python/slide_detect_pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe99333b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard PySceneDetect imports:\n",
    "from scenedetect import VideoManager\n",
    "from scenedetect import SceneManager\n",
    "from scenedetect.stats_manager import StatsManager\n",
    "\n",
    "# For content-aware scene detection:\n",
    "from scenedetect.detectors import ContentDetector, ThresholdDetector\n",
    "from customDetector import StdDetector\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1fcfd79a",
   "metadata": {},
   "outputs": [],
   "source": [
    "detector1 = ContentDetector(threshold=4, luma_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c83abbfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "detector = StdDetector(skip=50, threshold=10, average=20, window=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "963ff440",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_scenes(video_path, detector=None):\n",
    "    # Create our video & scene managers, then add the detector.\n",
    "    video_manager = VideoManager([video_path])\n",
    "    stats_manager = StatsManager()\n",
    "    scene_manager = SceneManager(stats_manager)\n",
    "    \n",
    "    if detector is None:\n",
    "        detector = StdDetector()\n",
    "    scene_manager.add_detector(detector)\n",
    "\n",
    "    # Improve processing speed by downscaling before processing.\n",
    "    video_manager.set_downscale_factor()\n",
    "    \n",
    "    # Start the video manager and perform the scene detection.\n",
    "    video_manager.start()\n",
    "    scene_manager.detect_scenes(frame_source=video_manager)\n",
    "    \n",
    "    # Each returned scene is a tuple of the (start, end) timecode.\n",
    "    return scene_manager.get_scene_list(), stats_manager\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2748b51a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████▉| 2012/2015 [00:21<00:00, 91.88frames/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 [(FrameTimecode(frame=0, fps=59.004392), FrameTimecode(frame=637, fps=59.004392)), (FrameTimecode(frame=637, fps=59.004392), FrameTimecode(frame=756, fps=59.004392)), (FrameTimecode(frame=756, fps=59.004392), FrameTimecode(frame=1141, fps=59.004392)), (FrameTimecode(frame=1141, fps=59.004392), FrameTimecode(frame=1479, fps=59.004392)), (FrameTimecode(frame=1479, fps=59.004392), FrameTimecode(frame=1509, fps=59.004392)), (FrameTimecode(frame=1509, fps=59.004392), FrameTimecode(frame=1798, fps=59.004392)), (FrameTimecode(frame=1798, fps=59.004392), FrameTimecode(frame=1939, fps=59.004392)), (FrameTimecode(frame=1939, fps=59.004392), FrameTimecode(frame=2012, fps=59.004392))]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "scenes, stats = find_scenes('../test/SlideChangeTest1.mov', detector = detector1)\n",
    "print(len(scenes), scenes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "110ed7d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scores(stats_manager):\n",
    "    frame_axis = []\n",
    "    metrics = {}\n",
    "    for key in stats_manager._registered_metrics:\n",
    "        metrics[key] = []\n",
    "\n",
    "    for frame, val_dict in stats_manager._frame_metrics.items():\n",
    "        frame_axis.append(frame)\n",
    "        for key in stats_manager._registered_metrics:\n",
    "            metrics[key].append(val_dict[key])\n",
    "\n",
    "\n",
    "    for key in stats_manager._registered_metrics:\n",
    "        plt.plot(frame_axis, metrics[key], label = key)      \n",
    "\n",
    "#     if expected is not None:\n",
    "#         for x in expected:\n",
    "#             plt.vlines(x, 0, 50)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba8c7f37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51f92957",
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_frames(scenes, method=\"last\"):\n",
    "    assert method in [\"last\", \"first\", \"middle\"]\n",
    "    \n",
    "    if method == \"last\":\n",
    "        return [x[1].get_frames()-1 for x in scenes]\n",
    "    elif method == \"first\":\n",
    "        return [x[0].get_frames() for x in scenes]\n",
    "    elif method == \"middle\":\n",
    "        return [(x[0].get_frames()+x[1].get_frames())//2 for x in scenes]\n",
    "    \n",
    "    return []\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b6be0852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[225, 475, 621]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame_lst = select_frames(scenes, method=\"middle\")\n",
    "frame_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3d0ff44f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def export_frames(file, frames):\n",
    "    cap = cv2.VideoCapture(file)\n",
    "    \n",
    "    # Find OpenCV version\n",
    "    (major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')\n",
    "    \n",
    "    if int(major_ver)  < 3 :\n",
    "        fps = cap.get(cv2.cv.CV_CAP_PROP_FPS)\n",
    "    else :\n",
    "        fps = cap.get(cv2.CAP_PROP_FPS)\n",
    "    \n",
    "    length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "    i = 0\n",
    "    while (cap.isOpened() and i < len(frames)):\n",
    "        j = frames[i]\n",
    "        seconds = \"{:.3f}\".format(j / 25)\n",
    "        cap.set(1, j)\n",
    "        ret, frame = cap.read()\n",
    "        if ret == False:\n",
    "            break\n",
    "        \n",
    "#         cv.imwrite(\"./trainingDataPreprocessing/img_y/\" + \"{:04d}\".format(i) + \".png\", frame)\n",
    "        cv2.imwrite(\"vv/p\"+str(i) + \".bmp\", frame)\n",
    "        print(j, \"p\", i)\n",
    "        i += 1\n",
    "        \n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4e3ed039",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000 p 0\n",
      "4050 p 1\n",
      "4200 p 2\n",
      "4950 p 3\n",
      "5625 p 4\n",
      "6025 p 5\n",
      "6475 p 6\n",
      "6900 p 7\n",
      "7525 p 8\n",
      "10575 p 9\n",
      "17544 p 10\n"
     ]
    }
   ],
   "source": [
    "export_frames('../test/SlideChangeTest2.mp4', sorted(frame_lst))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "968a3cf1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "expected = np.array([10, 12, 19, 25, 32, 34]) * 2010 / 34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6dc84e99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 591.17647059,  709.41176471, 1123.23529412, 1477.94117647,\n",
       "       1891.76470588, 2010.        ])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c0cbb0a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from slideDetect import SlideDetect\n",
    "from util import FileLocator\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9bbb98b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"../test/SlideChangeTest2.mp4\"\n",
    "locator = FileLocator(filename, os.getcwd() + \"/output12/temp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4171cc4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame_lst = [x[1].get_frames() for x in scenes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "72d22297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1460.8"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "36520/25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c05d0c23",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'start_time': 0,\n",
       "  'end_time': 160000,\n",
       "  'image_time': 52680,\n",
       "  'image': '/Users/kcluo/PycharmProjects/thoth/python/output12/temp/image/SlideChangeTest2_mp4_52680.png'},\n",
       " {'start_time': 160000,\n",
       "  'end_time': 164000,\n",
       "  'image_time': 161920,\n",
       "  'image': '/Users/kcluo/PycharmProjects/thoth/python/output12/temp/image/SlideChangeTest2_mp4_161920.png'},\n",
       " {'start_time': 164000,\n",
       "  'end_time': 172000,\n",
       "  'image_time': 164680,\n",
       "  'image': '/Users/kcluo/PycharmProjects/thoth/python/output12/temp/image/SlideChangeTest2_mp4_164680.png'},\n",
       " {'start_time': 172000,\n",
       "  'end_time': 224000,\n",
       "  'image_time': 205240,\n",
       "  'image': '/Users/kcluo/PycharmProjects/thoth/python/output12/temp/image/SlideChangeTest2_mp4_205240.png'},\n",
       " {'start_time': 224000,\n",
       "  'end_time': 226000,\n",
       "  'image_time': 225080,\n",
       "  'image': '/Users/kcluo/PycharmProjects/thoth/python/output12/temp/image/SlideChangeTest2_mp4_225080.png'},\n",
       " {'start_time': 226000,\n",
       "  'end_time': 256000,\n",
       "  'image_time': 235240,\n",
       "  'image': '/Users/kcluo/PycharmProjects/thoth/python/output12/temp/image/SlideChangeTest2_mp4_235240.png'},\n",
       " {'start_time': 256000,\n",
       "  'end_time': 262000,\n",
       "  'image_time': 256840,\n",
       "  'image': '/Users/kcluo/PycharmProjects/thoth/python/output12/temp/image/SlideChangeTest2_mp4_256840.png'},\n",
       " {'start_time': 262000,\n",
       "  'end_time': 290000,\n",
       "  'image_time': 264040,\n",
       "  'image': '/Users/kcluo/PycharmProjects/thoth/python/output12/temp/image/SlideChangeTest2_mp4_264040.png'},\n",
       " {'start_time': 290000,\n",
       "  'end_time': 312000,\n",
       "  'image_time': 300040,\n",
       "  'image': '/Users/kcluo/PycharmProjects/thoth/python/output12/temp/image/SlideChangeTest2_mp4_300040.png'},\n",
       " {'start_time': 312000,\n",
       "  'end_time': 534000,\n",
       "  'image_time': 386440,\n",
       "  'image': '/Users/kcluo/PycharmProjects/thoth/python/output12/temp/image/SlideChangeTest2_mp4_386440.png'},\n",
       " {'start_time': 534000,\n",
       "  'end_time': 869560,\n",
       "  'image_time': 696040,\n",
       "  'image': '/Users/kcluo/PycharmProjects/thoth/python/output12/temp/image/SlideChangeTest2_mp4_696040.png'}]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SlideDetect.export_frames(locator, frame_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a1384eab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/kcluo/PycharmProjects/thoth/python'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4062b245",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [{\"start_time\": 0, \"end_time\": 10795, \"image_time\": 33, \"image\": \"/Users/kcluo/PycharmProjects/thoth/output66/temp/image/SlideChangeTest1_mov_33.png\"}, {\"start_time\": 10795, \"end_time\": 12812, \"image_time\": 10812, \"image\": \"/Users/kcluo/PycharmProjects/thoth/output66/temp/image/SlideChangeTest1_mov_10812.png\"}, {\"start_time\": 12812, \"end_time\": 19337, \"image_time\": 12829, \"image\": \"/Users/kcluo/PycharmProjects/thoth/output66/temp/image/SlideChangeTest1_mov_12829.png\"}, {\"start_time\": 19337, \"end_time\": 25065, \"image_time\": 19354, \"image\": \"/Users/kcluo/PycharmProjects/thoth/output66/temp/image/SlideChangeTest1_mov_19354.png\"}, {\"start_time\": 25065, \"end_time\": 25574, \"image_time\": 25082, \"image\": \"/Users/kcluo/PycharmProjects/thoth/output66/temp/image/SlideChangeTest1_mov_25082.png\"}, {\"start_time\": 25574, \"end_time\": 30472, \"image_time\": 25591, \"image\": \"/Users/kcluo/PycharmProjects/thoth/output66/temp/image/SlideChangeTest1_mov_25591.png\"}, {\"start_time\": 30472, \"end_time\": 32861, \"image_time\": 30489, \"image\": \"/Users/kcluo/PycharmProjects/thoth/output66/temp/image/SlideChangeTest1_mov_30489.png\"}, {\"start_time\": 32861, \"end_time\": 34099, \"image_time\": 32878, \"image\": \"/Users/kcluo/PycharmProjects/thoth/output66/temp/image/SlideChangeTest1_mov_32878.png\"}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3bc9c676",
   "metadata": {},
   "outputs": [],
   "source": [
    "ff = [x['image_time']//40 for x in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bef0b78c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1317, 4048, 4117, 5131, 5627, 5881, 6421, 6601, 7501, 9661, 17401]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6bcdfc53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1317 p 0\n",
      "4048 p 1\n",
      "4117 p 2\n",
      "5131 p 3\n",
      "5627 p 4\n",
      "5881 p 5\n",
      "6421 p 6\n",
      "6601 p 7\n",
      "7501 p 8\n",
      "9661 p 9\n",
      "17401 p 10\n"
     ]
    }
   ],
   "source": [
    "export_frames('../test/SlideChangeTest2.mp4', sorted(ff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aadade09",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch",
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