image = cv2.imread("0.1/01.pgm", cv2.COLOR_BGR2GRAY)


IMAGE_THRESHOLD = 100

def get_coords_by_threshold(
        image: np.ndarray, threshold: int = IMAGE_THRESHOLD
) -> tuple:
    """Gets pixel coords with intensity more then 'theshold'."""
    coords = np.where(image > threshold)
    return coords


def delete_pixels_by_min_threshold(
        image: np.ndarray, min_threshold: int = IMAGE_THRESHOLD
) -> np.ndarray:
    """Deletes coords from image and returns this image."""
    anti_coords = np.where(image < min_threshold)
    image[anti_coords] = 0
    return image


def get_neigh_nums_list(
        coords: tuple, kernel_size: int = 3
) -> list:
    """
    Gets list with number of neighborhoods by kernel.
    Returns list like [[x1, y1, N1], [x2, y2, N2]],
    Where N - neighborhood's number.
    """
    neigh_nums = []
    offset = (kernel_size - 1) // 2

    for x, y in zip(coords[0], coords[1]):
        if x < 0 + offset or x > image.shape[0] - offset:
            continue
        if y < 0 + offset or y > image.shape[1] - offset:
            continue

        kernel = image[x-offset:x+offset+1, y-offset:y+offset+1]
        step_neigh_num = np.count_nonzero(kernel) - 1
        neigh_nums.append([x, y, step_neigh_num])

    return neigh_nums


def sort_neight_nums_by_N(neigh_nums: list) -> np.ndarray:
    """
    Sort neigh_nums by N parameter.
    Removes N=-1, N=0 and sorts by N desc.
    """
    np_neigh_nums = np.array(neigh_nums)

    np_neigh_nums = np_neigh_nums[
        (np_neigh_nums[:, 2] != -1) & (np_neigh_nums[:, 2] != 0)]

    np_neigh_nums = np_neigh_nums[
        np.argsort(-np_neigh_nums[:, 2])]

    return np_neigh_nums


def get_potential_points_coords(
        sorted_niegh_nums: np.ndarray, potential_points_num: int = 200
) -> np.ndarray:
    """
    Gets best candidates, potential points coords only.
    Returns np.ndarray like [[x1, y1], [x2, y2]].
    """
    sorted_neigh_nums = np.delete(sorted_niegh_nums, 2, axis=1)
    return sorted_neigh_nums[:potential_points_num]


# def get_best_lines(potential_points: np.ndarray) -> np.ndarray:
#     """
#     Gets best combinations of lines by all points.
#     Line y = kx + b.
#     Returns np.ndarray like [[k1, b1], [k2, b2]].
#     """
#     best_lines = []
#     best_inliers = []
#     iterations_num = 1000
#     threshold_distance = 3
#     threshold_k = 0.1
#     threshold_b = 10

#     for i in range(iterations_num):
#         sample_indices = np.random.choice(potential_points.shape[0], size=3, replace=False)

#         sample_points = potential_points[sample_indices]
#         sample_x = sample_points[:, 1]
#         sample_y = sample_points[:, 0]

#         coefficients = np.polyfit(sample_x, sample_y, deg=1)

#         distances = np.abs(potential_points[:, 0] - np.matmul(np.vstack((potential_points[:, 1], np.ones_like(potential_points[:, 1]))).T, coefficients)) / np.sqrt(coefficients[0] ** 2 + 1)
#         inliers = np.where(distances < threshold_distance)[0]

#         is_similar = False
#         for line in best_lines:
#             diff_k = abs(coefficients[0] - line[0])
#             diff_b = abs(coefficients[0] - line[0])

#             if diff_k <= threshold_k and diff_b <= threshold_b:
#                 is_similar = True
#                 break

#         if not is_similar and len(inliers) > len(best_inliers):
#             best_lines = [coefficients]
#             best_inliers = inliers
#         elif not is_similar and len(inliers) == len(best_inliers):
#             best_lines.append(coefficients)

#     return np.array(best_lines)


def get_best_lines(potential_points: np.ndarray) -> np.ndarray:
    """
    Gets best combinations of lines by all points.
    Line y = kx + b.
    Returns np.ndarray like [[k1, b1], [k2, b2]].
    """
    best_lines = []
    num_lines = 0
    iterations_num = 1000
    threshold_distance = 3
    threshold_k = 0.5
    threshold_b = 20

    while num_lines < 3:
        if num_lines == 0:
            remaining_points = np.copy(potential_points)
        print(f"remaining points: {len(remaining_points)}")
        best_inliers = []
        best_line = None

        for i in range(iterations_num):
            if len(remaining_points) < 2:
                break
            sample_indices = np.random.choice(remaining_points.shape[0], size=3, replace=False)
            sample_points = remaining_points[sample_indices]
            sample_x = sample_points[:, 1]
            sample_y = sample_points[:, 0]
            coefficients = np.polyfit(sample_x, sample_y, deg=1)
            distances = np.abs(remaining_points[:, 0] - np.matmul(np.vstack((remaining_points[:, 1], np.ones_like(remaining_points[:, 1]))).T, coefficients)) / np.sqrt(coefficients[0] ** 2 + 1)
            inliers = np.where(distances < threshold_distance)[0]

            is_similar = False
            for line in best_lines:
                diff_k = abs(coefficients[0] - line[0])
                diff_b = abs(coefficients[1] - line[1])

                if diff_k <= threshold_k and diff_b <= threshold_b:
                    is_similar = True
                    break

            if not is_similar and len(inliers) > len(best_inliers):
                best_line = coefficients
                best_inliers = inliers

        if best_line is not None:
            best_lines.append(best_line)
            buffer = remaining_points
            remaining_points = np.delete(remaining_points, best_inliers, axis=0)
            if len(remaining_points) < 1:
                remaining_points = np.append(remaining_points, buffer[:len(buffer)//2 + 1])

        num_lines += 1

    return np.array(best_lines)


# def angle_between_lines(line1: np.ndarray, line2: np.ndarray) -> float:
#     """Calculates the angle in degrees between two lines."""
#     k1, b1 = line1
#     k2, b2 = line2
#     angle_rad = np.arctan2(k2 - k1, 1 + k1 * k2)
#     angle_deg = np.degrees(angle_rad)
#     return angle_deg

# main
# image = cv2.GaussianBlur(image, (25, 25), 0)

coords = get_coords_by_threshold(image)

# image = delete_pixels_by_min_threshold(image)

neigh_nums = get_neigh_nums_list(coords)

sorted_neigh_nums = sort_neight_nums_by_N(neigh_nums)

potential_points = get_potential_points_coords(sorted_neigh_nums)

best_lines = get_best_lines(potential_points)
print(best_lines)

# Visualization
plt.imshow(image, cmap="gray")
plt.scatter(potential_points[:, 1], potential_points[:, 0], color="red", marker="o")
plt.show()

# Plot the lines on the image
for equation in best_lines:
    k, b = equation
    x = np.arange(0, image.shape[1])
    y = k * x + b
    plt.imshow(image, cmap="gray")
    plt.scatter(potential_points[:, 1], potential_points[:, 0], color="red", marker="o")
    plt.plot(x, y, color="green")
    plt.scatter(potential_points[:, 1], potential_points[:, 0], color="red", marker="o")
plt.show()

Нужно как-то улучшить нахождение potential_points на изображении? Есть идеи? Размытие по гауссу подойдет? Реализуй, только так, чтобы с кодом стыковалось нормально