"""The LangSAM model for segmenting objects from satellite images using text prompts.
The source code is adapted from the https://github.com/luca-medeiros/lang-segment-anything repository.
Credits to Luca Medeiros for the original implementation.
"""

import os
import warnings
import argparse
import numpy as np
import torch
from PIL import Image
from segment_anything import sam_model_registry
from segment_anything import SamPredictor
from huggingface_hub import hf_hub_download
from .common import *

try:
    import rasterio
except ImportError:
    print("Installing rasterio...")
    install_package("rasterio")

warnings.filterwarnings("ignore")


try:
    import groundingdino.datasets.transforms as T
    from groundingdino.models import build_model
    from groundingdino.util import box_ops
    from groundingdino.util.inference import predict
    from groundingdino.util.slconfig import SLConfig
    from groundingdino.util.utils import clean_state_dict

except ImportError:
    print("Installing GroundingDINO...")
    install_package("groundingdino-py")
    print("Please restart the kernel and run the notebook again.")

# Mode checkpoints
SAM_MODELS = {
    "vit_h": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
    "vit_l": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth",
    "vit_b": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth",
}

# Cache path
CACHE_PATH = os.environ.get(
    "TORCH_HOME", os.path.expanduser("~/.cache/torch/hub/checkpoints")
)


def load_model_hf(
    repo_id: str, filename: str, ckpt_config_filename: str, device: str = "cpu"
) -> torch.nn.Module:
    """
    Loads a model from HuggingFace Model Hub.

    Args:
        repo_id (str): Repository ID on HuggingFace Model Hub.
        filename (str): Name of the model file in the repository.
        ckpt_config_filename (str): Name of the config file for the model in the repository.
        device (str): Device to load the model onto. Default is 'cpu'.

    Returns:
        torch.nn.Module: The loaded model.
    """

    cache_config_file = hf_hub_download(
        repo_id=repo_id,
        filename=ckpt_config_filename,
        force_filename=ckpt_config_filename,
    )
    args = SLConfig.fromfile(cache_config_file)
    model = build_model(args)
    model.to(device)
    cache_file = hf_hub_download(
        repo_id=repo_id, filename=filename, force_filename=filename
    )
    checkpoint = torch.load(cache_file, map_location="cpu")
    model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
    model.eval()
    return model


def transform_image(image: Image) -> torch.Tensor:
    """
    Transforms an image using standard transformations for image-based models.

    Args:
        image (Image): The PIL Image to be transformed.

    Returns:
        torch.Tensor: The transformed image as a tensor.
    """
    transform = T.Compose(
        [
            T.RandomResize([800], max_size=1333),
            T.ToTensor(),
            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ]
    )
    image_transformed, _ = transform(image, None)
    return image_transformed


# Class definition for LangSAM
class LangSAM:
    """
    A Language-based Segment-Anything Model (LangSAM) class which combines GroundingDINO and SAM.
    """

    def __init__(self, model_type="vit_h"):
        """Initialize the LangSAM instance.

        Args:
            model_type (str, optional): The model type. It can be one of the following: vit_h, vit_l, vit_b.
                Defaults to 'vit_h'. See https://bit.ly/3VrpxUh for more details.
        """

        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.build_groundingdino()
        self.build_sam(model_type)

        self.source = None
        self.image = None
        self.masks = None
        self.boxes = None
        self.phrases = None
        self.logits = None
        self.prediction = None

    def build_sam(self, model_type):
        """Build the SAM model.

        Args:
            model_type (str, optional): The model type. It can be one of the following: vit_h, vit_l, vit_b.
                Defaults to 'vit_h'. See https://bit.ly/3VrpxUh for more details.
        """
        checkpoint_url = SAM_MODELS[model_type]
        sam = sam_model_registry[model_type]()
        state_dict = torch.hub.load_state_dict_from_url(checkpoint_url)
        sam.load_state_dict(state_dict, strict=True)
        sam.to(device=self.device)
        self.sam = SamPredictor(sam)

    def build_groundingdino(self):
        """Build the GroundingDINO model."""
        ckpt_repo_id = "ShilongLiu/GroundingDINO"
        ckpt_filename = "groundingdino_swinb_cogcoor.pth"
        ckpt_config_filename = "GroundingDINO_SwinB.cfg.py"
        self.groundingdino = load_model_hf(
            ckpt_repo_id, ckpt_filename, ckpt_config_filename, self.device
        )

    def predict_dino(self, image, text_prompt, box_threshold, text_threshold):
        """
        Run the GroundingDINO model prediction.

        Args:
            image (Image): Input PIL Image.
            text_prompt (str): Text prompt for the model.
            box_threshold (float): Box threshold for the prediction.
            text_threshold (float): Text threshold for the prediction.

        Returns:
            tuple: Tuple containing boxes, logits, and phrases.
        """

        image_trans = transform_image(image)
        boxes, logits, phrases = predict(
            model=self.groundingdino,
            image=image_trans,
            caption=text_prompt,
            box_threshold=box_threshold,
            text_threshold=text_threshold,
            device=self.device,
        )
        W, H = image.size
        boxes = box_ops.box_cxcywh_to_xyxy(boxes) * torch.Tensor([W, H, W, H])

        return boxes, logits, phrases

    def predict_sam(self, image, boxes):
        """
        Run the SAM model prediction.

        Args:
            image (Image): Input PIL Image.
            boxes (torch.Tensor): Tensor of bounding boxes.

        Returns:
            Masks tensor.
        """
        image_array = np.asarray(image)
        self.sam.set_image(image_array)
        transformed_boxes = self.sam.transform.apply_boxes_torch(
            boxes, image_array.shape[:2]
        )
        masks, _, _ = self.sam.predict_torch(
            point_coords=None,
            point_labels=None,
            boxes=transformed_boxes.to(self.sam.device),
            multimask_output=False,
        )
        return masks.cpu()

    def set_image(self, image):
        """Set the input image.

        Args:
            image (str): The path to the image file or a HTTP URL.
        """

        if isinstance(image, str):
            if image.startswith("http"):
                image = download_file(image)

            if not os.path.exists(image):
                raise ValueError(f"Input path {image} does not exist.")

            self.source = image
        else:
            self.source = None

    def predict(
        self,
        image,
        text_prompt,
        box_threshold,
        text_threshold,
        output=None,
        mask_multiplier=255,
        dtype=np.uint8,
        save_args={},
        return_results=False,
        return_coords=False,
        **kwargs,
    ):
        """
        Run both GroundingDINO and SAM model prediction.

        Parameters:
            image (Image): Input PIL Image.
            text_prompt (str): Text prompt for the model.
            box_threshold (float): Box threshold for the prediction.
            text_threshold (float): Text threshold for the prediction.
            output (str, optional): Output path for the prediction. Defaults to None.
            mask_multiplier (int, optional): Mask multiplier for the prediction. Defaults to 255.
            dtype (np.dtype, optional): Data type for the prediction. Defaults to np.uint8.
            save_args (dict, optional): Save arguments for the prediction. Defaults to {}.
            return_results (bool, optional): Whether to return the results. Defaults to False.

        Returns:
            tuple: Tuple containing masks, boxes, phrases, and logits.
        """

        if isinstance(image, str):
            if image.startswith("http"):
                image = download_file(image)

            if not os.path.exists(image):
                raise ValueError(f"Input path {image} does not exist.")

            self.source = image

            # Load the georeferenced image
            with rasterio.open(image) as src:
                image_np = src.read().transpose(
                    (1, 2, 0)
                )  # Convert rasterio image to numpy array
                self.transform = src.transform  # Save georeferencing information
                self.crs = src.crs  # Save the Coordinate Reference System
                image_pil = Image.fromarray(
                    image_np[:, :, :3]
                )  # Convert numpy array to PIL image, excluding the alpha channel
        else:
            image_pil = image
            image_np = np.array(image_pil)

        self.image = image_pil

        boxes, logits, phrases = self.predict_dino(
            image_pil, text_prompt, box_threshold, text_threshold
        )
        masks = torch.tensor([])
        if len(boxes) > 0:
            masks = self.predict_sam(image_pil, boxes)
            masks = masks.squeeze(1)

        if boxes.nelement() == 0:  # No "object" instances found
            print("No objects found in the image.")
            return
        else:
            # Create an empty image to store the mask overlays
            mask_overlay = np.zeros_like(
                image_np[..., 0], dtype=dtype
            )  # Adjusted for single channel

            for i, (box, mask) in enumerate(zip(boxes, masks)):
                # Convert tensor to numpy array if necessary and ensure it contains integers
                if isinstance(mask, torch.Tensor):
                    mask = (
                        mask.cpu().numpy().astype(dtype)
                    )  # If mask is on GPU, use .cpu() before .numpy()
                mask_overlay += ((mask > 0) * (i + 1)).astype(
                    dtype
                )  # Assign a unique value for each mask

            # Normalize mask_overlay to be in [0, 255]
            mask_overlay = (
                mask_overlay > 0
            ) * mask_multiplier  # Binary mask in [0, 255]

        if output is not None:
            array_to_image(mask_overlay, output, self.source, dtype=dtype, **save_args)

        self.masks = masks
        self.boxes = boxes
        self.phrases = phrases
        self.logits = logits
        self.prediction = mask_overlay

        if return_results:
            return masks, boxes, phrases, logits

        if return_coords:
            boxlist = []
            for box in self.boxes:
                box = box.cpu().numpy()
                boxlist.append((box[0], box[1]))
            return boxlist

    def predict_batch(
        self,
        images,
        out_dir,
        text_prompt,
        box_threshold,
        text_threshold,
        mask_multiplier=255,
        dtype=np.uint8,
        save_args={},
        merge=True,
        verbose=True,
        **kwargs,
    ):
        """
        Run both GroundingDINO and SAM model prediction for a batch of images.

        Parameters:
            images (list): List of input PIL Images.
            out_dir (str): Output directory for the prediction.
            text_prompt (str): Text prompt for the model.
            box_threshold (float): Box threshold for the prediction.
            text_threshold (float): Text threshold for the prediction.
            mask_multiplier (int, optional): Mask multiplier for the prediction. Defaults to 255.
            dtype (np.dtype, optional): Data type for the prediction. Defaults to np.uint8.
            save_args (dict, optional): Save arguments for the prediction. Defaults to {}.
            merge (bool, optional): Whether to merge the predictions into a single GeoTIFF file. Defaults to True.
        """

        import glob

        if not os.path.exists(out_dir):
            os.makedirs(out_dir)

        if isinstance(images, str):
            images = list(glob.glob(os.path.join(images, "*.tif")))
            images.sort()

        if not isinstance(images, list):
            raise ValueError("images must be a list or a directory to GeoTIFF files.")

        for i, image in enumerate(images):
            basename = os.path.splitext(os.path.basename(image))[0]
            if verbose:
                print(
                    f"Processing image {str(i+1).zfill(len(str(len(images))))} of {len(images)}: {image}..."
                )
            output = os.path.join(out_dir, f"{basename}_mask.tif")
            self.predict(
                image,
                text_prompt,
                box_threshold,
                text_threshold,
                output=output,
                mask_multiplier=mask_multiplier,
                dtype=dtype,
                save_args=save_args,
                **kwargs,
            )

        if merge:
            output = os.path.join(out_dir, "merged.tif")
            merge_rasters(out_dir, output)
            if verbose:
                print(f"Saved the merged prediction to {output}.")

    def save_boxes(self, output=None, dst_crs="EPSG:4326", **kwargs):
        """Save the bounding boxes to a vector file.

        Args:
            output (str): The path to the output vector file.
            dst_crs (str, optional): The destination CRS. Defaults to "EPSG:4326".
            **kwargs: Additional arguments for boxes_to_vector().
        """

        if self.boxes is None:
            print("Please run predict() first.")
            return
        else:
            boxes = self.boxes.tolist()
            coords = rowcol_to_xy(self.source, boxes=boxes, dst_crs=dst_crs, **kwargs)
            if output is None:
                return boxes_to_vector(coords, self.crs, dst_crs, output)
            else:
                boxes_to_vector(coords, self.crs, dst_crs, output)

    def show_anns(
        self,
        figsize=(12, 10),
        axis="off",
        cmap="viridis",
        alpha=0.4,
        add_boxes=True,
        box_color="r",
        box_linewidth=1,
        title=None,
        output=None,
        blend=True,
        **kwargs,
    ):
        """Show the annotations (objects with random color) on the input image.

        Args:
            figsize (tuple, optional): The figure size. Defaults to (12, 10).
            axis (str, optional): Whether to show the axis. Defaults to "off".
            cmap (str, optional): The colormap for the annotations. Defaults to "viridis".
            alpha (float, optional): The alpha value for the annotations. Defaults to 0.4.
            add_boxes (bool, optional): Whether to show the bounding boxes. Defaults to True.
            box_color (str, optional): The color for the bounding boxes. Defaults to "r".
            box_linewidth (int, optional): The line width for the bounding boxes. Defaults to 1.
            title (str, optional): The title for the image. Defaults to None.
            output (str, optional): The path to the output image. Defaults to None.
            blend (bool, optional): Whether to show the input image. Defaults to True.
            kwargs (dict, optional): Additional arguments for matplotlib.pyplot.savefig().
        """

        import warnings
        import matplotlib.pyplot as plt
        import matplotlib.patches as patches

        warnings.filterwarnings("ignore")

        anns = self.prediction

        if anns is None:
            print("Please run predict() first.")
            return
        elif len(anns) == 0:
            print("No objects found in the image.")
            return

        plt.figure(figsize=figsize)
        plt.imshow(self.image)

        if add_boxes:
            for box in self.boxes:
                # Draw bounding box
                box = box.cpu().numpy()  # Convert the tensor to a numpy array
                rect = patches.Rectangle(
                    (box[0], box[1]),
                    box[2] - box[0],
                    box[3] - box[1],
                    linewidth=box_linewidth,
                    edgecolor=box_color,
                    facecolor="none",
                )
                plt.gca().add_patch(rect)

        if "dpi" not in kwargs:
            kwargs["dpi"] = 100

        if "bbox_inches" not in kwargs:
            kwargs["bbox_inches"] = "tight"

        plt.imshow(anns, cmap=cmap, alpha=alpha)

        if title is not None:
            plt.title(title)
        plt.axis(axis)

        if output is not None:
            if blend:
                plt.savefig(output, **kwargs)
            else:
                array_to_image(self.prediction, output, self.source)

    def raster_to_vector(self, image, output, simplify_tolerance=None, **kwargs):
        """Save the result to a vector file.

        Args:
            image (str): The path to the image file.
            output (str): The path to the vector file.
            simplify_tolerance (float, optional): The maximum allowed geometry displacement.
                The higher this value, the smaller the number of vertices in the resulting geometry.
        """

        raster_to_vector(image, output, simplify_tolerance=simplify_tolerance, **kwargs)

    def show_map(self, basemap="SATELLITE", out_dir=None, **kwargs):
        """Show the interactive map.

        Args:
            basemap (str, optional): The basemap. It can be one of the following: SATELLITE, ROADMAP, TERRAIN, HYBRID.
            out_dir (str, optional): The path to the output directory. Defaults to None.

        Returns:
            leafmap.Map: The map object.
        """
        return text_sam_gui(self, basemap=basemap, out_dir=out_dir, **kwargs)


def main():
    parser = argparse.ArgumentParser(description="LangSAM")
    parser.add_argument("--image", required=True, help="path to the image")
    parser.add_argument("--prompt", required=True, help="text prompt")
    parser.add_argument(
        "--box_threshold", default=0.5, type=float, help="box threshold"
    )
    parser.add_argument(
        "--text_threshold", default=0.5, type=float, help="text threshold"
    )
    args = parser.parse_args()

    with rasterio.open(args.image) as src:
        image_np = src.read().transpose(
            (1, 2, 0)
        )  # Convert rasterio image to numpy array
        transform = src.transform  # Save georeferencing information
        crs = src.crs  # Save the Coordinate Reference System

    model = LangSAM()

    image_pil = Image.fromarray(
        image_np[:, :, :3]
    )  # Convert numpy array to PIL image, excluding the alpha channel
    image_np_copy = image_np.copy()  # Create a copy for modifications

    masks, boxes, phrases, logits = model.predict(
        image_pil, args.prompt, args.box_threshold, args.text_threshold
    )

    if boxes.nelement() == 0:  # No "object" instances found
        print("No objects found in the image.")
    else:
        # Create an empty image to store the mask overlays
        mask_overlay = np.zeros_like(
            image_np[..., 0], dtype=np.int64
        )  # Adjusted for single channel

        for i in range(len(boxes)):
            box = boxes[i].cpu().numpy()  # Convert the tensor to a numpy array
            mask = masks[i].cpu().numpy()  # Convert the tensor to a numpy array

            # Add the mask to the mask_overlay image
            mask_overlay += (mask > 0) * (i + 1)  # Assign a unique value for each mask

    # Normalize mask_overlay to be in [0, 255]
    mask_overlay = ((mask_overlay > 0) * 255).astype(
        rasterio.uint8
    )  # Binary mask in [0, 255]

    with rasterio.open(
        "mask.tif",
        "w",
        driver="GTiff",
        height=mask_overlay.shape[0],
        width=mask_overlay.shape[1],
        count=1,
        dtype=mask_overlay.dtype,
        crs=crs,
        transform=transform,
    ) as dst:
        dst.write(mask_overlay, 1)


if __name__ == "__main__":
    main()