polygons_processing/postpprocess_detectree2.py

import json
from shapely.geometry import Polygon, Point
from shapely.ops import unary_union
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon as MplPolygon
import numpy as np
import pandas as pd


def add_extreme_coordinates(polygon_data):
    polygon_coords = np.array(polygon_data["geometry"]["coordinates"][0])

    polygon_data["geometry"]["max_lat"] = max(polygon_coords[:, 1])
    polygon_data["geometry"]["min_lat"] = min(polygon_coords[:, 1])
    polygon_data["geometry"]["max_lon"] = max(polygon_coords[:, 0])
    polygon_data["geometry"]["min_lon"] = min(polygon_coords[:, 0])


def turn_into_dataframe(data):
    data_list = data["features"]

    for i in range(len(data_list)):
        add_extreme_coordinates(data_list[i])

    df = pd.DataFrame(data_list).drop(columns="type")

    dict_cols = ["properties", "geometry"]
    for dict_col in dict_cols:
        dict_df = pd.json_normalize(df[dict_col])
        # Merge the new columns back into the original DataFrame
        df = df.drop(columns=[dict_col]).join(dict_df)
    df["coordinates"] = df["coordinates"].apply(lambda x: x[0])
    df["polygon"] = df["coordinates"].apply(lambda x: Polygon(x))

    df = df.drop(columns=["type"])
    return df

# Function to plot a polygon
def plot_polygon(ax, polygon, color, label="label"):
    if not polygon.is_empty:
        x, y = polygon.exterior.xy
        ax.fill(x, y, color=color, alpha=0.5, label=label)


def plot_polygons(list_polygons, first_one_different=False, dpi=150):
    # Plot the polygons and their intersection
    plt.figure(dpi=dpi)
    fig, ax = plt.subplots()

    if first_one_different:
        plot_polygon(ax, list_polygons[0], "red", f"polygon {0}")
        for i, polygon in enumerate(list_polygons[1:]):
            plot_polygon(ax, polygon, "blue", f"polygon {i}")
    else:
        for i, polygon in enumerate(list_polygons):
            plot_polygon(ax, polygon, "blue", f"polygon {i}")

    # Plot the intersection
    # plot_polygon(ax, intersection, 'red', 'Intersection')

    # Add legend
    # ax.legend()

    # Set axis limits
    ax.set_aspect("equal")

    # Set title
    ax.set_title("Polygons and their Intersection")
    plt.ylabel("lat")
    plt.xlabel("lon")

    plt.show()


def plot_polygons_with_colors(list_polygons, list_colors, dpi=150):
    # Plot the polygons and their intersection
    plt.figure(dpi=dpi)
    fig, ax = plt.subplots()

    for polygon, color in zip(list_polygons, list_colors):
        plot_polygon(ax, polygon, color)

    # Set axis limits
    ax.set_aspect("equal")

    # Set title
    ax.set_title("Polygons and their Intersection")
    plt.ylabel("lat")
    plt.xlabel("lon")

    plt.show()


def plot_polygons_from_df(df, dpi=150):
    list_polygons = []
    for index, row in df.iterrows():
        list_polygons.append(row["polygon"])
    plot_polygons(list_polygons=list_polygons, dpi=dpi)


def map_color(id):
    return "blue"


def plot_polygons_from_df_with_color(df, dpi=150):

    df["plot_colors"] = df["id"].apply(map_color)
    list_polygons = []
    list_colors = []
    for index, row in df.iterrows():
        list_polygons.append(row["polygon"])
        list_colors.append(row["plot_colors"])
    plot_polygons_with_colors(
        list_polygons=list_polygons, list_colors=list_colors, dpi=dpi
    )

def intersection(polygon, polygon_comparison):
    return polygon.intersection(polygon_comparison)


def intersection_area(polygon, polygon_comparison):
    return intersection(polygon, polygon_comparison).area


def intersection_area_ratio(polygon, polygon_comparison):
    return intersection_area(polygon, polygon_comparison) / polygon.area

def containsPoint(polygonB, polygon):
    coordinatesB = get_coordinates(polygonB)
    for coord in coordinatesB:
        coord = Point(coord)
        if polygon.contains(coord):
            return True
        else:
            return False

def get_coordinates(polygon):
    coordinates = polygon.exterior.coords
    coordinates = [list(pair) for pair in coordinates]
    return coordinates

def mark_id_to_be_dropped(df, id_string):
    df.loc[df['id']== id_string , 'to_drop'] = True

def mark_id_to_be_merged(df, id_string):
    df.loc[df['id']== id_string , 'to_merge'] = True
    
def calc_overlapping_subset(df_input, index):
    max_lat = df_input.iloc[index]['max_lat']
    min_lat = df_input.iloc[index]['min_lat']
    max_lon = df_input.iloc[index]['max_lon']
    min_lon = df_input.iloc[index]['min_lon']
    relevant_subset = df_input.loc[( (( ((max_lat < df_input['max_lat']) & (max_lat > df_input['min_lat'])) | \
                                    ((min_lat < df_input['max_lat']) & (min_lat > df_input['min_lat'])) )| \
                                    ( ((df_input['max_lat'] < max_lat) & (df_input['max_lat'] > min_lat)) | \
                                    ((df_input['min_lat'] > min_lat ) & ( df_input['min_lat'] < max_lat)) ) )  & \
                                    (( ( ((max_lon < df_input['max_lon']) & (max_lon > df_input['min_lon'])) | \
                                    ((min_lon < df_input['max_lon']) & (min_lon > df_input['min_lon'])) ) ) |
                                    ( ((df_input['max_lon'] < max_lon ) & (df_input['max_lon'] > min_lon)) | \
                                    ((df_input['min_lon'] > min_lon) & (df_input['min_lon'] < max_lon)) ) ) )]
    return relevant_subset

def remove_contained_poylgons(df_input):
    df_result = df_input.copy()

    for i in range (len(df_result)):
        
        polygonA = df_input.iloc[i]['polygon']

        #relevant_subset = df_result[df_result['polygon'].apply(lambda polygonB: containsPoint(polygonA, polygonB))]
        #relevant_subset = relevant_subset[relevant_subset['id'] != df_input.iloc[i]['id']]
        relevant_subset = calc_overlapping_subset(df_input = df_result, index = i)

        # Experiment with this parameter to find the best threshold
        # It certainly has to be smaller than 0.9
        threshold = 0.85
        for j in range(len(relevant_subset)):
            ratio_current_choice = intersection_area_ratio(polygon = polygonA, polygon_comparison = relevant_subset.iloc[j]['polygon'])
            ratio_alternative_choice = intersection_area_ratio(polygon = relevant_subset.iloc[j]['polygon'], polygon_comparison= polygonA)
            if  (ratio_current_choice > threshold) or (ratio_alternative_choice > threshold): # or ratio_alternative_choice > threashold:
                if polygonA.area >  relevant_subset.iloc[j]['polygon'].area:
                    mark_id_to_be_dropped(df=df_result, id_string = relevant_subset.iloc[j]['id'])               
                else:
                    mark_id_to_be_dropped(df=df_result, id_string = df_input.iloc[i]['id'])  

    #remove all polygons that had a marked id
    df_result = df_result.loc[df_result["to_drop"] == False]
    return df_result

def merge(df_input, polygon_index, merge_subset):
    for j in range(len(merge_subset)):
        #merge merged_polygon with j-th polygon in merge_subset
        #delete j_th polygon in merge_subset from df_input
        merged_polygon = df_input.iloc[polygon_index]
        merged_polygon_id = df_input.iloc[polygon_index]['id']
        merged_polygon_index = merged_polygon.index

        #change by merge --> polygon, coordinates, min/max long lat, score (use max or min or avg)
        tmp = merged_polygon['polygon'].union(merge_subset.iloc[j]['polygon'])
        merged_coordinates = list(tmp.exterior.coords)
        merged_polygon = Polygon(merged_coordinates) #new polygon

        coordinates = [list(tup) for tup in merged_coordinates] #new coordinates
        #updating min/max long/lat
        min_lon = min([point[0] for point in coordinates])
        max_lon = max([point[0] for point in coordinates])
        min_lat = min([point[1] for point in coordinates])
        max_lat = max([point[1] for point in coordinates])
        polygon_score = merge_subset.iloc[j]['Confidence_score']

        #updating merged polygon
        df_input.loc[df_input['id'] == merged_polygon_id,'polygon'] = merged_polygon
        df_input.loc[df_input['id'] == merged_polygon_id,'min_lon'] = min_lon
        df_input.loc[df_input['id'] == merged_polygon_id,'max_lon'] = max_lon
        df_input.loc[df_input['id'] == merged_polygon_id,'min_lat'] = min_lat
        df_input.loc[df_input['id'] == merged_polygon_id,'max_lat'] = max_lat
        df_input.loc[df_input['id'] == merged_polygon_id,'Confidence_score'] = (df_input.iloc[polygon_index]['Confidence_score'] + polygon_score)/2
        df_input.loc[df_input['id'] == merged_polygon_id, 'coordinates'] = df_input.loc[df_input['id'] == merged_polygon_id, 'polygon'].apply(get_coordinates)
        df_input = df_input.loc[df_input['id'] != merge_subset.iloc[j]['id']]
    return df_input


def merge_overlapping(df_input):
    # Experiment with this parameter to get the best results
    threshold = 0.40
    #df_result = df_input.copy()

    for i in range(len(df_input)):
        polygon = df_input.iloc[i]['polygon']
        relevant_subset = calc_overlapping_subset(df_input=df_input, index=i)
        toBeMerged = False
        for j in range(len(relevant_subset)):
            ratio_current_choice = intersection_area_ratio(polygon = polygon, polygon_comparison = relevant_subset.iloc[j]['polygon'])
            ratio_alternative_choice = intersection_area_ratio(polygon = relevant_subset.iloc[j]['polygon'], polygon_comparison= polygon)
            if  (ratio_current_choice > threshold) or (ratio_alternative_choice > threshold):
                toBeMerged = True
                mark_id_to_be_merged(df=relevant_subset, id_string = relevant_subset.iloc[j]['id'])         
        
        if toBeMerged:
            # deleting is handled in this funciton as well
            df_input = merge(df_input=df_input, polygon_index=i, merge_subset=relevant_subset[relevant_subset['to_merge']==True])
            return True, df_input
    
    return False, df_input
    

def process(list_df):
    df_res = pd.concat(list_df)
    df_res = remove_contained_poylgons(df_input= df_res)
    i = 0
    merged, df_res = merge_overlapping(df_input=df_res)
    while(merged):
        i+=1
        if i%100 == 0:
            print(i)
        merged, df_res = merge_overlapping(df_input=df_res)
    return df_res


def combine_different_tile_size(df_smaller, df_bigger):

    df_result = df_bigger.copy()

    for i in range(len(df_smaller)):
        max_lat = df_smaller.iloc[i]["max_lat"]
        min_lat = df_smaller.iloc[i]["min_lat"]
        max_lon = df_smaller.iloc[i]["max_lon"]
        min_lon = df_smaller.iloc[i]["min_lon"]

        polygon = df_smaller.iloc[i]["polygon"]

        relevant_subset = df_bigger.loc[
            (
                ((max_lat < df_bigger["max_lat"]) & (max_lat > df_bigger["min_lat"]))
                | ((min_lat < df_bigger["max_lat"]) & (min_lat > df_bigger["min_lat"]))
            )
            & (
                ((max_lon < df_bigger["max_lon"]) & (max_lon > df_bigger["min_lon"]))
                | ((min_lon < df_bigger["max_lon"]) & (min_lon > df_bigger["min_lon"]))
            )
        ]

        list_polygons = [polygon]

        for index, row in relevant_subset.iterrows():
            list_polygons.append(row["polygon"])

        add_polygon = True
        threashold = 0.15
        for comparison_polygon in list_polygons[1:]:
            ratio = intersection_area_ratio(polygon, comparison_polygon)
            if ratio > threashold:
                add_polygon = False

        if add_polygon:
            # df_result = pd.concat([df_result, df_result.iloc[[i]]], axis= 1, ignore_index=True)#df_result.append(df_result.iloc[i], ignore_index=True)
            df_result = pd.concat(
                [df_result, df_smaller.iloc[[i]]], axis=0, join="outer"
            )  #

    return df_result


def clean(df, score_threashold=0.5):
    df = df.loc[df["score"] > score_threashold]
    return df

def row_to_feature(row):
    feature = {
        "id": row["id"],
        "type": "Feature",
        "properties": {"Confidence_score": row["Confidence_score"]},
        "geometry": {"type": "Polygon", "coordinates": [row["coordinates"]]},
    }
    return feature


def export_df_as_geojson(df, filename="output"):
    features = [row_to_feature(row) for idx, row in df.iterrows()]

    feature_collection = {
        "type": "FeatureCollection",
        "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG::32720"}},
        "features": features,
    }

    output_geojson = json.dumps(feature_collection)

    with open(f"{filename}.geojson", "w") as f:
        f.write(output_geojson)

    print(f"GeoJSON data exported to '{filename}.geojson' file.")

def convert_id_to_string(prefix, x):
    return prefix + str(x)

def postprocess(prediction_geojson_path, store_path):
    with open(prediction_geojson_path,"r",) as file:
        prediction_data = json.load(file)

    df = turn_into_dataframe(prediction_data)

    df["id"] = df.index

    df['Confidence_score'] = df['Confidence_score'].astype(float)

    df["id"] = df["id"].apply(lambda x: convert_id_to_string("df_", x))

    df["to_drop"] = False
    df["to_merge"] = False

    df_res = process([df])

    export_df_as_geojson(df=df_res, filename=store_path)

    return df_res