prototype_v1-video-feature-extraction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Essential Libraries\n",
    "import cv2\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#import av   # !pip install av\n",
    "import numpy as np\n",
    "import os\n",
    "import torch\n",
    "from transformers import VivitConfig, VivitModel, VivitImageProcessor\n",
    "from torchvision.transforms import Compose, Resize, Lambda, ToTensor, ToPILImage\n",
    "from PIL import Image\n",
    "\n",
    "# Display plots inline\n",
    "%matplotlib inline\n",
    "\n",
    "# Set plot parameters\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 6)\n",
    "plt.rcParams['axes.grid'] = False\n",
    "plt.style.use('dark_background')\n",
    "plt.rcParams[\"axes.spines.top\"] = False\n",
    "plt.rcParams[\"axes.spines.right\"] = False\n",
    "plt.rcParams[\"font.size\"] = 15\n",
    "#VID-20230703-WA0028_ beginning-cut and mid-cut"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare videos for read in with VideoVit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import av   # !pip install av\n",
    "\n",
    "# fpath = \"/Users/mikehemberger/Documents/vscode/totems/totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/\"\n",
    "# vid_filename = \"MVI_7423_pulse_wabbern-cropped.mp4\"\n",
    "#container = av.open(os.path.join(fpath, vid_filename))\n",
    "\n",
    "#for frame in container.decode(video=0):\n",
    "#    frame.to_image().save(fpath + 'images-from-av/frame-%04d.jpg' % frame.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = AutoModel.from_pretrained(\"bert-base-cased\")\n",
    "#configuration = VivitConfig()\n",
    "#model = VivitModel(configuration)\n",
    "#configuration = model.config\n",
    "\n",
    "\n",
    "def read_video_pyav(container):\n",
    "    frames = []\n",
    "    for frame in container.decode(video=0):\n",
    "        frames.append(frame.to_ndarray(format=\"rgb24\"))\n",
    "    return frames\n",
    "\n",
    "def process_frames(model, processor, frames):\n",
    "    inputs = processor(frames, return_tensors=\"pt\")\n",
    "    outputs = model(**inputs)\n",
    "    return outputs\n",
    "\n",
    "# Initialize model and processor\n",
    "#image_processor = VivitImageProcessor.from_pretrained(\"google/vivit-b-16x2\")\n",
    "#model = VivitModel.from_pretrained(\"google/vivit-b-16x2\")\n",
    "\n",
    "# Use a pipeline as a high-level helper\n",
    "# from transformers import pipeline\n",
    "# pipe = pipeline(model=\"google/vivit-b-16x2\", task=\"feature-extraction\")\n",
    "# from transformers import AutoTokenizer, ViViTForVideoClassification\n",
    "\n",
    "# tokenizer = AutoTokenizer.from_pretrained(\"google/vivit-b-16x2\")\n",
    "# model = ViViTForVideoClassification.from_pretrained(\"google/vivit-b-16x2\")\n",
    "\n",
    "# image_processor = VivitImageProcessor.from_pretrained(\"google/vivit-b-16x2\")\n",
    "\n",
    "# model = VivitForVideoClassification.from_pretrained(\"google/vivit-b-16x2\")\n",
    "\n",
    "# inputs = image_processor(list(video), return_tensors=\"pt\")\n",
    "\n",
    "# with torch.no_grad():\n",
    "#     outputs = pipe(**inputs)\n",
    "#     #outputs = model(**inputs)\n",
    "\n",
    "\n",
    "print(model.config.num_frames)\n",
    "\n",
    "# Open video file\n",
    "fpath = \"/Users/mikehemberger/Documents/vscode/totems/totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/\"\n",
    "vid_filename = \"MVI_7423_pulse_wabbern-cropped.mp4\"\n",
    "container = av.open(os.path.join(fpath, vid_filename))\n",
    "\n",
    "# Read video frames\n",
    "video = read_video_pyav(container)\n",
    "\n",
    "\n",
    "# Load your model\n",
    "#model = VivitModel.from_pretrained(\"google/vivit-b-16x2\")\n",
    "model.config.num_frames = 32\n",
    "num_frames_expected = model.config.num_frames  # replace this with actual value if not found in config\n",
    "\n",
    "# Define the frame processing function\n",
    "def process_frames(model, processor, frames):\n",
    "    inputs = processor(frames, return_tensors=\"pt\")\n",
    "    outputs = model(**inputs)\n",
    "    return outputs\n",
    "\n",
    "transform = Compose([\n",
    "    ToPILImage(),  # Convert tensor to PIL Image directly\n",
    "    Resize((224, 224)),  # Resize frames to the size your model expects\n",
    "    ToTensor()  # Convert frames to PyTorch tensors\n",
    "])\n",
    "\n",
    "# Apply transformation to all frames\n",
    "video = [transform(frame) for frame in video]\n",
    "feature_vectors = []\n",
    "num_frames_per_input = model.config.num_frames  # adjust as necessary\n",
    "\n",
    "#for i in range(0, len(video), num_frames_per_input):\n",
    "for i in range(0, num_frames_per_input*2, num_frames_per_input):\n",
    "    frames_batch = video[i : i+num_frames_per_input]\n",
    "\n",
    "    # if not enough frames for a full batch, pad with zeros\n",
    "    if len(frames_batch) < num_frames_per_input:\n",
    "        padding = [torch.zeros((3, 224, 224)) for _ in range(num_frames_per_input - len(frames_batch))]\n",
    "        frames_batch.extend(padding)\n",
    "\n",
    "    frames_batch = torch.stack(frames_batch)  # Combine frames into a tensor\n",
    "    frames_batch = frames_batch.unsqueeze(0)  # Add an extra batch dimension\n",
    "    print(frames_batch.shape)\n",
    "    inputs = {'pixel_values': frames_batch}\n",
    "    outputs = model(**inputs)\n",
    "    feature_vectors.append(np.squeeze(outputs.last_hidden_state.detach().cpu().numpy()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[np.squeeze(f).shape for f in feature_vectors]\n",
    "#feature_vectors[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fvs = np.array(np.squeeze(feature_vectors[0]))\n",
    "print(fvs.shape)\n",
    "\n",
    "fvs = np.array([np.squeeze(f) for f in feature_vectors])  # or concate\n",
    "print(fvs.shape)\n",
    "\n",
    "#np.save(f\"./totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/vivit_feature_vectors_224.npy\", fvs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(805, 768)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fvs = np.load(f\"./totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/vivit_feature_vectors_224.npy\")\n",
    "fvs = np.load(f\"../totems/totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/images-224/prototypev1-fv-MVI_7423_pulse_wabbern-cropped-224.npy\")  # vivit?!\n",
    "\n",
    "fvs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py:991: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "# from sklearn.decomposition import PCA\n",
    "# pca = PCA(n_components=500)\n",
    "# pca_result = pca.fit_transform(fv)\n",
    "# print(pca_result.shape)\n",
    "# print('Explained variation per principal component: {}'.format(\n",
    "#     pca.explained_variance_ratio_.sum()))\n",
    "\n",
    "\n",
    "tsne = TSNE(n_components=2, perplexity=45, early_exaggeration=250, learning_rate=45, random_state=42, init=\"pca\")\n",
    "features_embedded = tsne.fit_transform(fvs)\n",
    "#features_embedded = tsne.fit_transform(pca_result)\n",
    "#db = DBSCAN(eps=5.75, min_samples=50).fit(features_embedded)  # You can adjust eps and min_samples\n",
    "#clusters = db.labels_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x141af14e0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "# cluster_ids = sorted(np.unique(clusters))\n",
    "# cluster_means = np.array([np.mean(features_embedded[clusters == cluster_id], axis=0)\n",
    "#                           for cluster_id in cluster_ids])\n",
    "\n",
    "# n_clusters = len(cluster_ids)\n",
    "# jet_colors = cm.plasma(np.linspace(0, 1, n_clusters))\n",
    "plt.scatter(features_embedded[:, 0], features_embedded[:, 1], alpha=.6, c=\"w\", edgecolors=\"none\", s=10)\n",
    "# adjust tubelets > take less frames as input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# video classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import glob\n",
    "\n",
    "copy_files = False\n",
    "source_dir = '/Users/mikehemberger/Documents/vscode/totems/totem-prototype/roboflow-downloads/totem-prototype.v1i.yolov7pytorch'\n",
    "target_dir = \"./totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/\"\n",
    "os.makedirs(os.path.join(target_dir, \"images/renamed/\"), exist_ok=True)\n",
    "z_fill_ = 4\n",
    "if copy_files:\n",
    "    os.makedirs(target_dir, exist_ok=True)\n",
    "    prefix = \"MVI_7423_pulse_wabbern-cropped_mp4-\"\n",
    "    # Find all jpg and txt files in the source directory and its subdirectories\n",
    "    for filename in glob.glob(source_dir + '/**/*.jpg', recursive=True):\n",
    "        if os.path.basename(filename).startswith(prefix):\n",
    "            renamed = str(os.path.basename(filename).split(prefix)[1].split(\"_jpg\")[0]).zfill(z_fill_) # .split(prefix)[1]\n",
    "            renamed_filename = \"frame_\" + renamed + \".jpg\"\n",
    "            destination = os.path.join(target_dir, \"images/renamed/\", renamed_filename)\n",
    "            shutil.copyfile(filename, destination)\n",
    "\n",
    "    for filename in glob.glob(source_dir + '/**/*.txt', recursive=True):\n",
    "        if os.path.basename(filename).startswith(prefix):\n",
    "            renamed = str(os.path.basename(filename).split(prefix)[1].split(\"_jpg\")[0]).zfill(z_fill_) # .split(prefix)[1]\n",
    "            renamed_filename = \"frame_\" + renamed + \".txt\"\n",
    "            destination = os.path.join(target_dir, \"images/renamed/\", renamed_filename)\n",
    "            shutil.copyfile(filename, destination)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "805"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_dir = os.path.join(target_dir, \"images/renamed/\")\n",
    "filenames = sorted([f for f in os.listdir(target_dir) if f.endswith(\".jpg\")])\n",
    "target_fpaths = [os.path.join(target_dir, f) for f in os.listdir(target_dir) if f.endswith(\".jpg\")]\n",
    "\n",
    "df = pd.DataFrame(columns=[\"filename\", \"frame_idx\", \"frame_sec\", \"filepath\", \"cutout_fpath\", \"frame_str\", \"frame_int\"])\n",
    "sr = 40  # sampling rate\n",
    "df[\"filename\"] = filenames\n",
    "df[\"frame_idx\"] = [int(n) for n in range(len(df))]\n",
    "df[\"frame_sec\"] = df[\"frame_idx\"] * sr  # 25hz, 40ms sr\n",
    "df[\"filepath\"] = target_fpaths\n",
    "df[\"frame_int\"] = [int(fn.split(\"frame_\")[-1].split(\".jpg\")[0]) for fn in df[\"filename\"].tolist()] #  .astype(\"int64\")\n",
    "#df.to_csv(\"./prototypev1.csv\", index=False)\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "804\n",
      "['./totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/images/renamed/frame_0000.txt', './totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/images/renamed/frame_0001.txt', './totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/images/renamed/frame_0002.txt', './totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/images/renamed/frame_0003.txt', './totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/images/renamed/frame_0004.txt']\n",
      "['./totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/images-from-av/frame-0000.jpg', './totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/images-from-av/frame-0001.jpg', './totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/images-from-av/frame-0002.jpg', './totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/images-from-av/frame-0003.jpg', './totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/images-from-av/frame-0004.jpg']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/l1/s9h8bddx0mv1bqzrqffsxhp00000gn/T/ipykernel_41809/3399057814.py:6: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"filepath_txt\"] = sorted(df[\"filepath\"].str.replace(\".jpg\", \".txt\"))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>frame_idx</th>\n",
       "      <th>frame_sec</th>\n",
       "      <th>filepath</th>\n",
       "      <th>cutout_fpath</th>\n",
       "      <th>frame_str</th>\n",
       "      <th>frame_int</th>\n",
       "      <th>filepath_txt</th>\n",
       "      <th>ori_filepath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>frame_0000.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>frame_0001.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>frame_0002.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>frame_0003.jpg</td>\n",
       "      <td>3</td>\n",
       "      <td>120</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>frame_0004.jpg</td>\n",
       "      <td>4</td>\n",
       "      <td>160</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "      <td>./totem-prototype/videos/MVI_7423_pulse_wabber...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         filename  frame_idx  frame_sec  \\\n",
       "0  frame_0000.jpg          0          0   \n",
       "1  frame_0001.jpg          1         40   \n",
       "2  frame_0002.jpg          2         80   \n",
       "3  frame_0003.jpg          3        120   \n",
       "4  frame_0004.jpg          4        160   \n",
       "\n",
       "                                            filepath  cutout_fpath  frame_str  \\\n",
       "0  ./totem-prototype/videos/MVI_7423_pulse_wabber...           NaN        NaN   \n",
       "1  ./totem-prototype/videos/MVI_7423_pulse_wabber...           NaN        NaN   \n",
       "2  ./totem-prototype/videos/MVI_7423_pulse_wabber...           NaN        NaN   \n",
       "3  ./totem-prototype/videos/MVI_7423_pulse_wabber...           NaN        NaN   \n",
       "4  ./totem-prototype/videos/MVI_7423_pulse_wabber...           NaN        NaN   \n",
       "\n",
       "   frame_int                                       filepath_txt  \\\n",
       "0          0  ./totem-prototype/videos/MVI_7423_pulse_wabber...   \n",
       "1          1  ./totem-prototype/videos/MVI_7423_pulse_wabber...   \n",
       "2          2  ./totem-prototype/videos/MVI_7423_pulse_wabber...   \n",
       "3          3  ./totem-prototype/videos/MVI_7423_pulse_wabber...   \n",
       "4          4  ./totem-prototype/videos/MVI_7423_pulse_wabber...   \n",
       "\n",
       "                                        ori_filepath  \n",
       "0  ./totem-prototype/videos/MVI_7423_pulse_wabber...  \n",
       "1  ./totem-prototype/videos/MVI_7423_pulse_wabber...  \n",
       "2  ./totem-prototype/videos/MVI_7423_pulse_wabber...  \n",
       "3  ./totem-prototype/videos/MVI_7423_pulse_wabber...  \n",
       "4  ./totem-prototype/videos/MVI_7423_pulse_wabber...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../totems/prototypev1.csv\")\n",
    "T_idx = df[\"frame_idx\"].max()\n",
    "\n",
    "df = df.sort_values(by=\"frame_idx\")\n",
    "\n",
    "df[\"filepath_txt\"] = sorted(df[\"filepath\"].str.replace(\".jpg\", \".txt\"))\n",
    "df[\"ori_filepath\"] = sorted(df[\"filepath\"].str.replace(\"images/renamed\",\"images-from-av\").str.replace(\"frame_\",\"frame-\"))\n",
    "df = df.iloc[:-1]\n",
    "\n",
    "print(len(df))\n",
    "print(df[\"filepath_txt\"].tolist()[:5])\n",
    "print(df[\"ori_filepath\"].tolist()[:5])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['frame_0000.jpg',\n",
       " 'frame_0001.jpg',\n",
       " 'frame_0002.jpg',\n",
       " 'frame_0003.jpg',\n",
       " 'frame_0004.jpg']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# resize\n",
    "src = \"./totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/images/renamed/\"\n",
    "src_ = \"./totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/images-224/\"\n",
    "os.makedirs(src_, exist_ok=True)\n",
    "\n",
    "filenames = sorted([f for f in os.listdir(src) if f.endswith(\".jpg\")])\n",
    "resize = (224, 224)\n",
    "\n",
    "# for fn in filenames:\n",
    "#     img = Image.open(src+fn).resize(resize)\n",
    "#     img.save(src_+fn)\n",
    "\n",
    "# # VIDEO\n",
    "# images = sorted([img for img in os.listdir(src_) if img.endswith(\".jpg\")])\n",
    "# frame = cv2.imread(os.path.join(src_, images[0]))\n",
    "# height, width, layers = frame.shape\n",
    "\n",
    "# video = cv2.VideoWriter(src_ + 'video.avi', 0, 25, (width, height))\n",
    "# for image in images:\n",
    "#     video.write(cv2.imread(os.path.join(src_, image)))\n",
    "# cv2.destroyAllWindows()\n",
    "# video.release()\n",
    "df[\"filename\"].tolist()[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "        [ 462,  475]]),\n",
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       "        [ 424,  447]]),\n",
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       "        [ 429,  455]]),\n",
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       "        [303, 319]]),\n",
       " 'frame_0003_11': array([[717, 736],\n",
       "        [372, 384]]),\n",
       " 'frame_0003_12': array([[669, 681],\n",
       "        [192, 207]]),\n",
       " 'frame_0003_13': array([[651, 663],\n",
       "        [138, 149]]),\n",
       " 'frame_0003_14': array([[584, 599],\n",
       "        [130, 148]]),\n",
       " 'frame_0003_15': array([[592, 601],\n",
       "        [ 91, 100]]),\n",
       " 'frame_0003_16': array([[306, 332],\n",
       "        [355, 379]]),\n",
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       " 'frame_0003_18': array([[242, 262],\n",
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       " 'frame_0003_19': array([[ 85, 101],\n",
       "        [615, 642]]),\n",
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       "        [300, 316]]),\n",
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       "        [ 462,  475]]),\n",
       " 'frame_0004_3': array([[1079, 1094],\n",
       "        [ 424,  447]]),\n",
       " 'frame_0004_4': array([[1225, 1244],\n",
       "        [ 429,  455]]),\n",
       " 'frame_0004_5': array([[733, 744],\n",
       "        [418, 429]]),\n",
       " 'frame_0004_6': array([[594, 610],\n",
       "        [407, 420]]),\n",
       " 'frame_0004_7': array([[570, 593],\n",
       "        [527, 547]]),\n",
       " 'frame_0004_8': array([[485, 502],\n",
       "        [429, 442]]),\n",
       " 'frame_0004_9': array([[768, 786],\n",
       "        [294, 305]]),\n",
       " 'frame_0004_10': array([[711, 726],\n",
       "        [303, 319]]),\n",
       " 'frame_0004_11': array([[717, 736],\n",
       "        [372, 384]]),\n",
       " 'frame_0004_12': array([[669, 681],\n",
       "        [192, 207]]),\n",
       " 'frame_0004_13': array([[651, 663],\n",
       "        [138, 149]]),\n",
       " 'frame_0004_14': array([[584, 599],\n",
       "        [130, 148]]),\n",
       " 'frame_0004_15': array([[592, 601],\n",
       "        [ 91, 100]]),\n",
       " 'frame_0004_16': array([[306, 332],\n",
       "        [355, 379]]),\n",
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       "        [300, 316]]),\n",
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       "        [466, 478]]),\n",
       " 'frame_0005_2': array([[1063, 1081],\n",
       "        [ 462,  475]]),\n",
       " 'frame_0005_3': array([[1079, 1094],\n",
       "        [ 424,  447]]),\n",
       " 'frame_0005_4': array([[1225, 1244],\n",
       "        [ 429,  455]]),\n",
       " 'frame_0005_5': array([[733, 744],\n",
       "        [418, 429]]),\n",
       " 'frame_0005_6': array([[594, 610],\n",
       "        [407, 420]]),\n",
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       "        [391, 404]]),\n",
       " 'frame_0021_35': array([[361, 371],\n",
       "        [419, 435]]),\n",
       " 'frame_0021_36': array([[1036, 1052],\n",
       "        [ 409,  423]]),\n",
       " 'frame_0021_37': array([[723, 739],\n",
       "        [397, 410]]),\n",
       " 'frame_0021_38': array([[682, 697],\n",
       "        [147, 171]]),\n",
       " 'frame_0021_39': array([[673, 682],\n",
       "        [212, 228]]),\n",
       " 'frame_0021_40': array([[497, 512],\n",
       "        [298, 312]]),\n",
       " 'frame_0021_41': array([[413, 433],\n",
       "        [398, 411]]),\n",
       " 'frame_0021_42': array([[358, 372],\n",
       "        [418, 443]]),\n",
       " 'frame_0021_43': array([[653, 670],\n",
       "        [308, 326]]),\n",
       " 'frame_0021_44': array([[610, 626],\n",
       "        [307, 323]]),\n",
       " 'frame_0021_45': array([[541, 556],\n",
       "        [252, 265]]),\n",
       " 'frame_0021_46': array([[722, 737],\n",
       "        [403, 416]]),\n",
       " 'frame_0021_47': array([[1034, 1052],\n",
       "        [ 412,  430]]),\n",
       " 'frame_0021_48': array([[1166, 1179],\n",
       "        [ 514,  542]]),\n",
       " 'frame_0021_49': array([[1136, 1147],\n",
       "        [ 503,  512]]),\n",
       " 'frame_0021_50': array([[729, 745],\n",
       "        [665, 679]]),\n",
       " 'frame_0021_51': array([[818, 833],\n",
       "        [473, 485]]),\n",
       " 'frame_0021_52': array([[152, 169],\n",
       "        [442, 462]]),\n",
       " 'frame_0021_53': array([[133, 157],\n",
       "        [498, 519]]),\n",
       " 'frame_0021_54': array([[113, 134],\n",
       "        [502, 513]]),\n",
       " 'frame_0021_55': array([[ 80, 103],\n",
       "        [527, 545]]),\n",
       " 'frame_0022_0': array([[863, 888],\n",
       "        [505, 520]]),\n",
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       " 'frame_0022_2': array([[1063, 1081],\n",
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       " 'frame_0022_3': array([[1079, 1094],\n",
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       "        [ 429,  455]]),\n",
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       "        [418, 429]]),\n",
       " 'frame_0022_6': array([[594, 610],\n",
       "        [407, 420]]),\n",
       " 'frame_0022_7': array([[570, 593],\n",
       "        [527, 547]]),\n",
       " 'frame_0022_8': array([[485, 502],\n",
       "        [429, 442]]),\n",
       " 'frame_0022_9': array([[768, 786],\n",
       "        [294, 305]]),\n",
       " 'frame_0022_10': array([[711, 726],\n",
       "        [303, 319]]),\n",
       " 'frame_0022_11': array([[717, 736],\n",
       "        [372, 384]]),\n",
       " 'frame_0022_12': array([[669, 681],\n",
       "        [192, 207]]),\n",
       " 'frame_0022_13': array([[651, 663],\n",
       "        [138, 149]]),\n",
       " 'frame_0022_14': array([[584, 599],\n",
       "        [130, 148]]),\n",
       " 'frame_0022_15': array([[592, 601],\n",
       "        [ 91, 100]]),\n",
       " 'frame_0022_16': array([[306, 332],\n",
       "        [355, 379]]),\n",
       " 'frame_0022_17': array([[ 30,  48],\n",
       "        [409, 427]]),\n",
       " 'frame_0022_18': array([[242, 262],\n",
       "        [468, 481]]),\n",
       " 'frame_0022_19': array([[ 85, 101],\n",
       "        [615, 642]]),\n",
       " 'frame_0022_20': array([[361, 373],\n",
       "        [300, 316]]),\n",
       " 'frame_0022_21': array([[823, 832],\n",
       "        [468, 477]]),\n",
       " 'frame_0022_22': array([[733, 744],\n",
       "        [658, 668]]),\n",
       " 'frame_0022_23': array([[156, 167],\n",
       "        [443, 455]]),\n",
       " 'frame_0022_24': array([[ 92, 106],\n",
       "        [525, 537]]),\n",
       " 'frame_0022_25': array([[119, 151],\n",
       "        [493, 511]]),\n",
       " 'frame_0022_26': array([[1139, 1146],\n",
       "        [ 497,  503]]),\n",
       " 'frame_0022_27': array([[1171, 1180],\n",
       "        [ 517,  533]]),\n",
       " 'frame_0022_28': array([[683, 697],\n",
       "        [144, 163]]),\n",
       " 'frame_0022_29': array([[675, 683],\n",
       "        [206, 220]]),\n",
       " 'frame_0022_30': array([[655, 671],\n",
       "        [303, 319]]),\n",
       " 'frame_0022_31': array([[612, 627],\n",
       "        [302, 316]]),\n",
       " 'frame_0022_32': array([[544, 556],\n",
       "        [248, 258]]),\n",
       " 'frame_0022_33': array([[499, 513],\n",
       "        [293, 304]]),\n",
       " 'frame_0022_34': array([[415, 435],\n",
       "        [391, 404]]),\n",
       " 'frame_0022_35': array([[361, 371],\n",
       "        [419, 435]]),\n",
       " 'frame_0022_36': array([[1036, 1052],\n",
       "        [ 409,  423]]),\n",
       " 'frame_0022_37': array([[723, 739],\n",
       "        [397, 410]]),\n",
       " 'frame_0022_38': array([[682, 697],\n",
       "        [147, 171]]),\n",
       " 'frame_0022_39': array([[673, 682],\n",
       "        [212, 228]]),\n",
       " 'frame_0022_40': array([[497, 512],\n",
       "        [298, 312]]),\n",
       " 'frame_0022_41': array([[413, 433],\n",
       "        [398, 411]]),\n",
       " 'frame_0022_42': array([[358, 372],\n",
       "        [418, 443]]),\n",
       " 'frame_0022_43': array([[653, 670],\n",
       "        [308, 326]]),\n",
       " 'frame_0022_44': array([[610, 626],\n",
       "        [307, 323]]),\n",
       " 'frame_0022_45': array([[541, 556],\n",
       "        [252, 265]]),\n",
       " 'frame_0022_46': array([[722, 737],\n",
       "        [403, 416]]),\n",
       " 'frame_0022_47': array([[1034, 1052],\n",
       "        [ 412,  430]]),\n",
       " 'frame_0022_48': array([[1166, 1179],\n",
       "        [ 514,  542]]),\n",
       " 'frame_0022_49': array([[1136, 1147],\n",
       "        [ 503,  512]]),\n",
       " 'frame_0022_50': array([[729, 745],\n",
       "        [665, 679]]),\n",
       " 'frame_0022_51': array([[818, 833],\n",
       "        [473, 485]]),\n",
       " 'frame_0022_52': array([[152, 169],\n",
       "        [442, 462]]),\n",
       " 'frame_0022_53': array([[133, 157],\n",
       "        [498, 519]]),\n",
       " 'frame_0022_54': array([[113, 134],\n",
       "        [502, 513]]),\n",
       " 'frame_0022_55': array([[ 80, 103],\n",
       "        [527, 545]]),\n",
       " 'frame_0023_0': array([[863, 888],\n",
       "        [505, 520]]),\n",
       " 'frame_0023_1': array([[973, 992],\n",
       "        [466, 478]]),\n",
       " 'frame_0023_2': array([[1063, 1081],\n",
       "        [ 462,  475]]),\n",
       " 'frame_0023_3': array([[1079, 1094],\n",
       "        [ 424,  447]]),\n",
       " 'frame_0023_4': array([[1225, 1244],\n",
       "        [ 429,  455]]),\n",
       " 'frame_0023_5': array([[733, 744],\n",
       "        [418, 429]]),\n",
       " 'frame_0023_6': array([[594, 610],\n",
       "        [407, 420]]),\n",
       " 'frame_0023_7': array([[570, 593],\n",
       "        [527, 547]]),\n",
       " 'frame_0023_8': array([[485, 502],\n",
       "        [429, 442]]),\n",
       " 'frame_0023_9': array([[768, 786],\n",
       "        [294, 305]]),\n",
       " 'frame_0023_10': array([[711, 726],\n",
       "        [303, 319]]),\n",
       " 'frame_0023_11': array([[717, 736],\n",
       "        [372, 384]]),\n",
       " 'frame_0023_12': array([[669, 681],\n",
       "        [192, 207]]),\n",
       " 'frame_0023_13': array([[651, 663],\n",
       "        [138, 149]]),\n",
       " 'frame_0023_14': array([[584, 599],\n",
       "        [130, 148]]),\n",
       " 'frame_0023_15': array([[592, 601],\n",
       "        [ 91, 100]]),\n",
       " 'frame_0023_16': array([[306, 332],\n",
       "        [355, 379]]),\n",
       " 'frame_0023_17': array([[ 30,  48],\n",
       "        [409, 427]]),\n",
       " 'frame_0023_18': array([[242, 262],\n",
       "        [468, 481]]),\n",
       " 'frame_0023_19': array([[ 85, 101],\n",
       "        [615, 642]]),\n",
       " 'frame_0023_20': array([[361, 373],\n",
       "        [300, 316]]),\n",
       " 'frame_0023_21': array([[823, 832],\n",
       "        [468, 477]]),\n",
       " 'frame_0023_22': array([[733, 744],\n",
       "        [658, 668]]),\n",
       " 'frame_0023_23': array([[156, 167],\n",
       "        [443, 455]]),\n",
       " 'frame_0023_24': array([[ 92, 106],\n",
       "        [525, 537]]),\n",
       " 'frame_0023_25': array([[119, 151],\n",
       "        [493, 511]]),\n",
       " 'frame_0023_26': array([[1139, 1146],\n",
       "        [ 497,  503]]),\n",
       " 'frame_0023_27': array([[1171, 1180],\n",
       "        [ 517,  533]]),\n",
       " 'frame_0023_28': array([[683, 697],\n",
       "        [144, 163]]),\n",
       " 'frame_0023_29': array([[675, 683],\n",
       "        [206, 220]]),\n",
       " 'frame_0023_30': array([[655, 671],\n",
       "        [303, 319]]),\n",
       " 'frame_0023_31': array([[612, 627],\n",
       "        [302, 316]]),\n",
       " 'frame_0023_32': array([[544, 556],\n",
       "        [248, 258]]),\n",
       " 'frame_0023_33': array([[499, 513],\n",
       "        [293, 304]]),\n",
       " 'frame_0023_34': array([[415, 435],\n",
       "        [391, 404]]),\n",
       " 'frame_0023_35': array([[361, 371],\n",
       "        [419, 435]]),\n",
       " 'frame_0023_36': array([[1036, 1052],\n",
       "        [ 409,  423]]),\n",
       " 'frame_0023_37': array([[723, 739],\n",
       "        [397, 410]]),\n",
       " 'frame_0023_38': array([[682, 697],\n",
       "        [147, 171]]),\n",
       " 'frame_0023_39': array([[673, 682],\n",
       "        [212, 228]]),\n",
       " 'frame_0023_40': array([[497, 512],\n",
       "        [298, 312]]),\n",
       " 'frame_0023_41': array([[413, 433],\n",
       "        [398, 411]]),\n",
       " 'frame_0023_42': array([[358, 372],\n",
       "        [418, 443]]),\n",
       " 'frame_0023_43': array([[653, 670],\n",
       "        [308, 326]]),\n",
       " 'frame_0023_44': array([[610, 626],\n",
       "        [307, 323]]),\n",
       " 'frame_0023_45': array([[541, 556],\n",
       "        [252, 265]]),\n",
       " 'frame_0023_46': array([[722, 737],\n",
       "        [403, 416]]),\n",
       " 'frame_0023_47': array([[1034, 1052],\n",
       "        [ 412,  430]]),\n",
       " 'frame_0023_48': array([[1166, 1179],\n",
       "        [ 514,  542]]),\n",
       " 'frame_0023_49': array([[1136, 1147],\n",
       "        [ 503,  512]]),\n",
       " 'frame_0023_50': array([[729, 745],\n",
       "        [665, 679]]),\n",
       " 'frame_0023_51': array([[818, 833],\n",
       "        [473, 485]]),\n",
       " 'frame_0023_52': array([[152, 169],\n",
       "        [442, 462]]),\n",
       " 'frame_0023_53': array([[133, 157],\n",
       "        [498, 519]]),\n",
       " 'frame_0023_54': array([[113, 134],\n",
       "        [502, 513]]),\n",
       " 'frame_0023_55': array([[ 80, 103],\n",
       "        [527, 545]]),\n",
       " 'frame_0024_0': array([[863, 888],\n",
       "        [505, 520]]),\n",
       " 'frame_0024_1': array([[973, 992],\n",
       "        [466, 478]]),\n",
       " 'frame_0024_2': array([[1063, 1081],\n",
       "        [ 462,  475]]),\n",
       " 'frame_0024_3': array([[1079, 1094],\n",
       "        [ 424,  447]]),\n",
       " 'frame_0024_4': array([[1225, 1244],\n",
       "        [ 429,  455]]),\n",
       " 'frame_0024_5': array([[733, 744],\n",
       "        [418, 429]]),\n",
       " 'frame_0024_6': array([[594, 610],\n",
       "        [407, 420]]),\n",
       " 'frame_0024_7': array([[570, 593],\n",
       "        [527, 547]]),\n",
       " 'frame_0024_8': array([[485, 502],\n",
       "        [429, 442]]),\n",
       " 'frame_0024_9': array([[768, 786],\n",
       "        [294, 305]]),\n",
       " 'frame_0024_10': array([[711, 726],\n",
       "        [303, 319]]),\n",
       " 'frame_0024_11': array([[717, 736],\n",
       "        [372, 384]]),\n",
       " 'frame_0024_12': array([[669, 681],\n",
       "        [192, 207]]),\n",
       " 'frame_0024_13': array([[651, 663],\n",
       "        [138, 149]]),\n",
       " 'frame_0024_14': array([[584, 599],\n",
       "        [130, 148]]),\n",
       " 'frame_0024_15': array([[592, 601],\n",
       "        [ 91, 100]]),\n",
       " 'frame_0024_16': array([[306, 332],\n",
       "        [355, 379]]),\n",
       " 'frame_0024_17': array([[ 30,  48],\n",
       "        [409, 427]]),\n",
       " 'frame_0024_18': array([[242, 262],\n",
       "        [468, 481]]),\n",
       " 'frame_0024_19': array([[ 85, 101],\n",
       "        [615, 642]]),\n",
       " 'frame_0024_20': array([[361, 373],\n",
       "        [300, 316]]),\n",
       " 'frame_0024_21': array([[823, 832],\n",
       "        [468, 477]]),\n",
       " 'frame_0024_22': array([[733, 744],\n",
       "        [658, 668]]),\n",
       " 'frame_0024_23': array([[156, 167],\n",
       "        [443, 455]]),\n",
       " 'frame_0024_24': array([[ 92, 106],\n",
       "        [525, 537]]),\n",
       " 'frame_0024_25': array([[119, 151],\n",
       "        [493, 511]]),\n",
       " 'frame_0024_26': array([[1139, 1146],\n",
       "        [ 497,  503]]),\n",
       " 'frame_0024_27': array([[1171, 1180],\n",
       "        [ 517,  533]]),\n",
       " 'frame_0024_28': array([[683, 697],\n",
       "        [144, 163]]),\n",
       " 'frame_0024_29': array([[675, 683],\n",
       "        [206, 220]]),\n",
       " 'frame_0024_30': array([[655, 671],\n",
       "        [303, 319]]),\n",
       " 'frame_0024_31': array([[612, 627],\n",
       "        [302, 316]]),\n",
       " 'frame_0024_32': array([[544, 556],\n",
       "        [248, 258]]),\n",
       " 'frame_0024_33': array([[499, 513],\n",
       "        [293, 304]]),\n",
       " 'frame_0024_34': array([[415, 435],\n",
       "        [391, 404]]),\n",
       " 'frame_0024_35': array([[361, 371],\n",
       "        [419, 435]]),\n",
       " 'frame_0024_36': array([[1036, 1052],\n",
       "        [ 409,  423]]),\n",
       " 'frame_0024_37': array([[723, 739],\n",
       "        [397, 410]]),\n",
       " 'frame_0024_38': array([[682, 697],\n",
       "        [147, 171]]),\n",
       " 'frame_0024_39': array([[673, 682],\n",
       "        [212, 228]]),\n",
       " 'frame_0024_40': array([[497, 512],\n",
       "        [298, 312]]),\n",
       " 'frame_0024_41': array([[413, 433],\n",
       "        [398, 411]]),\n",
       " 'frame_0024_42': array([[358, 372],\n",
       "        [418, 443]]),\n",
       " 'frame_0024_43': array([[653, 670],\n",
       "        [308, 326]]),\n",
       " 'frame_0024_44': array([[610, 626],\n",
       "        [307, 323]]),\n",
       " 'frame_0024_45': array([[541, 556],\n",
       "        [252, 265]]),\n",
       " 'frame_0024_46': array([[722, 737],\n",
       "        [403, 416]]),\n",
       " 'frame_0024_47': array([[1034, 1052],\n",
       "        [ 412,  430]]),\n",
       " 'frame_0024_48': array([[1166, 1179],\n",
       "        [ 514,  542]]),\n",
       " 'frame_0024_49': array([[1136, 1147],\n",
       "        [ 503,  512]]),\n",
       " 'frame_0024_50': array([[729, 745],\n",
       "        [665, 679]]),\n",
       " 'frame_0024_51': array([[818, 833],\n",
       "        [473, 485]]),\n",
       " 'frame_0024_52': array([[152, 169],\n",
       "        [442, 462]]),\n",
       " 'frame_0024_53': array([[133, 157],\n",
       "        [498, 519]]),\n",
       " 'frame_0024_54': array([[113, 134],\n",
       "        [502, 513]]),\n",
       " 'frame_0024_55': array([[ 80, 103],\n",
       "        [527, 545]]),\n",
       " ...}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annots = df[\"filepath_txt\"].tolist()\n",
    "filenames = df[\"filename\"].tolist()\n",
    "img_filepaths = df[\"ori_filepath\"].tolist()\n",
    "\n",
    "d = dict()\n",
    "\n",
    "for idx, annot in enumerate(annots):\n",
    "    im = Image.open(img_filepaths[idx])\n",
    "    im_width, im_height = im.size\n",
    "    with open(annot, 'r') as f:\n",
    "        lines = f.read().splitlines()\n",
    "        for nth, line in enumerate(lines):\n",
    "            values = list(map(float, line.strip().split()))\n",
    "            while values:\n",
    "                class_id = int(values.pop(0))\n",
    "\n",
    "                x_values = []\n",
    "                y_values = []\n",
    "                while values and len(values) >= 2:\n",
    "                    x_values.append(values.pop(0))\n",
    "                    y_values.append(values.pop(0))\n",
    "                    x1 = int(min(x_values) * im_width)\n",
    "                    y1 = int(min(y_values) * im_height)\n",
    "                    x2 = int(max(x_values) * im_width)\n",
    "                    y2 = int(max(y_values) * im_height)\n",
    "                    #print(np.array([[x1, x2],[y1, y2]]))\n",
    "                d.update({filenames[idx].split(\".jpg\")[0] + \"_\" + str(nth): np.array([[x1, x2],[y1, y2]])})#np.array((x_values, y_values))})\n",
    "d        \n",
    "#d['frame_0000.jpg'][1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[873.0, 511.0],\n",
       " [980.6, 470.8],\n",
       " [1070.2, 467.2],\n",
       " [1085.0, 433.2],\n",
       " [1232.6, 439.4],\n",
       " [737.4, 422.4],\n",
       " [600.4, 412.2],\n",
       " [579.2, 535.0],\n",
       " [491.8, 434.2],\n",
       " [775.2, 298.4],\n",
       " [717.0, 309.4],\n",
       " [724.6, 376.8],\n",
       " [673.8, 198.0],\n",
       " [655.8, 142.4],\n",
       " [590.0, 137.2],\n",
       " [595.6, 94.6],\n",
       " [316.4, 364.6],\n",
       " [37.2, 416.2],\n",
       " [250.0, 473.2],\n",
       " [91.4, 625.8],\n",
       " [365.8, 306.4],\n",
       " [873.0, 511.0],\n",
       " [980.6, 470.8],\n",
       " [1070.2, 467.2],\n",
       " [1085.0, 433.2],\n",
       " [1232.6, 439.4],\n",
       " [737.4, 422.4],\n",
       " [600.4, 412.2],\n",
       " [579.2, 535.0],\n",
       " [491.8, 434.2],\n",
       " [775.2, 298.4],\n",
       " [717.0, 309.4],\n",
       " [724.6, 376.8],\n",
       " [673.8, 198.0],\n",
       " [655.8, 142.4],\n",
       " [590.0, 137.2],\n",
       " [595.6, 94.6],\n",
       " [316.4, 364.6],\n",
       " [37.2, 416.2],\n",
       " [250.0, 473.2],\n",
       " [91.4, 625.8],\n",
       " [365.8, 306.4],\n",
       " [873.0, 511.0],\n",
       " [980.6, 470.8],\n",
       " [1070.2, 467.2],\n",
       " [1085.0, 433.2],\n",
       " [1232.6, 439.4],\n",
       " [737.4, 422.4],\n",
       " [600.4, 412.2],\n",
       " [579.2, 535.0],\n",
       " [491.8, 434.2],\n",
       " [775.2, 298.4],\n",
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       " [728.0, 408.2],\n",
       " [1041.2, 419.2],\n",
       " [1171.2, 525.2],\n",
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       " [735.4, 670.6],\n",
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       " [142.6, 506.4],\n",
       " [121.4, 506.4],\n",
       " [89.2, 534.2],\n",
       " ...]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "def get_bbox(df:dict, frame:str):\n",
    "    x = df[frame][0,0], df[frame][0,1], df[frame][0,1], df[frame][0,0], df[frame][0,0]\n",
    "    y = df[frame][1,0], df[frame][1,0], df[frame][1,1], df[frame][1,1], df[frame][1,0]\n",
    "\n",
    "    return np.vstack((x,y)).T\n",
    "\n",
    "\n",
    "\n",
    "search_list = list(d.keys()) \n",
    "\n",
    "\n",
    "means = list()\n",
    "for nth in range(len(search_list)):\n",
    "    search_for = f\"frame_{str(nth).zfill(z_fill_)}_\"#'frame_0000_'\n",
    "    result_keys = [f for f in search_list if f.startswith(search_for)]\n",
    "\n",
    "    bboxes = []\n",
    "    for key in result_keys:\n",
    "        means.append([np.mean(get_bbox(d, key)[:,0]), np.mean(get_bbox(d, key)[:,1])])\n",
    "means\n",
    "#np.array(bboxes)\n",
    "    # for bbox in bboxes:\n",
    "    #     plt.plot(np.mean(bbox[:,0]),np.mean(bbox[:,1]),\".\", color=colors[nth])\n",
    "    #     plt.axis(\"off\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clusters 34\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.jet\n",
    "colors = cmap(np.linspace(0, 1, len(search_list)))\n",
    "means_ = np.array(means)\n",
    "colors = cmap(np.linspace(0, 1, len(means_)))[:,:3]\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.imshow(Image.open(df.loc[0,\"ori_filepath\"]))\n",
    "\n",
    "for m in means_:\n",
    "    plt.plot(m[0], m[1], \".\", color=colors[nth])\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "import numpy as np\n",
    "\n",
    "kmeans = KMeans(n_clusters=35, random_state=0)\n",
    "kmeans.fit(means_)\n",
    "assignments = kmeans.labels_\n",
    "centers = kmeans.cluster_centers_\n",
    "\n",
    "print(\"clusters\", max(assignments))\n",
    "\n",
    "for c_ in centers:\n",
    "    plt.plot(c_[0], c_[1], \"+\", color=\"w\", ms=6)\n",
    "\n",
    "# NEEDS COLORS TO TRACK CLUSERS AND VISUALIY CONFIRM time-series, plot the cluster id bsides the center"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- use the cluster centers to pool nth of knn search bboxes and average them\n",
    "- lastly follow them back to seg-mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "xoffset = 5  # in pixels from center for \"temporary\" bbox visialization\n",
    "yoffset = 10\n",
    "xframes = [int(n) for n in range(len(df))]\n",
    "mat = np.zeros((len(xframes), len(centers)))\n",
    "bbox_ = list()\n",
    "for ith, c__ in enumerate(centers):\n",
    "    x, y = c__ # centers[20]\n",
    "    x_min = max(0, x - xoffset)  # make sure indices are not negative\n",
    "    y_min = max(0, y - yoffset)\n",
    "    x_max = x + xoffset\n",
    "    y_max = y + yoffset\n",
    "    for nth in xframes:\n",
    "        intensity_matrix = np.array(Image.open(df.loc[nth,\"ori_filepath\"]))\n",
    "        x_max = min(x_max, intensity_matrix.shape[1])\n",
    "        y_max = min(y_max, intensity_matrix.shape[0])\n",
    "\n",
    "        # check those boxes!!!\n",
    "        # roi = intensity_matrix[int(y_min):int(y_max), int(x_min):int(x_max)]\n",
    "\n",
    "        bbox_.append([int(y_min), int(y_max), int(x_min), int(x_max)])\n",
    "        roi = intensity_matrix[int(y_min):int(y_max), int(x_min):int(x_max)]\n",
    "        Image.fromarray(roi).save(f\"./test/test_{ith}_{nth}.jpg\")\n",
    "\n",
    "        mat[nth, ith] = np.sum(roi)\n",
    "        #mat[nth, ith] = np.mean(roi)\n",
    "\n",
    "# slow... 4:40min:sec for 20 offset, but thats too much!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the cutouts on a black background but at their location!\n",
    "- this will make it clear I hope!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLOT IDENTIFIED ROI WITH NUMBERS, THEN USE NUMBERS TO LABEL TRACES / OR COLOR\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.imshow(Image.open(df.loc[0,\"ori_filepath\"]))\n",
    "\n",
    "for bbox in bbox_:\n",
    "    plt.plot(\n",
    "        [bbox[2], bbox[2], bbox[3], bbox[3], bbox[2]],\n",
    "        [bbox[0], bbox[0], bbox[1], bbox[1], bbox[0]],\n",
    "        \"-or\",)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = pd.DataFrame(mat, columns=[f\"center_{n}\" for n in range(len(centers))])\n",
    "mat = mat / mat.mean().mean()\n",
    "mat = mat / mat.max()\n",
    "#mat.max().max()\n",
    "# rollingmean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take xframe 483, sorty by activity and use the sorting for visualisation\n",
    "srt_idx = np.where([x == 483 for x in xframes])[0]\n",
    "sorted_indices = np.argsort(mat.iloc[srt_idx, :].values)[0]\n",
    "#mat.iloc[sorted_indices,:]\n",
    "#sorted_indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduce Threshold red-line for .5 of normalized instensity\n",
    "- also possible to set threshold at around .4 and start with a linear blue7red scale from there till 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/mikehemberger/Documents/vscode/totem/extract-video-features-prototypev1.ipynb Cell 26\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/mikehemberger/Documents/vscode/totem/extract-video-features-prototypev1.ipynb#X34sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m plt\u001b[39m.\u001b[39mfigure(figsize\u001b[39m=\u001b[39m(\u001b[39m16\u001b[39m,\u001b[39m4\u001b[39m))\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/mikehemberger/Documents/vscode/totem/extract-video-features-prototypev1.ipynb#X34sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m plt\u001b[39m.\u001b[39mplot(xframes, np\u001b[39m.\u001b[39mones(\u001b[39mlen\u001b[39m(xframes)), \u001b[39m\"\u001b[39m\u001b[39m-w\u001b[39m\u001b[39m\"\u001b[39m, lw\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/mikehemberger/Documents/vscode/totem/extract-video-features-prototypev1.ipynb#X34sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m plt\u001b[39m.\u001b[39mplot(xframes,mat\u001b[39m.\u001b[39miloc[:, \u001b[39m0\u001b[39m] \u001b[39m/\u001b[39m \u001b[39mmax\u001b[39m(mat\u001b[39m.\u001b[39miloc[:, \u001b[39m0\u001b[39m]), \u001b[39m\"\u001b[39m\u001b[39m-w\u001b[39m\u001b[39m\"\u001b[39m, lw\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "plt.plot(xframes, np.ones(len(xframes)), \"-w\", lw=1)\n",
    "plt.plot(xframes,mat.iloc[:, 0] / max(mat.iloc[:, 0]), \"-w\", lw=1)\n",
    "plt.plot(xframes,mat.iloc[:, 2] / max(mat.iloc[:, 2]), \"-b\", lw=2)\n",
    "\n",
    "plt.figure(figsize=(16,5))\n",
    "plt.plot(xframes,mat.iloc[:, 0] / max(mat.iloc[:, 0]), \"-w\", lw=2)\n",
    "#plt.plot(xframes,mat.iloc[:, 1] / max(mat.iloc[:, 1]), \"-r\", lw=2)\n",
    "plt.plot(xframes,mat.iloc[:, 2] / max(mat.iloc[:, 2]), \"-b\", lw=2)\n",
    "plt.xlim([400, 600])\n",
    "\n",
    "plt.figure(figsize=(16,5))\n",
    "plt.plot(xframes,mat.iloc[:, 0] / max(mat.iloc[:, 0]), \"-w\", lw=2)\n",
    "#plt.plot(xframes,mat.iloc[:, 1] / max(mat.iloc[:, 1]), \"-r\", lw=2)\n",
    "plt.plot(xframes,mat.iloc[:, 2] / max(mat.iloc[:, 2]), \"-b\", lw=2)\n",
    "plt.xlim([450, 525])\n",
    "\n",
    "plt.figure(figsize=(22,6.6))\n",
    "plt.imshow(mat.iloc[:, sorted_indices].T, cmap=\"bwr\")\n",
    "#plt.axis(\"square\")\n",
    "plt.colorbar()\n",
    "plt.xlim([450, 525])\n",
    "plt.ylim([0, 35])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cut out clusters and show the averGES!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(16,12))\n",
    "counter = 0\n",
    "for ith in sorted_indices:\n",
    "    plt.plot(xframes, counter + mat.iloc[:, ith], \"-w\", lw=2)\n",
    "    counter += 1\n",
    "    \n",
    "plt.xlim([200, 600])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "offset = 25\n",
    "\n",
    "\n",
    "for center in centers:\n",
    "    x, y = center\n",
    "    x_min = max(0, x - offset)  # make sure indices are not negative\n",
    "    y_min = max(0, y - offset)\n",
    "    x_max = x + offset\n",
    "    y_max = y + offset\n",
    "\n",
    "    # Make sure indices do not exceed the array dimensions\n",
    "    x_max = min(x_max, intensity_matrix.shape[1])\n",
    "    y_max = min(y_max, intensity_matrix.shape[0])\n",
    "\n",
    "    # Slice the intensity matrix to get the bounding box\n",
    "    roi = intensity_matrix[int(y_min):int(y_max), int(x_min):int(x_max)]\n",
    "    \n",
    "    # Compute the integrated intensity\n",
    "    integrated_intensity = np.sum(roi)\n",
    "    print(f\"Integrated intensity for center ({x},{y}): {integrated_intensity}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# if i want to use this i have to loop across bboxes and across frames! then compare across frames, not internally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all = np.array(())\n",
    "all_ = dict()\n",
    "\n",
    "for n in range(5):\n",
    "    bbox_0 = bboxes_frames[int(n)+1]\n",
    "    bbox_1 = bboxes_frames[int(n)+2]\n",
    "    ious = list()\n",
    "\n",
    "    for bbox in bbox_0:\n",
    "        entries = [calculate_iou(bbox, bbox_) == 1. for bbox_ in bbox_1] #len(np.unique([calculate_iou(bbox, bbox_) for bbox_ in bbox_1]))\n",
    "        if sum(entries) > 1:\n",
    "            print(sum(entries))\n",
    "            ious.append(np.where(entries))\n",
    "    if ious:  \n",
    "        all = np.concatenate(ious)\n",
    "        all = all[all>0]\n",
    "        all_[int(n)] = all\n",
    "\n",
    "all_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "xbins = np.arange(0, 1, step=0.1)\n",
    "c, bins = np.histogram(all_[2], xbins)\n",
    "c_, bins_ = np.histogram(all_[3], xbins)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(bins[:-1], c,'-w')\n",
    "plt.plot(bins_[:-1], c_,'-r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ious_ = dict()\n",
    "\n",
    "for ith, bboxes in bboxes_frames.items():\n",
    "    ious = list()\n",
    "    for nth in range(len(bboxes)):\n",
    "        if len(np.unique([calculate_iou(bboxes[nth], bbox) for bbox in bboxes])) > 2:\n",
    "            ious.append(np.unique([calculate_iou(bboxes[nth], bbox) for bbox in bboxes]))\n",
    "    \n",
    "    ious_[ith] = ious\n",
    "    # have to carry index over somehow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.unique(ious[3])\n",
    "\n",
    "all_ = list()\n",
    "for i, val in ious_.items():\n",
    "    for le in val:\n",
    "        #print(le[(le > 0) & (le < 1)])\n",
    "        all_.append(le[(le > 0) & (le < 1)])\n",
    "\n",
    "all_\n",
    "all = np.concatenate(all_)\n",
    "\n",
    "xbins = np.arange(0, 1, step=0.1)\n",
    "c, bins = np.histogram(all, xbins)\n",
    "\n",
    "plt.figure()\n",
    "plt.bar(bins[:-1], c, width=.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try to find across frames first!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "search_list = list(d.keys()) \n",
    "colors = [\"w\", \"g\", \"y\"]\n",
    "\n",
    "plt.figure()\n",
    "#plt.imshow(Image.open(df.loc[0,\"filepath\"]))\n",
    "\n",
    "for nth in [0,1,2]:\n",
    "    search_for = f\"frame_{str(nth).zfill(z_fill_)}_\"#'frame_0000_'\n",
    "    result_keys = [f for f in search_list if f.startswith(search_for)]\n",
    "\n",
    "    bboxes = []\n",
    "    for key in result_keys:\n",
    "        bboxes.append(get_bbox(d, key))\n",
    "\n",
    "    for bbox in bboxes:\n",
    "        plt.plot(bbox[:,0],bbox[:,1],\"-\", color=colors[nth])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ious = dict()\n",
    "\n",
    "for nth in range(len(bboxes)):\n",
    "    iou = [calculate_iou(bboxes[nth], bbox) for bbox]\n",
    "\n",
    "    ious.append(np.unique(iou))\n",
    "\n",
    "ious"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.draw import polygon\n",
    "\n",
    "def coords_to_mask(coords, shape):\n",
    "    \"\"\"\n",
    "    Erstellt eine binäre Maske von einer Liste von Koordinaten.\n",
    "    \"\"\"\n",
    "    mask = np.zeros(shape, dtype=np.uint8)\n",
    "    rr, cc = polygon(coords[:,1], coords[:,0])  # x, y vertauschen\n",
    "    mask[rr, cc] = 1\n",
    "    return mask\n",
    "\n",
    "\n",
    "def calculate_iou(coords1, coords2):\n",
    "    \"\"\"\n",
    "    Berechnet die Intersection over Union (IoU) zwischen zwei Listen von Koordinaten.\n",
    "    Bestimmt die Form basierend auf den maximalen x- und y-Koordinaten.\n",
    "    \"\"\"\n",
    "    max_coord = np.maximum(coords1.max(axis=0), coords2.max(axis=0))\n",
    "    shape = np.ceil(max_coord[::-1] + 1 + 10).astype(int)  # x, y vertauschen und einen Puffer von 10 hinzufügen\n",
    "\n",
    "    mask1 = coords_to_mask(coords1, shape)\n",
    "    mask2 = coords_to_mask(coords2, shape)\n",
    "\n",
    "    intersection = np.logical_and(mask1, mask2)\n",
    "    union = np.logical_or(mask1, mask2)\n",
    "    \n",
    "    if np.sum(union) == 0:\n",
    "        return 0.0\n",
    "    else:\n",
    "        iou_score = np.sum(intersection) / np.sum(union)\n",
    "        return iou_score\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Definiere die Koordinaten der Polygone\n",
    "polygon1 = np.array([[20, 20], [30, 50], [40, 20]])\n",
    "polygon2 = np.array([[30, 30], [40, 45], [50, 60], [40, 75], [30, 90], [20, 75]])\n",
    "polygon3 = np.array([[60, 60], [65, 70], [70, 80], [75, 90], [80, 100], [75, 110], [70, 120], [65, 130], [60, 140]])\n",
    "\n",
    "# Berechne die IoU zwischen den Polygonen\n",
    "iou_score1 = calculate_iou(polygon1, polygon1)\n",
    "iou_score2 = calculate_iou(polygon1, polygon3)\n",
    "iou_score3 = calculate_iou(polygon2, polygon3)\n",
    "\n",
    "# Drucke die IoU-Scores\n",
    "print(f\"IoU score between polygon1 and polygon1: {iou_score1}\")\n",
    "print(f\"IoU score between polygon1 and polygon3: {iou_score2}\")\n",
    "print(f\"IoU score between polygon2 and polygon3: {iou_score3}\")\n",
    "print(polygon1.shape, polygon2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(polygon1[:,0], polygon1[:,1])\n",
    "plt.plot(polygon2[:,0], polygon2[:,1], \"-b.\")\n",
    "plt.plot(polygon3[:,0], polygon3[:,1], \"-r.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import ViTFeatureExtractor, ViTModel\n",
    "feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')  # 244-in21k\n",
    "model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k', return_dict=True)  # 224-in21k\n",
    "\n",
    "filenames = df[\"filename\"].tolist()\n",
    "feature_vectors = []\n",
    "for i, image in enumerate(filenames): \n",
    "    image = Image.open(src_+image)\n",
    "    inputs = feature_extractor(images=image, return_tensors=\"pt\")\n",
    "    outputs = model(**inputs)\n",
    "    feature_vector = outputs.pooler_output.detach().cpu().numpy()\n",
    "    feature_vectors.append(feature_vector)\n",
    "\n",
    "feature_vectors = np.concatenate(feature_vectors, axis=0)\n",
    "#np.save(os.path.join(src_,\"prototypev1-fv-MVI_7423_pulse_wabbern-cropped-224.npy\"), feature_vectors)\n",
    "\n",
    "print(feature_vectors.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fv = np.load(src_ + \"prototypev1-fv-MVI_7423_pulse_wabbern-cropped-224.npy\")\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tsne = TSNE(n_components=2, perplexity=65, early_exaggeration=220, learning_rate=45, random_state=42, init=\"pca\")\n",
    "features_embedded = tsne.fit_transform(fv)\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.scatter(features_embedded[:, 0], features_embedded[:, 1], color=\"w\", alpha=.5, edgecolors=\"none\", s=30)\n",
    "plt.axis(\"square\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_x = [(-25, 5), (5.05, 15)]\n",
    "cluster_idx = []\n",
    "col = [\"w\", \"r\"]\n",
    "plt.figure()\n",
    "for nth, (filt, c) in enumerate(zip(filter_x, col)):\n",
    "    cidx = (features_embedded[:, 0] > filt[0]) & (features_embedded[:, 0] < filt[1])\n",
    "    cluster_idx.append(np.where(cidx)[0])\n",
    "    plt.scatter(features_embedded[cidx, 0], features_embedded[cidx, 1], color=c, alpha=.5, edgecolors=\"none\", s=30)\n",
    "    plt.axis(\"square\")\n",
    "\n",
    "\n",
    "# VIDEO\n",
    "filenames = df[\"filename\"].tolist()\n",
    "for nth, cid in enumerate(cluster_idx):\n",
    "    cfilenames = np.array(filenames)[cid]\n",
    "    frame = cv2.imread(os.path.join(src_, cfilenames[0]))\n",
    "    height, width, layers = frame.shape\n",
    "\n",
    "    video = cv2.VideoWriter(src_ + f'video_{nth}.avi', 0, 25, (width, height))\n",
    "    for image in cfilenames:\n",
    "        video.write(cv2.imread(os.path.join(src_, image)))\n",
    "    cv2.destroyAllWindows()\n",
    "    video.release()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get all annotation files and extract all seg masks / or transfered to bbox\n",
    "- Thrn use IoU to determine which LEDs overlap, visualize and recheck\n",
    "- set a threshold on IoU to merge LEDs together and stick with that final set of LEDs over all frames!!!\n",
    "- then integrate, mean, area-normalize integrated intensity values from images / bbox cut outs\n",
    "\n",
    "- HAS TO BE DONE ON ORIGINAL EXTRACTED IMAGES AND TXT ANNOTATIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importiere die notwendigen Bibliotheken\n",
    "import numpy as np\n",
    "from skimage.draw import polygon\n",
    "\n",
    "# Definiere die Funktionen zur Erstellung der Maske und zur Berechnung der IoU\n",
    "def coords_to_mask(coords, shape):\n",
    "    \"\"\"\n",
    "    Erstellt eine binäre Maske von einer Liste von Koordinaten.\n",
    "    \"\"\"\n",
    "    mask = np.zeros(shape, dtype=np.uint8)\n",
    "    rr, cc = polygon(coords[:,0], coords[:,1])\n",
    "    mask[rr, cc] = 1\n",
    "    return mask\n",
    "\n",
    "def calculate_iou(coords1, coords2, shape):\n",
    "    \"\"\"\n",
    "    Berechnet die Intersection over Union (IoU) zwischen zwei Listen von Koordinaten.\n",
    "    \"\"\"\n",
    "    mask1 = coords_to_mask(coords1, shape)\n",
    "    mask2 = coords_to_mask(coords2, shape)\n",
    "    intersection = np.logical_and(mask1, mask2)\n",
    "    union = np.logical_or(mask1, mask2)\n",
    "    iou_score = np.sum(intersection) / np.sum(union)\n",
    "    return iou_score\n",
    "\n",
    "# Definiere die Koordinaten der Polygone\n",
    "polygon1 = np.array([[20, 20], [30, 50], [40, 20]])\n",
    "polygon2 = np.array([[30, 30], [40, 60], [50, 30]])\n",
    "polygon3 = np.array([[60, 60], [70, 80], [80, 60]])\n",
    "\n",
    "# Berechne die IoU zwischen den Polygonen\n",
    "iou_score1 = calculate_iou(polygon1, polygon2, (100, 100))\n",
    "iou_score2 = calculate_iou(polygon1, polygon3, (100, 100))\n",
    "iou_score3 = calculate_iou(polygon2, polygon3, (100, 100))\n",
    "\n",
    "# Drucke die IoU-Scores\n",
    "print(f\"IoU score between polygon1 and polygon2: {iou_score1}\")\n",
    "print(f\"IoU score between polygon1 and polygon3: {iou_score2}\")\n",
    "print(f\"IoU score between polygon2 and polygon3: {iou_score3}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#target_dir = \"./totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/\"\n",
    "#os.makedirs(os.path.join(target_dir, \"images/renamed/\"), exist_ok=True)\n",
    "\n",
    "# load all annot files and structure data\n",
    "\n",
    "\n",
    "# for vis_class in num_vis_classes:\n",
    "#     frames = df_[\"filename\"].tolist()  # sorted([f for f in os.listdir(image_dir) if f.endswith(\".jpg\")])\n",
    "#     annots = df_[\"filename\"].str.replace(\".jpg\", \".txt\")#.tolist()  #sorted([f for f in os.listdir(image_dir) if f.endswith(\".txt\")])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_dir = \"./totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/\"\n",
    "os.makedirs(os.path.join(target_dir, \"images/renamed/\"), exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "target_dir = \"./totem-prototype/videos/MVI_7423_pulse_wabbern-cropped/\"\n",
    "\n",
    "# Specify the classes\n",
    "num_vis_classes = [\"0\"]  # Add or remove class ids here\n",
    "#os.makedirs(os.path.join(target_dir, f\"cutout-bbox-class{0}/\"), exist_ok=True)\n",
    "\n",
    "# Specify your directories\n",
    "image_dir = os.path.join(target_dir, \"images/renamed/\")\n",
    "\n",
    "for vis_class in num_vis_classes:\n",
    "    frames = df_[\"filename\"].tolist()  # sorted([f for f in os.listdir(image_dir) if f.endswith(\".jpg\")])\n",
    "    annots = df_[\"filename\"].str.replace(\".jpg\", \".txt\")#.tolist()  #sorted([f for f in os.listdir(image_dir) if f.endswith(\".txt\")])\n",
    "\n",
    "    # Print the number of images and annotations found\n",
    "    print(f\"Found {len(frames)} frames and {len(annots)} annotation files.\",\n",
    "          f\"{frames[:4]},{annots[:4]}\")\n",
    "\n",
    "    #Iterate through the images and annotations\n",
    "    for idx, frame in enumerate(frames):\n",
    "        im = Image.open(os.path.join(image_dir, frame))\n",
    "        im_width, im_height = im.size  # Image dimensions\n",
    "\n",
    "        with open(os.path.join(image_dir, annots[idx]), 'r') as f:\n",
    "            lines = f.read().splitlines()\n",
    "            for nth, line in enumerate(lines):\n",
    "                # Split the line into a list of values\n",
    "                values = list(map(float, line.strip().split()))\n",
    "                # Extract and process each class separately\n",
    "                while values:\n",
    "                    # Get the class ID and remove it from the list\n",
    "                    class_id = int(values.pop(0))\n",
    "                    # Only process the specified classes\n",
    "                    if str(class_id) not in num_vis_classes:\n",
    "                        break\n",
    "\n",
    "                    # Calculate the bounding box coordinates from the segmentation mask\n",
    "                    x_values = []\n",
    "                    y_values = []\n",
    "                    while values and len(values) >= 2:\n",
    "                        x_values.append(values.pop(0))\n",
    "                        y_values.append(values.pop(0))\n",
    "                    x1 = int(min(x_values) * im_width)\n",
    "                    y1 = int(min(y_values) * im_height)\n",
    "                    x2 = int(max(x_values) * im_width)\n",
    "                    y2 = int(max(y_values) * im_height)\n",
    "\n",
    "                    # Extract and save the bounding box as a separate image\n",
    "                    bbox_image = im.crop((x1, y1, x2, y2))\n",
    "                    os.makedirs(os.path.join(target_dir, f\"cutout-bbox-class{nth}/\"), exist_ok=True)\n",
    "                    bbox_image.save(os.path.join(target_dir, f\"cutout-bbox-class{nth}/\" + frame), \"JPEG\", quality=95)\n",
    "\n",
    "    # for idx, frame in enumerate(frames):\n",
    "    #         im = Image.open(os.path.join(image_dir, frame))\n",
    "    #         im_width, im_height = im.size  # Image dimensions\n",
    "    #         with open(os.path.join(image_dir, annots[idx]), 'r') as f:\n",
    "    #             for _, line in enumerate(f):\n",
    "    #                 #print(line.strip().split()[0])\n",
    "    #                 if int(line.strip().split()[0]) == int(vis_class):\n",
    "    #                     print(int(line.strip().split()[0]))\n",
    "    #                     print(len(line.strip().split()), frame)\n",
    "    #                     class_id, x_center, y_center, width, height = map(float, line.strip().split())\n",
    "    #                     class_id = int(class_id)\n",
    "            \n",
    "    #                     x1 = int((x_center - width / 2.) * im_width)\n",
    "    #                     y1 = int((y_center - height / 2.) * im_height)\n",
    "    #                     x2 = int((x_center + width / 2.) * im_width)\n",
    "    #                     y2 = int((y_center + height / 2.) * im_height)\n",
    "\n",
    "    #                     # Extract and save the bounding box as a separate image\n",
    "    #                     bbox_image = im.crop((x1, y1, x2, y2))\n",
    "                            #bbox_image = bbox_image.resize((224,224))\n",
    "                            #bbox_image.save(f\"./data/videos/fusion-2014/class{vis_class}-video/{os.path.basename(frame).replace('.jpg', '')}.jpg\", \"JPEG\", quality=95)\n",
    "\n",
    "                            # Save the image\n",
    "                        #bbox_image.save(os.path.join(\"./data/videos/VID-20230703-WA0028/annotated-roboflow-v12/images/\", f\"cutout-bbox-class{class_id}/frame_{str(idx).zfill(3)}.jpg\"), \"JPEG\", quality=95)\n"
   ]
  }
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