{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-04-02T19:20:43.810656Z",
     "start_time": "2024-04-02T19:20:43.798646Z"
    }
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import os\n",
    "import time\n",
    "import shutil\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-04-02T19:20:44.354065Z",
     "start_time": "2024-04-02T19:20:44.350075Z"
    }
   },
   "outputs": [],
   "source": [
    "class Object(object):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-04-02T19:20:44.905729Z",
     "start_time": "2024-04-02T19:20:44.886725Z"
    }
   },
   "outputs": [],
   "source": [
    "def extract_images(video_path, save_path):\n",
    "    dsize = (512, 512)\n",
    "    video_cap = cv2.VideoCapture(video_path)\n",
    "    success, image = video_cap.read()\n",
    "    frame_count = 0\n",
    "    while success:\n",
    "        frame_save_path = os.path.join(save_path, 'img{0}.jpg'.format(str(frame_count).zfill(6)))\n",
    "        #do pseudocoloring\n",
    "        # cv2.imwrite(frame_save_path,  cv2.applyColorMap(image, cv2.COLORMAP_JET))\n",
    "        #resize image to (512, 512)\n",
    "        # output = cv2.resize(image, dsize)\n",
    "        cv2.imwrite(frame_save_path, image)\n",
    "        success, image = video_cap.read()\n",
    "        frame_count +=1\n",
    "    # count frames for each video\n",
    "    with open(os.path.join(save_path, 'n_frames'), 'w') as file:\n",
    "        file.write(str(frame_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-04-02T19:20:45.440615Z",
     "start_time": "2024-04-02T19:20:45.423612Z"
    }
   },
   "outputs": [],
   "source": [
    "def split_to_train_test(root_directory_path, frames = 'frames_1'):\n",
    "    frames_path = os.path.join(root_directory_path, frames)\n",
    "    print(frames_path)\n",
    "    current_list = os.listdir(frames_path)\n",
    "    train_frame_count = 0\n",
    "    test_frame_count = 0\n",
    "\n",
    "    train_save_path = os.path.join(root_directory_path, 'train')\n",
    "    if not os.path.exists(train_save_path):\n",
    "        os.makedirs(train_save_path)\n",
    "\n",
    "    test_save_path = os.path.join(root_directory_path, 'test')\n",
    "    if not os.path.exists(test_save_path):\n",
    "        os.makedirs(test_save_path)\n",
    "\n",
    "    for cur_frames in current_list:\n",
    "        cur_frames_path = os.path.join(frames_path, cur_frames)\n",
    "        n_frames = 0\n",
    "        with open(os.path.join(cur_frames_path, 'n_frames'), 'r') as file:\n",
    "            n_frames = int(file.readline())\n",
    "\n",
    "        test = round(n_frames / 3)\n",
    "        train = n_frames - test\n",
    "\n",
    "        for i in range(train):\n",
    "            frame_path = os.path.join(cur_frames_path, 'img{0}.jpg'.format(str(i).zfill(6)))\n",
    "            frame_save_path = os.path.join(train_save_path, 'img{0}.jpg'.format(str(train_frame_count).zfill(6)))\n",
    "            shutil.copyfile(frame_path, frame_save_path)\n",
    "            train_frame_count +=1\n",
    "\n",
    "        for i in range(train, n_frames):\n",
    "            frame_path = os.path.join(cur_frames_path, 'img{0}.jpg'.format(str(i).zfill(6)))\n",
    "            frame_save_path = os.path.join(test_save_path, 'img{0}.jpg'.format(str(test_frame_count).zfill(6)))\n",
    "            shutil.copyfile(frame_path, frame_save_path)\n",
    "            test_frame_count +=1\n",
    "\n",
    "    with open(os.path.join(train_save_path, 'n_frames'), 'w') as file:\n",
    "        file.write(str(train_frame_count)) \n",
    "\n",
    "    with open(os.path.join(test_save_path, 'n_frames'), 'w') as file:\n",
    "        file.write(str(test_frame_count)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-04-02T19:20:47.327649Z",
     "start_time": "2024-04-02T19:20:47.307015Z"
    }
   },
   "outputs": [],
   "source": [
    "def main(opt):\n",
    "\n",
    "    current_video_list = os.listdir(opt.video_root_directory_path)\n",
    "    num_video = 0\n",
    "    for video in current_video_list:\n",
    "        video_source_path = os.path.join(opt.video_root_directory_path, video)\n",
    "        video_save_path = os.path.join(opt.save_root_directory_path, '{0}'.format((video.split('.')[0])))\n",
    "        if not os.path.exists(video_save_path):\n",
    "            os.makedirs(video_save_path)\n",
    "        # Раскадровка\n",
    "        extract_images(video_source_path, video_save_path)\n",
    "       # деление на тренировочную и тестовую выборки\n",
    "        split_to_train_test(opt.root_directory_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-04-02T19:29:45.945320Z",
     "start_time": "2024-04-02T19:20:51.982552Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Storyboard started...\n",
      "D:\\current\\lepidicid\\dataset\\frames_1\n",
      "D:\\current\\lepidicid\\dataset\\frames_1\n",
      "Total time: 9 minutes\n",
      "Storyboard ended success!\n"
     ]
    }
   ],
   "source": [
    "opt = Object()\n",
    "opt.video_root_directory_path = r'D:\\current\\lepidicid\\dataset\\video_1'\n",
    "opt.save_root_directory_path = r'D:\\current\\lepidicid\\dataset\\frames_1'\n",
    "opt.root_directory_path = r'D:\\current\\lepidicid\\dataset'\n",
    "print('Storyboard started...')\n",
    "total_start = time.time()\n",
    "main(opt)\n",
    "print('Total time: ' + str(round((time.time() - total_start) / 60)) + ' minutes')\n",
    "print('Storyboard ended success!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-04-02T19:20:40.621444Z"
    }
   },
   "outputs": [],
   "source": [
    "'Storyboard started... \\\n",
    "Total time: 73 minutes \\\n",
    "Storyboard ended success!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-04-02T19:20:40.601378Z"
    }
   },
   "outputs": [],
   "source": [
    "'Storyboard started... \\\n",
    "Total time: 58 minutes \\\n",
    "Storyboard ended success!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-04-02T19:20:40.626462Z"
    }
   },
   "outputs": [],
   "source": [
    "'Storyboard started... \\\n",
    "Total time: 22 minutes \\\n",
    "Storyboard ended success!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-04-02T19:20:40.629443Z"
    }
   },
   "outputs": [],
   "source": [
    "'Storyboard started... \\\n",
    "Total time: 17 minutes \\\n",
    "Storyboard ended success!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-04-02T19:20:40.630442Z"
    }
   },
   "outputs": [],
   "source": []
  }
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