pipeline.py

from pathlib import Path
from typing import Any

import numpy as np
import torch
from PIL import Image
from ultralytics import YOLO

import identification
import pose
import segmentation
from identification import IdentificationModel
from utils import (
    PictureLayout,
    crop,
    get_picture_layout,
    get_segmentation_mask_crop_box,
)


def load_pose_and_segmentation_models(
    filepath_weights_segmentation_model: Path,
    filepath_weights_pose_model: Path,
) -> dict[str, YOLO]:
    """
    Load into memory the models used by the pipeline.

    Returns:
        segmentation (YOLO): segmentation model.
        pose (YOLO): pose estimation model.
    """
    model_segmentation = segmentation.load_pretrained_model(
        str(filepath_weights_segmentation_model)
    )

    model_pose = pose.load_pretrained_model(str(filepath_weights_pose_model))
    return {
        "segmentation": model_segmentation,
        "pose": model_pose,
    }


def load_models(
    filepath_weights_segmentation_model: Path,
    filepath_weights_pose_model: Path,
    device: torch.device,
    filepath_identification_lightglue_features: Path,
    filepath_identification_db: Path,
    extractor_type: str,
    n_keypoints: int,
    threshold_wasserstein: float,
) -> dict[str, YOLO | IdentificationModel]:
    """
    Load into memory the models used by the pipeline.

    Returns:
        segmentation (YOLO): segmentation model.
        pose (YOLO): pose estimation model.
        identification (IdentificationModel): identification model.
    """
    loaded_pose_seg_models = load_pose_and_segmentation_models(
        filepath_weights_segmentation_model=filepath_weights_segmentation_model,
        filepath_weights_pose_model=filepath_weights_pose_model,
    )

    model_identification = identification.load(
        device=device,
        filepath_features=filepath_identification_lightglue_features,
        filepath_db=filepath_identification_db,
        n_keypoints=n_keypoints,
        extractor_type=extractor_type,
        threshold_wasserstein=threshold_wasserstein,
    )

    return {**loaded_pose_seg_models, "identification": model_identification}


def run_preprocess(pil_image: Image.Image) -> dict[str, Any]:
    """
    Run the preprocess stage of the pipeline.

    Args:
        pil_image (PIL): original image.

    Returns:
        pil_image (PIL): rotated image to make it a landscape.
        layout (PictureLayout): layout type of the input image.
    """
    picture_layout = get_picture_layout(pil_image=pil_image)

    # If the image is in Portrait Mode, we turn it into Landscape
    pil_image_preprocessed = (
        pil_image.rotate(angle=90, expand=True)
        if picture_layout == PictureLayout.PORTRAIT
        else pil_image
    )
    return {
        "pil_image": pil_image_preprocessed,
        "layout": picture_layout,
    }


def run_pose(model: YOLO, pil_image: Image.Image) -> dict[str, Any]:
    """
    Run the pose stage of the pipeline.

    Args:
        model (YOLO): loaded pose estimation model.
        pil_image (PIL): Image to run the model on.

    Returns:
        prediction: Raw prediction from the model.
        orig_image: original image used for inference after the preprocessing
        stages applied by ultralytics.
        keypoints_xy (np.ndarray): keypoints in xy format.
        keypoints_xyn (np.ndarray): keyoints in xyn format.
        theta (float): angle in radians to rotate the image to re-align it
        horizontally.
        side (FishSide): Predicted side of the fish.
    """
    return pose.predict(model=model, pil_image=pil_image)


def run_crop(
    pil_image_mask: Image.Image,
    pil_image_masked: Image.Image,
    padding: int,
) -> dict[str, Any]:
    """
    Run the crop on the mask and masked images.

    Args:
        pil_image_mask (PIL): Image containing the segmentation mask.
        pil_image_masked (PIL): Image containing the applied pil_image_mask on
        the original image.
        padding (int): by how much do we want to pad the result image?

    Returns:
        box (Tuple[int, int, int, int]): 4 tuple representing a rectangle (x1,
        y1, x2, y2) with the upper left corner given first.
        pil_image (PIL): cropped masked image.
    """

    box_crop = get_segmentation_mask_crop_box(
        pil_image_mask=pil_image_mask,
        padding=padding,
    )
    pil_image_masked_cropped = crop(
        pil_image=pil_image_masked,
        box=box_crop,
    )
    return {
        "box": box_crop,
        "pil_image": pil_image_masked_cropped,
    }


def run_rotation(
    pil_image: Image.Image,
    angle_rad: float,
    keypoints_xy: np.ndarray,
) -> dict[str, Any]:
    """
    Run the rotation stage of the pipeline.

    Args:
        pil_image (PIL): image to run the rotation on.
        angle_rad (float): angle in radian to rotate the image.
        keypoints_xy (np.ndarray): keypoints from the pose estimation
        prediction in xy format.

    Returns:
        array_image (np.ndarray): rotated array_image as a 2D numpy array.
        keypoints_xy (np.ndarray): rotated keypoints in xy format.
        pil_image (PIL): rotated PIL image.
    """
    results_rotation = pose.rotate_image_and_keypoints_xy(
        angle_rad=angle_rad,
        array_image=np.array(pil_image),
        keypoints_xy=keypoints_xy,
    )
    pil_image_rotated = Image.fromarray(results_rotation["array_image"])

    return {
        "pil_image": pil_image_rotated,
        "array_image": results_rotation["array_image"],
        "keypoints_xy": results_rotation["keypoints_xy"],
    }


def run_segmentation(model: YOLO, pil_image: Image.Image) -> dict[str, Any]:
    """
    Run the segmentation stage of the pipeline.

    Args:
        pil_image (PIL): image to run the rotation on.
        model (YOLO): segmentation model.
        prediction in xy format.

    Returns:
        prediction: Raw prediction from the model.
        orig_image: original image used for inference
        after preprocessing stages applied by
        ultralytics.
        mask (PIL): postprocessed mask in white and black format - used for visualization
        mask_raw (np.ndarray): Raw mask not postprocessed
        masked (PIL): mask applied to the pil_image.
    """
    results_segmentation = segmentation.predict(
        model=model,
        pil_image=pil_image,
    )
    return results_segmentation


def run_pre_identification_stages(
    loaded_models: dict[str, YOLO],
    pil_image: Image.Image,
    param_crop_padding: int = 0,
) -> dict[str, Any]:
    """
    Run the partial ML pipeline on `pil_image` up to identifying the fish. It
    prepares the input image `pil_image` to make it possible to identify it.

    Args:
        loaded_models (dict[str, YOLO]): resut of calling `load_models`.
        pil_image (PIL): Image to run the pipeline on.
        param_crop_padding (int): how much to pad the resulting segmentated
        image when cropped.

    Returns:
        order (list[str]): the stages and their order.
        stages (dict[str, Any]): the description of each stage, its
        input and output.
    """

    # Unpacking the loaded models
    model_pose = loaded_models["pose"]
    model_segmentation = loaded_models["segmentation"]

    # Stage: Preprocess
    results_preprocess = run_preprocess(pil_image=pil_image)

    # Stage: Pose estimation
    pil_image_preprocessed = results_preprocess["pil_image"]
    results_pose = run_pose(model=model_pose, pil_image=pil_image_preprocessed)

    # Stage: Rotation
    results_rotation = run_rotation(
        pil_image=pil_image_preprocessed,
        keypoints_xy=results_pose["keypoints_xy"],
        angle_rad=results_pose["theta"],
    )

    # Stage: Segmentation
    pil_image_rotated = Image.fromarray(results_rotation["array_image"])
    results_segmentation = run_segmentation(
        model=model_segmentation, pil_image=pil_image_rotated
    )

    # Stage: Crop
    results_crop = run_crop(
        pil_image_mask=results_segmentation["mask"],
        pil_image_masked=results_segmentation["masked"],
        padding=param_crop_padding,
    )

    return {
        "order": [
            "preprocess",
            "pose",
            "rotation",
            "segmentation",
            "crop",
        ],
        "stages": {
            "preprocess": {
                "input": {"pil_image": pil_image},
                "output": results_preprocess,
            },
            "pose": {
                "input": {"pil_image": pil_image_preprocessed},
                "output": results_pose,
            },
            "rotation": {
                "input": {
                    "pil_image": pil_image_preprocessed,
                    "angle_rad": results_pose["theta"],
                    "keypoints_xy": results_pose["keypoints_xy"],
                },
                "output": results_rotation,
            },
            "segmentation": {
                "input": {"pil_image": pil_image_rotated},
                "output": results_segmentation,
            },
            "crop": {
                "input": {
                    "pil_image_mask": results_segmentation["mask"],
                    "pil_image_masked": results_segmentation["masked"],
                    "padding": param_crop_padding,
                },
                "output": results_crop,
            },
        },
    }


def run(
    loaded_models: dict[str, YOLO | IdentificationModel],
    pil_image: Image.Image,
    param_crop_padding: int = 0,
    param_k: int = 3,
) -> dict[str, Any]:
    """
    Run the ML pipeline on `pil_image`.

    Args:
        loaded_models (dict[str, YOLO]): resut of calling `load_models`.
        pil_image (PIL): Image to run the pipeline on.
        param_crop_padding (int): how much to pad the resulting segmentated
        image when cropped.
        param_k (int): top k matches to return.

    Returns:
        order (list[str]): the stages and their order.
        stages (dict[str, Any]): the description of each stage, its
        input and output.
    """
    model_identification = loaded_models["identification"]

    results = run_pre_identification_stages(
        loaded_models=loaded_models,
        pil_image=pil_image,
        param_crop_padding=param_crop_padding,
    )

    results_crop = results["stages"]["crop"]["output"]
    results_identification = identification.predict(
        model=model_identification,
        pil_image=results_crop["pil_image"],
        k=param_k,
    )

    results["order"].append("identification")
    results["stages"]["identification"] = {
        "input": {"pil_image": results_crop["pil_image"]},
        "output": results_identification,
    }

    return results