sign-predictor/visualize_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.keypoint_extractor import KeypointExtractor\n",
    "\n",
    "# reload modules\n",
    "%load_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_name = 'A_robbe.mp4' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract keypoints\n",
    "keypoint_extractor = KeypointExtractor('data/fingerspelling/data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from IPython.display import HTML\n",
    "from base64 import b64encode\n",
    "import mediapy as media\n",
    "%matplotlib inline\n",
    "\n",
    "# Define the frames per second (fps) and duration of the video\n",
    "fps = 25\n",
    "duration = 10\n",
    "\n",
    "# Create a dummy video of random noise\n",
    "_, video_frames = keypoint_extractor.extract_keypoints_from_video(video_name, draw=True)\n",
    "\n",
    "# Convert the video to a numpy array\n",
    "video = np.array(video_frames)\n",
    "media.show_video(video, height=400, codec='gif', fps=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.model import SPOTER\n",
    "from src.identifiers import LANDMARKS\n",
    "import torch\n",
    "\n",
    "spoter_model = SPOTER(num_classes=5, hidden_dim=len(LANDMARKS) *2)\n",
    "spoter_model.load_state_dict(torch.load('models/spoter_40.pth'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get average number of frames in test set\n",
    "from src.keypoint_extractor import KeypointExtractor\n",
    "from src.datasets.finger_spelling_dataset import FingerSpellingDataset\n",
    "from src.identifiers import LANDMARKS\n",
    "import numpy as np\n",
    "\n",
    "keypoints_extractor = KeypointExtractor(\"data/fingerspelling/data/\")\n",
    "test_set = FingerSpellingDataset(\"data/fingerspelling/data/\", keypoints_extractor, keypoints_identifier=LANDMARKS, subset=\"val\")\n",
    "\n",
    "frames = []\n",
    "labels = []\n",
    "for sample, label in test_set:\n",
    "    frames.append(sample.shape[0])\n",
    "    labels.append(label)\n",
    "\n",
    "print(np.mean(frames))\n",
    "# get label frequency in the labels list\n",
    "from collections import Counter\n",
    "\n",
    "counter = Counter(labels)\n",
    "print(counter)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hand keypoint visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_hand_keypoints(dataframe, hand, frame):\n",
    "    hand_columns = np.array([i for i in range(66 + (42 if hand == \"right\" else 0), 108 + (42 if hand == \"right\" else 0))])\n",
    "    \n",
    "    # get the x, y coordinates of the hand keypoints\n",
    "    frame_df = dataframe.iloc[frame:frame+1, hand_columns]\n",
    "    hand_coords = frame_df.values.reshape(21, 2)\n",
    "    \n",
    "    x_coords = hand_coords[:, ::2] #Even indices\n",
    "    y_coords = hand_coords[:, 1::2] #Uneven indices\n",
    "    \n",
    "    #Plot the keypoints\n",
    "    plt.scatter(x_coords, y_coords)\n",
    "    return frame_df.style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set video, hand and frame to display\n",
    "video_name = 'A_victor.mp4'\n",
    "hand = \"right\"\n",
    "frame = 1\n",
    "%reload_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.keypoint_extractor import KeypointExtractor\n",
    "import numpy as np\n",
    "\n",
    "#Extract keypoints from requested video\n",
    "keypoints_extractor = KeypointExtractor(\"data/fingerspelling/data/\")\n",
    "\n",
    "\n",
    "#Plot the hand keypoints\n",
    "df = keypoints_extractor.extract_keypoints_from_video(video_name, normalize=False)\n",
    "plot_hand_keypoints(df, hand, frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot the NORMALIZED hand keypoints\n",
    "df = keypoints_extractor.extract_keypoints_from_video(video_name, normalize=True)\n",
    "plot_hand_keypoints(df, hand, frame)"
   ]
  }
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