final_1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qkzuA1v6dnRn"
      },
      "source": [
        "# Cloth Wraping\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "pw-wwhryBscH",
        "outputId": "00601542-21bf-4596-9dff-b756846dab2e"
      },
      "outputs": [
        {
          "ename": "MessageError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mMessageError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-d5df0069828e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms)\u001b[0m\n\u001b[1;32m    103\u001b[0m       \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m       \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout_ms\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m       ephemeral=True)\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral)\u001b[0m\n\u001b[1;32m    118\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mephemeral\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m     _message.blocking_request(\n\u001b[0;32m--> 120\u001b[0;31m         'request_auth', request={'authType': 'dfs_ephemeral'}, timeout_sec=None)\n\u001b[0m\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m   \u001b[0mmountpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_os\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpanduser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mblocking_request\u001b[0;34m(request_type, request, timeout_sec, parent)\u001b[0m\n\u001b[1;32m    169\u001b[0m   request_id = send_request(\n\u001b[1;32m    170\u001b[0m       request_type, request, parent=parent, expect_reply=True)\n\u001b[0;32m--> 171\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m    100\u001b[0m         reply.get('colab_msg_id') == message_id):\n\u001b[1;32m    101\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;34m'error'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mMessageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'error'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mMessageError\u001b[0m: Error: credential propagation was unsuccessful"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wYhPWhClTBVA",
        "outputId": "a0db4d7d-c886-4748-a5e0-07c07e9ce7cb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "cd /content/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZXzISl6wdk_n",
        "outputId": "37bc21db-af11-4604-aaeb-6bd8e7c5e00c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "!pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KQLBznQatuPE"
      },
      "outputs": [],
      "source": [
        "%rm -r inputs/*\n",
        "%rm -r inputs_cloth/*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ufCZbQoqeD8y",
        "outputId": "a69a68d1-2331-4d90-9aba-731bec129d45"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘inputs’: File exists\n",
            "mkdir: cannot create directory ‘inputs_cloth’: File exists\n"
          ]
        }
      ],
      "source": [
        "! mkdir inputs\n",
        "! mkdir inputs_cloth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "q51c_FiQO2zK",
        "outputId": "6cab3cc0-ede1-4c28-bb43-b53732ddaa3c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/inputs\n",
            "Upload the model image : \n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-fcda4fa7-f7ce-4c38-ae64-96f3b7ee175d\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-fcda4fa7-f7ce-4c38-ae64-96f3b7ee175d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving 00094_00.jpg to 00094_00.jpg\n",
            "/content\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "%cd inputs\n",
        "print('Upload the model image : ')\n",
        "uploaded = files.upload()\n",
        "%cd .."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "ukA9gEOSPGXC",
        "outputId": "4f912bb6-a235-48fe-8dba-1ead50ba3994"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/inputs_cloth\n",
            "upload the cloth image : \n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7d8596e7-1429-481d-91c6-3508de273f8d\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7d8596e7-1429-481d-91c6-3508de273f8d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving cloth1.jpg to cloth1.jpg\n",
            "/content\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "%cd inputs_cloth\n",
        "print('upload the cloth image : ')\n",
        "uploaded = files.upload()\n",
        "%cd .."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qPVbTEVbZgmY"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "model_image = os.listdir(\"/content/inputs\")\n",
        "cloth_image = os.listdir(\"/content/inputs_cloth\")\n",
        "\n",
        "model_image = model_image[0]\n",
        "cloth_image = cloth_image[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0dHIcVp7ak5o",
        "outputId": "f2a61167-60fd-469f-954e-cc240bdf5dc5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "00094_00.jpg\n",
            "cloth1.jpg\n"
          ]
        }
      ],
      "source": [
        "print(model_image)\n",
        "print(cloth_image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dVtJq_DCwvoE",
        "outputId": "372da3cb-93a3-4d18-f602-c27c419b1786"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 28\n",
            "drwx------ 5 root root 4096 Aug 19 14:37 \u001b[0m\u001b[01;34mdrive\u001b[0m/\n",
            "drwxr-xr-x 4 root root 4096 Aug 19 14:40 \u001b[01;34mfinal_output\u001b[0m/\n",
            "drwxr-xr-x 2 root root 4096 Aug 19 14:44 \u001b[01;34minputs\u001b[0m/\n",
            "drwxr-xr-x 2 root root 4096 Aug 19 14:44 \u001b[01;34minputs_cloth\u001b[0m/\n",
            "drwxr-xr-x 2 root root 4096 Aug 19 14:38 \u001b[01;34minputs_cloth_mask\u001b[0m/\n",
            "drwxr-xr-x 2 root root 4096 Aug 19 14:38 \u001b[01;34minputs_cloth_mask_modified\u001b[0m/\n",
            "drwxr-xr-x 1 root root 4096 Aug 15 13:44 \u001b[01;34msample_data\u001b[0m/\n"
          ]
        }
      ],
      "source": [
        "ls -l"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yMqkLgWSu9dR"
      },
      "outputs": [],
      "source": [
        "import glob \n",
        "\n",
        "paths = glob.glob('./inputs/*')\n",
        "paths.extend(glob.glob('./inputs_cloth/*'))\n",
        "for i in paths:\n",
        "  if '.jpg' not in i:\n",
        "    os.remove(i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h67VmiGJLCFa",
        "outputId": "cff765a9-612e-4e79-f62a-4c945a52e2f5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "00094_00.jpg\n",
            "(1024, 768, 3)\n",
            "(1024, 768, 3)\n"
          ]
        }
      ],
      "source": [
        "import cv2 \n",
        "import os \n",
        "\n",
        "for i in os.listdir('./inputs/'):\n",
        "    print(i)\n",
        "    im = cv2.imread('./inputs/'+i)\n",
        "    print(im.shape)\n",
        "    im = cv2.resize(im,(768,1024))\n",
        "    print(im.shape)\n",
        "    cv2.imwrite('./inputs/'+i.split('.')[0]+'.jpg',im)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_jZBO4CU2QET",
        "outputId": "e27b712c-283c-4303-938d-6aa679d72184"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cloth1.jpg\n",
            "(869, 670, 3)\n",
            "(1024, 768, 3)\n"
          ]
        }
      ],
      "source": [
        "import cv2 \n",
        "import os \n",
        "\n",
        "for i in os.listdir('./inputs_cloth/'):\n",
        "    print(i)\n",
        "    im = cv2.imread('./inputs_cloth/'+i)\n",
        "    print(im.shape)\n",
        "    im = cv2.resize(im,(768,1024))\n",
        "    print(im.shape)\n",
        "    cv2.imwrite('./inputs_cloth/'+i.split('.')[0]+'.jpg',im)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r9z5dQFvhnb1"
      },
      "source": [
        "## Cloth Segmentation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zuvm4L9Rhr8S",
        "outputId": "2c00ba27-0619-4943-be0e-a754af6e10a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "%cd /content/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IlRUb_yLzzCY"
      },
      "outputs": [],
      "source": [
        "! rm -r inputs_cloth_mask"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XV7Ev81mvz6G",
        "outputId": "50ca33d5-bc1f-4634-816f-ffa225b785d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "rm: cannot remove '/content/inputs_cloth/.ipynb_checkpoints': No such file or directory\n"
          ]
        }
      ],
      "source": [
        "rm -r /content/inputs_cloth/.ipynb_checkpoints"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bTzQCow0jC5G"
      },
      "outputs": [],
      "source": [
        "! mkdir inputs_cloth_mask"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hDIlw1tij_no",
        "outputId": "52adaadf-71fb-4f24-ff4c-0fe65888153e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/hr-viton/pipeline/HumanPose/clothsegment\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/hr-viton/pipeline/HumanPose/clothsegment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ja2JCafIkJL2",
        "outputId": "2190ca2e-f7f6-455e-8fab-b1bd58f9b1c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----checkpoints loaded from path: trained_checkpoint/cloth_segm_u2net_latest.pth----\n",
            "\r  0% 0/1 [00:00<?, ?it/s]['cloth1.jpg']\n",
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3722: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.\n",
            "  warnings.warn(\"nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.\")\n",
            "100% 1/1 [00:17<00:00, 17.15s/it]\n"
          ]
        }
      ],
      "source": [
        "! python3 infer.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QicvVGuc3DSF",
        "outputId": "3080ae88-cea8-4757-e526-08f8d8e886b3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "%cd /content/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sORL9lB_11HQ"
      },
      "outputs": [],
      "source": [
        "%rm -r inputs_cloth_mask_modified"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o7GzuZHYXVmt"
      },
      "outputs": [],
      "source": [
        "from PIL import Image, ImageChops\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DAmy27pH189v",
        "outputId": "7802220e-0733-4298-e51f-fd1cf7afe891"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "768 1024\n"
          ]
        }
      ],
      "source": [
        "! mkdir inputs_cloth_mask_modified\n",
        "images_list = os.listdir('./inputs_cloth_mask')\n",
        "for image in images_list:\n",
        "  image_path = './inputs_cloth_mask/' + image\n",
        "  image_save_path = './inputs_cloth_mask_modified/' + image.split('.')[0] + '.jpg'\n",
        "  img = Image.open(image_path)\n",
        "  img = img.convert(\"RGB\")\n",
        "  width, height = img.size\n",
        "  print(width, height)\n",
        "  for i in range(0, width):  \t\t\t\t\t  \n",
        "    for j in range(0, height):  \t\t\t\t  \n",
        "      data = img.getpixel((i, j))  \t\t\t\t  \n",
        "      if (data[0] >= 64):\n",
        "        img.putpixel((i, j), (255, 255, 255))\n",
        "  img = img.convert('L')\n",
        "  img.save(image_save_path)  \t\t "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jCn5O81JkY2N"
      },
      "source": [
        "## Wraping Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6sVEom4imAKN",
        "outputId": "4cefee19-b56b-4e4c-f798-2a04894781ca"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2020 NVIDIA Corporation\n",
            "Built on Mon_Oct_12_20:09:46_PDT_2020\n",
            "Cuda compilation tools, release 11.1, V11.1.105\n",
            "Build cuda_11.1.TC455_06.29190527_0\n"
          ]
        }
      ],
      "source": [
        "! nvcc --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "waVJJ2OO3bEk",
        "outputId": "5dd2aaf9-8c6d-4ae6-cb81-abdd8770a33d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fri Aug 19 14:45:27 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   39C    P0    28W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "24qP1Y9fmGYW"
      },
      "outputs": [],
      "source": [
        "#!pip install torchgeometry"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fe9o3p4zoIGC",
        "outputId": "7a7064a9-723f-4064-9e0d-f6cbe486b955"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/hr-viton/pipeline/HumanPose/HR-VITON\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/hr-viton/pipeline/HumanPose/HR-VITON"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1s6HkJWypC1a"
      },
      "outputs": [],
      "source": [
        "%rm -f ./my_data/test/cloth/*\n",
        "%rm -f ./my_data/test/cloth-mask/*\n",
        "%rm -f ./my_data/test/image/*\n",
        "%rm -f ./my_data/test/image-densepose/*\n",
        "%rm -f ./my_data/test/image-parse-agnostic-v3.2/*\n",
        "%rm -f ./my_data/test/image-parse-v3/*\n",
        "%rm -f ./my_data/test/openpose_img/*\n",
        "%rm -f ./my_data/test/openpose_json/*\n",
        "%rm ./my_data/test_pairs.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gAiTh3LhbKmj"
      },
      "outputs": [],
      "source": [
        "with open('./my_data/test_pairs.txt', 'w') as f:\n",
        "    f.write(model_image+\" \"+cloth_image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bnqVFeHe3cuu"
      },
      "outputs": [],
      "source": [
        "import shutil"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "QTwme7AeqZTn",
        "outputId": "dc552bd4-3d17-477f-d982-ae456020bf01"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'my_data/test/openpose_json/00094_00_keypoints.json'"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.core.display import JSON\n",
        "\n",
        "%cp -r /content/inputs_cloth/* ./my_data/test/cloth\n",
        "%cp -r /content/inputs_cloth_mask_modified/* ./my_data/test/cloth-mask\n",
        "%cp -r /content/inputs/* ./my_data/test/image\n",
        "\n",
        "src = os.path.join('data/test/image-densepose', model_image)\n",
        "dst = os.path.join('my_data/test/image-densepose', model_image)\n",
        "shutil.copyfile(src, dst)\n",
        "\n",
        "src = os.path.join('data/test/image-parse-v3', model_image.replace(\"jpg\", \"png\"))\n",
        "dst = os.path.join('my_data/test/image-parse-v3', model_image.replace(\"jpg\", \"png\"))\n",
        "shutil.copyfile(src, dst)\n",
        "\n",
        "src = os.path.join('data/test/image-parse-agnostic-v3.2', model_image.replace(\"jpg\", \"png\"))\n",
        "dst = os.path.join('my_data/test/image-parse-agnostic-v3.2', model_image.replace(\"jpg\", \"png\"))\n",
        "shutil.copyfile(src, dst)\n",
        "\n",
        "src = os.path.join('data/test/openpose_img', (model_image[0:8]+\"_rendered\"+model_image[8:]).replace(\"jpg\",\"png\"))\n",
        "dst = os.path.join('my_data/test/openpose_img', (model_image[0:8]+\"_rendered\"+model_image[8:]).replace(\"jpg\",\"png\"))\n",
        "shutil.copyfile(src, dst)\n",
        "\n",
        "src = os.path.join('data/test/openpose_json', (model_image[0:8]+\"_keypoints\"+model_image[8:]).replace(\"jpg\",\"json\"))\n",
        "dst = os.path.join('my_data/test/openpose_json', (model_image[0:8]+\"_keypoints\"+model_image[8:]).replace(\"jpg\",\"json\"))\n",
        "shutil.copyfile(src, dst)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VwbgUAFJ8BcC",
        "outputId": "acb4bc64-e373-49ca-9640-0deb4686e59d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (4.6.0.66)\n",
            "Requirement already satisfied: torchgeometry in /usr/local/lib/python3.7/dist-packages (0.1.2)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (7.1.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (4.64.0)\n",
            "Requirement already satisfied: tensorboardX in /usr/local/lib/python3.7/dist-packages (2.5.1)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (0.18.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (1.7.3)\n",
            "Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from opencv-python) (1.21.6)\n",
            "Requirement already satisfied: torch>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from torchgeometry) (1.12.1+cu113)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.0.0->torchgeometry) (4.1.1)\n",
            "Requirement already satisfied: protobuf<=3.20.1,>=3.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorboardX) (3.17.3)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf<=3.20.1,>=3.8.0->tensorboardX) (1.15.0)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image) (2021.11.2)\n",
            "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image) (2.9.0)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image) (1.3.0)\n",
            "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image) (3.2.2)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image) (2.6.3)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (2.8.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install opencv-python torchgeometry Pillow tqdm tensorboardX scikit-image scipy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V2FWQrP2n6KL",
        "outputId": "487ae04c-a4f7-459e-dca6-09422946c569"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Namespace(batch_size=1, checkpoint_dir='checkpoints', clothmask_composition='warp_grad', data_list='test_pairs.txt', datamode='test', dataroot='my_data/', datasetting='unpaired', fine_height=1024, fine_width=768, fp16=False, gen_checkpoint='checkpoints/gen.pth', gen_semantic_nc=7, gpu_ids='0', init_type='xavier', init_variance=0.02, ngf=64, norm_G='spectralaliasinstance', num_upsampling_layers='most', occlusion=True, out_layer='relu', output_dir=None, output_nc=13, semantic_nc=13, shuffle=False, tensorboard_count=100, tensorboard_dir='tensorboard', test_name='test15', tocg_checkpoint='checkpoints/mtviton.pth', upsample='bilinear', warp_feature='T1', workers=4)\n",
            "Start to test %s!\n",
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:566: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n",
            "/usr/local/lib/python3.7/dist-packages/torchvision/transforms/transforms.py:333: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.\n",
            "  \"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. \"\n",
            "Network [SPADEGenerator] was created. Total number of parameters: 100.5 million. To see the architecture, do print(network).\n",
            "/usr/local/lib/python3.7/dist-packages/torchvision/transforms/transforms.py:333: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.\n",
            "  \"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. \"\n",
            "test_generator.py:121: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  pre_clothes_mask = torch.FloatTensor((pre_clothes_mask.detach().cpu().numpy() > 0.5).astype(np.float)).cuda()\n",
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:4216: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.\n",
            "  \"Default grid_sample and affine_grid behavior has changed \"\n",
            "test_generator.py:143: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  warped_cm_onehot = torch.FloatTensor((warped_clothmask_paired.detach().cpu().numpy() > 0.5).astype(np.float)).cuda()\n",
            "1\n",
            "Test time 4.104851245880127\n",
            "Finished testing!\n"
          ]
        }
      ],
      "source": [
        "!python3 test_generator.py --occlusion --test_name test15 --tocg_checkpoint checkpoints/mtviton.pth --gpu_ids 0 --gen_checkpoint checkpoints/gen.pth --datasetting unpaired --dataroot my_data/ --data_list test_pairs.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_wzNNUz2B0f",
        "outputId": "44e861b0-9be9-4941-c75d-732394963330"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "%cd /content/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QgTKUCvO1suy"
      },
      "outputs": [],
      "source": [
        "%rm -r final_output/*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d23Wnew411Sc",
        "outputId": "3909560c-9b34-44ef-e9ab-0320e2c2ad05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘final_output’: File exists\n"
          ]
        }
      ],
      "source": [
        "!mkdir final_output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DuMll7CJ1btr"
      },
      "outputs": [],
      "source": [
        "%cp -r /content/drive/MyDrive/hr-viton/pipeline/HumanPose/HR-VITON/output/test15/test/unpaired/generator/* final_output/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DunogKnSjCn0"
      },
      "outputs": [],
      "source": [
        "#pip freeze > requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7jJdbSdTY7h3"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "final_1.ipynb",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3.10.5 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.5"
    },
    "vscode": {
      "interpreter": {
        "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}