Action Detection Personal Version.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Human Activity Recognition (HAR)** is a field in computer science that aims to create systems that recognize, classify and take actions based on human action data captured by sensors.\n",
    "\n",
    "This notebook contains the entire pipeline of a Data Science project that uses labeled data to train a **Machine Learning** system that can extract patterns from the data and generalize to unseen samples. The project consists on capturing frames from the webcam and predict the actions being performed into **4 classes**: \n",
    "- Hello signal;\n",
    "- Thanks signal; \n",
    "- I Love You signal;\n",
    "- Doing nothing;\n",
    "\n",
    "The pipeline is the following:\n",
    "\n",
    "* Collection of data to use in the training, validation and testing phases of a Machine Learning system;\n",
    "\n",
    "    * The data is collected using the webcam to capture frames. They are then passed to a pretrained ML model that extracts **Keypoints** from the body. Read more about this model [here](https://github.com/google-ai-edge/mediapipe/blob/master/docs/solutions/holistic.md).\n",
    "\n",
    "* Pre processing of the Keypoints captured to create **embeddings** of the frames. These embeddings encode the action being performed and can be processed as a sequence to be classified;\n",
    "\n",
    "* Creation and training of a **Recurrent Neural Network** using LSTM building blocks, which is capable of processing sequences and extracting patterns of them to perform predictions;\n",
    "\n",
    "    * The network is modeled with **2 LSTM layers** that receive the sequence and output the last hidden state after the processing phase. Then, **Fully Connected layers** are used to process the output of the RNN and output logits corresponding to the likelihood of the sequence being of one of the 4 classes.\n",
    "\n",
    "* After training and analysis of the results, predictions are performed on unlabeled data captured on **real time**; \n",
    "\n",
    "\n",
    "The project is based on [this YouTube video](https://youtu.be/doDUihpj6ro?si=9nu-WivTaBG_jJ4p)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "recover_checkpoints = True\n",
    "collect_data_points = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Imports and Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install tensorflow opencv-python mediapipe scikit-learn matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install lightning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import mediapipe as mp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Keypoints unsing MediaPipe Holistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cv2.typing import MatLike\n",
    "from typing import Tuple, NamedTuple\n",
    "from mediapipe.python.solutions.holistic import Holistic, HAND_CONNECTIONS, POSE_CONNECTIONS\n",
    "from mediapipe.python.solutions.drawing_utils import draw_landmarks, DrawingSpec, BLUE_COLOR, GREEN_COLOR, RED_COLOR, WHITE_COLOR, BLACK_COLOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Capture Keypoints from frame\n",
    "def mediapipe_detection(frame: MatLike, model: Holistic) -> Tuple[MatLike, NamedTuple]:\n",
    "    image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "    image.flags.writeable = False\n",
    "    results = model.process(image)\n",
    "    image.flags.writeable = True\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "    return image, results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw Keypoints and connections on the image to be displayed\n",
    "def draw_landmarks_image(image: MatLike, results: NamedTuple) -> None:\n",
    "    draw_landmarks(image, results.left_hand_landmarks, HAND_CONNECTIONS)\n",
    "    draw_landmarks(image, results.right_hand_landmarks, HAND_CONNECTIONS)\n",
    "    draw_landmarks(image, results.pose_landmarks, POSE_CONNECTIONS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw Keypoints and connections on the image to be displayed\n",
    "def draw_styled_landmarks_image(image: MatLike, results: NamedTuple) -> None:\n",
    "    draw_landmarks(image, results.left_hand_landmarks, HAND_CONNECTIONS,\n",
    "                   DrawingSpec(color=BLUE_COLOR, thickness=1, circle_radius=1),\n",
    "                   DrawingSpec(color=BLACK_COLOR, thickness=1, circle_radius=1),\n",
    "                   )\n",
    "    draw_landmarks(image, results.right_hand_landmarks, HAND_CONNECTIONS,\n",
    "                   DrawingSpec(color=BLUE_COLOR, thickness=1, circle_radius=1),\n",
    "                   DrawingSpec(color=BLACK_COLOR, thickness=1, circle_radius=1),\n",
    "                   )\n",
    "    draw_landmarks(image, results.pose_landmarks, POSE_CONNECTIONS,\n",
    "                   DrawingSpec(color=GREEN_COLOR, thickness=2, circle_radius=2),\n",
    "                   DrawingSpec(color=WHITE_COLOR, thickness=1, circle_radius=1),\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "with Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "        image, results = mediapipe_detection(frame, holistic)\n",
    "\n",
    "        draw_styled_landmarks_image(image, results)\n",
    "\n",
    "        cv2.imshow(\"OpenCV Feed\", image)\n",
    "\n",
    "        if cv2.waitKey(10) & 0xFF == ord(\"q\"):\n",
    "            break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Extracting Keypoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19c14de3020>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'landmark'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[11], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m results\u001b[38;5;241m.\u001b[39mface_landmarks\u001b[38;5;241m.\u001b[39mlandmark[\u001b[38;5;241m0\u001b[39m], results\u001b[38;5;241m.\u001b[39mpose_landmarks\u001b[38;5;241m.\u001b[39mlandmark[\u001b[38;5;241m0\u001b[39m], \u001b[43mresults\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mleft_hand_landmarks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlandmark\u001b[49m[\u001b[38;5;241m0\u001b[39m] \n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'landmark'"
     ]
    }
   ],
   "source": [
    "results.face_landmarks.landmark[0], results.pose_landmarks.landmark[0], results.left_hand_landmarks.landmark[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results.pose_landmarks.landmark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the keypoints from the response of the MediaPipe model and build the embedding for the frame\n",
    "def extract_keypoints(results: NamedTuple) -> np.array:\n",
    "    pose = np.array([[lm.x, lm.y, lm.z, lm.visibility] for lm in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks is not None else np.zeros((33*4,))\n",
    "    face = np.array([[lm.x, lm.y, lm.z] for lm in results.face_landmarks.landmark]).flatten() if results.face_landmarks is not None else np.zeros((468*3,))\n",
    "    left = np.array([[lm.x, lm.y, lm.z] for lm in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks is not None else np.zeros((21*3,))\n",
    "    right = np.array([[lm.x, lm.y, lm.z] for lm in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks is not None else np.zeros((21*3,))\n",
    "    return np.concatenate([pose, face, left, right])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding = extract_keypoints(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1662,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  3.73173505e-01,\n",
       "        6.85222149e-01, -3.23865095e-07,  3.89403194e-01,  6.10989630e-01,\n",
       "       -5.42135537e-03,  4.29089457e-01,  5.34335911e-01, -1.16982199e-02,\n",
       "        4.57169056e-01,  4.72424060e-01, -1.82271469e-02,  4.62127179e-01,\n",
       "        4.19827729e-01, -2.14945693e-02,  4.40933377e-01,  5.02701998e-01,\n",
       "       -1.53918387e-02,  5.15154660e-01,  5.38985968e-01, -3.37564535e-02,\n",
       "        4.93789166e-01,  5.64471364e-01, -4.20869179e-02,  4.68499839e-01,\n",
       "        5.58134973e-01, -4.72065210e-02,  4.41409290e-01,  5.33171952e-01,\n",
       "       -2.26650480e-02,  5.19568145e-01,  5.78167617e-01, -3.56830694e-02,\n",
       "        4.95648950e-01,  5.99782109e-01, -3.48277055e-02,  4.73716736e-01,\n",
       "        5.92805564e-01, -3.57019864e-02,  4.43930358e-01,  5.73642313e-01,\n",
       "       -3.13354880e-02,  5.16770780e-01,  6.15131974e-01, -4.24345247e-02,\n",
       "        4.93945599e-01,  6.34015858e-01, -3.50070111e-02,  4.72966522e-01,\n",
       "        6.27201855e-01, -3.10023520e-02,  4.50297147e-01,  6.18886590e-01,\n",
       "       -4.08503190e-02,  5.10274649e-01,  6.50038302e-01, -4.55989279e-02,\n",
       "        4.90335435e-01,  6.65378869e-01, -3.96174230e-02,  4.69349563e-01,\n",
       "        6.59235835e-01, -3.58481519e-02])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding[-(21*3*2):]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Setup Folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_PATH = os.path.join(\"MP_DATA\")\n",
    "\n",
    "actions = np.array(['hello', 'thanks', 'iloveyou', 'nothing'])\n",
    "\n",
    "n_videos = 60\n",
    "\n",
    "video_length = 30\n",
    "\n",
    "video_to_start = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for action in actions:\n",
    "    #dirmax = np.max(np.array(os.listdir(os.path.join(DATA_PATH, action))).astype(int))\n",
    "    for video in range(video_to_start, video_to_start + n_videos):\n",
    "        try:\n",
    "            os.makedirs(os.path.join(DATA_PATH, action, str(video)))\n",
    "        except:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Collect Keypoints for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For each of the actions to be classified,  a number of videos are captures with a certain amount of frames. The keypoints of these frames\n",
    "# are extracted and the corresponding embeddings are saved for posterior usage\n",
    "if collect_data_points:\n",
    "    cap = cv2.VideoCapture(0)\n",
    "    with Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "        for action in actions:\n",
    "            \n",
    "            ret, initial_frame = cap.read()\n",
    "\n",
    "            cv2.putText(initial_frame, \"ACTION {}\".format(action), (120, 250),\n",
    "                                cv2.FONT_HERSHEY_SIMPLEX, 1, GREEN_COLOR, cv2.LINE_4)\n",
    "            cv2.imshow(\"OpenCV Feed\", initial_frame)\n",
    "            cv2.waitKey(1000)\n",
    "\n",
    "            for video in range(video_to_start, video_to_start + n_videos):\n",
    "                for frame_num in range(0, video_length):\n",
    "                    ret, frame = cap.read()\n",
    "\n",
    "                    image, results = mediapipe_detection(frame, holistic)\n",
    "\n",
    "                    draw_styled_landmarks_image(image, results)\n",
    "                    \n",
    "                    cv2.putText(image, \"Collecting frames for {}\".format(action), (15, 30),\n",
    "                            cv2.FONT_HERSHEY_SIMPLEX, 0.8, BLACK_COLOR, cv2.LINE_4)\n",
    "                \n",
    "                    cv2.putText(image, \"Video Number {}\".format(video), (15, 80),\n",
    "                            cv2.FONT_HERSHEY_SIMPLEX, 0.8, BLACK_COLOR, cv2.LINE_4)\n",
    "                \n",
    "                    cv2.imshow(\"OpenCV Feed\", image)\n",
    "\n",
    "                    npy_path = os.path.join(DATA_PATH, action, str(video), str(frame_num))\n",
    "                    \n",
    "                    embedding = extract_keypoints(results)\n",
    "\n",
    "                    np.save(npy_path, embedding)\n",
    "\n",
    "                    if cv2.waitKey(10) & 0xFF == ord(\"q\"):\n",
    "                        break\n",
    "\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Preprocess Data and Create Labels and Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_map = {action:num for num, action in enumerate(actions)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hello': 0, 'thanks': 1, 'iloveyou': 2, 'nothing': 3}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ACTION hello\n",
      "\n",
      "Reading Video 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading Video 1\n",
      "Reading Video 2\n",
      "Reading Video 3\n",
      "Reading Video 4\n",
      "Reading Video 5\n",
      "Reading Video 6\n",
      "Reading Video 7\n",
      "Reading Video 8\n",
      "Reading Video 9\n",
      "Reading Video 10\n",
      "Reading Video 11\n",
      "Reading Video 12\n",
      "Reading Video 13\n",
      "Reading Video 14\n",
      "Reading Video 15\n",
      "Reading Video 16\n",
      "Reading Video 17\n",
      "Reading Video 18\n",
      "Reading Video 19\n",
      "Reading Video 20\n",
      "Reading Video 21\n",
      "Reading Video 22\n",
      "Reading Video 23\n",
      "Reading Video 24\n",
      "Reading Video 25\n",
      "Reading Video 26\n",
      "Reading Video 27\n",
      "Reading Video 28\n",
      "Reading Video 29\n",
      "Reading Video 30\n",
      "Reading Video 31\n",
      "Reading Video 32\n",
      "Reading Video 33\n",
      "Reading Video 34\n",
      "Reading Video 35\n",
      "Reading Video 36\n",
      "Reading Video 37\n",
      "Reading Video 38\n",
      "Reading Video 39\n",
      "Reading Video 40\n",
      "Reading Video 41\n",
      "Reading Video 42\n",
      "Reading Video 43\n",
      "Reading Video 44\n",
      "Reading Video 45\n",
      "Reading Video 46\n",
      "Reading Video 47\n",
      "Reading Video 48\n",
      "Reading Video 49\n",
      "Reading Video 50\n",
      "Reading Video 51\n",
      "Reading Video 52\n",
      "Reading Video 53\n",
      "Reading Video 54\n",
      "Reading Video 55\n",
      "Reading Video 56\n",
      "Reading Video 57\n",
      "Reading Video 58\n",
      "Reading Video 59\n",
      "\n",
      "ACTION thanks\n",
      "\n",
      "Reading Video 0\n",
      "Reading Video 1\n",
      "Reading Video 2\n",
      "Reading Video 3\n",
      "Reading Video 4\n",
      "Reading Video 5\n",
      "Reading Video 6\n",
      "Reading Video 7\n",
      "Reading Video 8\n",
      "Reading Video 9\n",
      "Reading Video 10\n",
      "Reading Video 11\n",
      "Reading Video 12\n",
      "Reading Video 13\n",
      "Reading Video 14\n",
      "Reading Video 15\n",
      "Reading Video 16\n",
      "Reading Video 17\n",
      "Reading Video 18\n",
      "Reading Video 19\n",
      "Reading Video 20\n",
      "Reading Video 21\n",
      "Reading Video 22\n",
      "Reading Video 23\n",
      "Reading Video 24\n",
      "Reading Video 25\n",
      "Reading Video 26\n",
      "Reading Video 27\n",
      "Reading Video 28\n",
      "Reading Video 29\n",
      "Reading Video 30\n",
      "Reading Video 31\n",
      "Reading Video 32\n",
      "Reading Video 33\n",
      "Reading Video 34\n",
      "Reading Video 35\n",
      "Reading Video 36\n",
      "Reading Video 37\n",
      "Reading Video 38\n",
      "Reading Video 39\n",
      "Reading Video 40\n",
      "Reading Video 41\n",
      "Reading Video 42\n",
      "Reading Video 43\n",
      "Reading Video 44\n",
      "Reading Video 45\n",
      "Reading Video 46\n",
      "Reading Video 47\n",
      "Reading Video 48\n",
      "Reading Video 49\n",
      "Reading Video 50\n",
      "Reading Video 51\n",
      "Reading Video 52\n",
      "Reading Video 53\n",
      "Reading Video 54\n",
      "Reading Video 55\n",
      "Reading Video 56\n",
      "Reading Video 57\n",
      "Reading Video 58\n",
      "Reading Video 59\n",
      "\n",
      "ACTION iloveyou\n",
      "\n",
      "Reading Video 0\n",
      "Reading Video 1\n",
      "Reading Video 2\n",
      "Reading Video 3\n",
      "Reading Video 4\n",
      "Reading Video 5\n",
      "Reading Video 6\n",
      "Reading Video 7\n",
      "Reading Video 8\n",
      "Reading Video 9\n",
      "Reading Video 10\n",
      "Reading Video 11\n",
      "Reading Video 12\n",
      "Reading Video 13\n",
      "Reading Video 14\n",
      "Reading Video 15\n",
      "Reading Video 16\n",
      "Reading Video 17\n",
      "Reading Video 18\n",
      "Reading Video 19\n",
      "Reading Video 20\n",
      "Reading Video 21\n",
      "Reading Video 22\n",
      "Reading Video 23\n",
      "Reading Video 24\n",
      "Reading Video 25\n",
      "Reading Video 26\n",
      "Reading Video 27\n",
      "Reading Video 28\n",
      "Reading Video 29\n",
      "Reading Video 30\n",
      "Reading Video 31\n",
      "Reading Video 32\n",
      "Reading Video 33\n",
      "Reading Video 34\n",
      "Reading Video 35\n",
      "Reading Video 36\n",
      "Reading Video 37\n",
      "Reading Video 38\n",
      "Reading Video 39\n",
      "Reading Video 40\n",
      "Reading Video 41\n",
      "Reading Video 42\n",
      "Reading Video 43\n",
      "Reading Video 44\n",
      "Reading Video 45\n",
      "Reading Video 46\n",
      "Reading Video 47\n",
      "Reading Video 48\n",
      "Reading Video 49\n",
      "Reading Video 50\n",
      "Reading Video 51\n",
      "Reading Video 52\n",
      "Reading Video 53\n",
      "Reading Video 54\n",
      "Reading Video 55\n",
      "Reading Video 56\n",
      "Reading Video 57\n",
      "Reading Video 58\n",
      "Reading Video 59\n",
      "\n",
      "ACTION nothing\n",
      "\n",
      "Reading Video 0\n",
      "Reading Video 1\n",
      "Reading Video 2\n",
      "Reading Video 3\n",
      "Reading Video 4\n",
      "Reading Video 5\n",
      "Reading Video 6\n",
      "Reading Video 7\n",
      "Reading Video 8\n",
      "Reading Video 9\n",
      "Reading Video 10\n",
      "Reading Video 11\n",
      "Reading Video 12\n",
      "Reading Video 13\n",
      "Reading Video 14\n",
      "Reading Video 15\n",
      "Reading Video 16\n",
      "Reading Video 17\n",
      "Reading Video 18\n",
      "Reading Video 19\n",
      "Reading Video 20\n",
      "Reading Video 21\n",
      "Reading Video 22\n",
      "Reading Video 23\n",
      "Reading Video 24\n",
      "Reading Video 25\n",
      "Reading Video 26\n",
      "Reading Video 27\n",
      "Reading Video 28\n",
      "Reading Video 29\n",
      "Reading Video 30\n",
      "Reading Video 31\n",
      "Reading Video 32\n",
      "Reading Video 33\n",
      "Reading Video 34\n",
      "Reading Video 35\n",
      "Reading Video 36\n",
      "Reading Video 37\n",
      "Reading Video 38\n",
      "Reading Video 39\n",
      "Reading Video 40\n",
      "Reading Video 41\n",
      "Reading Video 42\n",
      "Reading Video 43\n",
      "Reading Video 44\n",
      "Reading Video 45\n",
      "Reading Video 46\n",
      "Reading Video 47\n",
      "Reading Video 48\n",
      "Reading Video 49\n",
      "Reading Video 50\n",
      "Reading Video 51\n",
      "Reading Video 52\n",
      "Reading Video 53\n",
      "Reading Video 54\n",
      "Reading Video 55\n",
      "Reading Video 56\n",
      "Reading Video 57\n",
      "Reading Video 58\n",
      "Reading Video 59\n"
     ]
    }
   ],
   "source": [
    "# To simplify the model, 15 frames are used to train the models instead of 30\n",
    "VIDEOS_TO_READ = 60\n",
    "FRAMES_TO_READ = 15\n",
    "\n",
    "X = []\n",
    "y = []\n",
    "\n",
    "for action in actions:\n",
    "    print(\"\\nACTION {}\\n\".format(action))\n",
    "    for video in range(0, VIDEOS_TO_READ):\n",
    "        print(\"Reading Video {}\".format(video))\n",
    "        frames_video = []\n",
    "        y.append(label_map[action])\n",
    "        for frame_num in range(0, FRAMES_TO_READ):\n",
    "            path = os.path.join(DATA_PATH, action, str(video), \"{}.npy\".format(frame_num))\n",
    "            embedding = np.load(path)\n",
    "            frames_video.append(embedding)\n",
    "        X.append(frames_video)\n",
    "\n",
    "X = np.array(X)\n",
    "y = np.array(y)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To simplify the model, the face landmarks are removed from the saved embeddings\n",
    "def exclude_face_landmarks(landmarks_matrix: np.array) -> np.array:\n",
    "    X_pose = landmarks_matrix[:, :, :(33*4)]\n",
    "    X_hands = landmarks_matrix[:, :,(468*3) + (33*4):]\n",
    "\n",
    "    return np.concatenate((X_pose, X_hands), axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_after_reshape = exclude_face_landmarks(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 15, 258)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_after_reshape.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = to_categorical(y).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 15, 1662)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 4)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_temp, X_test, y_temp, y_test = train_test_split(X_after_reshape, y, test_size=0.05, )\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(216, 15, 258)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 15, 258)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 15, 258)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(216, 4)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.1. Build and Train LSTM model - Tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense, Input, Bidirectional\n",
    "from tensorflow.keras.callbacks import TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH = 4\n",
    "SHAPE = X_train.shape[1:]\n",
    "OUTPUT_SHAPE = actions.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 258)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SHAPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OUTPUT_SHAPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_dir = os.path.join('logs')\n",
    "tb_callback = TensorBoard(log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Input(shape=SHAPE, batch_size=BATCH))\n",
    "model.add(LSTM(128, return_sequences=True, activation='relu'))\n",
    "model.add(LSTM(64, return_sequences=False, activation='relu'))\n",
    "model.add(Dense(64,  activation='relu'))\n",
    "model.add(Dense(32,  activation='relu'))\n",
    "model.add(Dense(OUTPUT_SHAPE, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">198,144</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │        <span style=\"color: #00af00; text-decoration-color: #00af00\">49,408</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)                 │           <span style=\"color: #00af00; text-decoration-color: #00af00\">132</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │       \u001b[38;5;34m198,144\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │        \u001b[38;5;34m49,408\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │         \u001b[38;5;34m4,160\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)                │         \u001b[38;5;34m2,080\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m)                 │           \u001b[38;5;34m132\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">253,924</span> (991.89 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m253,924\u001b[0m (991.89 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">253,924</span> (991.89 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m253,924\u001b[0m (991.89 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Cliente\\Documents\\Pessoal\\projetos\\dfki\\vevn\\Lib\\site-packages\\keras\\src\\saving\\saving_lib.py:713: UserWarning: Skipping variable loading for optimizer 'adam', because it has 2 variables whereas the saved optimizer has 26 variables. \n",
      "  saveable.load_own_variables(weights_store.get(inner_path))\n"
     ]
    }
   ],
   "source": [
    "if recover_checkpoints:\n",
    "    model.load_weights('model_checkpoint_keras/action_v6_LSTM_UNIDIRECT_4_labels_2_layers_15_frames.keras')\n",
    "else:\n",
    "    model.fit(X_train, y_train, batch_size=BATCH, epochs=100, callbacks=[tb_callback], validation_data=(X_val, y_val), shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import multilabel_confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test, batch_size=BATCH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 4)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = np.argmax(predictions, axis=1)\n",
    "y_true = np.argmax(y_test, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12,)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9166666666666666"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.2. Build an Train Model - Pytorch/Pylightning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import pytorch_lightning as pl\n",
    "from torch.utils.data import DataLoader\n",
    "from sklearn.model_selection import train_test_split\n",
    "import torchmetrics\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "import multiprocessing as mp\n",
    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
    "from pytorch_lightning.loggers import TensorBoardLogger"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset and Data Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PointsDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, frames: torch.tensor, labels: torch.tensor):\n",
    "        self.frames = frames\n",
    "        self.labels = labels\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "    def __getitem__(self, index: int):\n",
    "        video = self.frames[index, :, :]\n",
    "        label = self.labels[index]\n",
    "        return video, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SignalDataModule(pl.LightningDataModule):\n",
    "    def __init__(self, batch_size: int, X: np.ndarray, y: np.ndarray):\n",
    "        super().__init__()\n",
    "        self.batch_size = batch_size\n",
    "        self.X = X.astype(np.float32)\n",
    "        self.y = y.astype(np.float32)\n",
    "\n",
    "    def setup(self, stage=None):\n",
    "\n",
    "        X_temp, X_test, y_temp, y_test = train_test_split(self.X, self.y, test_size=0.05, )\n",
    "        X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=0.05)\n",
    "        \n",
    "        X_train = torch.from_numpy(X_train)\n",
    "        X_test = torch.from_numpy(X_test)\n",
    "        X_val = torch.from_numpy(X_val)\n",
    "        y_train = torch.from_numpy(y_train)\n",
    "        y_test = torch.from_numpy(y_test)\n",
    "        y_val = torch.from_numpy(y_val)\n",
    "        \n",
    "        self.train_dataset = PointsDataset(X_train,y_train)\n",
    "        self.validation_dataset = PointsDataset(X_val, y_val)\n",
    "        self.test_dataset = PointsDataset(X_test, y_test)\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True)\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        return DataLoader(self.validation_dataset, batch_size=self.batch_size)\n",
    "\n",
    "    def test_dataloader(self):\n",
    "        return DataLoader(self.test_dataset, batch_size=self.batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SignalDetector(pl.LightningModule):\n",
    "    def __init__(self, n_classes, n_features, hidden_size, n_layers):\n",
    "        super().__init__()\n",
    "\n",
    "        self.lstm = nn.LSTM(n_features, hidden_size, n_layers, batch_first=True)\n",
    "\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(hidden_size, 64),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(64, 32),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(32, n_classes),\n",
    "        )\n",
    "\n",
    "        self.test_acc = torchmetrics.Accuracy(task='multiclass', num_classes=n_classes)\n",
    "\n",
    "        self.metrics = {\n",
    "            'train': {'loss': [], 'acc': [], 'auc': []},\n",
    "            'val':   {'loss': [], 'acc': [], 'auc': []},\n",
    "            'test':  {'loss': [], 'acc': [], 'auc': []}\n",
    "        }\n",
    "\n",
    "        self.lists = {\n",
    "            'train': {'loss': [], 'logits': [], 'labels': []},\n",
    "            'val':   {'loss': [], 'logits': [], 'labels': []},\n",
    "            'test':  {'loss': [], 'logits': [], 'labels': []}\n",
    "        }\n",
    "\n",
    "    #How the data is passed through the model to result in the likelihoods\n",
    "    def forward(self, X, y=None):\n",
    "        out, (h, c) = self.lstm(X)\n",
    "        logits = self.model(h[-1])\n",
    "\n",
    "        loss = 0\n",
    "        if y is not None:\n",
    "            loss = torch.nn.functional.cross_entropy(logits, y)\n",
    "        return loss, logits\n",
    "\n",
    "    #What is performed in the steps of training, validation and testing\n",
    "    def step(self, batch, batch_idx, moment):\n",
    "        X, y = batch[0], batch[1]\n",
    "\n",
    "        loss, logits = self(X, y)\n",
    "        self.lists[moment]['loss'].append(loss)\n",
    "        self.lists[moment]['logits'].append(logits)\n",
    "        self.lists[moment]['labels'].append(y)\n",
    "\n",
    "        self.log(\"{}_loss\".format(moment), loss, prog_bar=True)\n",
    "\n",
    "        return dict(loss=loss, prediction=logits, labels=y)\n",
    "\n",
    "\n",
    "    def register_metrics(self, moment):\n",
    "        print(f\"\\nGetting metrics for moment {moment} at epoch {self.current_epoch}\")\n",
    "        \n",
    "        loss = self.lists[moment]['loss']\n",
    "        logits = self.lists[moment]['logits']\n",
    "        labels = self.lists[moment]['labels']\n",
    "\n",
    "        print(f\"Len logits: {len(logits)} Len labels: {len(labels)}\")\n",
    "\n",
    "        loss_mean = torch.stack(loss).mean()\n",
    "        logits = torch.cat(logits, dim=0)\n",
    "        labels = torch.cat(labels, dim=0)\n",
    "\n",
    "        # Calculation of metrics\n",
    "        true_labels = torch.argmax(labels, dim=1)\n",
    "        acc = self.test_acc(logits.detach().cpu(), true_labels.detach().cpu())\n",
    "\n",
    "        print(\"Loss: {:.3f} ACC: {:.3f}\".format(loss_mean.item(), acc))\n",
    "        print(\"\\n\\n\")\n",
    "\n",
    "        # Registering Metrics\n",
    "        self.log('{}_loss_epoch'.format(moment), loss_mean, on_epoch=True)\n",
    "        self.log('{}_acc'.format(moment), acc, on_epoch=True)\n",
    "\n",
    "        self.metrics[moment]['loss'].append(loss_mean.item())\n",
    "        self.metrics[moment]['acc'].append(acc)\n",
    "\n",
    "    def accessMetrics(self):\n",
    "      return self.metrics\n",
    "\n",
    "    def resetLists(self, moment: str):\n",
    "      self.lists[moment]['loss'] = []\n",
    "      self.lists[moment]['logits'] = []\n",
    "      self.lists[moment]['labels'] = []\n",
    "\n",
    "\n",
    "    def on_train_epoch_start(self) -> None:\n",
    "      super().on_train_epoch_start()\n",
    "      self.resetLists('train')\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "      return self.step(batch, batch_idx, 'train')\n",
    "\n",
    "    def on_train_epoch_end(self):\n",
    "      self.register_metrics('train')\n",
    "\n",
    "\n",
    "    def on_validation_epoch_start(self) -> None:\n",
    "      super().on_validation_epoch_start()\n",
    "      self.resetLists('val')\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "      return self.step(batch, batch_idx, 'val')\n",
    "\n",
    "    def on_validation_epoch_end(self):\n",
    "        self.register_metrics('val')\n",
    "\n",
    "\n",
    "    def on_test_epoch_start(self) -> None:\n",
    "      super().on_test_epoch_start()\n",
    "      self.resetLists('test')\n",
    "\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "      return self.step(batch, batch_idx, 'test')\n",
    "\n",
    "    def on_test_epoch_end(self):\n",
    "      self.register_metrics('test')\n",
    "\n",
    "\n",
    "    def prediction_step(self, X):\n",
    "      out, (h, c) = self.lstm(X)\n",
    "        \n",
    "      logits = self.model(h[-1])\n",
    "      return logits\n",
    "\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.AdamW(self.parameters(), lr=2e-5)\n",
    "        \"\"\"scheduler = get_linear_schedule_with_warmup(\n",
    "            optimizer,\n",
    "            num_warmup_steps=self.n_warmup_steps,\n",
    "            num_training_steps=self.n_training_steps\n",
    "        )\n",
    "        return dict(\n",
    "            optimizer=optimizer,\n",
    "            lr_scheduler=dict(\n",
    "                scheduler=scheduler,\n",
    "                interval='step'\n",
    "            )\n",
    "        )\"\"\"\n",
    "        return optimizer\n",
    "\n",
    "    def on_save_checkpoint(self, checkpoint):\n",
    "      checkpoint['metrics'] = self.metrics\n",
    "\n",
    "    def on_load_checkpoint(self, checkpoint):\n",
    "       if 'metrics' in checkpoint:\n",
    "            self.metrics = checkpoint['metrics']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    }
   ],
   "source": [
    "modelDir = 'model_checkpoint'\n",
    "log_dir = 'model_log'\n",
    "\n",
    "os.makedirs(log_dir, exist_ok=True)\n",
    "logger = TensorBoardLogger(save_dir=log_dir, name='signal_prediction')\n",
    "\n",
    "# Configurations for training\n",
    "BATCH_SIZE = 4\n",
    "N_EPOCHS = 100\n",
    "\n",
    "N_FEATURES = X_train.shape[-1]\n",
    "N_CLASSES = actions.shape[0]\n",
    "\n",
    "# Data Module\n",
    "data_module = SignalDataModule(\n",
    "    BATCH_SIZE, \n",
    "    X_after_reshape, y\n",
    "    )\n",
    "\n",
    "if recover_checkpoints:\n",
    "    modelDetector = SignalDetector.load_from_checkpoint(\n",
    "        checkpoint_path=os.path.join(modelDir, \"epoch=79-val_loss=0.342.ckpt\"),\n",
    "        n_classes=N_CLASSES,\n",
    "        n_features=N_FEATURES,\n",
    "        hidden_size=64,\n",
    "        n_layers=2\n",
    "    )\n",
    "else:\n",
    "    modelDetector = SignalDetector(\n",
    "        N_CLASSES,\n",
    "        N_FEATURES,\n",
    "        64,\n",
    "        2\n",
    "    )\n",
    "\n",
    "#Save the current model if the val_loss for the current epoch is the best among the previous ones\n",
    "checkpoint_callback = ModelCheckpoint(\n",
    "        dirpath=modelDir,\n",
    "        filename=\"{epoch}-{val_loss:.3f}\",\n",
    "        verbose=True,\n",
    "        monitor=\"val_loss\",\n",
    "        mode='min'\n",
    ")\n",
    "\n",
    "\n",
    "trainer = pl.Trainer(\n",
    "    max_epochs=N_EPOCHS,\n",
    "    accelerator=\"gpu\" if torch.cuda.is_available() else \"cpu\",\n",
    "    callbacks=[checkpoint_callback],\n",
    "    logger=logger\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not recover_checkpoints:\n",
    "  trainer.fit(modelDetector,  data_module)\n",
    "  print('Best model saved at:', checkpoint_callback.best_model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = modelDetector.accessMetrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train': {'loss': [1.3887419700622559,\n",
       "   1.387747883796692,\n",
       "   1.3866865634918213,\n",
       "   1.385604977607727,\n",
       "   1.3842507600784302,\n",
       "   1.3826006650924683,\n",
       "   1.3802603483200073,\n",
       "   1.3768079280853271,\n",
       "   1.372119665145874,\n",
       "   1.365506887435913,\n",
       "   1.3556803464889526,\n",
       "   1.3431282043457031,\n",
       "   1.3271565437316895,\n",
       "   1.3072476387023926,\n",
       "   1.2842459678649902,\n",
       "   1.2544395923614502,\n",
       "   1.2183165550231934,\n",
       "   1.1748594045639038,\n",
       "   1.1276023387908936,\n",
       "   1.0787396430969238,\n",
       "   1.0275578498840332,\n",
       "   0.9850005507469177,\n",
       "   0.938727617263794,\n",
       "   0.9032489061355591,\n",
       "   0.8630813956260681,\n",
       "   0.8305160403251648,\n",
       "   0.7968361377716064,\n",
       "   0.7664415836334229,\n",
       "   0.7367825508117676,\n",
       "   0.7082728743553162,\n",
       "   0.6837705969810486,\n",
       "   0.6561650633811951,\n",
       "   0.6339935064315796,\n",
       "   0.6109579801559448,\n",
       "   0.5897524952888489,\n",
       "   0.5705227851867676,\n",
       "   0.5545588731765747,\n",
       "   0.5388139486312866,\n",
       "   0.5236677527427673,\n",
       "   0.5083341002464294,\n",
       "   0.49433618783950806,\n",
       "   0.48242834210395813,\n",
       "   0.4731828272342682,\n",
       "   0.46317312121391296,\n",
       "   0.45224323868751526,\n",
       "   0.441196084022522,\n",
       "   0.43361130356788635,\n",
       "   0.4263719916343689,\n",
       "   0.41608351469039917,\n",
       "   0.4075319766998291,\n",
       "   0.40025806427001953,\n",
       "   0.39144837856292725,\n",
       "   0.38357654213905334,\n",
       "   0.37944862246513367,\n",
       "   0.37048470973968506,\n",
       "   0.36536598205566406,\n",
       "   0.3605005741119385,\n",
       "   0.34977155923843384,\n",
       "   0.343880295753479,\n",
       "   0.3354339003562927,\n",
       "   0.3363149166107178,\n",
       "   0.32449817657470703,\n",
       "   0.3183988332748413,\n",
       "   0.3064267337322235,\n",
       "   0.3024255931377411,\n",
       "   0.29695048928260803,\n",
       "   0.287974089384079,\n",
       "   0.28419411182403564,\n",
       "   0.27276405692100525,\n",
       "   0.2672048807144165,\n",
       "   0.2603745758533478,\n",
       "   0.25171446800231934,\n",
       "   0.2531951367855072,\n",
       "   0.23890039324760437,\n",
       "   0.23141613602638245,\n",
       "   0.2269117683172226,\n",
       "   0.21260660886764526,\n",
       "   0.2102978527545929,\n",
       "   0.2142106294631958,\n",
       "   0.21460451185703278],\n",
       "  'acc': [tensor(0.2546),\n",
       "   tensor(0.2546),\n",
       "   tensor(0.2546),\n",
       "   tensor(0.2546),\n",
       "   tensor(0.2546),\n",
       "   tensor(0.2546),\n",
       "   tensor(0.2546),\n",
       "   tensor(0.2546),\n",
       "   tensor(0.2546),\n",
       "   tensor(0.2546),\n",
       "   tensor(0.2546),\n",
       "   tensor(0.2639),\n",
       "   tensor(0.3333),\n",
       "   tensor(0.4769),\n",
       "   tensor(0.5787),\n",
       "   tensor(0.5833),\n",
       "   tensor(0.5741),\n",
       "   tensor(0.5694),\n",
       "   tensor(0.5833),\n",
       "   tensor(0.6667),\n",
       "   tensor(0.6991),\n",
       "   tensor(0.7083),\n",
       "   tensor(0.7176),\n",
       "   tensor(0.7176),\n",
       "   tensor(0.7315),\n",
       "   tensor(0.7315),\n",
       "   tensor(0.7407),\n",
       "   tensor(0.7639),\n",
       "   tensor(0.7731),\n",
       "   tensor(0.7824),\n",
       "   tensor(0.7778),\n",
       "   tensor(0.7824),\n",
       "   tensor(0.8009),\n",
       "   tensor(0.8009),\n",
       "   tensor(0.8194),\n",
       "   tensor(0.8241),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8194),\n",
       "   tensor(0.8287),\n",
       "   tensor(0.8380),\n",
       "   tensor(0.8380),\n",
       "   tensor(0.8380),\n",
       "   tensor(0.8565),\n",
       "   tensor(0.8657),\n",
       "   tensor(0.8565),\n",
       "   tensor(0.8704),\n",
       "   tensor(0.8704),\n",
       "   tensor(0.8796),\n",
       "   tensor(0.8750),\n",
       "   tensor(0.8750),\n",
       "   tensor(0.8796),\n",
       "   tensor(0.8750),\n",
       "   tensor(0.8796),\n",
       "   tensor(0.8611),\n",
       "   tensor(0.8750),\n",
       "   tensor(0.8843),\n",
       "   tensor(0.8796),\n",
       "   tensor(0.8796),\n",
       "   tensor(0.8843),\n",
       "   tensor(0.8889),\n",
       "   tensor(0.8843),\n",
       "   tensor(0.8796),\n",
       "   tensor(0.8935),\n",
       "   tensor(0.8935),\n",
       "   tensor(0.8981),\n",
       "   tensor(0.9028),\n",
       "   tensor(0.9028),\n",
       "   tensor(0.8981),\n",
       "   tensor(0.9259),\n",
       "   tensor(0.9213),\n",
       "   tensor(0.9213),\n",
       "   tensor(0.9444),\n",
       "   tensor(0.9306),\n",
       "   tensor(0.9352),\n",
       "   tensor(0.9444),\n",
       "   tensor(0.9398),\n",
       "   tensor(0.9630),\n",
       "   tensor(0.9444),\n",
       "   tensor(0.9491),\n",
       "   tensor(0.9398)],\n",
       "  'auc': []},\n",
       " 'val': {'loss': [1.4140985012054443,\n",
       "   1.4093695878982544,\n",
       "   1.4080842733383179,\n",
       "   1.406907558441162,\n",
       "   1.405515193939209,\n",
       "   1.4041675329208374,\n",
       "   1.402136206626892,\n",
       "   1.399220585823059,\n",
       "   1.3950022459030151,\n",
       "   1.3908730745315552,\n",
       "   1.3842331171035767,\n",
       "   1.3764853477478027,\n",
       "   1.366921067237854,\n",
       "   1.3529620170593262,\n",
       "   1.335601806640625,\n",
       "   1.3126745223999023,\n",
       "   1.2816334962844849,\n",
       "   1.2429128885269165,\n",
       "   1.20012629032135,\n",
       "   1.1519455909729004,\n",
       "   1.1023893356323242,\n",
       "   1.0541263818740845,\n",
       "   1.013016700744629,\n",
       "   0.9764164090156555,\n",
       "   0.9409019351005554,\n",
       "   0.9081571102142334,\n",
       "   0.8760325908660889,\n",
       "   0.8470675349235535,\n",
       "   0.8156639933586121,\n",
       "   0.7847604751586914,\n",
       "   0.74741530418396,\n",
       "   0.68714839220047,\n",
       "   0.6551620364189148,\n",
       "   0.6200065612792969,\n",
       "   0.6120168566703796,\n",
       "   0.5742337107658386,\n",
       "   0.5523874163627625,\n",
       "   0.5383329391479492,\n",
       "   0.5143516063690186,\n",
       "   0.4976554214954376,\n",
       "   0.4793201982975006,\n",
       "   0.47200894355773926,\n",
       "   0.4507180154323578,\n",
       "   0.4422004520893097,\n",
       "   0.42776647210121155,\n",
       "   0.4191267192363739,\n",
       "   0.4129914939403534,\n",
       "   0.40467071533203125,\n",
       "   0.40062642097473145,\n",
       "   0.39360103011131287,\n",
       "   0.388081818819046,\n",
       "   0.38510265946388245,\n",
       "   0.3812621533870697,\n",
       "   0.37537384033203125,\n",
       "   0.370353102684021,\n",
       "   0.36898383498191833,\n",
       "   0.36697396636009216,\n",
       "   0.361286997795105,\n",
       "   0.36025288701057434,\n",
       "   0.3608701229095459,\n",
       "   0.35903432965278625,\n",
       "   0.35404738783836365,\n",
       "   0.3535235822200775,\n",
       "   0.35449790954589844,\n",
       "   0.3529951572418213,\n",
       "   0.35199272632598877,\n",
       "   0.35209891200065613,\n",
       "   0.35315120220184326,\n",
       "   0.3494265079498291,\n",
       "   0.3482872545719147,\n",
       "   0.3480600118637085,\n",
       "   0.3471165597438812,\n",
       "   0.3482762277126312,\n",
       "   0.3453003466129303,\n",
       "   0.3465825021266937,\n",
       "   0.3417775332927704,\n",
       "   0.3480483293533325,\n",
       "   0.3481302261352539,\n",
       "   0.3460569381713867,\n",
       "   0.3510260283946991,\n",
       "   0.3415232002735138],\n",
       "  'acc': [tensor(0.2500),\n",
       "   tensor(0.1667),\n",
       "   tensor(0.1667),\n",
       "   tensor(0.1667),\n",
       "   tensor(0.1667),\n",
       "   tensor(0.1667),\n",
       "   tensor(0.1667),\n",
       "   tensor(0.1667),\n",
       "   tensor(0.1667),\n",
       "   tensor(0.1667),\n",
       "   tensor(0.1667),\n",
       "   tensor(0.1667),\n",
       "   tensor(0.1667),\n",
       "   tensor(0.3333),\n",
       "   tensor(0.5000),\n",
       "   tensor(0.4167),\n",
       "   tensor(0.4167),\n",
       "   tensor(0.4167),\n",
       "   tensor(0.4167),\n",
       "   tensor(0.5000),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.9167),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.7500),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8333),\n",
       "   tensor(0.8333)],\n",
       "  'auc': []},\n",
       " 'test': {'loss': [], 'acc': [], 'auc': []}}"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = np.arange(1, len(metrics['train']['loss']) + 1)  # Número de épocas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "plt.plot(epochs, metrics['val']['acc'][1:], label='Validation ACC')\n",
    "plt.plot(epochs, metrics['train']['acc'], label='Train ACC')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('ACC')\n",
    "plt.title('Validation and Train ACC over Epochs')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Cliente\\Documents\\Pessoal\\projetos\\dfki\\vevn\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:424: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3a7e3a65e464cdc8709221e2cb3ddcb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Getting metrics for moment test at epoch 0\n",
      "Len logits: 3 Len labels: 3\n",
      "Loss: 0.159 ACC: 1.000\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">            1.0            </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_loss         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.15919731557369232    </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">      test_loss_epoch      </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.15919731557369232    </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m           1.0           \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_loss        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.15919731557369232   \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m     test_loss_epoch     \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.15919731557369232   \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'test_loss': 0.15919731557369232,\n",
       "  'test_loss_epoch': 0.15919731557369232,\n",
       "  'test_acc': 1.0}]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(modelDetector, data_module)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Test in Real Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from abc import ABC, abstractmethod\n",
    "\n",
    "class SignalDetectorABC(ABC):\n",
    "\n",
    "    @abstractmethod\n",
    "    def predict(self, frames: np.ndarray) -> np.ndarray:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import ndarray\n",
    "\n",
    "\n",
    "class SignalDetectorKeras(SignalDetectorABC):\n",
    "    def __init__(self, model: Sequential) -> None:\n",
    "        super().__init__()\n",
    "        self.model = model\n",
    "\n",
    "    def predict(self, frames: np.ndarray) -> np.ndarray:\n",
    "        res = self.model.predict(frames)[0]\n",
    "\n",
    "        return res\n",
    "    \n",
    "class SignalDetectorPytorch(SignalDetectorABC):\n",
    "    def __init__(self, model: pl.LightningModule) -> None:\n",
    "        super().__init__()\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "\n",
    "    def predict(self, frames: np.ndarray) -> np.ndarray:\n",
    "        with torch.no_grad():\n",
    "            loss, logits = self.model(torch.tensor(frames, dtype=torch.float32))\n",
    "        return torch.nn.functional.softmax(logits, dim=1).numpy()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "signalDKeras = SignalDetectorKeras(model)\n",
    "signalDetectorPytorch = SignalDetectorPytorch(modelDetector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = [BLACK_COLOR, GREEN_COLOR, RED_COLOR, BLUE_COLOR]\n",
    "def prob_viz(res, actions, input_frame, colors):\n",
    "    output_frame = input_frame.copy()\n",
    "    for num, prob in enumerate(res):\n",
    "        cv2.rectangle(output_frame, (0,60+num*40), (int(prob*100), 90+num*40), colors[num], -1)\n",
    "        cv2.putText(output_frame, actions[num], (0, 85+num*40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_4)\n",
    "        \n",
    "    return output_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Cliente\\Documents\\Pessoal\\projetos\\dfki\\vevn\\Lib\\site-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"
     ]
    }
   ],
   "source": [
    "# 1. New detection variables\n",
    "sequence = []\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "# Set mediapipe model \n",
    "with Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "\n",
    "        # Read feed\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "        # Make detections\n",
    "        image, results = mediapipe_detection(frame, holistic)\n",
    "        \n",
    "        # Draw landmarks\n",
    "        draw_styled_landmarks_image(image, results)\n",
    "        \n",
    "        # 2. Prediction logic\n",
    "        keypoints = extract_keypoints(results)\n",
    "        sequence.append(keypoints)\n",
    "        sequence = sequence[-FRAMES_TO_READ:]\n",
    "        \n",
    "        if len(sequence) == FRAMES_TO_READ:\n",
    "            sequence_reshaped = exclude_face_landmarks(np.expand_dims(sequence, axis=0))\n",
    "            res = signalDetectorPytorch.predict(sequence_reshaped)\n",
    "            \n",
    "            # Viz probabilities\n",
    "            image = prob_viz(res, actions, image, colors)\n",
    "            \n",
    "        # Show to screen\n",
    "        cv2.imshow('OpenCV Feed', image)\n",
    "\n",
    "        # Break gracefully\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}