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.gitattributes

@@ -53,3 +53,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+video_1/Bacillus_thuringiensis_var_kurstaki_Z_52_1.mp4 filter=lfs diff=lfs merge=lfs -text
+video_1/Bacillus_thuringiensis_var_kurstaki_Z_52_2.mp4 filter=lfs diff=lfs merge=lfs -text

extract_frames_from_videos.ipynb

@@ -0,0 +1,258 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": [],
+    "ExecuteTime": {
+     "end_time": "2024-03-22T20:03:54.743603Z",
+     "start_time": "2024-03-22T20:03:54.736584Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import os\n",
+    "import time\n",
+    "import shutil\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": [],
+    "ExecuteTime": {
+     "end_time": "2024-03-22T20:03:55.436507Z",
+     "start_time": "2024-03-22T20:03:55.419464Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Object(object):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": [],
+    "ExecuteTime": {
+     "end_time": "2024-03-22T20:03:56.190578Z",
+     "start_time": "2024-03-22T20:03:56.167941Z"
+    }
+   },
+   "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": 10,
+   "metadata": {
+    "tags": [],
+    "ExecuteTime": {
+     "end_time": "2024-03-22T20:03:57.682935Z",
+     "start_time": "2024-03-22T20:03:57.662276Z"
+    }
+   },
+   "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\\\\lepidicid')\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\\\\lepidicid')\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": 11,
+   "metadata": {
+    "tags": [],
+    "ExecuteTime": {
+     "end_time": "2024-03-22T20:03:58.405510Z",
+     "start_time": "2024-03-22T20:03:58.395452Z"
+    }
+   },
+   "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": 12,
+   "metadata": {
+    "tags": [],
+    "ExecuteTime": {
+     "end_time": "2024-03-22T20:17:35.178001Z",
+     "start_time": "2024-03-22T20:03:59.230421Z"
+    }
+   },
+   "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: 14 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": {},
+   "outputs": [],
+   "source": [
+    "'Storyboard started... \\\n",
+    "Total time: 73 minutes \\\n",
+    "Storyboard ended success!'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'Storyboard started... \\\n",
+    "Total time: 58 minutes \\\n",
+    "Storyboard ended success!'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'Storyboard started... \\\n",
+    "Total time: 22 minutes \\\n",
+    "Storyboard ended success!'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'Storyboard started... \\\n",
+    "Total time: 17 minutes \\\n",
+    "Storyboard ended success!'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}