CNN_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "33643b46-2032-409a-9a55-fd27b7c3db23",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-04 16:37:25.269288: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-06-04 16:37:25.859356: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "import time\n",
    "import mediapipe as mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce5402bc-ff99-49a0-b1e1-a415bb1bd2e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_holistic = mp.solutions.holistic\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "\n",
    "def mediapipe_detection(image, model):\n",
    "    image = cv2.cvtColor(image, 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\n",
    "\n",
    "def draw_styled_landmarks(image, results):\n",
    "    if results.face_landmarks:\n",
    "        mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION)\n",
    "    if results.pose_landmarks:\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)\n",
    "    if results.left_hand_landmarks:\n",
    "        mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)\n",
    "    if results.right_hand_landmarks:\n",
    "        mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)\n",
    "\n",
    "def extract_keypoints(results):\n",
    "    pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)\n",
    "    face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(468*3)\n",
    "    lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)\n",
    "    rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)\n",
    "    return np.concatenate([pose, face, lh, rh])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7fae29b0-68c9-4ab6-887c-e98585d0ccfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = os.path.join('TrashData')\n",
    "actions = np.array(['hello', 'thanks', 'eat', 'help'])\n",
    "no_sequences = 30\n",
    "sequence_length = 30\n",
    "\n",
    "for action in actions:\n",
    "    for sequence in range(1, no_sequences + 1):\n",
    "        try:\n",
    "            os.makedirs(os.path.join(data_path, action, str(sequence)))\n",
    "        except FileExistsError:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c62a34d-1009-4d00-b525-00002aecf959",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[40;33;01m/dev/video0\u001b[0m  \u001b[40;33;01m/dev/video1\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "ls /dev/video*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e57c4e96-f214-4cc9-9dfc-63670c1770a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2? sheesh! index 1 doesn't work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "96ae4799-31cf-4a16-989e-a393595c7c6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1717499252.860692   31129 gl_context_egl.cc:85] Successfully initialized EGL. Major : 1 Minor: 5\n",
      "I0000 00:00:1717499252.866377   52676 gl_context.cc:357] GL version: 3.2 (OpenGL ES 3.2 Mesa 24.1.0-arch1.1), renderer: Mesa Intel(R) UHD Graphics (CML GT2)\n",
      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n",
      "W0000 00:00:1717499252.953451   52667 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "W0000 00:00:1717499252.964962   52667 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "W0000 00:00:1717499252.968855   52666 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "W0000 00:00:1717499252.968855   52667 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "W0000 00:00:1717499252.969057   52668 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "W0000 00:00:1717499252.975790   52668 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "W0000 00:00:1717499252.979660   52669 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "W0000 00:00:1717499252.981547   52671 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "/home/VintellX/.local/lib/python3.12/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",
      "qt.qpa.plugin: Could not find the Qt platform plugin \"wayland\" in \"/home/VintellX/.local/lib/python3.12/site-packages/cv2/qt/plugins\"\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    for action in actions:\n",
    "        for sequence in range(1, no_sequences + 1):\n",
    "            for frame_num in range(sequence_length):\n",
    "                ret, frame = cap.read()\n",
    "                image, results = mediapipe_detection(frame, holistic)\n",
    "                draw_styled_landmarks(image, results)\n",
    "\n",
    "                if frame_num == 0:\n",
    "                    cv2.putText(image, 'STARTING COLLECTION', (120, 200),\n",
    "                                cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 4, cv2.LINE_AA)\n",
    "                    cv2.putText(image, f'Collecting frames for {action} Video Number {sequence}', (15, 12),\n",
    "                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)\n",
    "                    cv2.imshow('OpenCV Feed', image)\n",
    "                    cv2.waitKey(500)\n",
    "                else:\n",
    "                    cv2.putText(image, f'Collecting frames for {action} Video Number {sequence}', (15, 12),\n",
    "                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)\n",
    "                    cv2.imshow('OpenCV Feed', image)\n",
    "\n",
    "                keypoints = extract_keypoints(results)\n",
    "                npy_path = os.path.join(data_path, action, str(sequence), str(frame_num))\n",
    "                np.save(npy_path, keypoints)\n",
    "\n",
    "                if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "                    break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10b38eff-717f-41f5-aabe-8663a8be05ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of sequences: (120, 30, 1662)\n",
      "Shape of labels: (120,)\n",
      "Shape of:\n",
      "X_train: (96, 30, 1662, 1)\n",
      "X_test: (24, 30, 1662, 1)\n",
      "y_train: (96, 4)\n",
      "y_test: (24, 4)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "no_sequences = 30\n",
    "sequence_length = 30\n",
    "\n",
    "data_path = 'TrashData'\n",
    "actions = np.array(['hello', 'thanks', 'eat', 'help'])\n",
    "\n",
    "label_map = {label: num for num, label in enumerate(actions)}\n",
    "sequences, labels = [], []\n",
    "\n",
    "for action in actions:\n",
    "    for sequence in range(1, no_sequences + 1):\n",
    "        window = []\n",
    "        for frame_num in range(sequence_length):\n",
    "            res = np.load(os.path.join(data_path, action, str(sequence), \"{}.npy\".format(frame_num)))\n",
    "            window.append(res)\n",
    "        sequences.append(window)\n",
    "        labels.append(label_map[action])\n",
    "\n",
    "sequences = np.array(sequences)\n",
    "labels = np.array(labels)\n",
    "\n",
    "print(f\"Shape of sequences: {sequences.shape}\")\n",
    "print(f\"Shape of labels: {labels.shape}\")\n",
    "\n",
    "sequence_length = sequences.shape[1]  # bruhh ~30\n",
    "keypoints_size = sequences.shape[2]   # 1662\n",
    "\n",
    "# (samples, sequence_length, keypoints_size, 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(sequences, labels, test_size=0.2, random_state=42)\n",
    "y_train = to_categorical(y_train, num_classes=len(actions))\n",
    "y_test = to_categorical(y_test, num_classes=len(actions))\n",
    "\n",
    "X_train = X_train.reshape(-1, sequence_length, keypoints_size, 1)\n",
    "X_test = X_test.reshape(-1, sequence_length, keypoints_size, 1)\n",
    "\n",
    "print(\"Shape of:\")\n",
    "print(f\"X_train: {X_train.shape}\")\n",
    "print(f\"X_test: {X_test.shape}\")\n",
    "print(f\"y_train: {y_train.shape}\")\n",
    "print(f\"y_test: {y_test.shape}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dad9d5dc-e9b5-4a04-b6ac-d144d5e2a130",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/VintellX/.local/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:25: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.\n",
      "  warnings.warn(\n",
      "2024-06-04 16:43:48.858566: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-06-04 16:43:48.858929: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\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\"><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",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1662</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">831</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">831</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">415</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">415</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">207</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">79488</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │     <span style=\"color: #00af00; text-decoration-color: #00af00\">5,087,296</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">260</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",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m1662\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m320\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m831\u001b[0m, \u001b[38;5;34m32\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m831\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │        \u001b[38;5;34m18,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m415\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m415\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m207\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m79488\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │     \u001b[38;5;34m5,087,296\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │           \u001b[38;5;34m260\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\">5,180,228</span> (19.76 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m5,180,228\u001b[0m (19.76 MB)\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\">5,180,228</span> (19.76 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m5,180,228\u001b[0m (19.76 MB)\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": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, InputLayer, Conv1D, MaxPooling1D\n",
    "\n",
    "sequence_length = 30\n",
    "keypoints_size = 1662  # size(keypoints) after flattening\n",
    "\n",
    "model = Sequential([\n",
    "    InputLayer(input_shape=(sequence_length, keypoints_size, 1)),\n",
    "    Conv2D(32, (3, 3), activation='relu', padding='same'),\n",
    "    MaxPooling2D((2, 2)),\n",
    "    Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
    "    MaxPooling2D((2, 2)),\n",
    "    Conv2D(128, (3, 3), activation='relu', padding='same'),\n",
    "    MaxPooling2D((2, 2)),\n",
    "    Flatten(),\n",
    "    Dense(64, activation='relu'),\n",
    "    Dropout(0.5),\n",
    "    Dense(len(actions), activation='softmax')\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])\n",
    "\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7588bbc4-24f3-4931-9afa-983b7fb2e31e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequences shape: (120, 30, 1662)\n",
      "Total elements: 5983200\n"
     ]
    }
   ],
   "source": [
    "print(f\"Sequences shape: {sequences.shape}\")\n",
    "\n",
    "print(f\"Total elements: {sequences.size}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9818edfd-5710-4196-8473-3e029c20a7d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequences after reshaping: (120, 30, 1662, 1)\n"
     ]
    }
   ],
   "source": [
    "sequences = sequences.reshape(sequences.shape[0], sequences.shape[1], sequences.shape[2], 1)\n",
    "\n",
    "print(f\"Sequences after reshaping: {sequences.shape}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "98929c6c-623e-4572-9d2c-e87e9c00d4b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapes of:\n",
      "\n",
      "X_train:\t(96, 30, 1662, 1)\n",
      "X_test:\t\t(24, 30, 1662, 1)\n",
      "y_train:\t(96, 4)\n",
      "y_test:\t(24, 4)\n",
      "Epoch 1/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 809ms/step - accuracy: 0.2630 - loss: 2.9518 - val_accuracy: 0.2917 - val_loss: 1.3656\n",
      "Epoch 2/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 753ms/step - accuracy: 0.2708 - loss: 1.3776 - val_accuracy: 0.4167 - val_loss: 1.3928\n",
      "Epoch 3/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 740ms/step - accuracy: 0.2370 - loss: 1.3997 - val_accuracy: 0.7083 - val_loss: 1.3771\n",
      "Epoch 4/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 753ms/step - accuracy: 0.3737 - loss: 1.3799 - val_accuracy: 0.6667 - val_loss: 1.3663\n",
      "Epoch 5/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 753ms/step - accuracy: 0.3424 - loss: 1.3646 - val_accuracy: 0.4167 - val_loss: 1.3562\n",
      "Epoch 6/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 751ms/step - accuracy: 0.3685 - loss: 1.3479 - val_accuracy: 0.4167 - val_loss: 1.3353\n",
      "Epoch 7/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 758ms/step - accuracy: 0.3268 - loss: 1.3434 - val_accuracy: 0.6667 - val_loss: 1.2999\n",
      "Epoch 8/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 759ms/step - accuracy: 0.4245 - loss: 1.2836 - val_accuracy: 0.4167 - val_loss: 1.2602\n",
      "Epoch 9/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 767ms/step - accuracy: 0.4948 - loss: 1.2105 - val_accuracy: 0.6667 - val_loss: 1.1786\n",
      "Epoch 10/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 758ms/step - accuracy: 0.4857 - loss: 1.1806 - val_accuracy: 0.4583 - val_loss: 1.0859\n",
      "Epoch 11/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 772ms/step - accuracy: 0.5091 - loss: 1.0583 - val_accuracy: 0.6667 - val_loss: 0.9803\n",
      "Epoch 12/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 772ms/step - accuracy: 0.5456 - loss: 1.0252 - val_accuracy: 0.6250 - val_loss: 0.8913\n",
      "Epoch 13/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 763ms/step - accuracy: 0.5911 - loss: 0.9170 - val_accuracy: 0.6667 - val_loss: 0.7832\n",
      "Epoch 14/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 766ms/step - accuracy: 0.6159 - loss: 0.8523 - val_accuracy: 0.6667 - val_loss: 0.6617\n",
      "Epoch 15/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 771ms/step - accuracy: 0.6029 - loss: 0.8930 - val_accuracy: 0.5833 - val_loss: 0.7794\n",
      "Epoch 16/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 816ms/step - accuracy: 0.6120 - loss: 0.8268 - val_accuracy: 0.6667 - val_loss: 0.7060\n",
      "Epoch 17/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 774ms/step - accuracy: 0.5625 - loss: 0.8327 - val_accuracy: 0.7083 - val_loss: 0.6775\n",
      "Epoch 18/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 862ms/step - accuracy: 0.6354 - loss: 0.8005 - val_accuracy: 0.7083 - val_loss: 0.6270\n",
      "Epoch 19/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 798ms/step - accuracy: 0.6146 - loss: 0.7751 - val_accuracy: 0.7083 - val_loss: 0.6063\n",
      "Epoch 20/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 780ms/step - accuracy: 0.7500 - loss: 0.6626 - val_accuracy: 0.9583 - val_loss: 0.5773\n",
      "Epoch 21/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 770ms/step - accuracy: 0.6992 - loss: 0.7037 - val_accuracy: 0.7083 - val_loss: 0.5186\n",
      "Epoch 22/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 774ms/step - accuracy: 0.6914 - loss: 0.6790 - val_accuracy: 0.8750 - val_loss: 0.4806\n",
      "Epoch 23/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 773ms/step - accuracy: 0.6250 - loss: 0.6537 - val_accuracy: 0.6250 - val_loss: 0.6818\n",
      "Epoch 24/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 781ms/step - accuracy: 0.6445 - loss: 0.7621 - val_accuracy: 0.8333 - val_loss: 0.5589\n",
      "Epoch 25/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 774ms/step - accuracy: 0.7057 - loss: 0.6707 - val_accuracy: 0.9583 - val_loss: 0.4647\n",
      "Epoch 26/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 769ms/step - accuracy: 0.7188 - loss: 0.5042 - val_accuracy: 0.9167 - val_loss: 0.4794\n",
      "Epoch 27/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 781ms/step - accuracy: 0.6510 - loss: 0.6753 - val_accuracy: 0.9583 - val_loss: 0.4280\n",
      "Epoch 28/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 779ms/step - accuracy: 0.6615 - loss: 0.7193 - val_accuracy: 0.9583 - val_loss: 0.4028\n",
      "Epoch 29/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 784ms/step - accuracy: 0.6536 - loss: 0.7643 - val_accuracy: 0.7917 - val_loss: 0.6138\n",
      "Epoch 30/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 773ms/step - accuracy: 0.7083 - loss: 0.7038 - val_accuracy: 0.9583 - val_loss: 0.4545\n",
      "Epoch 31/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 786ms/step - accuracy: 0.7526 - loss: 0.6200 - val_accuracy: 0.9583 - val_loss: 0.3415\n",
      "Epoch 32/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 815ms/step - accuracy: 0.6823 - loss: 0.6342 - val_accuracy: 0.7083 - val_loss: 0.3582\n",
      "Epoch 33/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 775ms/step - accuracy: 0.6849 - loss: 0.5713 - val_accuracy: 0.9583 - val_loss: 0.2883\n",
      "Epoch 34/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 776ms/step - accuracy: 0.7461 - loss: 0.6379 - val_accuracy: 0.9167 - val_loss: 0.3585\n",
      "Epoch 35/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 778ms/step - accuracy: 0.7578 - loss: 0.5778 - val_accuracy: 0.9583 - val_loss: 0.2875\n",
      "Epoch 36/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 785ms/step - accuracy: 0.7044 - loss: 0.5507 - val_accuracy: 0.9583 - val_loss: 0.2386\n",
      "Epoch 37/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 771ms/step - accuracy: 0.7604 - loss: 0.4318 - val_accuracy: 0.9583 - val_loss: 0.2222\n",
      "Epoch 38/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 782ms/step - accuracy: 0.7852 - loss: 0.4175 - val_accuracy: 0.9583 - val_loss: 0.2421\n",
      "Epoch 39/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 782ms/step - accuracy: 0.7852 - loss: 0.4429 - val_accuracy: 0.9583 - val_loss: 0.1729\n",
      "Epoch 40/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 784ms/step - accuracy: 0.8659 - loss: 0.3450 - val_accuracy: 0.9583 - val_loss: 0.1566\n",
      "Epoch 41/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 779ms/step - accuracy: 0.7513 - loss: 0.4313 - val_accuracy: 0.9583 - val_loss: 0.2222\n",
      "Epoch 42/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 824ms/step - accuracy: 0.7956 - loss: 0.4145 - val_accuracy: 0.9583 - val_loss: 0.1542\n",
      "Epoch 43/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 774ms/step - accuracy: 0.8099 - loss: 0.4512 - val_accuracy: 0.9583 - val_loss: 0.1483\n",
      "Epoch 44/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 781ms/step - accuracy: 0.7956 - loss: 0.3681 - val_accuracy: 0.9583 - val_loss: 0.1722\n",
      "Epoch 45/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 779ms/step - accuracy: 0.7995 - loss: 0.3595 - val_accuracy: 0.9583 - val_loss: 0.1877\n",
      "Epoch 46/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 780ms/step - accuracy: 0.7891 - loss: 0.4134 - val_accuracy: 0.9583 - val_loss: 0.1433\n",
      "Epoch 47/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 784ms/step - accuracy: 0.7786 - loss: 0.3970 - val_accuracy: 0.9583 - val_loss: 0.1902\n",
      "Epoch 48/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 777ms/step - accuracy: 0.8294 - loss: 0.3965 - val_accuracy: 0.9583 - val_loss: 0.1663\n",
      "Epoch 49/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 785ms/step - accuracy: 0.8893 - loss: 0.2945 - val_accuracy: 1.0000 - val_loss: 0.1227\n",
      "Epoch 50/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 778ms/step - accuracy: 0.7799 - loss: 0.3519 - val_accuracy: 1.0000 - val_loss: 0.1346\n",
      "Epoch 51/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 782ms/step - accuracy: 0.7552 - loss: 0.4639 - val_accuracy: 0.9583 - val_loss: 0.1215\n",
      "Epoch 52/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 768ms/step - accuracy: 0.7526 - loss: 0.5078 - val_accuracy: 0.9583 - val_loss: 0.1295\n",
      "Epoch 53/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 773ms/step - accuracy: 0.7917 - loss: 0.3826 - val_accuracy: 0.9583 - val_loss: 0.1813\n",
      "Epoch 54/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 771ms/step - accuracy: 0.8138 - loss: 0.4349 - val_accuracy: 0.9583 - val_loss: 0.1601\n",
      "Epoch 55/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 769ms/step - accuracy: 0.8776 - loss: 0.3326 - val_accuracy: 0.9583 - val_loss: 0.1469\n",
      "Epoch 56/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 775ms/step - accuracy: 0.8164 - loss: 0.4030 - val_accuracy: 0.9583 - val_loss: 0.1222\n",
      "Epoch 57/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 777ms/step - accuracy: 0.8464 - loss: 0.3442 - val_accuracy: 0.9583 - val_loss: 0.0917\n",
      "Epoch 58/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 776ms/step - accuracy: 0.7904 - loss: 0.3940 - val_accuracy: 0.9583 - val_loss: 0.0731\n",
      "Epoch 59/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 781ms/step - accuracy: 0.8346 - loss: 0.3236 - val_accuracy: 0.9583 - val_loss: 0.0628\n",
      "Epoch 60/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 776ms/step - accuracy: 0.8190 - loss: 0.3637 - val_accuracy: 0.9583 - val_loss: 0.1512\n",
      "Epoch 61/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 779ms/step - accuracy: 0.8229 - loss: 0.4516 - val_accuracy: 0.9583 - val_loss: 0.0796\n",
      "Epoch 62/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 776ms/step - accuracy: 0.8307 - loss: 0.3689 - val_accuracy: 1.0000 - val_loss: 0.1098\n",
      "Epoch 63/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 769ms/step - accuracy: 0.8060 - loss: 0.3957 - val_accuracy: 0.9167 - val_loss: 0.2504\n",
      "Epoch 64/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 768ms/step - accuracy: 0.8490 - loss: 0.3659 - val_accuracy: 0.9167 - val_loss: 0.2913\n",
      "Epoch 65/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 775ms/step - accuracy: 0.8789 - loss: 0.4196 - val_accuracy: 1.0000 - val_loss: 0.1312\n",
      "Epoch 66/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 778ms/step - accuracy: 0.8750 - loss: 0.3484 - val_accuracy: 0.9583 - val_loss: 0.1172\n",
      "Epoch 67/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 770ms/step - accuracy: 0.7799 - loss: 0.3596 - val_accuracy: 0.9167 - val_loss: 0.1783\n",
      "Epoch 68/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 768ms/step - accuracy: 0.8151 - loss: 0.3698 - val_accuracy: 1.0000 - val_loss: 0.0953\n",
      "Epoch 69/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 779ms/step - accuracy: 0.8815 - loss: 0.2792 - val_accuracy: 1.0000 - val_loss: 0.0780\n",
      "Epoch 70/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 770ms/step - accuracy: 0.8802 - loss: 0.3051 - val_accuracy: 0.9583 - val_loss: 0.0867\n",
      "Epoch 71/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 782ms/step - accuracy: 0.8398 - loss: 0.2662 - val_accuracy: 0.9583 - val_loss: 0.0809\n",
      "Epoch 72/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 779ms/step - accuracy: 0.8646 - loss: 0.2505 - val_accuracy: 1.0000 - val_loss: 0.0553\n",
      "Epoch 73/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 770ms/step - accuracy: 0.8776 - loss: 0.2714 - val_accuracy: 0.9583 - val_loss: 0.0811\n",
      "Epoch 74/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 772ms/step - accuracy: 0.8151 - loss: 0.3020 - val_accuracy: 0.9583 - val_loss: 0.0986\n",
      "Epoch 75/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 785ms/step - accuracy: 0.8503 - loss: 0.2508 - val_accuracy: 0.9583 - val_loss: 0.0946\n",
      "Epoch 76/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 772ms/step - accuracy: 0.8919 - loss: 0.2066 - val_accuracy: 0.9583 - val_loss: 0.1147\n",
      "Epoch 77/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 773ms/step - accuracy: 0.8880 - loss: 0.2783 - val_accuracy: 0.9583 - val_loss: 0.1310\n",
      "Epoch 78/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 779ms/step - accuracy: 0.8945 - loss: 0.2072 - val_accuracy: 0.9583 - val_loss: 0.0686\n",
      "Epoch 79/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 780ms/step - accuracy: 0.8607 - loss: 0.2943 - val_accuracy: 1.0000 - val_loss: 0.0533\n",
      "Epoch 80/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 779ms/step - accuracy: 0.8372 - loss: 0.3038 - val_accuracy: 1.0000 - val_loss: 0.0620\n",
      "Epoch 81/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 777ms/step - accuracy: 0.8320 - loss: 0.2627 - val_accuracy: 1.0000 - val_loss: 0.0620\n",
      "Epoch 82/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 778ms/step - accuracy: 0.8932 - loss: 0.2675 - val_accuracy: 0.9583 - val_loss: 0.0820\n",
      "Epoch 83/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 777ms/step - accuracy: 0.8151 - loss: 0.3340 - val_accuracy: 1.0000 - val_loss: 0.0655\n",
      "Epoch 84/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 783ms/step - accuracy: 0.8880 - loss: 0.1811 - val_accuracy: 1.0000 - val_loss: 0.0480\n",
      "Epoch 85/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 774ms/step - accuracy: 0.9466 - loss: 0.1306 - val_accuracy: 1.0000 - val_loss: 0.0570\n",
      "Epoch 86/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 777ms/step - accuracy: 0.9258 - loss: 0.1944 - val_accuracy: 1.0000 - val_loss: 0.0521\n",
      "Epoch 87/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 782ms/step - accuracy: 0.8867 - loss: 0.2455 - val_accuracy: 1.0000 - val_loss: 0.0271\n",
      "Epoch 88/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 784ms/step - accuracy: 0.8451 - loss: 0.2751 - val_accuracy: 1.0000 - val_loss: 0.0315\n",
      "Epoch 89/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 776ms/step - accuracy: 0.8763 - loss: 0.2879 - val_accuracy: 0.9583 - val_loss: 0.1457\n",
      "Epoch 90/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 778ms/step - accuracy: 0.8893 - loss: 0.2353 - val_accuracy: 0.9583 - val_loss: 0.0536\n",
      "Epoch 91/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 782ms/step - accuracy: 0.8633 - loss: 0.2762 - val_accuracy: 0.9583 - val_loss: 0.0489\n",
      "Epoch 92/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 773ms/step - accuracy: 0.8763 - loss: 0.2792 - val_accuracy: 0.9583 - val_loss: 0.0915\n",
      "Epoch 93/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 785ms/step - accuracy: 0.8503 - loss: 0.3022 - val_accuracy: 0.9167 - val_loss: 0.1765\n",
      "Epoch 94/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 776ms/step - accuracy: 0.8477 - loss: 0.2869 - val_accuracy: 1.0000 - val_loss: 0.0719\n",
      "Epoch 95/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 777ms/step - accuracy: 0.8932 - loss: 0.2600 - val_accuracy: 1.0000 - val_loss: 0.0597\n",
      "Epoch 96/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 790ms/step - accuracy: 0.8711 - loss: 0.2532 - val_accuracy: 0.9583 - val_loss: 0.0632\n",
      "Epoch 97/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 781ms/step - accuracy: 0.9154 - loss: 0.1788 - val_accuracy: 0.9583 - val_loss: 0.0671\n",
      "Epoch 98/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 775ms/step - accuracy: 0.8919 - loss: 0.2130 - val_accuracy: 1.0000 - val_loss: 0.0492\n",
      "Epoch 99/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 777ms/step - accuracy: 0.8659 - loss: 0.2182 - val_accuracy: 1.0000 - val_loss: 0.0558\n",
      "Epoch 100/100\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 783ms/step - accuracy: 0.8724 - loss: 0.2150 - val_accuracy: 1.0000 - val_loss: 0.0324\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "sequences = sequences.reshape((sequences.shape[0], 30, 1662, 1))\n",
    "\n",
    "labels = to_categorical(labels, num_classes=len(actions))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(sequences, labels, test_size=0.2, random_state=42)\n",
    "print(\"Shapes of:\")\n",
    "print()\n",
    "print(f\"X_train:\\t{X_train.shape}\")\n",
    "print(f\"X_test:\\t\\t{X_test.shape}\")\n",
    "print(f\"y_train:\\t{y_train.shape}\")\n",
    "print(f\"y_test:\\t\\t{y_test.shape}\")\n",
    "\n",
    "\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X_train, y_train, epochs=100, validation_data=(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "da8c0029-87c1-4508-9cb9-09171f6b0e77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1662\n"
     ]
    }
   ],
   "source": [
    "print(keypoints.size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f4facf4-302a-4f37-a7ca-747409e815f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f6ce63f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 181ms/step\n",
      "Predicted:\t[1 1 0 1 0 2 2 0 1 3 0 2 0 1 2 3 3 0 2 3 2 1 1 2]\n",
      "For Real:\t[1 1 0 1 0 2 2 0 1 3 0 2 0 1 2 3 3 0 2 3 2 1 1 2]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "predicted = np.argmax(predictions, axis=1)\n",
    "forReal = np.argmax(y_test, axis=1)\n",
    "\n",
    "print(f\"Predicted:\\t{predicted}\")\n",
    "print(f\"For Real:\\t{forReal}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "09903be6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1717500008.279289   31129 gl_context_egl.cc:85] Successfully initialized EGL. Major : 1 Minor: 5\n",
      "I0000 00:00:1717500008.279786   56100 gl_context.cc:357] GL version: 3.2 (OpenGL ES 3.2 Mesa 24.1.0-arch1.1), renderer: Mesa Intel(R) UHD Graphics (CML GT2)\n",
      "W0000 00:00:1717500008.333352   56094 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "W0000 00:00:1717500008.344506   56090 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "W0000 00:00:1717500008.345261   56095 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "W0000 00:00:1717500008.345261   56092 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "W0000 00:00:1717500008.345466   56094 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "W0000 00:00:1717500008.348559   56094 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "W0000 00:00:1717500008.356089   56093 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
      "W0000 00:00:1717500008.357507   56092 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/VintellX/.local/lib/python3.12/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n",
      "keypoints sahpe: (1, 30, 1662, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        if not ret:\n",
    "            break\n",
    "\n",
    "        image, results = mediapipe_detection(frame, holistic)\n",
    "        draw_styled_landmarks(image, results)\n",
    "\n",
    "        keypoints = extract_keypoints(results)\n",
    "        if keypoints.size == 1662:\n",
    "            keypoints = np.expand_dims(keypoints, axis=0)\n",
    "            keypoints = np.repeat(keypoints, 30, axis=0)\n",
    "            keypoints = keypoints.reshape(1, 30, 1662, 1)\n",
    "        else:\n",
    "            raise ValueError(f\"Fuck it! Expected 1662 keypoints, but got {keypoints.size}\")\n",
    "        \n",
    "        print(f\"keypoints sahpe: {keypoints.shape}\")\n",
    "        \n",
    "        predictions = model.predict(keypoints)\n",
    "        predicted_class = actions[np.argmax(predictions)]\n",
    "        \n",
    "        cv2.putText(image, f'Prediction: {predicted_class}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)\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": "code",
   "execution_count": null,
   "id": "974547df-70f6-4bca-a0e1-390b28401016",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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