main.py

import numpy as np
from shapely.geometry.point import Point
from skimage.draw import circle_perimeter_aa
from tqdm import tqdm

from core import inference


def draw_circle(img, row, col, rad):
    rr, cc, val = circle_perimeter_aa(row, col, rad)
    valid = (
            (rr >= 0) &
            (rr < img.shape[0]) &
            (cc >= 0) &
            (cc < img.shape[1])
    )
    img[rr[valid], cc[valid]] = val[valid]


def noisy_circle(size, radius, noise):
    img = np.zeros((size, size), dtype=np.float)

    # Circle
    row = np.random.randint(size)
    col = np.random.randint(size)
    rad = np.random.randint(10, max(10, radius))
    draw_circle(img, row, col, rad)

    # Noise
    img += noise * np.random.rand(*img.shape)
    return (row, col, rad), img


def find_circle(img):
    return inference.find_circle(img).numpy()


def iou(params0, params1):
    row0, col0, rad0 = params0
    row1, col1, rad1 = params1

    shape0 = Point(row0, col0).buffer(rad0)
    shape1 = Point(row1, col1).buffer(rad1)

    return (
            shape0.intersection(shape1).area /
            shape0.union(shape1).area
    )


def main():
    results = []
    for _ in tqdm(range(1000)):
        params, img = noisy_circle(200, 50, 2)
        detected = find_circle(img)
        results.append(iou(params, detected))
    results = np.array(results)
    print((results > 0.7).mean())


if __name__ == "__main__":
    inference.load_model()
    main()