dataset_handlemultiembers_py.ipynb

{
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  "metadata": {
    "colab": {
      "name": "dataset_handlemultiembers.py",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "mount_file_id": "1CRpre_sTWlynM5mBtSb7BpUO9vZ5bVON",
      "authorship_tag": "ABX9TyPGE7RkjqzfbXH4GX/2mjYI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ERGOWHO/emberdatect_python_dataset/blob/main/dataset_handlemultiembers_py.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# Step 1: initialize & Setup\n",
        "\n"
      ],
      "metadata": {
        "id": "g5XlRnJCZmow"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 1.1: Link to GOOGLE DRIVE\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "[reference](https://zhuanlan.zhihu.com/p/109634905)\n"
      ],
      "metadata": {
        "id": "chHeUyrdK3ey"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eEtzBKa14eA1",
        "outputId": "33566a6f-33c4-4b16-b836-2c6630b829ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 1.2: Define folder paths"
      ],
      "metadata": {
        "id": "kh9NZdKtvMJw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## handle current folder path\n",
        "import os\n",
        "import re\n",
        "import math\n",
        "import json\n",
        "import time \n",
        "import shutil\n",
        "import cv2\n",
        "from google.colab import output\n",
        "import imageio\n",
        "import os.path as osp\n",
        "\n",
        "\n",
        "#modify here only\n",
        "filename = 'scenes5_2ember_daytime'\n",
        "PATH = '/content/drive/MyDrive/lab/embertrackingdataset/model_evaluation_tests/withforest_nofire/scenes5nofire/multiembers'\n",
        "output_path = PATH + '/imgs/'\n",
        "imgs_with_bbox_path = PATH + '/imgswithbbox/'\n",
        "\n",
        "################\n",
        "#input files:\n",
        "################\n",
        "file_path = PATH + '/' + filename + '.txt' # input data file\n",
        "video_path = PATH + '/' + filename + 'ProceduralForest.avi'  # input video file\n",
        "#mark_img_path = PATH+ '/' + filename + 'ProceduralForest_Moment.jpg' #input jpg\n",
        "\n",
        "################\n",
        "#Output files:\n",
        "################\n",
        "#marked_imgoutput_path = PATH + '/ember_straces.png' #\n",
        "#check file already exists\n",
        "folder1 = os.path.exists(output_path)\n",
        "if not folder1: #create new folder if not exists\n",
        "  os.makedirs(output_path) #video slip frams (imgs folder)\n",
        "#folder2 = os.path.exists(imgs_with_bbox_path)\n",
        "#if not folder2:\n",
        "  #os.makedirs(imgs_with_bbox_path) #imgs with bbox for checking\n",
        "\n",
        "path_jsonfile = PATH + '/' + filename + '.json'\n",
        "\n",
        "path_x_ember = PATH + '/x_ember.txt'\n",
        "path_y_ember = PATH + '/y_ember.txt'\n",
        "path_id_ember = PATH + '/id_ember.txt'\n",
        "\n",
        "####################\n",
        "#get current time\n",
        "####################\n",
        "\n",
        "data_cap = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n",
        "\n"
      ],
      "metadata": {
        "id": "w7AEReQ-K9fL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 1.3:  Read data from UE4 log\n",
        "#save data to list\n"
      ],
      "metadata": {
        "id": "IBjG5sdS4Y0o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open(file_path, \"r\", encoding=\"utf-8\") as f:\n",
        "  lines = f.readlines()\n",
        "\n",
        "##Setting data from UE4, 1 frame 1 data, \n",
        "#if there is x ember co-exist at current frame, \n",
        "#there will be x data at the current frame\n",
        "UE4_Life_time = 10\n",
        "UE4_Loop_during = 2\n",
        "UE4_Samplerate = 30 # unit: frames per sencond\n",
        "Max_ember_onscreen = UE4_Life_time / UE4_Loop_during # the max number of embers on screen at same time\n",
        "Frames_per_ember = UE4_Life_time * UE4_Samplerate\n",
        "One_loop_frames = UE4_Loop_during * UE4_Samplerate # Start a new loop (generate a new ember) every 'One_loop_frames' frames \n",
        "#One_ember_lifetime_frames = UE4_Life_time / UE4_Loop_during * One_loop_frames\n",
        "\n",
        "\n",
        "k = 0\n",
        "num1 = 0\n",
        "ScreenLoc = []\n",
        "UniqueID = []\n",
        "NormalizedAge = []\n",
        "Loc_3D = []\n",
        "Bbox_vertex_3D = []\n",
        "\n",
        "for k in range(len(lines)-int(len(lines) % 3)):\n",
        "  if num1 == 1:\n",
        "    ScreenLoc.append(re.findall('[+-]?\\d+\\.?\\d*',lines[k]))\n",
        "  elif num1 == 2:\n",
        "    NormalizedAge.append(re.findall('[+-]?\\d+\\.?\\d*',lines[k]))\n",
        "  else:\n",
        "    num1 = 0\n",
        "    UniqueID.append(re.findall('[+-]?\\d+\\.?\\d*',lines[k]))\n",
        "  num1 += 1\n",
        "\n",
        "i=0\n",
        "x_ember = []\n",
        "y_ember = []\n",
        "id_ember = []\n",
        "\n",
        "scale_x = []\n",
        "scale_y = []\n",
        "scale_z = []\n",
        "mesh_size = []\n",
        "\n",
        "for i in range(len(ScreenLoc)):\n",
        "  x_ember.append(float(ScreenLoc[i][2]))\n",
        "  y_ember.append(float(ScreenLoc[i][3]))\n",
        "  id_ember.append(float(UniqueID[i][2]))\n",
        "\n",
        "  #mesh size scale          \n",
        "  scale_x.append(float(NormalizedAge[i][2]))\n",
        "  scale_y.append(float(NormalizedAge[i][3]))\n",
        "  #scale_z.append(float(NormalizedAge[i][3]))\n",
        "\n",
        "  #mesh_size.append(float(NormalizedAge[i][1]) * float(NormalizedAge[i][2]) * float(NormalizedAge[i][3]))\n",
        "  mesh_size.append(float(NormalizedAge[i][1]) * float(NormalizedAge[i][2]))\n",
        "\n",
        "num_ember = id_ember[i]\n",
        "\n",
        "print(\"number of ember is : \", num_ember)\n",
        "print(\"max scale.x: \", max(scale_x))\n",
        "print(\"max scale.y: \", max(scale_y))\n",
        "\n",
        "print(\"x_ember: \", x_ember)\n",
        "print(\"y_ember: \", y_ember)\n",
        "print(\"id_ember: \", id_ember)\n",
        "\n",
        "print(\"scale.x: \", scale_x)\n",
        "print(\"scale.y: \", scale_y)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JU0atAY00BmK",
        "outputId": "91f508a3-5000-4bcd-8326-e0f5382841f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of ember is :  11.0\n",
            "max scale.x:  2.518\n",
            "max scale.y:  1.956\n",
            "x_ember:  [563.16, 564.682, 566.201, 567.718, 569.233, 570.746, 572.257, 573.766, 575.272, 576.777, 578.279, 579.78, 581.278, 582.774, 584.269, 585.761, 587.251, 588.739, 590.225, 591.709, 593.191, 594.672, 596.15, 597.625, 599.099, 600.571, 602.041, 603.509, 604.975, 606.439, 607.901, 609.361, 610.819, 612.275, 613.729, 615.181, 616.631, 618.079, 619.525, 620.969, 622.411, 623.852, 625.29, 626.726, 628.161, 629.594, 631.024, 632.453, 633.879, 635.304, 636.727, 638.148, 639.567, 640.984, 642.4, 643.813, 645.225, 646.634, 648.042, 649.448, 650.852, 652.255, 541.998, 653.655, 544.155, 655.053, 546.31, 656.45, 548.461, 657.844, 550.61, 659.237, 552.755, 660.628, 554.897, 662.017, 557.036, 663.404, 559.172, 664.79, 561.304, 666.174, 563.434, 667.555, 565.561, 668.935, 567.685, 670.314, 569.806, 671.69, 571.923, 673.065, 574.038, 674.438, 576.149, 675.808, 578.258, 677.177, 580.363, 678.545, 582.466, 679.91, 584.565, 681.274, 586.662, 682.636, 588.755, 683.996, 590.846, 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1.136, 1.567, 1.185, 1.565, 1.224, 1.564, 1.231, 1.562, 1.229, 1.56, 1.226, 1.558, 1.224, 1.556, 1.222, 1.554, 1.219]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 2: Handle Json file\n"
      ],
      "metadata": {
        "id": "fYq-_5NbT6hq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 2.1: Json \"info\" and \"cate\""
      ],
      "metadata": {
        "id": "5bKzyi7Ec0zh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Info\n",
        "info = []\n",
        "inf = {}\n",
        "inf[\"year\"] = \"2022\"\n",
        "inf[\"version\"] = \"1.0\"\n",
        "inf[\"descributor\"] = \"0508life2dur1emb1 pure forest scenes #3 background without fires or smoke, UE4 mesh scale setting: 1~2, screen size about 80*80 pixel, loop during 1, life time 2， 26 embers, 2 ember max at same time\"\n",
        "inf[\"data_created\"] = data_cap\n",
        "info.append(inf)\n",
        "\n",
        "#CATEGORIES\n",
        "category_id = 1\n",
        "cate = []\n",
        "cat = {}\n",
        "cat[\"id\"] = category_id\n",
        "cat[\"name\"] = \"ember\"\n",
        "cate.append(cat)\n"
      ],
      "metadata": {
        "id": "1PB-4bHPTxuA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 2.2: get dataset imgs from video"
      ],
      "metadata": {
        "id": "3UFNOzeHcFRY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "interval = 1  # 每间隔10帧取一张图片\n",
        "\n",
        "#存img\n",
        "imgarr = []\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    num = 1\n",
        "    video = cv2.VideoCapture(video_path)\n",
        "    fps = video.get(cv2.CAP_PROP_FPS)#fps\n",
        "    frames = video.get(cv2.CAP_PROP_FRAME_COUNT)#total frame\n",
        "    width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
        "    height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
        "    print(\"fps=\",int(fps),\"frames=\",int(frames))\n",
        "\n",
        "    while video.isOpened():\n",
        "        is_read, frame = video.read()\n",
        "        if is_read:\n",
        "          if num % interval == 0:\n",
        "            #if num % interval == 1:\n",
        "            file_name = '%d' % num\n",
        "            cv2.imwrite(output_path + str(file_name) + '.jpg', frame)#输出图片\n",
        "            #cv2.imwrite(imgs_with_bbox_path + str(file_name) + '.jpg', frame)#输出图片for bbox check\n",
        "            #cv2.waitKey(1)\n",
        "            img = {}\n",
        "            img[\"id\"] = num\n",
        "            img[\"width\"] = width\n",
        "            img[\"height\"] = height\n",
        "            img[\"file_name\"] = str(file_name) + \".jpg\"\n",
        "            img[\"date_captured\"] = data_cap\n",
        "            #save to one dict\n",
        "            imgarr.append(img)\n",
        "            print(num)\n",
        "          num += 1\n",
        "        else:\n",
        "          break\n",
        "\n",
        "output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')\n",
        "\n",
        "shutil.copytree(output_path, imgs_with_bbox_path)\n",
        "print('copy dir finished!')\n",
        "\n",
        "# End of code alert\n",
        "# Play an audio beep. Any audio URL will do.\n",
        "\n",
        "output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')\n",
        "cv2.waitKey(1)\n",
        "output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kw8izjnk4Fb4",
        "outputId": "752e3995-3a93-4547-a00f-7467e3575df7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 2.3: (For multi embers) handle bboxes\n",
        "\n",
        "\n",
        "1.   Map ids to embers \n",
        "2.   Add bbox for imgs according to each data\n",
        "\n",
        "*   仅输出含有bbox的图像/ out put imgs only when it has bbox\n",
        "*   在每个有bbox的图像上标注/mark bbox for checking进块\n",
        "\n",
        "3.   Generate ember[ ] for json \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "nALE8OKP_Itl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#存ember\n",
        "embarr = []\n",
        "\n",
        "x=x_ember\n",
        "y=y_ember\n",
        "ember_number = 2\n",
        "#ue4 mesh size VS pixel ratio adjust\n",
        "pixel_mesh_scale_ratio = 17\n",
        "\n",
        "#对于data中每个数据来说\n",
        "for i in range(len(x_ember)):\n",
        "  \n",
        "  x[i]=round(x[i])\n",
        "  y[i]=round(y[i])\n",
        "\n",
        "  #if the current ember is in the frame:\n",
        "  if 0 < x[i] < 2560 and 0 < y[i] < 1080: \n",
        "    ############## make json ember file\n",
        "    #calculate the bbox\n",
        "    bbx_width = pixel_mesh_scale_ratio * scale_x[i]\n",
        "    bbx_height = pixel_mesh_scale_ratio * scale_x[i]\n",
        "    area = bbx_width * bbx_height \n",
        "    # top left coordinate of the current ember\n",
        "    x_top_left = int(x[i] - bbx_width/2)\n",
        "    y_top_left = int(y[i] - bbx_height/2)\n",
        "    bbox = [x_top_left, y_top_left, bbx_width, bbx_height]\n",
        "    #for each ember data, turn data sequence to correspond frames:\n",
        "    if i <= Frames_per_ember/2:\n",
        "      image_id = i+1\n",
        "    else:\n",
        "      image_id = math.ceil((i+1 - Frames_per_ember/2) /2 + Frames_per_ember/2) # /2 if ember = 2\n",
        "    #json.ember{}\n",
        "    ember = {}\n",
        "    ember[\"area\"] = area\n",
        "    ember[\"image_id\"] = image_id\n",
        "    ember[\"bbox\"] = bbox\n",
        "    ember[\"id\"] = 1000000 * int(id_ember[i]) + 10000000000 + i\n",
        "    ember[\"ember_id\"] = int(id_ember[i])\n",
        "    ember[\"category_id\"] = category_id\n",
        "    embarr.append(ember)\n",
        "\n",
        "    ########## mark bbox on graph for checking:\n",
        "\n",
        "    #打开本数据对应的img  \n",
        "    image = cv2.imread(imgs_with_bbox_path + str(image_id) + '.jpg')\n",
        "    #将本数据的ember坐标中心标注在对应的图上\n",
        "    cv2.circle(image,(x[i],y[i]),1,(255,255,255),-1)\n",
        "\n",
        "    #get vertex\n",
        "    x_bottom_right = int(x[i] + bbx_width/2)\n",
        "    y_bottom_right = int(y[i] + bbx_height/2)\n",
        "\n",
        "    #The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners are pt1 and pt2.\n",
        "    draw_bbox = cv2.rectangle(image,(x_top_left,y_top_left),(x_bottom_right, y_bottom_right),(255,255,255),1)\n",
        "\n",
        "    cv2.imwrite(imgs_with_bbox_path + str(image_id) + '.jpg', draw_bbox) \n",
        "\n",
        "    \n",
        "# End of code alert\n",
        "# Play an audio beep. Any audio URL will do.\n",
        "output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')\n",
        "cv2.waitKey(1)\n",
        "output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')\n",
        "cv2.waitKey(1)\n",
        "output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"
      ],
      "metadata": {
        "id": "KrB1eYxqkcZk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 3: Pick up all the contains for json file + Make output files \n"
      ],
      "metadata": {
        "id": "WnbteZRR1ian"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "j_info = json.dumps(info,ensure_ascii=False)\n",
        "j_imges = json.dumps(imgarr,ensure_ascii=False)\n",
        "j_categories = json.dumps(cate,ensure_ascii=False)\n",
        "j_annotations = json.dumps(embarr,ensure_ascii=False)\n",
        "\n",
        "print(\"info\"+j_info)\n",
        "print(\"img\"+j_imges)\n",
        "print(\"cat\"+j_categories)\n",
        "print(\"ann\"+j_annotations)\n",
        "\n",
        "with open(path_jsonfile, \"w\") as f:\n",
        " f.write(\"{\")\n",
        " f.write(''' \"info\": ''')\n",
        " f.write(j_info)\n",
        " f.write(\", \") \n",
        " f.write('''\"images\": ''')\n",
        " f.write(j_imges)\n",
        " f.write(\", \")\n",
        " f.write('''\"categories\": ''')\n",
        " f.write(j_categories)\n",
        " f.write(\", \")\n",
        " f.write('''\"annotations\": ''')\n",
        " f.write(j_annotations)\n",
        " f.write(\", \")\n",
        " f.write(\"}\")\n",
        "\n",
        "print(\"x_ember: \", x_ember)\n",
        "print(\"y_ember: \", y_ember)\n",
        "print(\"id_ember: \", id_ember)\n",
        "#print(\"age_ember: \", age_ember)\n",
        "\n",
        "with open(path_x_ember, \"w\") as f:\n",
        " f.write(\"x_ember:\")\n",
        " f.write(str(x_ember))\n",
        "with open(path_y_ember, \"w\") as f:\n",
        " f.write(\"y_ember:\")\n",
        " f.write(str(y_ember))\n",
        "with open(path_id_ember, \"w\") as f:\n",
        " f.write(\"id_ember:\")\n",
        " f.write(str(id_ember))\n",
        "#with open(\"/content/drive/MyDrive/lab/embertrackingdataset/0414take2dataset/age_ember.txt\", \"w\") as f:\n",
        " #f.write(\"age_ember:\")\n",
        " #f.write(str(age_ember))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lpd_Q4dkA3z6",
        "outputId": "48f55fe0-154a-4518-dc9f-9771afe6fd96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "info[{\"year\": \"2022\", \"version\": \"1.0\", \"descributor\": \"0508life2dur1emb1 pure forest scenes #3 background without fires or smoke, UE4 mesh scale setting: 1~2, screen size about 80*80 pixel, loop during 1, life time 2， 26 embers, 2 ember max at same time\", \"data_created\": \"2022-05-21 21:31:14\"}]\n",
            "img[{\"id\": 1, \"width\": 2560, \"height\": 1080, \"file_name\": \"1.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 2, \"width\": 2560, \"height\": 1080, \"file_name\": \"2.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 3, \"width\": 2560, \"height\": 1080, \"file_name\": \"3.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 4, \"width\": 2560, \"height\": 1080, \"file_name\": \"4.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 5, \"width\": 2560, \"height\": 1080, \"file_name\": \"5.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 6, \"width\": 2560, \"height\": 1080, \"file_name\": \"6.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 7, \"width\": 2560, \"height\": 1080, \"file_name\": \"7.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 8, \"width\": 2560, \"height\": 1080, \"file_name\": \"8.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 9, \"width\": 2560, \"height\": 1080, \"file_name\": \"9.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 10, \"width\": 2560, \"height\": 1080, \"file_name\": \"10.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 11, \"width\": 2560, \"height\": 1080, \"file_name\": \"11.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 12, \"width\": 2560, \"height\": 1080, \"file_name\": \"12.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 13, \"width\": 2560, \"height\": 1080, \"file_name\": \"13.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 14, \"width\": 2560, \"height\": 1080, \"file_name\": \"14.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 15, \"width\": 2560, \"height\": 1080, \"file_name\": \"15.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 16, \"width\": 2560, \"height\": 1080, \"file_name\": \"16.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 17, \"width\": 2560, \"height\": 1080, \"file_name\": \"17.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 18, \"width\": 2560, \"height\": 1080, \"file_name\": \"18.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 19, \"width\": 2560, \"height\": 1080, \"file_name\": \"19.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 20, \"width\": 2560, \"height\": 1080, \"file_name\": \"20.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 21, \"width\": 2560, \"height\": 1080, \"file_name\": \"21.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 22, \"width\": 2560, \"height\": 1080, \"file_name\": \"22.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 23, \"width\": 2560, \"height\": 1080, \"file_name\": \"23.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 24, \"width\": 2560, \"height\": 1080, \"file_name\": \"24.jpg\", \"date_captured\": \"2022-05-21 21:31:14\"}, {\"id\": 25, \"width\": 2560, \"height\": 1080, \"file_name\": \"25.jpg\", \"date_captured\": 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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 4：make a gif"
      ],
      "metadata": {
        "id": "1NeQ-fYDy4ZI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def img2gif(img_dir, gif_path, duration):\n",
        "    \"\"\"\n",
        "\n",
        "    :param img_dir: 包含图片的文件夹\n",
        "    :param gif_path: 输出的gif的路径\n",
        "    :param duration: 每张图片切换的时间间隔，与fps的关系：duration = 1 / fps\n",
        "    :return:\n",
        "    \"\"\"\n",
        "    frames = []\n",
        "    for idx in sorted(os.listdir(img_dir)):\n",
        "        img = osp.join(img_dir, idx)\n",
        "        frames.append(imageio.imread(img))\n",
        "\n",
        "    imageio.mimsave(gif_path, frames[100:130], 'GIF', duration=duration)\n",
        "    print('Finish changing!')\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    gif_path = osp.join(PATH, 'output.gif')\n",
        "    img2gif(\"/content/drive/MyDrive/lab/embertrackingdataset/model_evaluation_tests/withforest_nofire/scenes5nofire/multiembers/2embers/imgswithbbox\", gif_path=gif_path, duration=0.1)\n",
        "\n",
        "# End of code alert\n",
        "# Play an audio beep. Any audio URL will do.\n",
        "output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')\n",
        "cv2.waitKey(2)\n",
        "output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')\n",
        "cv2.waitKey(5)\n",
        "output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')\n",
        "cv2.waitKey(2)\n",
        "output.eval_js('new Audio(\"https://upload.wikimedia.org/wikipedia/commons/0/05/Beep-09.ogg\").play()')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "H3SVleXEeTaX",
        "outputId": "c94c3184-5515-40a6-8d3a-7f2c558c9551"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finish changing!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#test\n"
      ],
      "metadata": {
        "id": "EwqdlS0BCez9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def accSum(n):\n",
        "    sum = 0\n",
        "    for i in range(1, n+1): #[1,n+1)\n",
        "        sum += i\n",
        "    return sum\n",
        "\n",
        "print(accSum(5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3f-zb4-Ced7",
        "outputId": "9f7dc311-c5fb-4bd9-9a45-e71132a6089b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "UE4_Life_time = 10\n",
        "UE4_Loop_during = 2\n",
        "UE4_Samplerate = 30 # unit: frames per sencond\n",
        "Max_ember_onscreen = UE4_Life_time / UE4_Loop_during # the max number of embers on screen at same time\n",
        "Frames_per_ember = UE4_Life_time * UE4_Samplerate\n",
        "One_loop_frames = UE4_Loop_during * UE4_Samplerate # Start a new loop (generate a new ember) every 'One_loop_frames' frames \n",
        "#One_ember_lifetime_frames = UE4_Life_time / UE4_Loop_during * One_loop_frames\n",
        "\n",
        "print(\"Max_ember_onscreen\",Max_ember_onscreen)\n",
        "print(\"Frames_per_ember\",Frames_per_ember)\n",
        "print(\"One_loop_frames\",One_loop_frames)\n",
        "print(\"One_ember_lifetime_frames\",One_ember_lifetime_frames)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CgYpzg6j_5-0",
        "outputId": "35493880-d6c8-485c-c5f5-0508c42d985f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max_ember_onscreen 5.0\n",
            "Frames_per_ember 300\n",
            "One_loop_frames 60\n",
            "One_ember_lifetime_frames 300.0\n"
          ]
        }
      ]
    }
  ]
}