Quickly adding log-chroma analysis files, restructure to follow.

viviehn · Dec 3, 2019 · 5bf9414 · 5bf9414
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diff --git a/temporal/find_cuts.ipynb b/temporal/find_cuts.ipynb
@@ -0,0 +1,155 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install Dependencies\n",
+    "#!pip install scikit-image\n",
+    "#!pip install scenedetect[opencv,progress_bar]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import io\n",
+    "import os\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "from skimage import color\n",
+    "import cv2\n",
+    "\n",
+    "import scenedetect\n",
+    "from scenedetect.video_manager import VideoManager\n",
+    "from scenedetect.scene_manager import SceneManager\n",
+    "from scenedetect.frame_timecode import FrameTimecode\n",
+    "from scenedetect.stats_manager import StatsManager\n",
+    "from scenedetect.detectors import ContentDetector\n",
+    "\n",
+    "STATS_FILE_PATH = 'testvideo.stats.csv'\n",
+    "dataset_folder = \"../../data/\"\n",
+    "cuts_folder = \"../../data/cuts/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Find video cuts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_video_cuts(dataset_folder, cuts_folder):\n",
+    "    videos_to_detect = [\n",
+    "        os.path.join(dataset_folder, f)\n",
+    "        for f \n",
+    "        in os.listdir(dataset_folder)\n",
+    "        if os.path.isfile(os.path.join(dataset_folder, f))\n",
+    "    ]\n",
+    "    for i, video_dir in enumerate(videos_to_detect):\n",
+    "        print(f\"Finding cuts of Video {video_dir}: {i+1}/{len(videos_to_detect)}\", end='\\r')\n",
+    "\n",
+    "        # Check for already existing files and skip if already calculated\n",
+    "        yt_id = vid_file.split('.')[0]\n",
+    "        vid_cut_file = f'{yt_id}_cuts.npy'\n",
+    "        vid_cut_dir = os.path.join(cuts_folder, vid_cut_file)\n",
+    "        if os.path.exists(vid_cut_dir):\n",
+    "            continue\n",
+    "\n",
+    "        # Create a video_manager point to video file testvideo.mp4. Note that multiple\n",
+    "        # videos can be appended by simply specifying more file paths in the list\n",
+    "        # passed to the VideoManager constructor. Note that appending multiple videos\n",
+    "        # requires that they all have the same frame size, and optionally, framerate.\n",
+    "        try:\n",
+    "            video_manager = VideoManager([video_dir])\n",
+    "            stats_manager = StatsManager()\n",
+    "            scene_manager = SceneManager(stats_manager)\n",
+    "            # Add ContentDetector algorithm (constructor takes detector options like threshold).\n",
+    "            scene_manager.add_detector(ContentDetector())\n",
+    "            base_timecode = video_manager.get_base_timecode()\n",
+    "\n",
+    "            try:\n",
+    "                # Set downscale factor to improve processing speed (no args means default).\n",
+    "                video_manager.set_downscale_factor()\n",
+    "\n",
+    "                # Start video_manager.\n",
+    "                video_manager.start()\n",
+    "\n",
+    "                # Perform scene detection on video_manager.\n",
+    "                scene_manager.detect_scenes(frame_source=video_manager)\n",
+    "\n",
+    "                # Obtain list of detected scenes.\n",
+    "                scene_list = scene_manager.get_scene_list(base_timecode)\n",
+    "                # Like FrameTimecodes, each scene in the scene_list can be sorted if the\n",
+    "                # list of scenes becomes unsorted.\n",
+    "\n",
+    "                times = np.array([s[0].get_frames() for s in scene_list])\n",
+    "                np.save(vid_cut_dir, times)\n",
+    "            finally:\n",
+    "                video_manager.release()\n",
+    "        except:\n",
+    "            pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find Cinematic Video Cuts\n",
+    "find_video_cuts(\n",
+    "    dataset_folder=\"../../data/\",\n",
+    "    cuts_folder=\"../../data/cuts/\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find Casual Video Cuts\n",
+    "find_video_cuts(\n",
+    "    dataset_folder=\"../../data/casual/\",\n",
+    "    cuts_folder=\"../../data/casual/cuts/\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}