# <<< CornerDetector >>>
RANDOM_ANSWER = np.array([[1, 2], [3, 4], [6, 9]])
COEFFS_KERNEL = np.array(
    [[1, 1, 1, 1, 1],
     [1, 5, 5, 5, 1],
     [1, 5, 0, 5, 1],
     [1, 5, 5, 5, 1],
     [1, 1, 1, 1, 1]]
)
# noise 0.0-0.2
POTENTIAL_POINTS_NUM_0_2 = 200
ITERATIONS_NUM_0_2 = 1000
IMAGE_THRESHOLD_0_2 = 0
THRESHOLD_DISTANCE_0_2 = 1
THRESHOLD_K_0_2 = 0.5
THRESHOLD_B_0_2 = 20
NOISE_LESS_0_2 = 100
KERNEL_SIZE_0_2 = 3

# noise 0.3-0.5
POTENTIAL_POINTS_NUM_3_5 = 200
ITERATIONS_NUM_3_5 = 1000
IMAGE_THRESHOLD_3_5 = 100
THRESHOLD_DISTANCE_3_5 = 1
THRESHOLD_K_3_5 = 0.5
THRESHOLD_B_3_5 = 20
NOISE_LESS_3_5 = 75
KERNEL_SIZE_3_5 = 3

# <<< TimerService >>>
TIME_MAX = 1.95


class TimeService:
    """Service for time management."""
    def __init__(self, time_max: float):
        self.time_max = time_max
        self.start_time = None

    def start_timer(self) -> None:
        """Start time point. Sets 'self.start_time'."""
        self.start_time = time.time()

    def check_timer(self):
        """Checks if time more than 'self.time_max'."""
        end_time = time.time()
        delta_time = end_time - self.start_time
        if delta_time > self.time_max:
            return False
        return delta_time


class CornerDetector(object):
    """Class for corner detection on image."""
    def __init__(
            self,
    ):
        self.__potential_points_num = None
        self.__iterations_num = None
        self.__image_threshold = None
        self.__threshold_k = None
        self.__threshold_b = None
        self.__threshold_distance = None
        self.__need_coeffs_kernel = None
        self.__image = None
        self.__kernel_size = 3
        self.__time_service = TimeService(TIME_MAX)
        self.__need_time_check = False
        self.random_answer = RANDOM_ANSWER

    def __get_coords_by_threshold(
            self,
    ) -> tuple:
        """Gets coordinates with intensity more than 'image_threshold'."""
        coords = np.where(self.__image > self.__image_threshold)
        return coords

    def __get_neigh_nums_list(
            self,
            coords: tuple,
            kernel_size: int
    ) -> 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 > self.__image.shape[0] - offset:
                continue
            if y < 0 + offset or y > self.__image.shape[1] - offset:
                continue

            kernel = self.__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 __get_neigh_coeffs_list(
            self, coords: tuple, kernel: np.array
    ) -> list:
        """
        Gets list with number of neighborhoods by kernel.
        Returns list like [[x1, y1, N1], [x2, y2, N2]],
        Where N - neighborhood's coeffs.
        """
        neigh_coeffs = []

        offset = (kernel.shape[0] - 1) // 2
        print(offset)

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

            try:
                image_kernel = self.__image[x - offset:x + offset + 1,
                               y - offset:y + offset + 1]
                step_neigh_coeff = np.sum(kernel * image_kernel)
                neigh_coeffs.append([x, y, step_neigh_coeff])
            except ValueError as e:
                continue

        return neigh_coeffs

    @staticmethod
    def __sort_neigh_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(
            self,
            sorted_neigh_nums: np.ndarray
    ) -> np.ndarray:
        """
        Gets best candidates, potential points coords only.
        Returns np.ndarray like [[x1, y1], [x2, y2]].
        """
        sorted_neigh_nums = np.delete(sorted_neigh_nums, 2, axis=1)
        return sorted_neigh_nums[:self.__potential_points_num]

    def __get_best_lines(
            self,
            potential_points: np.ndarray
    ) -> np.ndarray:
        """
        Gets the best combinations of lines by all points.
        Line y = kx + b.
        Returns np.ndarray like [[k1, b1], [k2, b2]].
        """
        remaining_points = 0
        best_lines = []
        num_lines = 0

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

            if len(remaining_points) == 0:
                remaining_points = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])

            for i in range(self.__iterations_num):
                if not self.__time_service.check_timer():
                    return self.random_answer
                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)
                inlines = np.where(distances < self.__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 <= self.__threshold_k and diff_b <= self.__threshold_b:
                        is_similar = True
                        break

                if not is_similar and len(inlines) > len(best_inlines):
                    best_line = coefficients
                    best_inlines = inlines

            if best_line is not None:
                best_lines.append(best_line)
                remaining_points = np.delete(remaining_points, best_inlines, axis=0)

            num_lines += 1

        return np.array(best_lines)

    def find_corners(
            self,
            image: np.ndarray,
    ) -> np.ndarray:
        """Finding triangle corners on image process."""
        self.__time_service.start_timer()
        self.__image = image

        coords = self.__get_coords_by_threshold()
        if self.__need_coeffs_kernel:
            neigh_nums = self.__get_neigh_coeffs_list(coords, COEFFS_KERNEL)
        else:
            neigh_nums = self.__get_neigh_nums_list(coords, self.__kernel_size)
        sorted_neigh_nums = self.__sort_neigh_nums_by_N(neigh_nums)
        potential_points = self.__get_potential_points_coords(sorted_neigh_nums)
        best_lines = self.__get_best_lines(potential_points)

        intersection_points = np.array([
            np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[1][0], -1]]), np.array([-best_lines[0][1], -best_lines[1][1]])),
            np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[0][1], -best_lines[2][1]])),
            np.linalg.solve(np.array([[best_lines[1][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[1][1], -best_lines[2][1]]))
        ], dtype=np.float32)

        return intersection_points

    def set_potential_points_num(self, potential_points_num: int) -> None:
        """Potential points numbers setter."""
        self.__potential_points_num = potential_points_num

    def set_iterations_num(self, iterations_num: int) -> None:
        """Iteration numbers setter."""
        self.__iterations_num = iterations_num

    def set_image_threshold(self, image_threshold: int) -> None:
        """Image threshold setter."""
        self.__image_threshold = image_threshold

    def set_threshold_k(self, threshold_k: float) -> None:
        """Threshold of k parameter setter."""
        self.__threshold_k = threshold_k

    def set_threshold_b(self, threshold_b: int) -> None:
        """Threshold of b parameter setter."""
        self.__threshold_b = threshold_b

    def set_threshold_distance(self, threshold_distance: int) -> None:
        """Threshold of distance area parameter setter."""
        self.__threshold_distance = threshold_distance

    def set_need_time_check(self, need_time_check: bool = True):
        """If you need time checking var setter."""
        self.__need_time_check = need_time_check

    def set_need_coeffs_kernel(self, need_coeffs_kernel: bool = True):
        """If you need coeffs kernel, not just neighbors var setter."""
        self.__need_coeffs_kernel = need_coeffs_kernel

    def set_kernel_size(self, kernel_size: int) -> None:
        """Kernel size setter."""
        self.__kernel_size = kernel_size


# noise 0.6
POTENTIAL_POINTS_NUM_6 = 1200
ITERATIONS_NUM_6 = 1500
IMAGE_THRESHOLD_6 = 170
THRESHOLD_DISTANCE_6 = 3
THRESHOLD_K_6 = 0.5
THRESHOLD_B_6 = 500
NOISE_LESS_6 = 45
KERNEL_SIZE_6 = 11

image = cv2.imread("0.6/00.pgm", cv2.COLOR_BGR2GRAY)
solver = Solver()
solver.solve(image)

Визуализируй потенциальные точки и прямые, которые получились на изображении