PIL wandb images

pil-wandb.ipynb

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      "metadata": {
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/ariG23498/c5c9b32a6b34028eaa7e32a49f3f7172/scratchpad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIYdn1woOS1n"
      },
      "source": [
        "import PIL\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EdezNUV0Wxtx"
      },
      "source": [
        "ar = [(np.random.rand(20,20,3)*225).astype('uint8') for i in range(10)]"
      ],
      "execution_count": 33,
      "outputs": []
    },
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        },
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        "outputId": "857c0b27-1fe6-48ab-dfea-2a092e76f3e6"
      },
      "source": [
        "plt.imshow(ar[0])"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f851defdc10>"
            ]
          },
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          },
          "execution_count": 34
        },
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            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-rFeoIuEWuaS"
      },
      "source": [
        "images = [PIL.Image.fromarray(image, 'RGB') for image in ar]"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "-0ewy_nQYGPA",
        "outputId": "57e3e174-5bee-4500-b098-1fd5d6e4c3b3"
      },
      "source": [
        "plt.imshow(images[1])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f851e9c7950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Ly_gA1OXTXN",
        "outputId": "1e7bebe3-abbf-4560-c4bc-3d5f3397863d"
      },
      "source": [
        "!pip install wandb -qqq\n",
        "import wandb"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 1.8MB 5.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 133kB 28.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 102kB 6.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 163kB 33.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 71kB 5.7MB/s \n",
            "\u001b[?25h  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517,
          "referenced_widgets": [
            "11cbc06d63fe453d89fc766a2c6583eb",
            "bdf02a7c3afd42039686f10116104425",
            "5774efd745814abaa20b6cb4e6ad034e",
            "9e49b297ba4d4160a1d90fb2b435881d",
            "2019cb7b13d041a6895a14d0ad99c3cc",
            "a5a748c18b3e42e2883746646b710137",
            "899918d96a2a4444b3e427ff81932db8",
            "fd9ee87194964cb79573ba64e98238bc"
          ]
        },
        "id": "RIPkDXUSXC7Y",
        "outputId": "69f8e471-c4ef-49b2-aea0-9fad5424959b"
      },
      "source": [
        "run = wandb.init(entity=\"repro\", project=\"PIL_Images\")\n",
        "with run:\n",
        "    run.log({\"img\":[wandb.Image(image) for image in images]})"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Tracking run with wandb version 0.10.30<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">amber-wildflower-6</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/repro/PIL_Images\" target=\"_blank\">https://wandb.ai/repro/PIL_Images</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/repro/PIL_Images/runs/wy2bait9\" target=\"_blank\">https://wandb.ai/repro/PIL_Images/runs/wy2bait9</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210512_162938-wy2bait9</code><br/><br/>\n",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 388<br/>Program ended successfully."
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "11cbc06d63fe453d89fc766a2c6583eb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "VBox(children=(Label(value=' 0.00MB of 0.01MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=0.0, max=1.0)…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Find user logs for this run at: <code>/content/wandb/run-20210512_162938-wy2bait9/logs/debug.log</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Find internal logs for this run at: <code>/content/wandb/run-20210512_162938-wy2bait9/logs/debug-internal.log</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<h3>Run summary:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>_runtime</td><td>3</td></tr><tr><td>_timestamp</td><td>1620836981</td></tr><tr><td>_step</td><td>0</td></tr></table>"
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              "<h3>Run history:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>_runtime</td><td>▁</td></tr><tr><td>_timestamp</td><td>▁</td></tr><tr><td>_step</td><td>▁</td></tr></table><br/>"
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          "data": {
            "text/html": [
              "Synced 4 W&B file(s), 10 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "\n",
              "                    <br/>Synced <strong style=\"color:#cdcd00\">amber-wildflower-6</strong>: <a href=\"https://wandb.ai/repro/PIL_Images/runs/wy2bait9\" target=\"_blank\">https://wandb.ai/repro/PIL_Images/runs/wy2bait9</a><br/>\n",
              "                "
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