destroy_lang_sam.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bRbzj7Uy9Uo"
      },
      "source": [
        "#Install and import packeges"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dummjDYWy46K",
        "outputId": "1b91669e-f43b-4dce-c286-55c4af18ad0c"
      },
      "outputs": [],
      "source": [
        "if True:\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\n",
        "    !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/segment-anything.git'\n",
        "\n",
        "    !wget -nc https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth -O sam_vit_h_4b8939.pth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q3_rbUeUzC-h",
        "outputId": "1f265917-edb1-4a40-d456-e723aa4f1609"
      },
      "outputs": [],
      "source": [
        "!pip install -U git+https://github.com/luca-medeiros/lang-segment-anything.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YMEzt3eOzEsR"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import torch\n",
        "from torchvision import transforms\n",
        "from torch.nn import functional as F\n",
        "from PIL import Image\n",
        "from io import BytesIO\n",
        "from lang_sam import LangSAM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "DngZJp08zIX0",
        "outputId": "c7165201-1633-402c-8125-dcae0083333f"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "sys.path.append(\"..\")\n",
        "from segment_anything import sam_model_registry, SamPredictor\n",
        "\n",
        "sam_checkpoint = \"sam_vit_h_4b8939.pth\"\n",
        "model_type = \"vit_h\"\n",
        "\n",
        "device = \"cuda\"\n",
        "\n",
        "sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)\n",
        "sam.to(device=device)\n",
        "model = LangSAM()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cEND3EfhzKS-"
      },
      "outputs": [],
      "source": [
        "from types import MethodType\n",
        "from torch.utils.checkpoint import checkpoint_sequential, checkpoint\n",
        "\n",
        "def patched_forward(self, x: torch.Tensor) -> torch.Tensor:\n",
        "      x = self.patch_embed(x)\n",
        "      if self.pos_embed is not None:\n",
        "          x = x + self.pos_embed\n",
        "\n",
        "      for blk in self.blocks:\n",
        "          x = checkpoint(blk, x)\n",
        "\n",
        "      x = checkpoint_sequential(self.neck, segments=len(self.neck), input=x.permute(0, 3, 1, 2))\n",
        "\n",
        "      return x\n",
        "\n",
        "sam.image_encoder.forward = MethodType(patched_forward, sam.image_encoder)\n",
        "predictor = SamPredictor(sam)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eNdEOFsxRtFn"
      },
      "source": [
        "#My versions of models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WskYXh7sRyzR"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "from torch.nn import functional as F\n",
        "from torchvision.transforms.functional import resize, to_pil_image  # type: ignore\n",
        "\n",
        "from copy import deepcopy\n",
        "from typing import Tuple\n",
        "\n",
        "\n",
        "class ResizeLongestSide:\n",
        "    \"\"\"\n",
        "    Resizes images to the longest side 'target_length', as well as provides\n",
        "    methods for resizing coordinates and boxes. Provides methods for\n",
        "    transforming both numpy array and batched torch tensors.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, target_length: int) -> None:\n",
        "        self.target_length = target_length\n",
        "\n",
        "    def apply_image(self, image: np.ndarray) -> np.ndarray:\n",
        "        \"\"\"\n",
        "        Expects a numpy array with shape HxWxC in uint8 format.\n",
        "        \"\"\"\n",
        "        target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)\n",
        "        return np.array(resize(to_pil_image(image), target_size))\n",
        "\n",
        "    def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:\n",
        "        \"\"\"\n",
        "        Expects a numpy array of length 2 in the final dimension. Requires the\n",
        "        original image size in (H, W) format.\n",
        "        \"\"\"\n",
        "        old_h, old_w = original_size\n",
        "        new_h, new_w = self.get_preprocess_shape(\n",
        "            original_size[0], original_size[1], self.target_length\n",
        "        )\n",
        "        coords = deepcopy(coords).astype(float)\n",
        "        coords[..., 0] = coords[..., 0] * (new_w / old_w)\n",
        "        coords[..., 1] = coords[..., 1] * (new_h / old_h)\n",
        "        return coords\n",
        "\n",
        "    def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:\n",
        "        \"\"\"\n",
        "        Expects a numpy array shape Bx4. Requires the original image size\n",
        "        in (H, W) format.\n",
        "        \"\"\"\n",
        "        boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)\n",
        "        return boxes.reshape(-1, 4)\n",
        "\n",
        "    def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:\n",
        "        \"\"\"\n",
        "        Expects batched images with shape BxCxHxW and float format. This\n",
        "        transformation may not exactly match apply_image. apply_image is\n",
        "        the transformation expected by the model.\n",
        "        \"\"\"\n",
        "        # Expects an image in BCHW format. May not exactly match apply_image.\n",
        "        target_size = self.get_preprocess_shape(image.shape[2], image.shape[3], self.target_length)\n",
        "        return F.interpolate(\n",
        "            image, target_size, mode=\"bilinear\", align_corners=False, antialias=True\n",
        "        )\n",
        "\n",
        "    def apply_coords_torch(\n",
        "        self, coords: torch.Tensor, original_size: Tuple[int, ...]\n",
        "    ) -> torch.Tensor:\n",
        "        \"\"\"\n",
        "        Expects a torch tensor with length 2 in the last dimension. Requires the\n",
        "        original image size in (H, W) format.\n",
        "        \"\"\"\n",
        "        old_h, old_w = original_size\n",
        "        new_h, new_w = self.get_preprocess_shape(\n",
        "            original_size[0], original_size[1], self.target_length\n",
        "        )\n",
        "        coords = deepcopy(coords).to(torch.float)\n",
        "        coords[..., 0] = coords[..., 0] * (new_w / old_w)\n",
        "        coords[..., 1] = coords[..., 1] * (new_h / old_h)\n",
        "        return coords\n",
        "\n",
        "    def apply_boxes_torch(\n",
        "        self, boxes: torch.Tensor, original_size: Tuple[int, ...]\n",
        "    ) -> torch.Tensor:\n",
        "        \"\"\"\n",
        "        Expects a torch tensor with shape Bx4. Requires the original image\n",
        "        size in (H, W) format.\n",
        "        \"\"\"\n",
        "        boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)\n",
        "        return boxes.reshape(-1, 4)\n",
        "\n",
        "    @staticmethod\n",
        "    def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:\n",
        "        \"\"\"\n",
        "        Compute the output size given input size and target long side length.\n",
        "        \"\"\"\n",
        "        scale = long_side_length * 1.0 / max(oldh, oldw)\n",
        "        newh, neww = oldh * scale, oldw * scale\n",
        "        neww = int(neww + 0.5)\n",
        "        newh = int(newh + 0.5)\n",
        "        return (newh, neww)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nIvxVHT6RuQU"
      },
      "outputs": [],
      "source": [
        "from typing import Optional, Tuple\n",
        "\n",
        "class Predictor:\n",
        "    def __init__(\n",
        "        self,\n",
        "        sam_model,\n",
        "    ) -> None:\n",
        "        \"\"\"\n",
        "        Uses SAM to calculate the image embedding for an image, and then\n",
        "        allow repeated, efficient mask prediction given prompts.\n",
        "\n",
        "        Arguments:\n",
        "          sam_model (Sam): The model to use for mask prediction.\n",
        "        \"\"\"\n",
        "        super().__init__()\n",
        "        self.model = sam_model\n",
        "        self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)\n",
        "        self.reset_image()\n",
        "\n",
        "    def set_image(\n",
        "        self,\n",
        "        image: np.ndarray,\n",
        "        image_format: str = \"RGB\",\n",
        "    ) -> None:\n",
        "        \"\"\"\n",
        "        Calculates the image embeddings for the provided image, allowing\n",
        "        masks to be predicted with the 'predict' method.\n",
        "\n",
        "        Arguments:\n",
        "          image (np.ndarray): The image for calculating masks. Expects an\n",
        "            image in HWC uint8 format, with pixel values in [0, 255].\n",
        "          image_format (str): The color format of the image, in ['RGB', 'BGR'].\n",
        "        \"\"\"\n",
        "        assert image_format in [\n",
        "            \"RGB\",\n",
        "            \"BGR\",\n",
        "        ], f\"image_format must be in ['RGB', 'BGR'], is {image_format}.\"\n",
        "        if image_format != self.model.image_format:\n",
        "            image = image[..., ::-1]\n",
        "\n",
        "        # Transform the image to the form expected by the model\n",
        "        input_image = self.transform.apply_image(image)\n",
        "        input_image_torch = leaf = torch.tensor(input_image.astype(np.float32), device=self.device, requires_grad=True)\n",
        "        input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]\n",
        "\n",
        "        self.set_torch_image(input_image_torch, image.shape[:2])\n",
        "        return leaf\n",
        "\n",
        "    def set_torch_image(\n",
        "        self,\n",
        "        transformed_image: torch.Tensor,\n",
        "        original_image_size: Tuple[int, ...],\n",
        "    ) -> None:\n",
        "        \"\"\"\n",
        "        Calculates the image embeddings for the provided image, allowing\n",
        "        masks to be predicted with the 'predict' method. Expects the input\n",
        "        image to be already transformed to the format expected by the model.\n",
        "\n",
        "        Arguments:\n",
        "          transformed_image (torch.Tensor): The input image, with shape\n",
        "            1x3xHxW, which has been transformed with ResizeLongestSide.\n",
        "          original_image_size (tuple(int, int)): The size of the image\n",
        "            before transformation, in (H, W) format.\n",
        "        \"\"\"\n",
        "        assert (\n",
        "            len(transformed_image.shape) == 4\n",
        "            and transformed_image.shape[1] == 3\n",
        "            and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size\n",
        "        ), f\"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}.\"\n",
        "        self.reset_image()\n",
        "\n",
        "        self.original_size = original_image_size\n",
        "        self.input_size = tuple(transformed_image.shape[-2:])\n",
        "        input_image = self.model.preprocess(transformed_image)\n",
        "        self.features = self.model.image_encoder.forward(input_image)\n",
        "        self.is_image_set = True\n",
        "\n",
        "    def predict(\n",
        "        self,\n",
        "        point_coords: Optional[np.ndarray] = None,\n",
        "        point_labels: Optional[np.ndarray] = None,\n",
        "        box: Optional[np.ndarray] = None,\n",
        "        mask_input: Optional[np.ndarray] = None,\n",
        "        multimask_output: bool = True,\n",
        "        return_logits: bool = True,\n",
        "    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n",
        "        \"\"\"\n",
        "        Predict masks for the given input prompts, using the currently set image.\n",
        "\n",
        "        Arguments:\n",
        "          point_coords (np.ndarray or None): A Nx2 array of point prompts to the\n",
        "            model. Each point is in (X,Y) in pixels.\n",
        "          point_labels (np.ndarray or None): A length N array of labels for the\n",
        "            point prompts. 1 indicates a foreground point and 0 indicates a\n",
        "            background point.\n",
        "          box (np.ndarray or None): A length 4 array given a box prompt to the\n",
        "            model, in XYXY format.\n",
        "          mask_input (np.ndarray): A low resolution mask input to the model, typically\n",
        "            coming from a previous prediction iteration. Has form 1xHxW, where\n",
        "            for SAM, H=W=256.\n",
        "          multimask_output (bool): If true, the model will return three masks.\n",
        "            For ambiguous input prompts (such as a single click), this will often\n",
        "            produce better masks than a single prediction. If only a single\n",
        "            mask is needed, the model's predicted quality score can be used\n",
        "            to select the best mask. For non-ambiguous prompts, such as multiple\n",
        "            input prompts, multimask_output=False can give better results.\n",
        "          return_logits (bool): If true, returns un-thresholded masks logits\n",
        "            instead of a binary mask.\n",
        "\n",
        "        Returns:\n",
        "          (np.ndarray): The output masks in CxHxW format, where C is the\n",
        "            number of masks, and (H, W) is the original image size.\n",
        "          (np.ndarray): An array of length C containing the model's\n",
        "            predictions for the quality of each mask.\n",
        "          (np.ndarray): An array of shape CxHxW, where C is the number\n",
        "            of masks and H=W=256. These low resolution logits can be passed to\n",
        "            a subsequent iteration as mask input.\n",
        "        \"\"\"\n",
        "        if not self.is_image_set:\n",
        "            raise RuntimeError(\"An image must be set with .set_image(...) before mask prediction.\")\n",
        "\n",
        "        # Transform input prompts\n",
        "        coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None\n",
        "        if point_coords is not None:\n",
        "            assert (\n",
        "                point_labels is not None\n",
        "            ), \"point_labels must be supplied if point_coords is supplied.\"\n",
        "            point_coords = self.transform.apply_coords(point_coords, self.original_size)\n",
        "            coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)\n",
        "            labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)\n",
        "            coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]\n",
        "        if box is not None:\n",
        "            box = self.transform.apply_boxes(box, self.original_size)\n",
        "            box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)\n",
        "            box_torch = box_torch[None, :]\n",
        "        if mask_input is not None:\n",
        "            mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)\n",
        "            mask_input_torch = mask_input_torch[None, :, :, :]\n",
        "\n",
        "        masks, iou_predictions, low_res_masks = self.predict_torch(\n",
        "            coords_torch,\n",
        "            labels_torch,\n",
        "            box_torch,\n",
        "            mask_input_torch,\n",
        "            multimask_output,\n",
        "            return_logits=return_logits,\n",
        "        )\n",
        "\n",
        "        #masks_np = masks[0].detach().cpu().numpy()\n",
        "        iou_predictions_np = iou_predictions[0].detach().cpu().numpy()\n",
        "        low_res_masks_np = low_res_masks[0].detach().cpu().numpy()\n",
        "        return masks\n",
        "\n",
        "    def predict_torch(\n",
        "        self,\n",
        "        point_coords: Optional[torch.Tensor],\n",
        "        point_labels: Optional[torch.Tensor],\n",
        "        boxes: Optional[torch.Tensor] = None,\n",
        "        mask_input: Optional[torch.Tensor] = None,\n",
        "        multimask_output: bool = True,\n",
        "        return_logits: bool = False,\n",
        "    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n",
        "        \"\"\"\n",
        "        Predict masks for the given input prompts, using the currently set image.\n",
        "        Input prompts are batched torch tensors and are expected to already be\n",
        "        transformed to the input frame using ResizeLongestSide.\n",
        "\n",
        "        Arguments:\n",
        "          point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the\n",
        "            model. Each point is in (X,Y) in pixels.\n",
        "          point_labels (torch.Tensor or None): A BxN array of labels for the\n",
        "            point prompts. 1 indicates a foreground point and 0 indicates a\n",
        "            background point.\n",
        "          boxes (np.ndarray or None): A Bx4 array given a box prompt to the\n",
        "            model, in XYXY format.\n",
        "          mask_input (np.ndarray): A low resolution mask input to the model, typically\n",
        "            coming from a previous prediction iteration. Has form Bx1xHxW, where\n",
        "            for SAM, H=W=256. Masks returned by a previous iteration of the\n",
        "            predict method do not need further transformation.\n",
        "          multimask_output (bool): If true, the model will return three masks.\n",
        "            For ambiguous input prompts (such as a single click), this will often\n",
        "            produce better masks than a single prediction. If only a single\n",
        "            mask is needed, the model's predicted quality score can be used\n",
        "            to select the best mask. For non-ambiguous prompts, such as multiple\n",
        "            input prompts, multimask_output=False can give better results.\n",
        "          return_logits (bool): If true, returns un-thresholded masks logits\n",
        "            instead of a binary mask.\n",
        "\n",
        "        Returns:\n",
        "          (torch.Tensor): The output masks in BxCxHxW format, where C is the\n",
        "            number of masks, and (H, W) is the original image size.\n",
        "          (torch.Tensor): An array of shape BxC containing the model's\n",
        "            predictions for the quality of each mask.\n",
        "          (torch.Tensor): An array of shape BxCxHxW, where C is the number\n",
        "            of masks and H=W=256. These low res logits can be passed to\n",
        "            a subsequent iteration as mask input.\n",
        "        \"\"\"\n",
        "        if not self.is_image_set:\n",
        "            raise RuntimeError(\"An image must be set with .set_image(...) before mask prediction.\")\n",
        "\n",
        "        if point_coords is not None:\n",
        "            points = (point_coords, point_labels)\n",
        "        else:\n",
        "            points = None\n",
        "\n",
        "        # Embed prompts\n",
        "        sparse_embeddings, dense_embeddings = self.model.prompt_encoder(\n",
        "            points=points,\n",
        "            boxes=boxes,\n",
        "            masks=mask_input,\n",
        "        )\n",
        "        print(sparse_embeddings.shape, dense_embeddings.shape)\n",
        "\n",
        "        # Predict masks\n",
        "        low_res_masks, iou_predictions = self.model.mask_decoder(\n",
        "            image_embeddings=self.features,\n",
        "            image_pe=self.model.prompt_encoder.get_dense_pe(),\n",
        "            sparse_prompt_embeddings=sparse_embeddings,\n",
        "            dense_prompt_embeddings=dense_embeddings,\n",
        "            multimask_output=multimask_output,\n",
        "        )\n",
        "\n",
        "        # Upscale the masks to the original image resolution\n",
        "        masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)\n",
        "\n",
        "        if not return_logits:\n",
        "            masks = masks > self.model.mask_threshold\n",
        "\n",
        "        return masks, iou_predictions, low_res_masks\n",
        "\n",
        "    def get_image_embedding(self) -> torch.Tensor:\n",
        "        \"\"\"\n",
        "        Returns the image embeddings for the currently set image, with\n",
        "        shape 1xCxHxW, where C is the embedding dimension and (H,W) are\n",
        "        the embedding spatial dimension of SAM (typically C=256, H=W=64).\n",
        "        \"\"\"\n",
        "        if not self.is_image_set:\n",
        "            raise RuntimeError(\n",
        "                \"An image must be set with .set_image(...) to generate an embedding.\"\n",
        "            )\n",
        "        assert self.features is not None, \"Features must exist if an image has been set.\"\n",
        "        return self.features\n",
        "\n",
        "    @property\n",
        "    def device(self) -> torch.device:\n",
        "        return self.model.device\n",
        "\n",
        "    def reset_image(self) -> None:\n",
        "        \"\"\"Resets the currently set image.\"\"\"\n",
        "        self.is_image_set = False\n",
        "        self.features = None\n",
        "        self.orig_h = None\n",
        "        self.orig_w = None\n",
        "        self.input_h = None\n",
        "        self.input_w = None"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6jt7GD5zdE3"
      },
      "source": [
        "#Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "odc8qWPczffx"
      },
      "outputs": [],
      "source": [
        "def find_boxes(image_path, text_prompt = \"person\", box_threshold = 0.3, text_threshold = 0.25):\n",
        "    image_pil = Image.open(image_path).convert(\"RGB\")\n",
        "    width, height = image_pil.size\n",
        "    image_pil = image_pil.resize((1024,int(1024*height/width)))\n",
        "    boxes, logits, phrases = model.predict_dino(image_pil, text_prompt, box_threshold, text_threshold)\n",
        "    return image_pil, boxes, logits, phrases"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ErxgqvVWzi8w"
      },
      "outputs": [],
      "source": [
        "def predict_sam(image_pil, boxes):\n",
        "      image_array = np.asarray(image_pil)\n",
        "      predictor.set_image(image_array)\n",
        "      transformed_boxes = predictor.transform.apply_boxes_torch(boxes, image_array.shape[:2])\n",
        "      masks, _, _ = predictor.predict_torch(\n",
        "            point_coords=None,\n",
        "            point_labels=None,\n",
        "            boxes=transformed_boxes.to(predictor.device),\n",
        "            multimask_output=False,\n",
        "            return_logits=False\n",
        "      )\n",
        "      masks = masks.squeeze(1)\n",
        "      return masks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VuGBb2tuzlFX"
      },
      "outputs": [],
      "source": [
        "def IoU(mask_attacked, ground_truth):\n",
        "  mask_attacked = (mask_attacked > 0.5)\n",
        "  return torch.sum(torch.logical_and(ground_truth,mask_attacked))/torch.sum(torch.logical_or(ground_truth,mask_attacked))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XLymgMVh8k8F"
      },
      "outputs": [],
      "source": [
        "def count_pixels(mask_attacked):\n",
        "    mask_attacked = (mask_attacked > 0.5)\n",
        "    return torch.sum(mask_attacked)/torch.sum(mask_attacked>-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZaqlqfXaznxc"
      },
      "outputs": [],
      "source": [
        "def display_image_with_masks(image, masks):\n",
        "    num_masks = len(masks)\n",
        "\n",
        "    fig, axes = plt.subplots(1, num_masks + 1, figsize=(15, 5))\n",
        "    axes[0].imshow(image)\n",
        "    axes[0].set_title(\"Image\")\n",
        "    axes[0].axis('off')\n",
        "\n",
        "    for i, mask_np in enumerate(masks):\n",
        "        axes[i+1].imshow(mask_np, cmap='gray')\n",
        "        axes[i+1].set_title(f\"Mask {i+1}\")\n",
        "        axes[i+1].axis('off')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jXRyZozlz1wF"
      },
      "source": [
        "#Attack"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9XUc2hkZz4sK"
      },
      "outputs": [],
      "source": [
        "def attack(image_path, mask, text_prompt = \"person\"):\n",
        "\n",
        "    image_pil, boxes, logits, phrases = find_boxes(image_path, text_prompt)\n",
        "    ground_truth = predict_sam(image_pil, boxes)\n",
        "    max_iter = 25\n",
        "    if mask == \"reverse\":\n",
        "      goal = -ground_truth.type(torch.float32)+1\n",
        "    elif mask == \"zero\":\n",
        "      goal = ground_truth.type(torch.float32)*0\n",
        "    else:\n",
        "      goal_image = cv2.imread(mask)\n",
        "      gray = cv2.cvtColor(goal_image, cv2.COLOR_BGR2GRAY)\n",
        "      _, binary_image = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY)\n",
        "      goal = -(binary_image/255)+1\n",
        "      goal = torch.tensor(goal).type(torch.float32).unsqueeze(dim=0).to(device)\n",
        "      max_iter = 150\n",
        "\n",
        "    new_image = image_pil\n",
        "    sigmoid = torch.nn.Sigmoid()\n",
        "    goal_numpy = goal.squeeze(dim=0).cpu().numpy()\n",
        "\n",
        "    for i in range(max_iter):\n",
        "      my_predictor = None\n",
        "      leaf = None\n",
        "      noise = None\n",
        "      loss = None\n",
        "      my_predictor = Predictor(sam)\n",
        "      image_array = np.asarray(new_image)\n",
        "      leaf = my_predictor.set_image(image_array)\n",
        "      transformed_boxes = my_predictor.transform.apply_boxes_torch(boxes, image_array.shape[:2])\n",
        "      masks = my_predictor.predict(\n",
        "            point_coords=None,\n",
        "            point_labels=None,\n",
        "            box=transformed_boxes.numpy(),\n",
        "            multimask_output=False,\n",
        "            return_logits=True\n",
        "      )\n",
        "      masks = sigmoid(masks)\n",
        "      if mask == \"reverse\":\n",
        "        iou = IoU(masks,ground_truth)\n",
        "        print(iou)\n",
        "        if iou<0.01:\n",
        "          break\n",
        "      elif mask == \"zero\":\n",
        "        non_zero = count_pixels(masks)\n",
        "        print(non_zero)\n",
        "        if non_zero<0.01:\n",
        "          break\n",
        "      else:\n",
        "        iou = IoU(masks,goal)\n",
        "        print(iou)\n",
        "        if iou>0.98:\n",
        "          break\n",
        "      loss = F.mse_loss(masks, goal.squeeze(dim=0))\n",
        "      loss.backward()\n",
        "      noise = leaf.grad.cpu().numpy()\n",
        "      new_image = np.clip(np.subtract(image_array,np.sign(noise)),0,255).astype(np.uint8)\n",
        "\n",
        "    im = Image.fromarray(new_image)\n",
        "    im.save(image_path[:-4]+\"_attack.png\")"
      ]
    }
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