video_analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "video_analysis.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cjstntjd/project_study/blob/master/video_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "76-KBVpnUYRc",
        "colab_type": "code",
        "outputId": "b62aa5a5-32e0-4a6b-ea17-7898872a1191",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive',force_remount = True)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBxQ5UmJWGOm",
        "colab_type": "code",
        "outputId": "44d2d4a0-bff6-441e-d2b1-9885ca0a8f2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "\n",
        "def save_image(path, success, is_resize, end_frame):\n",
        "    \n",
        "    cnt=0\n",
        "    while success:\n",
        "        successs,image= vidcap.read()\n",
        "        if is_resize==1:\n",
        "            image2= cv2.resize(image,(800,400))\n",
        "        else:\n",
        "            image2=image\n",
        "        \n",
        "        file_path = os.path.join(path, \"frame%.5d.jpg\" % cnt )\n",
        "        cv2.imwrite(file_path, image2)\n",
        "        cnt+=1\n",
        "        \n",
        "        print(\"%d image done \" %cnt)\n",
        "        \n",
        "        if cnt== end_frame:\n",
        "            break\n",
        "\n",
        "end_frame = 400\n",
        "\n",
        "print(\"save resize image\")\n",
        "vidcap = cv2.VideoCapture('/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/Frames/mov1.mp4')\n",
        "success, image = vidcap.read()\n",
        "save_image('/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/Frames/jpg', success, 1, end_frame)\n",
        "\n",
        "print(\"save original image\")\n",
        "vidcap = cv2.VideoCapture('/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/Frames/mov1.mp4')\n",
        "success, image = vidcap.read()\n",
        "save_image('/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/Frames/jpg_ori', success, 0, end_frame)\n",
        "#여기서부터 sub\n",
        "\n",
        "image_path = '/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/Frames/jpg'\n",
        "target_path = '/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/Frames/sub'\n",
        "img_list = os.listdir(image_path)\n",
        "\n",
        "cnt=0\n",
        "is_save=1\n",
        "\n",
        "for name in img_list:\n",
        "    \n",
        "    if cnt==0:\n",
        "        prev_name = name\n",
        "        \n",
        "    else:\n",
        "        prev_image_path = os.path.join(image_path, prev_name)\n",
        "        cur_image_path = os.path.join(image_path, name)\n",
        "        \n",
        "        img_prev = cv2.imread(prev_image_path, cv2.IMREAD_GRAYSCALE)\n",
        "        img_cur = cv2.imread(cur_image_path, cv2.IMREAD_GRAYSCALE)\n",
        "        img_sub = cv2.subtract(img_prev, img_cur)\n",
        "        \n",
        "        prev_name = name\n",
        "        \n",
        "        if is_save==1:\n",
        "            save_name = str(\"frame_sub%.5d.jpg\" % cnt)\n",
        "            save_path = os.path.join(target_path,save_name)\n",
        "            cv2.imwrite(save_path, img_sub)\n",
        "        \n",
        "    cnt+=1\n",
        "\n",
        "print(\"substract image process is complete\")\n",
        "#/////////////\n",
        "img_path = '/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/Frames/sub'\n",
        "target_path = '/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/Frames/sub_stack'\n",
        "img_list = os.listdir(img_path)\n",
        "\n",
        "img_H = 400\n",
        "img_W = 800\n",
        "\n",
        "window_size = 5\n",
        "\n",
        "sub_tile_stack = np.zeros((img_H,img_W,window_size), dtype='f')\n",
        "sub_tile_c = np.zeros((img_H,img_W), dtype='f')\n",
        "sub_tile = np.zeros((img_H,img_W), dtype='f')\n",
        "\n",
        "cnt = 0\n",
        "stack_cnt = 0\n",
        "is_save = 1\n",
        "\n",
        "for name in img_list:\n",
        "\n",
        "    cur_img_path  = os.path.join(img_path,name)\n",
        "    \n",
        "    \n",
        "    sub_tile = cv2.imread(cur_img_path, cv2.IMREAD_GRAYSCALE)\n",
        "\n",
        "    if stack_cnt >= window_size:\n",
        "        sub_tile_c = sub_tile_c + sub_tile - sub_tile_stack[:,:,stack_cnt%window_size]\n",
        "        sub_tile_stack[:,:,stack_cnt%window_size] = sub_tile\n",
        "    else:\n",
        "        sub_tile_stack[:,:,stack_cnt%window_size] = sub_tile\n",
        "        sub_tile_c = sub_tile_c + sub_tile\n",
        " \n",
        "\n",
        "    if is_save == 1:\n",
        "        save_name = str(\"frame_sub%.5d.jpg\" % cnt)\n",
        "        save_path = os.path.join(target_path,save_name)\n",
        "        cv2.imwrite(save_path, sub_tile_c) \n",
        "\n",
        "    cnt = cnt+1\n",
        "    stack_cnt = stack_cnt+1\n",
        "    \n",
        "print('Stack images done')\n",
        "#//////////////////////////////////////////////block\n",
        "img_path = '/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/Frames/sub_stack'\n",
        "target_path = '/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/Frames/sub_block'\n",
        "img_list = os.listdir(img_path)\n",
        "\n",
        "img_H = 400\n",
        "img_W = 800\n",
        "\n",
        "step_size = 10\n",
        "step_H = round(img_H/step_size)\n",
        "step_W = round(img_W/step_size)\n",
        "\n",
        "print(step_H, step_W)\n",
        "sub_tile = np.zeros((img_H,img_W))\n",
        "\n",
        "cnt = 0\n",
        "is_save = 1\n",
        "\n",
        "thr = 40;\n",
        "\n",
        "for name in img_list:\n",
        "\n",
        "    cur_img_path  = os.path.join(img_path,name)\n",
        "\n",
        "    # load two concecutive images\n",
        "    img_cur = cv2.imread(cur_img_path, cv2.IMREAD_GRAYSCALE)\n",
        "   \n",
        "    for i in range(step_H):\n",
        "        for j in range(step_W):\n",
        "            img_crop=img_cur[i*step_size:(i+1)*step_size,j*step_size:(j+1)*step_size]\n",
        "            crop_sum = np.sum(img_crop)\n",
        "            sub_tile[i*step_size:(i+1)*step_size,j*step_size:(j+1)*step_size] = crop_sum/step_size/step_size*2\n",
        "\n",
        "            if crop_sum/step_size/step_size*2 < thr:\n",
        "                sub_tile[i*step_size:(i+1)*step_size,j*step_size:(j+1)*step_size] = 0\n",
        "            \n",
        "\n",
        "    if is_save == 1:\n",
        "        save_name = str(\"frame_sub%.5d.jpg\" % cnt)\n",
        "        save_path = os.path.join(target_path,save_name)\n",
        "        cv2.imwrite(save_path, sub_tile) \n",
        "\n",
        "    cnt = cnt+1\n",
        "    \n",
        "print('Block image done')\n",
        "#///////////////////////\n",
        "img_path = '/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/Frames/jpg_ori'\n",
        "img_path_sub = '/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/Frames/sub_block'\n",
        "target_path = '/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/Frames/highlight'\n",
        "\n",
        "img_list = os.listdir(img_path)\n",
        "img_list_sub = os.listdir(img_path_sub)\n",
        "\n",
        "\n",
        "cnt = 0\n",
        "is_save = 1\n",
        "\n",
        "temp = np.zeros((360,640,3), dtype='f')\n",
        "for i in range(len(img_list)):\n",
        "\n",
        "    print('%s processing...' %i)\n",
        "    if i == 0:\n",
        "        print('skip first frame')\n",
        "    else:\n",
        "        img_file_path  = os.path.join(img_path,img_list[i])\n",
        "        img_sub_file_path  = os.path.join(img_path_sub,img_list_sub[i-1])\n",
        "\n",
        "        img = cv2.imread(img_file_path)\n",
        "        img = cv2.resize(img, (640,360))\n",
        "        img_sub = cv2.imread(img_sub_file_path, cv2.IMREAD_GRAYSCALE)\n",
        "        img_sub = cv2.resize(img_sub, (640,360))\n",
        "        \n",
        "        \n",
        "        temp[:,:,0] = img[:,:,0]\n",
        "        temp[:,:,1] = img[:,:,1]\n",
        "        temp[:,:,2] = img[:,:,2]        \n",
        "        temp[:,:,2] = temp[:,:,2] + img_sub\n",
        "    \n",
        "        save_name = str(\"frame_sub%.5d.jpg\" % cnt)\n",
        "        save_path = os.path.join(target_path,save_name)\n",
        "        cv2.imwrite(save_path, temp) \n",
        "        \n",
        "        cnt = cnt+1\n",
        "\n",
        "print('highlighting done')\n",
        "#////////////////////////////////\n",
        "image_folder = '/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/Frames/highlight'\n",
        "video_name = 'car_move.mp4'\n",
        "\n",
        "images = [img for img in os.listdir(image_folder) if img.endswith(\".jpg\")]\n",
        "frame = cv2.imread(os.path.join(image_folder, images[0]))\n",
        "height, width, layers = frame.shape\n",
        "\n",
        "video = cv2.VideoWriter(video_name, 0, 22.5*33/31, (width,height))\n",
        "\n",
        "for image in images:\n",
        "    video.write(cv2.imread(os.path.join(image_folder, image)))\n",
        "\n",
        "cv2.destroyAllWindows()\n",
        "video.release()\n",
        "\n",
        "print('finish')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "save resize image\n",
            "1 image done \n",
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            "384 processing...\n",
            "385 processing...\n",
            "386 processing...\n",
            "387 processing...\n",
            "388 processing...\n",
            "389 processing...\n",
            "390 processing...\n",
            "391 processing...\n",
            "392 processing...\n",
            "393 processing...\n",
            "394 processing...\n",
            "395 processing...\n",
            "396 processing...\n",
            "397 processing...\n",
            "398 processing...\n",
            "399 processing...\n",
            "highlighting done\n",
            "finish\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XuXx2BuIe5de",
        "colab_type": "code",
        "outputId": "fd13f3ea-f2c1-4791-c3a4-7e56c6ad0b74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd /gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/gdrive/My Drive/스터디/개인과제/과속방지_이미지/car video\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}