Mask_Extraction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Read me**\n",
        "\n",
        "The follow code is referenced from the Segment Anything GitHub repository by META AI research and FAIR. \n",
        "\n",
        "The code was modified to extract the masks from an input image. First, images of people wearing real masks were collected. Then, this code was used to detect and segment the mask away from the rest of the image. The purpose of this codeis to create the original image and mask that are used as input for our main model. \n",
        "\n",
        "Changes we made are added as comments throughout the code, with a Mask_Away tag in the front (Ctrl+F Mask_Away to find where we changed the code).\n",
        "\n",
        "Original code can be found at: https://github.com/facebookresearch/segment-anything"
      ],
      "metadata": {
        "id": "b1Te19lQHF7H"
      },
      "id": "b1Te19lQHF7H"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "901c8ef3",
      "metadata": {
        "id": "901c8ef3"
      },
      "outputs": [],
      "source": [
        "# Copyright (c) Meta Platforms, Inc. and affiliates."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1662bb7c",
      "metadata": {
        "id": "1662bb7c"
      },
      "source": [
        "# Produces masks from prompts using an ONNX model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7fcc21a0",
      "metadata": {
        "id": "7fcc21a0"
      },
      "source": [
        "SAM's prompt encoder and mask decoder are very lightweight, which allows for efficient computation of a mask given user input. This notebook shows an example of how to export and use this lightweight component of the model in ONNX format, allowing it to run on a variety of platforms that support an ONNX runtime."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "86daff77",
      "metadata": {
        "id": "86daff77",
        "outputId": "a8dccd29-7ab7-4a31-b6a5-8b61185d8c28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 41
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<a target=\"_blank\" href=\"https://colab.research.google.com/github/facebookresearch/segment-anything/blob/main/notebooks/onnx_model_example.ipynb\">\n",
              "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
              "</a>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from IPython.display import display, HTML\n",
        "display(HTML(\n",
        "\"\"\"\n",
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/facebookresearch/segment-anything/blob/main/notebooks/onnx_model_example.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>\n",
        "\"\"\"\n",
        "))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "55ae4e00",
      "metadata": {
        "id": "55ae4e00"
      },
      "source": [
        "## Environment Set-up"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "109a5cc2",
      "metadata": {
        "id": "109a5cc2"
      },
      "source": [
        "If running locally using jupyter, first install `segment_anything` in your environment using the [installation instructions](https://github.com/facebookresearch/segment-anything#installation) in the repository. The latest stable versions of PyTorch and ONNX are recommended for this notebook. If running from Google Colab, set `using_colab=True` below and run the cell. In Colab, be sure to select 'GPU' under 'Edit'->'Notebook Settings'->'Hardware accelerator'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "39b99fc4",
      "metadata": {
        "id": "39b99fc4"
      },
      "outputs": [],
      "source": [
        "using_colab = True\n",
        "\n",
        "# Mask_Away\n",
        "# we completely our testing on Google collab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "296a69be",
      "metadata": {
        "id": "296a69be",
        "outputId": "993a4d8b-919c-4718-c8c9-d6949b208c11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PyTorch version: 2.0.0+cu118\n",
            "Torchvision version: 0.15.1+cu118\n",
            "CUDA is available: True\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Collecting onnx\n",
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            "\u001b[?25hRequirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.9/dist-packages (from sympy->onnxruntime) (1.3.0)\n",
            "Installing collected packages: onnx, humanfriendly, coloredlogs, onnxruntime\n",
            "Successfully installed coloredlogs-15.0.1 humanfriendly-10.0 onnx-1.13.1 onnxruntime-1.14.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/facebookresearch/segment-anything.git\n",
            "  Cloning https://github.com/facebookresearch/segment-anything.git to /tmp/pip-req-build-op7jkrbp\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/facebookresearch/segment-anything.git /tmp/pip-req-build-op7jkrbp\n",
            "  Resolved https://github.com/facebookresearch/segment-anything.git to commit 3518c86b78b3bc9cf4fbe3d18e682fad1c79dc51\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: segment-anything\n",
            "  Building wheel for segment-anything (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for segment-anything: filename=segment_anything-1.0-py3-none-any.whl size=36602 sha256=1b4029c1fbac57b92211686053557c40b5db7df62eb3bd7a8eb234f4bc471f96\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-7zjwubjl/wheels/d5/11/03/7aca746a2c0e09f279b10436ced7175926bc38f650b736a648\n",
            "Successfully built segment-anything\n",
            "Installing collected packages: segment-anything\n",
            "Successfully installed segment-anything-1.0\n",
            "--2023-04-14 02:59:48--  https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg\n",
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            "--2023-04-14 02:59:48--  https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\n",
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            "\n"
          ]
        }
      ],
      "source": [
        "if using_colab:\n",
        "    import torch\n",
        "    import torchvision\n",
        "    print(\"PyTorch version:\", torch.__version__)\n",
        "    print(\"Torchvision version:\", torchvision.__version__)\n",
        "    print(\"CUDA is available:\", torch.cuda.is_available())\n",
        "    import sys\n",
        "    !{sys.executable} -m pip install opencv-python matplotlib onnx onnxruntime\n",
        "    !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/segment-anything.git'\n",
        "    \n",
        "    !mkdir images\n",
        "    !wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg\n",
        "        \n",
        "    !wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dc4a58be",
      "metadata": {
        "id": "dc4a58be"
      },
      "source": [
        "## Set-up"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "42396e8d",
      "metadata": {
        "id": "42396e8d"
      },
      "source": [
        "Note that this notebook requires both the `onnx` and `onnxruntime` optional dependencies, in addition to `opencv-python` and `matplotlib` for visualization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "2c712610",
      "metadata": {
        "id": "2c712610"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "from segment_anything import sam_model_registry, SamPredictor\n",
        "from segment_anything.utils.onnx import SamOnnxModel\n",
        "\n",
        "import onnxruntime\n",
        "from onnxruntime.quantization import QuantType\n",
        "from onnxruntime.quantization.quantize import quantize_dynamic"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "f29441b9",
      "metadata": {
        "id": "f29441b9"
      },
      "outputs": [],
      "source": [
        "# Mask_Away\n",
        "# The mask was edited to better suit our purposes for the main model. \n",
        "# We changed the mask to black and solid colour instead of a blue transparent mask. \n",
        "\n",
        "def show_mask(mask, ax):\n",
        "    color = np.array([0/255, 0/255, 0/255, 1])\n",
        "    h, w = mask.shape[-2:]\n",
        "    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n",
        "    ax.imshow(mask_image)\n",
        "    \n",
        "def show_points(coords, labels, ax, marker_size=375):\n",
        "    pos_points = coords[labels==1]\n",
        "    neg_points = coords[labels==0]\n",
        "    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)\n",
        "    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)   \n",
        "    \n",
        "def show_box(box, ax):\n",
        "    x0, y0 = box[0], box[1]\n",
        "    w, h = box[2] - box[0], box[3] - box[1]\n",
        "    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))   "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bd0f6b2b",
      "metadata": {
        "id": "bd0f6b2b"
      },
      "source": [
        "## Export an ONNX model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1540f719",
      "metadata": {
        "id": "1540f719"
      },
      "source": [
        "Set the path below to a SAM model checkpoint, then load the model. This will be needed to both export the model and to calculate embeddings for the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "76fc53f4",
      "metadata": {
        "id": "76fc53f4"
      },
      "outputs": [],
      "source": [
        "checkpoint = \"sam_vit_h_4b8939.pth\"\n",
        "model_type = \"vit_h\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "11bfc8aa",
      "metadata": {
        "id": "11bfc8aa"
      },
      "outputs": [],
      "source": [
        "sam = sam_model_registry[model_type](checkpoint=checkpoint)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "450c089c",
      "metadata": {
        "id": "450c089c"
      },
      "source": [
        "The script `segment-anything/scripts/export_onnx_model.py` can be used to export the necessary portion of SAM. Alternatively, run the following code to export an ONNX model. If you have already exported a model, set the path below and skip to the next section. Assure that the exported ONNX model aligns with the checkpoint and model type set above. This notebook expects the model was exported with the parameter `return_single_mask=True`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "38a8add8",
      "metadata": {
        "id": "38a8add8"
      },
      "outputs": [],
      "source": [
        "onnx_model_path = None  # Set to use an already exported model, then skip to the next section."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "7da638ba",
      "metadata": {
        "scrolled": false,
        "id": "7da638ba",
        "outputId": "a1bff882-9a61-4640-c71f-65ee2dd3dae5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============= Diagnostic Run torch.onnx.export version 2.0.0+cu118 =============\n",
            "verbose: False, log level: Level.ERROR\n",
            "======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import warnings\n",
        "\n",
        "onnx_model_path = \"sam_onnx_example.onnx\"\n",
        "\n",
        "onnx_model = SamOnnxModel(sam, return_single_mask=True)\n",
        "\n",
        "dynamic_axes = {\n",
        "    \"point_coords\": {1: \"num_points\"},\n",
        "    \"point_labels\": {1: \"num_points\"},\n",
        "}\n",
        "\n",
        "embed_dim = sam.prompt_encoder.embed_dim\n",
        "embed_size = sam.prompt_encoder.image_embedding_size\n",
        "mask_input_size = [4 * x for x in embed_size]\n",
        "dummy_inputs = {\n",
        "    \"image_embeddings\": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),\n",
        "    \"point_coords\": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float),\n",
        "    \"point_labels\": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),\n",
        "    \"mask_input\": torch.randn(1, 1, *mask_input_size, dtype=torch.float),\n",
        "    \"has_mask_input\": torch.tensor([1], dtype=torch.float),\n",
        "    \"orig_im_size\": torch.tensor([1500, 2250], dtype=torch.float),\n",
        "}\n",
        "output_names = [\"masks\", \"iou_predictions\", \"low_res_masks\"]\n",
        "\n",
        "with warnings.catch_warnings():\n",
        "    warnings.filterwarnings(\"ignore\", category=torch.jit.TracerWarning)\n",
        "    warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
        "    with open(onnx_model_path, \"wb\") as f:\n",
        "        torch.onnx.export(\n",
        "            onnx_model,\n",
        "            tuple(dummy_inputs.values()),\n",
        "            f,\n",
        "            export_params=True,\n",
        "            verbose=False,\n",
        "            opset_version=17,\n",
        "            do_constant_folding=True,\n",
        "            input_names=list(dummy_inputs.keys()),\n",
        "            output_names=output_names,\n",
        "            dynamic_axes=dynamic_axes,\n",
        "        )    "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c450cf1a",
      "metadata": {
        "id": "c450cf1a"
      },
      "source": [
        "If desired, the model can additionally be quantized and optimized. We find this improves web runtime significantly for negligible change in qualitative performance. Run the next cell to quantize the model, or skip to the next section otherwise."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "235d39fe",
      "metadata": {
        "id": "235d39fe"
      },
      "outputs": [],
      "source": [
        "onnx_model_quantized_path = \"sam_onnx_quantized_example.onnx\"\n",
        "quantize_dynamic(\n",
        "    model_input=onnx_model_path,\n",
        "    model_output=onnx_model_quantized_path,\n",
        "    optimize_model=True,\n",
        "    per_channel=False,\n",
        "    reduce_range=False,\n",
        "    weight_type=QuantType.QUInt8,\n",
        ")\n",
        "onnx_model_path = onnx_model_quantized_path"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "927a928b",
      "metadata": {
        "id": "927a928b"
      },
      "source": [
        "## Example Image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 237,
      "id": "6be6eb55",
      "metadata": {
        "id": "6be6eb55"
      },
      "outputs": [],
      "source": [
        "# Mask_Away\n",
        "# We used our own images for data processing. \n",
        "# The were imported into Google collab and were imported using the following code. \n",
        "\n",
        "image = cv2.imread('images/tom-zendaya-benedict-cumberbatch-kimmel-14.jpg')\n",
        "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 238,
      "id": "b7e9a27a",
      "metadata": {
        "id": "b7e9a27a",
        "outputId": "2dacdc57-23eb-41c7-a3eb-a2b7c82606fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 834
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "plt.imshow(image)\n",
        "plt.axis('on')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "027b177b",
      "metadata": {
        "id": "027b177b"
      },
      "source": [
        "## Using an ONNX model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "778d4593",
      "metadata": {
        "id": "778d4593"
      },
      "source": [
        "Here as an example, we use `onnxruntime` in python on CPU to execute the ONNX model. However, any platform that supports an ONNX runtime could be used in principle. Launch the runtime session below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 239,
      "id": "9689b1bf",
      "metadata": {
        "id": "9689b1bf"
      },
      "outputs": [],
      "source": [
        "ort_session = onnxruntime.InferenceSession(onnx_model_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7708ead6",
      "metadata": {
        "id": "7708ead6"
      },
      "source": [
        "To use the ONNX model, the image must first be pre-processed using the SAM image encoder. This is a heavier weight process best performed on GPU. SamPredictor can be used as normal, then `.get_image_embedding()` will retreive the intermediate features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 240,
      "id": "26e067b4",
      "metadata": {
        "id": "26e067b4"
      },
      "outputs": [],
      "source": [
        "sam.to(device='cuda')\n",
        "predictor = SamPredictor(sam)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 241,
      "id": "7ad3f0d6",
      "metadata": {
        "id": "7ad3f0d6"
      },
      "outputs": [],
      "source": [
        "predictor.set_image(image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 242,
      "id": "8a6f0f07",
      "metadata": {
        "id": "8a6f0f07"
      },
      "outputs": [],
      "source": [
        "image_embedding = predictor.get_image_embedding().cpu().numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 206,
      "id": "5e112f33",
      "metadata": {
        "id": "5e112f33",
        "outputId": "3317489d-c40a-4ec9-8007-a41c69f8679c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 256, 64, 64)"
            ]
          },
          "metadata": {},
          "execution_count": 206
        }
      ],
      "source": [
        "image_embedding.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6337b654",
      "metadata": {
        "id": "6337b654"
      },
      "source": [
        "The ONNX model has a different input signature than `SamPredictor.predict`. The following inputs must all be supplied. Note the special cases for both point and mask inputs. All inputs are `np.float32`.\n",
        "* `image_embeddings`: The image embedding from `predictor.get_image_embedding()`. Has a batch index of length 1.\n",
        "* `point_coords`: Coordinates of sparse input prompts, corresponding to both point inputs and box inputs. Boxes are encoded using two points, one for the top-left corner and one for the bottom-right corner. *Coordinates must already be transformed to long-side 1024.* Has a batch index of length 1.\n",
        "* `point_labels`: Labels for the sparse input prompts. 0 is a negative input point, 1 is a positive input point, 2 is a top-left box corner, 3 is a bottom-right box corner, and -1 is a padding point. *If there is no box input, a single padding point with label -1 and coordinates (0.0, 0.0) should be concatenated.*\n",
        "* `mask_input`: A mask input to the model with shape 1x1x256x256. This must be supplied even if there is no mask input. In this case, it can just be zeros.\n",
        "* `has_mask_input`: An indicator for the mask input. 1 indicates a mask input, 0 indicates no mask input.\n",
        "* `orig_im_size`: The size of the input image in (H,W) format, before any transformation. \n",
        "\n",
        "Additionally, the ONNX model does not threshold the output mask logits. To obtain a binary mask, threshold at `sam.mask_threshold` (equal to 0.0)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bf5a9f55",
      "metadata": {
        "id": "bf5a9f55"
      },
      "source": [
        "### Example point input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 245,
      "id": "1c0deef0",
      "metadata": {
        "id": "1c0deef0"
      },
      "outputs": [],
      "source": [
        "# Mask_Away\n",
        "# Based on the dimensions of the image (which can be seen from the previously outputed image)\n",
        "# We determined where the mask is located and changed input_point to match accordingly. \n",
        "# For example, the mask for the image we inputted was centered at [100, 125].\n",
        "\n",
        "input_point = np.array([[100, 125]])\n",
        "input_label = np.array([1])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7256394c",
      "metadata": {
        "id": "7256394c"
      },
      "source": [
        "Add a batch index, concatenate a padding point, and transform."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 246,
      "id": "4f69903e",
      "metadata": {
        "id": "4f69903e"
      },
      "outputs": [],
      "source": [
        "onnx_coord = np.concatenate([input_point, np.array([[0.0, 0.0]])], axis=0)[None, :, :]\n",
        "onnx_label = np.concatenate([input_label, np.array([-1])], axis=0)[None, :].astype(np.float32)\n",
        "\n",
        "onnx_coord = predictor.transform.apply_coords(onnx_coord, image.shape[:2]).astype(np.float32)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b188dc53",
      "metadata": {
        "id": "b188dc53"
      },
      "source": [
        "Create an empty mask input and an indicator for no mask."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 247,
      "id": "5cb52bcf",
      "metadata": {
        "id": "5cb52bcf"
      },
      "outputs": [],
      "source": [
        "onnx_mask_input = np.zeros((1, 1, 256, 256), dtype=np.float32)\n",
        "onnx_has_mask_input = np.zeros(1, dtype=np.float32)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a99c2cc5",
      "metadata": {
        "id": "a99c2cc5"
      },
      "source": [
        "Package the inputs to run in the onnx model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 248,
      "id": "b1d7ea11",
      "metadata": {
        "id": "b1d7ea11"
      },
      "outputs": [],
      "source": [
        "ort_inputs = {\n",
        "    \"image_embeddings\": image_embedding,\n",
        "    \"point_coords\": onnx_coord,\n",
        "    \"point_labels\": onnx_label,\n",
        "    \"mask_input\": onnx_mask_input,\n",
        "    \"has_mask_input\": onnx_has_mask_input,\n",
        "    \"orig_im_size\": np.array(image.shape[:2], dtype=np.float32)\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4b6409c9",
      "metadata": {
        "id": "4b6409c9"
      },
      "source": [
        "Predict a mask and threshold it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 249,
      "id": "dc4cc082",
      "metadata": {
        "scrolled": false,
        "id": "dc4cc082"
      },
      "outputs": [],
      "source": [
        "masks, _, low_res_logits = ort_session.run(None, ort_inputs)\n",
        "masks = masks > predictor.model.mask_threshold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 183,
      "id": "d778a8fb",
      "metadata": {
        "id": "d778a8fb",
        "outputId": "879b1dcd-6341-4e6b-efac-d53630938eab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 1, 452, 452)"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ],
      "source": [
        "masks.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 250,
      "id": "badb1175",
      "metadata": {
        "id": "badb1175",
        "outputId": "d6ca760f-0407-47f9-831c-0b7531b01090",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Mask_Away\n",
        "# We separated the mask from the original image to save the image separately. \n",
        "# Original dimensions were retained. \n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "#plt.imshow(image)\n",
        "show_mask(masks, plt.gca())\n",
        "#show_points(input_point, input_label, plt.gca())\n",
        "plt.axis('off')\n",
        "plt.show() "
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.8.0"
    },
    "colab": {
      "provenance": []
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}