import argparse

import cv2
import geojson
import numpy as np
import pandas as pd


def draw_poly(input_df, input_img, col=(0, 0, 0), fill=False):
    s = np.array(input_df)
    if fill:
        output_img = cv2.fillPoly(input_img, pts=np.int32([s]), color=col)
    else:
        output_img = cv2.polylines(input_img, np.int32([s]), True, color=col, thickness=1)
    return output_img


def draw_roi(input_roi, input_img, fill):
    if len(input_roi["geometry"]["coordinates"]) == 1:
        # Polygon w/o holes
        input_img = draw_poly(input_roi["geometry"]["coordinates"][0], input_img, fill=fill)
    else:
        first_roi = True
        for sub_roi in input_roi["geometry"]["coordinates"]:
            # Polygon with holes
            if not isinstance(sub_roi[0][0], list):
                if first_roi:
                    first_roi = False
                    col = (0, 0, 0)
                else:
                    # holes in ROI
                    col = (255, 255, 255) if not fill else (0, 0, 0)
                input_img = draw_poly(sub_roi, input_img, col=col, fill=fill)
            else:
                # MultiPolygon with holes
                for sub_coord in sub_roi:
                    if first_roi:
                        first_roi = False
                        col = (0, 0, 0)
                    else:
                        # holes in ROI
                        col = (255, 255, 255) if not fill else (0, 0, 0)
                    input_img = draw_poly(sub_coord, input_img, col=col, fill=fill)

    return input_img


def split_qupath_roi(in_roi):
    with open(in_roi) as file:
        qupath_roi = geojson.load(file)

    # HE dimensions
    dim_plt = [qupath_roi["dim"]["width"], qupath_roi["dim"]["height"]]

    tma_name = qupath_roi["name"]
    cell_types = [ct.rsplit(" - ", 1)[-1] for ct in qupath_roi["featureNames"]]

    for cell_type in cell_types:
        # create numpy array with white background
        img = np.zeros((dim_plt[1], dim_plt[0], 3), dtype="uint8")
        img.fill(255)

        for i, roi in enumerate(qupath_roi["features"]):
            if not args.all:
                if "classification" not in roi["properties"]:
                    continue
                if roi["properties"]["classification"]["name"] == cell_type:
                    img = draw_roi(roi, img, args.fill)
            else:
                img = draw_roi(roi, img, args.fill)

        # get all black pixel
        coords_arr = np.column_stack(np.where(img == (0, 0, 0)))

        # remove duplicated rows
        coords_arr_xy = coords_arr[coords_arr[:, 2] == 0]

        # remove last column
        coords_arr_xy = np.delete(coords_arr_xy, 2, axis=1)

        # to pandas and rename columns to x and y
        coords_df = pd.DataFrame(coords_arr_xy, columns=['y', 'x'])

        # reorder columns
        coords_df = coords_df[['x', 'y']]

        # drop duplicates
        coords_df = coords_df.drop_duplicates(
            subset=['x', 'y'],
            keep='last').reset_index(drop=True)

        coords_df.to_csv("{}_{}.txt".format(tma_name, cell_type), sep='\t', index=False)

        # img save
        if args.img:
            cv2.imwrite("{}_{}.png".format(tma_name, cell_type), img)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Split ROI coordinates of QuPath TMA annotation by cell type (classfication)")
    parser.add_argument("--qupath_roi", default=False, help="Input QuPath annotation (GeoJSON file)")
    parser.add_argument("--fill", action="store_true", required=False, help="Fill pixels in ROIs")
    parser.add_argument('--version', action='version', version='%(prog)s 0.1.0')
    parser.add_argument("--all", action="store_true", required=False, help="Extracts all ROIs")
    parser.add_argument("--img", action="store_true", required=False, help="Generates image of ROIs")
    args = parser.parse_args()

    if args.qupath_roi:
        split_qupath_roi(args.qupath_roi)