "    prediction = model.predict(keypoints)\n",
        "\n",
        "    if prediction > 0.5:\n",
        "        return 'Correct Exercise'\n",
        "    else:\n",
        "        return 'Incorrect Exercise'\n",
        "\n",
        "# Example usage:\n",
        "uploaded_video_path = '/content/drive/MyDrive/correct/correct/decline bench press/dbp_1.mp4'\n",
        "result = predict_exercise(uploaded_video_path, model)\n",
        "print(f'Result: {result}')"
      ],
      "metadata": {
        "id": "8kcyfLGmBNtZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24c01dea-14fc-4467-ec26-d4635ff022ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 334ms/step\n",
            "Result: Correct Exercise\n"
          ]
        }
      ]
    }
  ]
}