videoNesneTanima.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "videoNesneTanima.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN0WJvUByngN3K5VTyw5ygO",
      "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/RayetSena/Rayet/blob/master/videoNesneTanima.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sWc0QgUIdB31",
        "colab_type": "text"
      },
      "source": [
        "# **Install**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KzHaUxqPk9wv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "outputId": "fb659d84-c7d1-4995-b477-77ec5af5eef6"
      },
      "source": [
        "#install the tensorflow\n",
        "!pip install -U --pre tensorflow==\"2.*\""
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied, skipping upgrade: keras-preprocessing<1.2,>=1.1.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.*) (1.1.2)\n",
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            "Requirement already satisfied, skipping upgrade: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (1.0.1)\n",
            "Requirement already satisfied, skipping upgrade: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (0.4.1)\n",
            "Requirement already satisfied, skipping upgrade: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (49.1.0)\n",
            "Requirement already satisfied, skipping upgrade: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (1.7.0)\n",
            "Requirement already satisfied, skipping upgrade: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (3.2.2)\n",
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            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (2.10)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (2020.6.20)\n",
            "Requirement already satisfied, skipping upgrade: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (1.3.0)\n",
            "Requirement already satisfied, skipping upgrade: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (1.7.0)\n",
            "Requirement already satisfied, skipping upgrade: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (4.1.1)\n",
            "Requirement already satisfied, skipping upgrade: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (0.2.8)\n",
            "Requirement already satisfied, skipping upgrade: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (4.6)\n",
            "Requirement already satisfied, skipping upgrade: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (3.1.0)\n",
            "Requirement already satisfied, skipping upgrade: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (3.1.0)\n",
            "Requirement already satisfied, skipping upgrade: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.*) (0.4.8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fh_XyWI8k9nJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "4071d4a6-0fd2-4696-f5ac-c17efd8eb8c6"
      },
      "source": [
        "#install the pycocotools\n",
        "!pip install pycocotools"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pycocotools in /usr/local/lib/python3.6/dist-packages (2.0.1)\n",
            "Requirement already satisfied: matplotlib>=2.1.0 in /usr/local/lib/python3.6/dist-packages (from pycocotools) (3.2.2)\n",
            "Requirement already satisfied: setuptools>=18.0 in /usr/local/lib/python3.6/dist-packages (from pycocotools) (49.1.0)\n",
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            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.0->pycocotools) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.0->pycocotools) (1.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.0->pycocotools) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.0->pycocotools) (0.10.0)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.0->pycocotools) (1.18.5)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib>=2.1.0->pycocotools) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TzOcjNTRyqp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "b68c4553-189d-4ba8-8f3f-7ce3a86d7bca"
      },
      "source": [
        "#install the tf_slim\n",
        "!pip install tf_slim"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tf_slim in /usr/local/lib/python3.6/dist-packages (1.1.0)\n",
            "Requirement already satisfied: absl-py>=0.2.2 in /usr/local/lib/python3.6/dist-packages (from tf_slim) (0.9.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from absl-py>=0.2.2->tf_slim) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RGIwbyvfk9Zx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Get tensorflow/models or cd to parent directory of the repository\n",
        "import os\n",
        "import pathlib\n",
        "\n",
        "if \"models\" in pathlib.Path.cwd().parts:\n",
        "  while \"models\" in pathlib.Path.cwd().parts:\n",
        "    os.chdir('..')\n",
        "elif not pathlib.Path('models').exists():\n",
        "  !git clone --depth 1 https://github.com/tensorflow/models"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QRIqexxeLh2",
        "colab_type": "text"
      },
      "source": [
        "**Compile protobufs and install the object_detection package**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5l6P9ItOk9GR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f7049bfa-7799-4394-bfbc-04b39805493d"
      },
      "source": [
        "%%bash\n",
        "cd models/research/\n",
        "protoc object_detection/protos/*.proto --python_out=."
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "object_detection/protos/input_reader.proto: warning: Import object_detection/protos/image_resizer.proto but not used.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qdENGZVlHum",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "ee379ca1-cb37-476c-9722-7c153be00886"
      },
      "source": [
        "%%bash \n",
        "cd models/research\n",
        "pip install ."
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Processing /content/models/research\n",
            "Requirement already satisfied: Pillow>=1.0 in /usr/local/lib/python3.6/dist-packages (from object-detection==0.1) (7.0.0)\n",
            "Requirement already satisfied: Matplotlib>=2.1 in /usr/local/lib/python3.6/dist-packages (from object-detection==0.1) (3.2.2)\n",
            "Requirement already satisfied: Cython>=0.28.1 in /usr/local/lib/python3.6/dist-packages (from object-detection==0.1) (0.29.21)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from Matplotlib>=2.1->object-detection==0.1) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from Matplotlib>=2.1->object-detection==0.1) (0.10.0)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from Matplotlib>=2.1->object-detection==0.1) (1.18.5)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from Matplotlib>=2.1->object-detection==0.1) (1.2.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from Matplotlib>=2.1->object-detection==0.1) (2.4.7)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->Matplotlib>=2.1->object-detection==0.1) (1.15.0)\n",
            "Building wheels for collected packages: object-detection\n",
            "  Building wheel for object-detection (setup.py): started\n",
            "  Building wheel for object-detection (setup.py): finished with status 'done'\n",
            "  Created wheel for object-detection: filename=object_detection-0.1-cp36-none-any.whl size=1304063 sha256=9e34a317f27cdbd63a562681830db4a37492ec090ac2089f10449edcc2c1d011\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-w9330yxy/wheels/94/49/4b/39b051683087a22ef7e80ec52152a27249d1a644ccf4e442ea\n",
            "Successfully built object-detection\n",
            "Installing collected packages: object-detection\n",
            "  Found existing installation: object-detection 0.1\n",
            "    Uninstalling object-detection-0.1:\n",
            "      Successfully uninstalled object-detection-0.1\n",
            "Successfully installed object-detection-0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Y955Vw0efKE",
        "colab_type": "text"
      },
      "source": [
        "# **Imports**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NR-TC_EejuKE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import six.moves.urllib as urllib\n",
        "import sys\n",
        "import tarfile\n",
        "\n",
        "import tensorflow.compat.v1 as tf\n",
        "import zipfile\n",
        "\n",
        "from collections import defaultdict\n",
        "from io import StringIO\n",
        "from matplotlib import pyplot as plt\n",
        "from PIL import Image"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ic7S_0T8jzPW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#selecting and processing the video\n",
        "import imageio\n",
        "reader = imageio.get_reader('models/research/object_detection/test_video/1.mp4')\n",
        "fps = reader.get_meta_data()['fps']\n",
        "writer = imageio.get_writer('models/research/object_detection/test_video/output.mp4', fps=fps)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgksSBkZj5hc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Import the object detection module.\n",
        "from object_detection.utils import ops as utils_ops\n",
        "from object_detection.utils import label_map_util\n",
        "from object_detection.utils import visualization_utils as vis_util"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Svduvw9aj971",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# What model to download\n",
        "#'faster_rcnn_inception_v2_coco_2018_01_28'\n",
        "MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'\n",
        "MODEL_FILE = MODEL_NAME + '.tar.gz'\n",
        "DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'\n",
        "\n",
        "# Path to frozen detection graph. This is the actual model that is used for the object detection.\n",
        "PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'\n",
        "\n",
        "# List of the strings that is used to add correct label for each box.\n",
        "PATH_TO_LABELS = os.path.join('models/research/object_detection/data/mscoco_label_map.pbtxt')\n",
        "\n",
        "NUM_CLASSES = 90"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Egd_egJkDOU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Download model\n",
        "opener = urllib.request.URLopener()\n",
        "opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)\n",
        "tar_file = tarfile.open(MODEL_FILE)\n",
        "for file in tar_file.getmembers():\n",
        "  file_name = os.path.basename(file.name)\n",
        "  if 'frozen_inference_graph.pb' in file_name:\n",
        "    tar_file.extract(file, os.getcwd())"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-nah_stOkG9S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Load a Tensorflow model into memory.\n",
        "detection_graph = tf.Graph()\n",
        "with detection_graph.as_default():\n",
        "  od_graph_def = tf.compat.v1.GraphDef() \n",
        "  with tf.compat.v2.io.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n",
        "    serialized_graph = fid.read()\n",
        "    od_graph_def.ParseFromString(serialized_graph)\n",
        "    tf.import_graph_def(od_graph_def, name='')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9lfjmrekKOu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Loading label map\n",
        "label_map = label_map_util.load_labelmap(PATH_TO_LABELS)\n",
        "categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)\n",
        "category_index = label_map_util.create_category_index(categories)\n"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vbZXyJhbkRhy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Helper code\n",
        "PATH_TO_TEST_IMAGES_DIR = 'test_images'\n",
        "TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]\n",
        "\n",
        "# Size, in inches, of the output images.\n",
        "IMAGE_SIZE = (12, 8)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FCO4TKYfTzG3",
        "colab_type": "text"
      },
      "source": [
        "# **Detection**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPzpLi-0kXDu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def run_inference_for_single_image(image, graph):\n",
        "  with graph.as_default():\n",
        "\n",
        "      # Get handles to input and output tensors\n",
        "      ops = tf.get_default_graph().get_operations()\n",
        "      all_tensor_names = {output.name for op in ops for output in op.outputs}\n",
        "      tensor_dict = {}\n",
        "      for key in [\n",
        "          'num_detections', 'detection_boxes', 'detection_scores',\n",
        "          'detection_classes', 'detection_masks'\n",
        "      ]:\n",
        "        tensor_name = key + ':0'\n",
        "        if tensor_name in all_tensor_names:\n",
        "          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(\n",
        "              tensor_name)\n",
        "      if 'detection_masks' in tensor_dict:\n",
        "        # The following processing is only for single image\n",
        "        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])\n",
        "        detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])\n",
        "        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.\n",
        "        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)\n",
        "        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])\n",
        "        detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])\n",
        "        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(\n",
        "            detection_masks, detection_boxes, image.shape[0], image.shape[1])\n",
        "        detection_masks_reframed = tf.cast(\n",
        "            tf.greater(detection_masks_reframed, 0.5), tf.uint8)\n",
        "        # Follow the convention by adding back the batch dimension\n",
        "        tensor_dict['detection_masks'] = tf.expand_dims(\n",
        "            detection_masks_reframed, 0)\n",
        "      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')\n",
        "\n",
        "      # Run inference\n",
        "      output_dict = sess.run(tensor_dict,\n",
        "                             feed_dict={image_tensor: np.expand_dims(image, 0)})\n",
        "\n",
        "      # all outputs are float32 numpy arrays, so convert types as appropriate\n",
        "      output_dict['num_detections'] = int(output_dict['num_detections'][0])\n",
        "      output_dict['detection_classes'] = output_dict[\n",
        "          'detection_classes'][0].astype(np.uint8)\n",
        "      output_dict['detection_boxes'] = output_dict['detection_boxes'][0]\n",
        "      output_dict['detection_scores'] = output_dict['detection_scores'][0]\n",
        "      if 'detection_masks' in output_dict:\n",
        "        output_dict['detection_masks'] = output_dict['detection_masks'][0]\n",
        "  return output_dict"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LktLx2XckcfF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "eeed42a5-dc8d-47ed-fd88-6cce21affba7"
      },
      "source": [
        "with tf.Session(graph=detection_graph) as sess:\n",
        "    for i, image_np in enumerate(reader):\n",
        "      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
        "      image_np_expanded = np.expand_dims(image_np, axis=0)\n",
        "      # Actual detection.\n",
        "      output_dict = run_inference_for_single_image(image_np, detection_graph)\n",
        "      # Visualization of the results of a detection.\n",
        "      vis_util.visualize_boxes_and_labels_on_image_array(\n",
        "          image_np,\n",
        "          output_dict['detection_boxes'],\n",
        "          output_dict['detection_classes'],\n",
        "          output_dict['detection_scores'],\n",
        "          category_index,\n",
        "          instance_masks=output_dict.get('detection_masks'),\n",
        "          use_normalized_coordinates=True,\n",
        "          line_thickness=8)\n",
        "      writer.append_data(image_np)\n",
        "      print(i)\n",
        "    writer.close()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:IMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=16, resizing from (1036, 1848) to (1040, 1856) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to None (risking incompatibility). You may also see a FFMPEG warning concerning speedloss due to data not being aligned.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
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      ]
    }
  ]
}