import numpy as np

from pathlib import Path
from skimage.draw import polygon
from tifffile import TiffFile

from src.database.db_manager import DatabaseManager


def read_image(image_path: Path) -> np.ndarray:
    metadata = {}
    with TiffFile(image_path) as tif:
        image = tif.asarray()
        metadata = tif.imagej_metadata
    return image, metadata


def main():

    polygon_vertices = np.array([[10, 10], [50, 10], [50, 50], [10, 50]])
    image = np.zeros((100, 100), dtype=np.uint8)
    rr, cc = polygon(polygon_vertices[:, 0], polygon_vertices[:, 1])
    image[rr, cc] = 255


if __name__ == "__main__":

    db = DatabaseManager()
    model_name = "c17"
    model_id = db.get_models(filters={"model_name": model_name})[0]["id"]
    print(f"Model name {model_name}, id {model_id}")
    detections = db.get_detections(filters={"model_id": model_id})

    file_stems = set()

    for detection in detections:
        file_stems.add(detection["image_filename"].split("_")[0])

    print("Files:", file_stems)

    for stem in file_stems:
        print(stem)
        detections = db.get_detections(filters={"model_id": model_id, "i.filename": f"LIKE %{stem}%"})
        annotations = []
        for detection in detections:
            source_path = Path(detection["metadata"]["source_path"])
            image, metadata = read_image(source_path)

            offset = np.array(list(map(int, metadata["tile_section"].split(","))))[::-1]
            scale = np.array(list(map(int, metadata["patch_size"].split(","))))[::-1]
            # tile_size = np.array(list(map(int, metadata["tile_size"].split(","))))
            segmentation = np.array(detection["segmentation_mask"])  # * tile_size

            # print(source_path, image, metadata, segmentation.shape)
            # print(offset)
            # print(scale)
            # print(segmentation)

            # segmentation = (segmentation + offset * tile_size) / (tile_size * scale)
            segmentation = (segmentation + offset) / scale

            yolo_annotation = f"{detection['metadata']['class_id']} " + " ".join(
                [f"{x:.6f} {y:.6f}" for x, y in segmentation]
            )
            annotations.append(yolo_annotation)
            # print(segmentation)
            # print(yolo_annotation)

            # aa
            print(
                "  ",
                detection["model_name"],
                detection["image_id"],
                detection["image_filename"],
                source_path,
                metadata["label_path"],
            )
            # section_i_section_j = detection["image_filename"].split("_")[1].split(".")[0]
            # print("    ", section_i_section_j)

        label_path = metadata["label_path"]
        print("  ", label_path)
        with open(label_path, "w") as f:
            f.write("\n".join(annotations))

    exit()

    for detection in detections:
        print(detection["model_name"], detection["image_id"], detection["image_filename"])

    print(detections[0])
    # polygon_vertices = np.array([[10, 10], [50, 10], [50, 50], [10, 50]])

    # image = np.zeros((100, 100), dtype=np.uint8)

    # rr, cc = polygon(polygon_vertices[:, 0], polygon_vertices[:, 1])

    # image[rr, cc] = 255

    # import matplotlib.pyplot as plt

    # plt.imshow(image, cmap='gray')
    # plt.show()