ProPainter_20201201_success_V1.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "mount_file_id": "1xoAh9O58WZDCOQne2JLIh1VM1pVHxxZj",
      "authorship_tag": "ABX9TyNf/GY5p0stHwXWshQCbP55",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/7788po/colab-notebook/blob/main/ProPainter_20201201_success_V1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "moJkxivqiHcy",
        "outputId": "ff09969d-d03c-49c8-9a8c-bce01c59a801"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m32.9/32.9 MB\u001b[0m \u001b[31m52.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q timm einops av"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/sczhou/ProPainter.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n9i2hpnJjk5K",
        "outputId": "31effcb4-efe1-414f-daee-aeb55ea19894"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'ProPainter'...\n",
            "remote: Enumerating objects: 593, done.\u001b[K\n",
            "remote: Counting objects: 100% (112/112), done.\u001b[K\n",
            "remote: Compressing objects: 100% (82/82), done.\u001b[K\n",
            "remote: Total 593 (delta 30), reused 91 (delta 23), pack-reused 481\u001b[K\n",
            "Receiving objects: 100% (593/593), 55.21 MiB | 25.36 MiB/s, done.\n",
            "Resolving deltas: 100% (83/83), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qdRUJRF7nZex",
        "outputId": "495de19b-5fb5-493c-92ed-ba6868aadb08"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hMHrQNOVoG49",
        "outputId": "0fe86d1d-d8a2-49d2-91f8-44d68bd257bc"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ProPainter  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd ProPainter"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hF55cQXXoIr1",
        "outputId": "987be58f-9aca-4ba2-fb7f-f32b9de8705d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ProPainter\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lu85-3RyoNqT",
        "outputId": "c1c07b43-8e17-4e2f-fd13-aa77a2b27d1a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "assets\t datasets\t\t  LICENSE  README.md\t     train.py\tweights\n",
            "configs  inference_propainter.py  model    requirements.txt  utils\n",
            "core\t inputs\t\t\t  RAFT\t   scripts\t     web-demos\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cAylYFXoS3E",
        "outputId": "19db7d05-2440-41c0-d5fe-df68796c7c03"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: av in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 1)) (11.0.0)\n",
            "Collecting addict (from -r requirements.txt (line 2))\n",
            "  Downloading addict-2.4.0-py3-none-any.whl (3.8 kB)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 14)) (2.31.0)\n",
            "Requirement already satisfied: timm in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 15)) (0.9.12)\n",
            "Collecting yapf (from -r requirements.txt (line 16))\n",
            "  Downloading yapf-0.40.2-py3-none-any.whl (254 kB)\n",
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            "\u001b[?25hRequirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->-r requirements.txt (line 8)) (1.2.0)\n",
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            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from imageio-ffmpeg->-r requirements.txt (line 12)) (67.7.2)\n",
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            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from timm->-r requirements.txt (line 15)) (0.19.4)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->-r requirements.txt (line 8)) (1.16.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->timm->-r requirements.txt (line 15)) (4.66.1)\n",
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            "Installing collected packages: addict, yapf\n",
            "Successfully installed addict-2.4.0 yapf-0.40.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd&&ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZiyXDs0qbaW",
        "outputId": "a23c5ecc-d79d-4d4e-eee7-89dc5cb21f51"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ProPainter\n",
            "assets\t datasets\t\t  LICENSE  README.md\t     train.py\tweights\n",
            "configs  inference_propainter.py  model    requirements.txt  utils\n",
            "core\t inputs\t\t\t  RAFT\t   scripts\t     web-demos\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p inputs/object_removal/1 inputs/object_removal/1_mask"
      ],
      "metadata": {
        "id": "o-pbHctyYU-3"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p inputs/object_removal/2 inputs/object_removal/2_mask"
      ],
      "metadata": {
        "id": "iuwCh_0OYrj6"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p inputs/object_removal/3 inputs/object_removal/3_mask"
      ],
      "metadata": {
        "id": "zJpQjcjnYrwp"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p inputs/object_removal/4 inputs/object_removal/4_mask"
      ],
      "metadata": {
        "id": "dKBg9QmkYr7H"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p inputs/object_removal/5 inputs/object_removal/5_mask"
      ],
      "metadata": {
        "id": "p7nPUNRvYsF0"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/ProPainter/inputs/object_removal/1_mask\n",
        "!ls\n",
        "!unzip 337218362_pred_mask.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GRIz6rQbZ3Dl",
        "outputId": "cd97c49e-15ad-4a58-ff2d-82bc70b3e87d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ProPainter/inputs/object_removal/1_mask\n",
            "337218362_pred_mask.zip\n",
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            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd\n",
        "!ls\n",
        "%cd content\n",
        "!pwd\n",
        "!ls\n",
        "%cd content\n",
        "!pwd\n",
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Gm9nZakbE0V",
        "outputId": "36371404-0d66-47ff-fd9f-84524748f07a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ProPainter/inputs/object_removal/1_mask\n",
            "337218362_pred_mask.zip  content\n",
            "/content/ProPainter/inputs/object_removal/1_mask/content\n",
            "/content/ProPainter/inputs/object_removal/1_mask/content\n",
            "Segment-and-Track-Anything\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/ProPainter/inputs/object_removal/2_mask\n",
        "!ls\n",
        "!unzip 514114609_pred_mask\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3GT8rbtbj_t",
        "outputId": "618f176c-1b0d-41dd-b5d2-bdd7f46aadea"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ProPainter/inputs/object_removal/2_mask\n",
            "514114609_pred_mask.zip\n",
            "Archive:  514114609_pred_mask.zip\n",
            "   creating: content/Segment-and-Track-Anything/tracking_results/514114609/514114609_masks/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/ProPainter/inputs/object_removal/3_mask\n",
        "!ls\n",
        "!unzip 1217778986_pred_mask"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ofHApSN3b4GT",
        "outputId": "de6a7bcd-b954-4734-8f78-5d254264a8c4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ProPainter/inputs/object_removal/3_mask\n",
            "1217778986_pred_mask.zip\n",
            "Archive:  1217778986_pred_mask.zip\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/ProPainter/inputs/object_removal/4_mask\n",
        "!ls\n",
        "!unzip 1384620913_pred_mask"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vGCTYnduafNd",
        "outputId": "3e903a63-657d-4844-cc99-9d44ee84a92b"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ProPainter/inputs/object_removal/4_mask\n",
            "1384620913_pred_mask.zip\n",
            "Archive:  1384620913_pred_mask.zip\n",
            "   creating: content/Segment-and-Track-Anything/tracking_results/1384620913/1384620913_masks/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/ProPainter/inputs/object_removal/5_mask\n",
        "!ls\n",
        "!unzip 1704936041_pred_mask"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-D6cqq0cE-z",
        "outputId": "08970872-26e6-437a-b51f-0b95c0559292"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ProPainter/inputs/object_removal/5_mask\n",
            "1704936041_pred_mask.zip\n",
            "Archive:  1704936041_pred_mask.zip\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_propainter.py --video inputs/object_removal/1 --mask inputs/object_removal/1_mask/content/Segment-and-Track-Anything/tracking_results/337218362/337218362_masks"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPZny2zucXT7",
        "outputId": "00c42941-64b4-485a-ee36-8ae62de6fb2e"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "python3: can't open file '/content/ProPainter/inputs/object_removal/5_mask/inference_propainter.py': [Errno 2] No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MchrbhxQcXgu",
        "outputId": "6858fd9d-fc09-45f4-cd97-98e86f182023"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ProPainter/inputs/object_removal/5_mask\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/ProPainter/\n",
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "grDBOaOacXq9",
        "outputId": "e8a782c3-1146-44fd-86c3-a56fcbe478d7"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ProPainter\n",
            "/content/ProPainter\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_propainter.py --video inputs/object_removal/1 --mask inputs/object_removal/1_mask/content/Segment-and-Track-Anything/tracking_results/337218362/337218362_masks"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n8AqthrwdYOR",
        "outputId": "c9bdbd0c-febd-4fc6-affe-01883a2e2200"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/content/ProPainter/inference_propainter.py\", line 224, in <module>\n",
            "    frames, fps, size, video_name = read_frame_from_videos(args.video)\n",
            "  File \"/content/ProPainter/inference_propainter.py\", line 62, in read_frame_from_videos\n",
            "    frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
            "cv2.error: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_propainter.py --video inputs/object_removal/1/337218362.mp4 --mask inputs/object_removal/1_mask/content/Segment-and-Track-Anything/tracking_results/337218362/337218362_masks"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fxzGVZeKiLtK",
        "outputId": "fcda8fc8-427b-4552-d15e-76cd983251a9"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading: \"https://github.com/sczhou/ProPainter/releases/download/v0.1.0/raft-things.pth\" to /content/ProPainter/weights/raft-things.pth\n",
            "\n",
            "100% 20.1M/20.1M [00:00<00:00, 249MB/s]\n",
            "Downloading: \"https://github.com/sczhou/ProPainter/releases/download/v0.1.0/recurrent_flow_completion.pth\" to /content/ProPainter/weights/recurrent_flow_completion.pth\n",
            "\n",
            "100% 19.4M/19.4M [00:00<00:00, 75.0MB/s]\n",
            "Pretrained flow completion model has loaded...\n",
            "Downloading: \"https://github.com/sczhou/ProPainter/releases/download/v0.1.0/ProPainter.pth\" to /content/ProPainter/weights/ProPainter.pth\n",
            "\n",
            "100% 150M/150M [00:00<00:00, 274MB/s]\n",
            "Pretrained ProPainter has loaded...\n",
            "Network [InpaintGenerator] was created. Total number of parameters: 39.4 million. To see the architecture, do print(network).\n",
            "\n",
            "Processing: 337218362 [115 frames]...\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/ProPainter/inference_propainter.py\", line 372, in <module>\n",
            "    masked_frames = frames * (1 - masks_dilated)\n",
            "RuntimeError: The size of tensor a (115) must match the size of tensor b (116) at non-singleton dimension 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_propainter.py --video inputs/object_removal/2/514114609.mp4 --mask inputs/object_removal/2_mask/content/Segment-and-Track-Anything/tracking_results/514114609/514114609_masks"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pL1xfup2jeWE",
        "outputId": "2d980a3b-8c08-462e-a708-28e8e012f481"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pretrained flow completion model has loaded...\n",
            "Pretrained ProPainter has loaded...\n",
            "Network [InpaintGenerator] was created. Total number of parameters: 39.4 million. To see the architecture, do print(network).\n",
            "\n",
            "Processing: 514114609 [420 frames]...\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/ProPainter/inference_propainter.py\", line 350, in <module>\n",
            "    pred_flows_bi_sub, _ = fix_flow_complete.forward_bidirect_flow(\n",
            "  File \"/content/ProPainter/model/recurrent_flow_completion.py\", line 327, in forward_bidirect_flow\n",
            "    pred_flows_forward, pred_edges_forward = self.forward(masked_flows_forward, masks_forward)\n",
            "  File \"/content/ProPainter/model/recurrent_flow_completion.py\", line 300, in forward\n",
            "    flow = self.upsample(feat_d1)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n",
            "    return self._call_impl(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n",
            "    return forward_call(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py\", line 215, in forward\n",
            "    input = module(input)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n",
            "    return self._call_impl(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n",
            "    return forward_call(*args, **kwargs)\n",
            "  File \"/content/ProPainter/model/recurrent_flow_completion.py\", line 141, in forward\n",
            "    x = F.interpolate(x,\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\", line 4020, in interpolate\n",
            "    return torch._C._nn.upsample_bilinear2d(input, output_size, align_corners, scale_factors)\n",
            "torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.39 GiB. GPU 0 has a total capacty of 14.75 GiB of which 1.91 GiB is free. Process 113198 has 12.84 GiB memory in use. Of the allocated memory 8.35 GiB is allocated by PyTorch, and 3.43 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_propainter.py --video inputs/object_removal/3/1217778986.mp4 --mask /content/ProPainter/inputs/object_removal/3_mask/content/Segment-and-Track-Anything/tracking_results/1217778986/1217778986_masks"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Au_ALnE0lcdG",
        "outputId": "44124167-275b-443e-801a-831c31680312"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pretrained flow completion model has loaded...\n",
            "Pretrained ProPainter has loaded...\n",
            "Network [InpaintGenerator] was created. Total number of parameters: 39.4 million. To see the architecture, do print(network).\n",
            "\n",
            "Processing: 1217778986 [172 frames]...\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/ProPainter/inference_propainter.py\", line 372, in <module>\n",
            "    masked_frames = frames * (1 - masks_dilated)\n",
            "RuntimeError: The size of tensor a (172) must match the size of tensor b (173) at non-singleton dimension 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp /content/ProPainter/inputs/object_removal/3_mask/content/Segment-and-Track-Anything/tracking_results/1217778986/1217778986_masks/00171.png /content/ProPainter/inputs/object_removal/3_mask/content/Segment-and-Track-Anything/tracking_results/1217778986/1217778986_masks/00172.png"
      ],
      "metadata": {
        "id": "4WjvgeK4nZR3"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_propainter.py --video inputs/object_removal/3/1217778986.mp4 --mask inputs/object_removal/3_mask/content/Segment-and-Track-Anything/tracking_results/1217778986/1217778986_masks/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rvfNIh4snvAu",
        "outputId": "9ab4656b-192d-4b58-9844-49e83c12fcf8"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pretrained flow completion model has loaded...\n",
            "Pretrained ProPainter has loaded...\n",
            "Network [InpaintGenerator] was created. Total number of parameters: 39.4 million. To see the architecture, do print(network).\n",
            "\n",
            "Processing: 1217778986 [172 frames]...\n",
            "100% 35/35 [02:00<00:00,  3.45s/it]\n",
            "\n",
            "All results are saved in results/1217778986\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_propainter.py --video inputs/object_removal/1/337218362.mp4 --mask inputs/object_removal/1_mask/content/Segment-and-Track-Anything/tracking_results/337218362/337218362_masks/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EYm2bkSKqZrA",
        "outputId": "e4b4cbea-a56e-476c-fc06-823a9dc3d2b5"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pretrained flow completion model has loaded...\n",
            "Pretrained ProPainter has loaded...\n",
            "Network [InpaintGenerator] was created. Total number of parameters: 39.4 million. To see the architecture, do print(network).\n",
            "\n",
            "Processing: 337218362 [115 frames]...\n",
            "100% 23/23 [01:14<00:00,  3.24s/it]\n",
            "\n",
            "All results are saved in results/337218362\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_propainter.py --video inputs/object_removal/4/1384620913.mp4 --mask inputs/object_removal/4_mask/content/Segment-and-Track-Anything/tracking_results/1384620913/1384620913_masks/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AUeVxTbwr8SL",
        "outputId": "5961dfac-1077-418d-c59a-16d353523fad"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pretrained flow completion model has loaded...\n",
            "Pretrained ProPainter has loaded...\n",
            "Network [InpaintGenerator] was created. Total number of parameters: 39.4 million. To see the architecture, do print(network).\n",
            "\n",
            "Processing: 1384620913 [196 frames]...\n",
            "100% 40/40 [02:25<00:00,  3.64s/it]\n",
            "\n",
            "All results are saved in results/1384620913\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_propainter.py --video inputs/object_removal/5/1704936041.mp4 --mask /content/ProPainter/inputs/object_removal/5_mask/content/Segment-and-Track-Anything/tracking_results/1704936041/1704936041_masks/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pg-MEuQOtVnK",
        "outputId": "c27c6928-fd3a-4305-9dba-367f3c02ce23"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pretrained flow completion model has loaded...\n",
            "Pretrained ProPainter has loaded...\n",
            "Network [InpaintGenerator] was created. Total number of parameters: 39.4 million. To see the architecture, do print(network).\n",
            "\n",
            "Processing: 1704936041 [133 frames]...\n",
            "100% 27/27 [01:28<00:00,  3.29s/it]\n",
            "\n",
            "All results are saved in results/1704936041\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_propainter.py --video inputs/object_removal/2/514114609.mp4 --mask /content/ProPainter/inputs/object_removal/2_mask/content/Segment-and-Track-Anything/tracking_results/514114609/514114609_masks/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GEFFOWdNujOn",
        "outputId": "43edd01d-db77-49a2-e6da-b0c2ec794965"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pretrained flow completion model has loaded...\n",
            "Pretrained ProPainter has loaded...\n",
            "Network [InpaintGenerator] was created. Total number of parameters: 39.4 million. To see the architecture, do print(network).\n",
            "\n",
            "Processing: 514114609 [420 frames]...\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/ProPainter/inference_propainter.py\", line 350, in <module>\n",
            "    pred_flows_bi_sub, _ = fix_flow_complete.forward_bidirect_flow(\n",
            "  File \"/content/ProPainter/model/recurrent_flow_completion.py\", line 327, in forward_bidirect_flow\n",
            "    pred_flows_forward, pred_edges_forward = self.forward(masked_flows_forward, masks_forward)\n",
            "  File \"/content/ProPainter/model/recurrent_flow_completion.py\", line 300, in forward\n",
            "    flow = self.upsample(feat_d1)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n",
            "    return self._call_impl(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n",
            "    return forward_call(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py\", line 215, in forward\n",
            "    input = module(input)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n",
            "    return self._call_impl(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n",
            "    return forward_call(*args, **kwargs)\n",
            "  File \"/content/ProPainter/model/recurrent_flow_completion.py\", line 141, in forward\n",
            "    x = F.interpolate(x,\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\", line 4020, in interpolate\n",
            "    return torch._C._nn.upsample_bilinear2d(input, output_size, align_corners, scale_factors)\n",
            "torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.39 GiB. GPU 0 has a total capacty of 14.75 GiB of which 1.90 GiB is free. Process 206536 has 12.85 GiB memory in use. Of the allocated memory 8.35 GiB is allocated by PyTorch, and 3.44 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf /content/ProPainter/inputs/object_removal/2_mask/content/Segment-and-Track-Anything/tracking_results/514114609/514114609_masks/.ipynb_checkpoints"
      ],
      "metadata": {
        "id": "0YoRxi92u10G"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf /content/ProPainter/inputs/object_removal/5_mask/content/Segment-and-Track-Anything/tracking_results/1704936041/1704936041_masks/.ipynb_checkpoints"
      ],
      "metadata": {
        "id": "QL6JPb2dt3kF"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf inputs/object_removal/4_mask/content/Segment-and-Track-Anything/tracking_results/1384620913/1384620913_masks/.ipynb_checkpoints"
      ],
      "metadata": {
        "id": "3Usum2-nsU0d"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf inputs/object_removal/1_mask/content/Segment-and-Track-Anything/tracking_results/337218362/337218362_masks/.ipynb_checkpoints"
      ],
      "metadata": {
        "id": "gfcoyXrarVYC"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf inputs/object_removal/3_mask/content/Segment-and-Track-Anything/tracking_results/1217778986/1217778986_masks/.ipynb_checkpoints"
      ],
      "metadata": {
        "id": "Qd2UW7Yuo8ta"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_propainter.py --video inputs/object_removal/3/1217778 --mask"
      ],
      "metadata": {
        "id": "oR0EN3fxluQL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_propainter.py --video inputs/object_removal/2 --mask inputs/object_removal/2_mask/content/Segment-and-Track-Anything/tracking_results/514114609/514114609_masks"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k6Sb1TMzd6NV",
        "outputId": "6029af1c-3827-426b-b5ee-f03d32bb45c0"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/content/ProPainter/inference_propainter.py\", line 224, in <module>\n",
            "    frames, fps, size, video_name = read_frame_from_videos(args.video)\n",
            "  File \"/content/ProPainter/inference_propainter.py\", line 62, in read_frame_from_videos\n",
            "    frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
            "cv2.error: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_propainter.py --video inputs/object_removal/bmx-trees --mask inputs/object_removal/bmx-trees_mask"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-pEf3TDooB1",
        "outputId": "de3867d1-2ec2-499c-f378-f29b3dec734c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading: \"https://github.com/sczhou/ProPainter/releases/download/v0.1.0/raft-things.pth\" to /content/ProPainter/weights/raft-things.pth\n",
            "\n",
            "100% 20.1M/20.1M [00:00<00:00, 161MB/s]\n",
            "Downloading: \"https://github.com/sczhou/ProPainter/releases/download/v0.1.0/recurrent_flow_completion.pth\" to /content/ProPainter/weights/recurrent_flow_completion.pth\n",
            "\n",
            "100% 19.4M/19.4M [00:00<00:00, 168MB/s]\n",
            "Pretrained flow completion model has loaded...\n",
            "Downloading: \"https://github.com/sczhou/ProPainter/releases/download/v0.1.0/ProPainter.pth\" to /content/ProPainter/weights/ProPainter.pth\n",
            "\n",
            "100% 150M/150M [00:00<00:00, 207MB/s]\n",
            "Pretrained ProPainter has loaded...\n",
            "Network [InpaintGenerator] was created. Total number of parameters: 39.4 million. To see the architecture, do print(network).\n",
            "\n",
            "Processing: bmx-trees [80 frames]...\n",
            "100% 16/16 [00:16<00:00,  1.02s/it]\n",
            "\n",
            "All results are saved in results/bmx-trees\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_propainter.py --video inputs/video_completion/running_car.mp4 --mask inputs/video_completion/mask_square.png --height 240 --width 432\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZViAZNozs0Fj",
        "outputId": "0b4b4fa3-3b9b-4c79-c6ed-d2ee46e20970"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pretrained flow completion model has loaded...\n",
            "Pretrained ProPainter has loaded...\n",
            "Network [InpaintGenerator] was created. Total number of parameters: 39.4 million. To see the architecture, do print(network).\n",
            "\n",
            "Processing: running_car [292 frames]...\n",
            "100% 59/59 [01:12<00:00,  1.23s/it]\n",
            "\n",
            "All results are saved in results/running_car\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Lt-Q7b8mvoZX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TAGkp2Envoxf",
        "outputId": "affd1b23-7319-46eb-fcb0-7764102c8fc6"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/ProPainter\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r /content/ProPainter/results   /content/drive/MyDrive/"
      ],
      "metadata": {
        "id": "5NqqliTowC3m"
      },
      "execution_count": 63,
      "outputs": []
    }
  ]
}