TheDistanceAssessor.py

import cv2
import torch
import numpy as np
from skimage import morphology
import albumentations as A
import torch.nn.functional as F
import torch.nn as nn
from albumentations.pytorch import ToTensorV2
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
import matplotlib.path as mplPath
import matplotlib.patches as patches
from ultralyticsplus import YOLO

def image_morpho(mask_prediction):
        selem2 = morphology.disk(2)
        closed = morphology.closing(mask_prediction, selem2)
        
        return closed

def get_segformer_img(image_in, input_size=[224,224]):
        transform_img = A.Compose([
                        A.Resize(height=input_size[0], width=input_size[1], interpolation=cv2.INTER_NEAREST),
                        A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, p=1.0),
                        ToTensorV2(p=1.0),
                        ])
        
        image_in = cv2.resize(image_in, (1920, 1080))
        
        image_tr = transform_img(image=image_in)['image']
        image_tr = image_tr.unsqueeze(0)
        image_tr = image_tr.cpu()
        
        return image_tr, image_in

def load_segformer(path_model):
        
        model = torch.load(path_model, map_location=torch.device('cpu'))
        model = model.cpu()
        model.eval()
        return model

def load_yolo(PATH_model):
        model = YOLO(PATH_model)

        model.overrides['conf'] = 0.25  # NMS confidence threshold
        model.overrides['iou'] = 0.45  # NMS IoU threshold
        model.overrides['agnostic_nms'] = False  # NMS class-agnostic
        model.overrides['max_det'] = 1000  # maximum number of detections per image
        return model

def find_extreme_y_values(arr, values=[0, 6]):
        """
        Optimized function to find the lowest and highest y-values (row indices) in a 2D array where 0 or 6 appears.
        
        Parameters:
        - arr: The input 2D NumPy array.
        - values: The values to search for (default is [0, 6]).
        
        Returns:
        A tuple (lowest_y, highest_y) representing the lowest and highest y-values. If values are not found, returns None.
        """
        mask = np.isin(arr, values)
        rows_with_values = np.any(mask, axis=1)
        
        y_indices = np.nonzero(rows_with_values)[0]  # Directly finding non-zero (True) indices
        
        if y_indices.size == 0:
                return None, None  # Early return if values not found
        
        return y_indices[0], y_indices[-1]

def find_nearest_pairs(arr1, arr2):
        # Convert lists to numpy arrays for vectorized operations
        arr1_np = np.array(arr1)
        arr2_np = np.array(arr2)
        
        # Determine which array is shorter
        if len(arr1_np) < len(arr2_np):
                base_array, compare_array = arr1_np, arr2_np
        else:
                base_array, compare_array = arr2_np, arr1_np

        paired_base = []
        paired_compare = []

        # Mask to keep track of paired elements
        paired_mask = np.zeros(len(compare_array), dtype=bool)

        for item in base_array:
                # Calculate distances from the current item to all items in the compare_array
                distances = np.linalg.norm(compare_array - item, axis=1)
                nearest_index = np.argmin(distances)
                paired_base.append(item)
                paired_compare.append(compare_array[nearest_index])
                # Mark the paired element to exclude it from further pairing
                paired_mask[nearest_index] = True

                # Check if all elements from the compare_array have been paired
                if paired_mask.all():
                        break

        paired_base = np.array(paired_base)
        paired_compare = compare_array[paired_mask]

        return (paired_base, paired_compare) if len(arr1_np) < len(arr2_np) else (paired_compare, paired_base)

def filter_crossings(image, edges_dict):
        filtered_edges = {}
        for key, values in edges_dict.items():
                merged = [values[0]]
                for start, end in values[1:]:
                        if start - merged[-1][1] < 50:
                                
                                key_up = max([0, key-10])
                                key_down = min([image.shape[0]-1, key+10])
                                if key_up == 0:
                                        key_up = key+20
                                if key_down == image.shape[0]-1:
                                        key_down = key-20
                                
                                edges_to_test_slope1 = robust_edges(image, [key_up], values=[0, 6], min_width=19)
                                edges_to_test_slope2 = robust_edges(image, [key_down], values=[0, 6], min_width=19)
                                
                                values1, edges_to_test_slope1 = find_nearest_pairs(values, edges_to_test_slope1)
                                values2, edges_to_test_slope2 = find_nearest_pairs(values, edges_to_test_slope2)
                                
                                differences_y = []
                                for i, value in enumerate(values1):
                                        if start in value:
                                                idx = list(value).index(start)
                                                try:
                                                        differences_y.append(abs(start-edges_to_test_slope1[i][idx]))
                                                except:
                                                        pass
                                        if merged[-1][1] in value:
                                                idx = list(value).index(merged[-1][1])
                                                try:
                                                        differences_y.append(abs(merged[-1][1]-edges_to_test_slope1[i][idx]))
                                                except:
                                                        pass
                                for i, value in enumerate(values2):
                                        if start in value:
                                                idx = list(value).index(start)
                                                try:
                                                        differences_y.append(abs(start-edges_to_test_slope2[i][idx]))
                                                except:
                                                        pass
                                        if merged[-1][1] in value:
                                                idx = list(value).index(merged[-1][1])
                                                try:
                                                        differences_y.append(abs(merged[-1][1]-edges_to_test_slope2[i][idx]))
                                                except:
                                                        pass
                                
                                if any(element > 30 for element in differences_y):
                                        merged[-1] = (merged[-1][0], end)
                                else:
                                        merged.append((start, end))
                        else:
                                merged.append((start, end))
                filtered_edges[key] = merged
                
        return filtered_edges

def robust_edges(image, y_levels, values=[0, 6], min_width=19):
        
        for y in y_levels:
                row = image[y, :]
                mask = np.isin(row, values).astype(int)
                padded_mask = np.pad(mask, (1, 1), 'constant', constant_values=0)
                diff = np.diff(padded_mask)
                starts = np.where(diff == 1)[0]
                ends = np.where(diff == -1)[0] - 1

                # Filter sequences based on the minimum width criteria
                filtered_edges = [(start, end) for start, end in zip(starts, ends) if end - start + 1 >= min_width]
                filtered_edges = [(start, end) for start, end in filtered_edges if 0 not in (start, end) and 1919 not in (start, end)]
        
        return filtered_edges

def find_edges(image, y_levels, values=[0, 6], min_width=19):
        """
        Find start and end positions of continuous sequences of specified values at given y-levels in a 2D array,
        filtering for sequences that meet or exceed a specified minimum width.

        Parameters:
        - arr: 2D NumPy array to search within.
        - y_levels: List of y-levels (row indices) to examine.
        - values: Values to search for (default is [0, 6]).
        - min_width: Minimum width of sequences to be included in the results.

        Returns:
        A dict with y-levels as keys and lists of (start, end) tuples for each sequence found in that row that meets the width criteria.
        """
        edges_dict = {}
        for y in y_levels:
                row = image[y, :]
                mask = np.isin(row, values).astype(int)
                padded_mask = np.pad(mask, (1, 1), 'constant', constant_values=0)
                diff = np.diff(padded_mask)
                starts = np.where(diff == 1)[0]
                ends = np.where(diff == -1)[0] - 1

                # Filter sequences based on the minimum width criteria
                filtered_edges = [(start, end) for start, end in zip(starts, ends) if end - start + 1 >= min_width]
                filtered_edges = [(start, end) for start, end in filtered_edges if 0 not in (start, end) and 1919 not in (start, end)]
                
                edges_with_guard_rails = []
                for edge in filtered_edges:
                        cutout_left = image[y,edge[0]-50:edge[0]][::-1]
                        cutout_right = image[y,edge[1]:edge[1]+50]
                        
                        not_ones = np.where(cutout_left != 1)[0]
                        if len(not_ones) > 0 and not_ones[0] > 0:
                                last_one_index = not_ones[0] - 1
                                edge = (edge[0] - last_one_index,) + edge[1:]
                        else:
                                last_one_index = None if len(not_ones) == 0 else not_ones[-1] - 1
                        
                        not_ones = np.where(cutout_right != 1)[0]
                        if len(not_ones) > 0 and not_ones[0] > 0:
                                last_one_index = not_ones[0] - 1
                                edge = (edge[0], edge[1] - last_one_index) + edge[2:]
                        else:
                                last_one_index = None if len(not_ones) == 0 else not_ones[-1] - 1
                        
                        edges_with_guard_rails.append(edge)

                edges_dict[y] = edges_with_guard_rails
        
        edges_dict = {k: v for k, v in edges_dict.items() if v}
        
        edges_dict = filter_crossings(image, edges_dict)
        
        return edges_dict

def find_rails(arr, y_levels, values=[9, 10], min_width=5):
        edges_all = []
        for y in y_levels:
                row = arr[y, :]
                mask = np.isin(row, values).astype(int)
                padded_mask = np.pad(mask, (1, 1), 'constant', constant_values=0)
                diff = np.diff(padded_mask)
                starts = np.where(diff == 1)[0]
                ends = np.where(diff == -1)[0] - 1

                # Filter sequences based on the minimum width criteria
                filtered_edges = [(start, end) for start, end in zip(starts, ends) if end - start + 1 >= min_width]
                filtered_edges = [(start, end) for start, end in filtered_edges if 0 not in (start, end) and 1919 not in (start, end)]
                edges_all = filtered_edges
        
        return edges_all

def mark_edges(arr, edges_dict, mark_value=30):
        """
        Marks a 5x5 zone around the edges found in the array with a specific value.

        Parameters:
        - arr: The original 2D NumPy array.
        - edges_dict: A dictionary with y-levels as keys and lists of (start, end) tuples for edges.
        - mark_value: The value used to mark the edges.

        Returns:
        The modified array with marked zones.
        """
        marked_arr = np.copy(arr)  # Create a copy of the array to avoid modifying the original
        offset = 2  # To mark a 5x5 area, we go 2 pixels in each direction from the center

        for y, edges in edges_dict.items():
                for start, end in edges:
                        # Mark a 5x5 zone around the start and end positions
                        for dy in range(-offset, offset + 1):
                                for dx in range(-offset, offset + 1):
                                        # Check array bounds before marking
                                        if 0 <= y + dy < marked_arr.shape[0] and 0 <= start + dx < marked_arr.shape[1]:
                                                marked_arr[y + dy, start + dx] = mark_value
                                        if 0 <= y + dy < marked_arr.shape[0] and 0 <= end + dx < marked_arr.shape[1]:
                                                marked_arr[y + dy, end + dx] = mark_value

        return marked_arr

def find_rail_sides(img, edges_dict):
        left_border = []
        right_border = []
        for y,xs in edges_dict.items():
                rails = find_rails(img, [y], values=[9,10], min_width=5)
                left_border_actual = [min(xs)[0],y]
                right_border_actual = [max(xs)[1],y]
                
                for zone in rails:
                        if abs(zone[1]-left_border_actual[0]) < y*0.04: # dynamic treshold
                                left_border_actual[0] = zone[0]
                        if abs(zone[0]-right_border_actual[0]) < y*0.04:
                                right_border_actual[0] = zone[1]
                
                left_border.append(left_border_actual)
                right_border.append(right_border_actual)

        # removing detected uncontioussness
        left_border, flags_l, _ = robust_rail_sides(left_border) # filter outliers
        right_border, flags_r, _ = robust_rail_sides(right_border)
        
        return left_border, right_border, flags_l, flags_r

def robust_rail_sides(border, threshold=7):
        border = np.array(border)
        if border.size > 0:
                # delete borders found on the bottom side of the image
                border = border[border[:, 1] != 1079]
                
                steps_x = np.diff(border[:, 0])
                median_step = np.median(np.abs(steps_x))
                
                threshold_step = np.abs(threshold*np.abs(median_step))
                treshold_overcommings = abs(steps_x) > abs(threshold_step)
                
                flags = []
                
                if True not in treshold_overcommings:
                        return border, flags, []
                else:
                        overcommings_indices = [i for i, element in enumerate(treshold_overcommings) if element == True]
                        if overcommings_indices and np.all(np.diff(overcommings_indices) == 1):
                                overcommings_indices = [overcommings_indices[0]]
                        
                        filtered_border = border
                        
                        previously_deleted = []
                        for i in overcommings_indices:
                                for item in previously_deleted:
                                        if item[0] < i:
                                                i -= item[1]
                                first_part = filtered_border[:i+1]
                                second_part = filtered_border[i+1:]
                                if len(second_part)<2:
                                        filtered_border = first_part
                                        previously_deleted.append([i,len(second_part)])
                                elif len(first_part)<2:
                                        filtered_border = second_part
                                        previously_deleted.append([i,len(first_part)])
                                else:
                                        first_b, _, deleted_first = robust_rail_sides(first_part)
                                        second_b, _, _ = robust_rail_sides(second_part)
                                        filtered_border = np.concatenate((first_b,second_b), axis=0)
                                        
                                        if deleted_first:
                                                for deleted_item in deleted_first:
                                                        if deleted_item[0]<=i:
                                                                i -= deleted_item[1]
                                                
                                        flags.append(i)
                        return filtered_border, flags, previously_deleted
        else:
                return border, [], []

def find_dist_from_edges(id_map, image, edges_dict, left_border, right_border, real_life_width_mm, real_life_target_mm, mark_value=30):
        """
        Mark regions representing a real-life distance (e.g., 2 meters) to the left and right from the furthest edges.
        
        Parameters:
        - arr: 2D NumPy array representing the id_map.
        - edges_dict: Dictionary with y-levels as keys and lists of (start, end) tuples for edges.
        - real_life_width_mm: The real-world width in millimeters that the average sequence width represents.
        - real_life_target_mm: The real-world distance in millimeters to mark from the edges.
        
        Returns:
        - A NumPy array with the marked regions.
        """
        # Calculate the rail widths
        diffs_widths = {k: sum(e-s for s, e in v) / len(v) for k, v in edges_dict.items() if v}
        diffs_width = {k: max(e-s for s, e in v) for k, v in edges_dict.items() if v}

        # Pixel to mm scale factor
        scale_factors = {k: real_life_width_mm / v for k, v in diffs_width.items()}
        # Converting the real-life target distance to pixels
        target_distances_px = {k: int(real_life_target_mm / v) for k, v in scale_factors.items()}
        
        # Mark the regions representing the target distance to the left and right from the furthest edges
        end_points_left = {}
        region_levels_left = []
        for point in left_border:
                min_edge = point[0]
                
                # Ensure we stay within the image bounds
                #left_mark_start = max(0, min_edge - int(target_distances_px[point[1]]))
                left_mark_start = min_edge - int(target_distances_px[point[1]])
                end_points_left[point[1]] = left_mark_start
                
                # Left region points
                if left_mark_start < min_edge:
                        y_values = np.arange(left_mark_start, min_edge)
                        x_values = np.full_like(y_values, point[1])
                        region_line = np.column_stack((x_values, y_values))
                        region_levels_left.append(region_line)
                        
        end_points_right = {}
        region_levels_right = []
        for point in right_border:
                max_edge = point[0]
                
                # Ensure we stay within the image bounds
                right_mark_end = min(id_map.shape[1], max_edge + int(target_distances_px[point[1]]))
                if right_mark_end != id_map.shape[1]:
                        end_points_right[point[1]] = right_mark_end

                # Right region points
                if max_edge < right_mark_end:
                        y_values = np.arange(max_edge, right_mark_end)
                        x_values = np.full_like(y_values, point[1])
                        region_line = np.column_stack((x_values, y_values))
                        region_levels_right.append(region_line)

        return id_map, end_points_left, end_points_right, region_levels_left, region_levels_right

def bresenham_line(x0, y0, x1, y1):
        """
        Generate the coordinates of a line from (x0, y0) to (x1, y1) using Bresenham's algorithm.
        """
        line = []
        dx = abs(x1 - x0)
        dy = -abs(y1 - y0)
        sx = 1 if x0 < x1 else -1
        sy = 1 if y0 < y1 else -1
        err = dx + dy  # error value e_xy

        while True:
                line.append((x0, y0))  # Add the current point to the line
                if x0 == x1 and y0 == y1:
                        break
                e2 = 2 * err
                if e2 >= dy:  # e_xy+e_x > 0
                        err += dy
                        x0 += sx
                if e2 <= dx:  # e_xy+e_y < 0
                        err += dx
                        y0 += sy

        return line

def interpolate_end_points(end_points_dict, flags):
        line_arr = []
        ys = list(end_points_dict.keys())
        xs = list(end_points_dict.values())
        
        if flags and len(flags) == 1:
                pass
        elif flags and np.all(np.diff(flags) == 1):
                flags = [flags[0]]
        
        for i in range(0, len(ys) - 1):
                if i in flags:
                        continue
                y1, y2 = ys[i], ys[i + 1]
                x1, x2 = xs[i], xs[i + 1]
                line = np.array(bresenham_line(x1, y1, x2, y2))
                if np.any(line[:, 0] < 0):
                        line = line[line[:, 0] > 0]
                line_arr = line_arr + list(line)
        
        return line_arr

def extrapolate_line(pixels, image, min_y=None, extr_pixels=10):
        """
        Extrapolate a line based on the last segment using linear regression.
        
        Parameters:
        - pixels: List of (x, y) tuples representing line pixel coordinates.
        - image: 2D numpy array representing the image.
        - min_y: Minimum y-value to extrapolate to (optional).
        
        Returns:
        - A list of new extrapolated (x, y) pixel coordinates.
        """
        if len(pixels) < extr_pixels:
                print("Not enough pixels to perform extrapolation.")
                return []

        recent_pixels = np.array(pixels[-extr_pixels:])
        
        X = recent_pixels[:, 0].reshape(-1, 1)  # Reshape for sklearn
        y = recent_pixels[:, 1]
        
        model = LinearRegression()
        model.fit(X, y)
        
        slope = model.coef_[0]
        intercept = model.intercept_

        extrapolate = lambda x: slope * x + intercept
        
        # Calculate direction based on last two pixels
        dx, dy = 0, 0  # Default values
        
        x_diffs = []
        y_diffs = []
        for i in range(1,extr_pixels-1):
                x_diffs.append(pixels[-i][0] - pixels[-(i+1)][0])
                y_diffs.append(pixels[-i][1] - pixels[-(i+1)][1])
                
        x_diff = x_diffs[np.argmax(np.abs(x_diffs))]
        y_diff = y_diffs[np.argmax(np.abs(y_diffs))]
        
        if abs(int(x_diff)) >= abs(int(y_diff)):
                dx = 1 if x_diff >= 0 else -1
        else:
                dy = 1 if y_diff >= 0 else -1

        last_pixel = pixels[-1]
        new_pixels = []
        x, y = last_pixel

        min_y = min_y if min_y is not None else image.shape[0] - 1
        
        while 0 <= x < image.shape[1] and min_y <= y < image.shape[0]:
                if dx != 0:  # Horizontal or diagonal movement
                        x += dx
                        y = int(extrapolate(x))
                elif dy != 0:  # Vertical movement
                        y += dy
                        # For vertical lines, approximate x based on the last known value
                        x = int(x)
                        
                if 0 <= y < image.shape[0] and 0 <= x < image.shape[1]:
                        new_pixels.append((x, y))
                else:
                        break

        return new_pixels

def extrapolate_borders(dist_marked_id_map, border_l, border_r, lowest_y):
        
        #border_extrapolation_l1 = extrapolate_line(border_l, dist_marked_id_map, lowest_y)
        border_extrapolation_l2 = extrapolate_line(border_l[::-1], dist_marked_id_map, lowest_y)
        
        #border_extrapolation_r1 = extrapolate_line(border_r, dist_marked_id_map, lowest_y)
        border_extrapolation_r2 = extrapolate_line(border_r[::-1], dist_marked_id_map, lowest_y)
        
        #border_l = border_extrapolation_l2[::-1] + border_l + border_extrapolation_l1
        #border_r = border_extrapolation_r2[::-1] + border_r + border_extrapolation_r1
        
        border_l = border_extrapolation_l2[::-1] + border_l
        border_r = border_extrapolation_r2[::-1] + border_r
        
        return border_l, border_r

def find_zone_border(id_map, image, edges, irl_width_mm=1435, irl_target_mm=1000, lowest_y = 0):
        
        left_border, right_border, flags_l, flags_r = find_rail_sides(id_map, edges)
        
        dist_marked_id_map, end_points_left, end_points_right, left_region, right_region = find_dist_from_edges(id_map, image, edges, left_border, right_border, irl_width_mm, irl_target_mm)
        
        border_l = interpolate_end_points(end_points_left, flags_l)
        border_r = interpolate_end_points(end_points_right, flags_r)
        
        border_l, border_r = extrapolate_borders(dist_marked_id_map, border_l, border_r, lowest_y)
        
        return [border_l, border_r],[left_region, right_region]

def get_clues(segmentation_mask, number_of_clues):
        
        lowest, highest = find_extreme_y_values(segmentation_mask)
        if lowest is not None and highest is not None:
                clue_step = int((highest - lowest) / number_of_clues+1)
                clues = []
                for i in range(number_of_clues):
                        clues.append(highest - (i*clue_step))
                clues.append(lowest+int(0.5*clue_step))
                        
                return clues
        else:
                return []

def border_handler(id_map, image, edges, target_distances):
        
        lowest, _ = find_extreme_y_values(id_map)
        borders = []
        regions = []
        for target in target_distances:
                borders_regions = find_zone_border(id_map, image, edges, irl_target_mm=target, lowest_y = lowest)
                borders.append(borders_regions[0])
                regions.append(borders_regions[1])
                
        return borders, id_map, regions

def segment(input_image, model_seg, image_size):
        image_norm, image = get_segformer_img(input_image, image_size)

        outputs = model_seg(image_norm) 
        
        logits = outputs.logits
        upsampled_logits = nn.functional.interpolate(
                logits,
                size=image_norm.shape[-2:],
                mode="bilinear",
                align_corners=False
        )
        
        output  = upsampled_logits.float()
                
        confidence_scores = F.softmax(output, dim=1).cpu().detach().numpy().squeeze()
        id_map = np.argmax(confidence_scores, axis=0).astype(np.uint8)
        id_map = image_morpho(id_map)       
        
        id_map = cv2.resize(id_map, [input_image.shape[1],input_image.shape[0]], interpolation=cv2.INTER_NEAREST)
        return id_map, image

def detect(model_det, image):
        
        results = model_det.predict(image)

        return results, model_det, image

def manage_detections(results, model):
        bbox = results[0].boxes.xywh.tolist()
        cls = results[0].boxes.cls.tolist()
        accepted_stationary = np.array([24,25,28,36])
        accepted_moving = np.array([0,1,2,3,7,15,16,17,18,19])
        boxes_moving = {}
        boxes_stationary = {}
        if len(bbox) > 0:
                for xywh, clss in zip(bbox, cls):
                        if clss in accepted_moving:
                                if clss in boxes_moving.keys() and len(boxes_moving[clss]) > 0:
                                        boxes_moving[clss].append(xywh)
                                else:
                                        boxes_moving[clss] = [xywh]
                        if clss in accepted_stationary:
                                if clss in boxes_stationary.keys() and len(boxes_stationary[clss]) > 0:
                                        boxes_stationary[clss].append(xywh)
                                else:
                                        boxes_stationary[clss] = [xywh]

        return boxes_moving, boxes_stationary

def compute_detection_borders(borders, output_dims=[1080,1920]):
        det_height = output_dims[0]-1
        det_width = output_dims[1]-1
        
        for i,border in enumerate(borders):
                border_l = np.array(border[0])
                
                if list(border_l):
                        pass
                else:
                        border_l=np.array([[0,0],[0,0]])
                
                endpoints_l = [border_l[0],border_l[-1]]
                
                border_r = np.array(border[1])
                if list(border_r):
                        pass
                else:
                        border_r=np.array([[0,0],[0,0]])
                        
                endpoints_r = [border_r[0],border_r[-1]]
                
                if np.array_equal(np.array([[0,0],[0,0]]), endpoints_l):
                        endpoints_l = [[0,endpoints_r[0][1]],[0,endpoints_r[1][1]]]
                        
                if np.array_equal(np.array([[0,0],[0,0]]), endpoints_r):
                        endpoints_r = [[det_width,endpoints_l[0][1]],[det_width,endpoints_l[1][1]]]
                
                interpolated_top = bresenham_line(endpoints_l[1][0],endpoints_l[1][1],endpoints_r[1][0],endpoints_r[1][1])

                zero_range = [0,1,2,3]
                height_range = [det_height,det_height-1,det_height-2,det_height-3]
                width_range = [det_width,det_width-1,det_width-2,det_width-3]

                if (endpoints_l[0][0] in zero_range and endpoints_r[0][1] in height_range):
                        y_values = np.arange(endpoints_l[0][1], det_height)
                        x_values = np.full_like(y_values, 0)
                        bottom1 = np.column_stack((x_values, y_values))
                        
                        x_values = np.arange(0, endpoints_r[0][0])
                        y_values = np.full_like(x_values, det_height)
                        bottom2 = np.column_stack((x_values, y_values))
                        
                        interpolated_bottom = np.vstack((bottom1, bottom2))
                        
                elif (endpoints_l[0][1] in height_range and endpoints_r[0][0] in width_range):
                        y_values = np.arange(endpoints_r[0][1], det_height)
                        x_values = np.full_like(y_values, det_width)
                        bottom1 = np.column_stack((x_values, y_values))
                        
                        x_values = np.arange(endpoints_l[0][0], det_width)
                        y_values = np.full_like(x_values, det_height)
                        bottom2 = np.column_stack((x_values, y_values))
                        
                        interpolated_bottom = np.vstack((bottom1, bottom2))
                        
                elif endpoints_l[0][0] in zero_range and endpoints_r[0][0] in width_range:
                        y_values = np.arange(endpoints_l[0][1], det_height)
                        x_values = np.full_like(y_values, 0)
                        bottom1 = np.column_stack((x_values, y_values))
                        
                        y_values = np.arange(endpoints_r[0][1], det_height)
                        x_values = np.full_like(y_values, det_width)
                        bottom2 = np.column_stack((x_values, y_values))
                        
                        bottom3_mid = bresenham_line(bottom1[-1][0],bottom1[-1][1],bottom2[-1][0],bottom2[-1][1])
                        
                        interpolated_bottom = np.vstack((bottom1, bottom2, bottom3_mid))

                        
                else:
                        interpolated_bottom = bresenham_line(endpoints_l[0][0],endpoints_l[0][1],endpoints_r[0][0],endpoints_r[0][1])
                
                borders[i].append(interpolated_bottom)
                borders[i].append(interpolated_top)
                
        return borders

def get_bounding_box_points(cx, cy, w, h):
        top_left = (cx - w / 2, cy - h / 2)
        top_right = (cx + w / 2, cy - h / 2)
        bottom_right = (cx + w / 2, cy + h / 2)
        bottom_left = (cx - w / 2, cy + h / 2)
        
        corners = [top_left, top_right, bottom_right, bottom_left]
        
        def interpolate(point1, point2, fraction):
                """Interpolate between two points at a given fraction of the distance."""
                return (point1[0] + fraction * (point2[0] - point1[0]), 
                        point1[1] + fraction * (point2[1] - point1[1]))

        points = []
        for i in range(4):
                next_i = (i + 1) % 4
                points.append(corners[i])
                points.append(interpolate(corners[i], corners[next_i], 1 / 3))
                points.append(interpolate(corners[i], corners[next_i], 2 / 3))

        return points

def classify_detections(boxes_moving, boxes_stationary, borders, img_dims, output_dims=[1080,1920]):
        img_h, img_w, _ = img_dims
        img_h_scaletofullHD = output_dims[1]/img_w
        img_w_scaletofullHD = output_dims[0]/img_h
        colors = ["yellow","orange","red","green","blue"]
        
        borders = compute_detection_borders(borders,output_dims)
        
        boxes_info = []
        
        if boxes_moving or boxes_stationary:
                if boxes_moving:
                        for item, coords in boxes_moving.items():
                                for coord in coords:
                                        x = coord[0]*img_w_scaletofullHD
                                        y = coord[1]*img_h_scaletofullHD
                                        w = coord[2]*img_w_scaletofullHD
                                        h = coord[3]*img_h_scaletofullHD
                                        
                                        points_to_test = get_bounding_box_points(x, y, w, h)
                                        
                                        complete_border = []
                                        criticality = -1
                                        color = None
                                        for i,border in enumerate(reversed(borders)):
                                                border_nonempty = [np.array(arr) for arr in border if np.array(arr).size > 0]
                                                complete_border = np.vstack((border_nonempty))
                                                instance_border_path = mplPath.Path(np.array(complete_border))
                                                
                                                is_inside_borders = False
                                                for point in points_to_test:
                                                        is_inside = instance_border_path.contains_point(point)
                                                        if is_inside:
                                                                is_inside_borders = True
                                                
                                                if is_inside_borders:
                                                        criticality = i
                                                        color = colors[i]
                                                        
                                        if criticality == -1:
                                                color = colors[3]
                                                
                                        boxes_info.append([item, criticality, color, [x, y], [w, h], 1])
                                                
                if boxes_stationary:
                        for item, coords in boxes_stationary.items():
                                for coord in coords:
                                        x = coord[0]*img_w_scaletofullHD
                                        y = coord[1]*img_h_scaletofullHD
                                        w = coord[2]*img_w_scaletofullHD
                                        h = coord[3]*img_h_scaletofullHD
                                        
                                        points_to_test = get_bounding_box_points(x, y, w, h)
                                        
                                        complete_border = []
                                        criticality = -1
                                        color = None
                                        is_inside_borders = 0
                                        for i,border in enumerate(reversed(borders), start=len(borders) - 1):
                                                border_nonempty = [np.array(arr) for arr in border if np.array(arr).size > 0]
                                                complete_border = np.vstack(border_nonempty)
                                                instance_border_path = mplPath.Path(np.array(complete_border))
                                                
                                                is_inside_borders = False
                                                for point in points_to_test:
                                                        is_inside = instance_border_path.contains_point(point)
                                                        if is_inside:
                                                                is_inside_borders = True
                                                
                                                if is_inside_borders:
                                                        criticality = i
                                                        color = colors[4]
                                                
                                        if criticality == -1:
                                                color = colors[3]
                                                
                                        boxes_info.append([item, criticality, color, [x, y], [w, h], 0])
        
                return boxes_info
        
        else:
                print("No accepted detections in this image.")
                return []

def draw_classification(classification, id_map):
        if classification:                
                for box in classification:
                        x,y = box[3]
                        mark_value = 30
                        
                        x_start = int(max(x - 2, 0))
                        x_end = int(min(x + 3, id_map.shape[1]))
                        y_start = int(max(y - 2, 0))
                        y_end = int(min(y + 3, id_map.shape[0]))
                        
                        id_map[y_start:y_end, x_start:x_end] = mark_value
        else:
                return

def get_result(classification, id_map, names, borders, image, regions):
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = cv2.resize(image, (id_map.shape[1], id_map.shape[0]), interpolation = cv2.INTER_LINEAR)
        
        ratio = image.shape[0] / image.shape[1]
        
        fig = plt.figure(figsize=(16, 16*ratio), dpi=100)
        plt.imshow(image, cmap='gray')
        
        if classification:
                for box in classification:
                        
                        boxes = True
                        cx,cy = box[3]
                        name = names[box[0]]
                        if boxes:
                                w,h = box[4]
                                x = cx - w / 2
                                y = cy - h / 2
                                rect = patches.Rectangle((x, y), w, h, linewidth=2, edgecolor=box[2], facecolor='none')
                                
                                ax = plt.gca()
                                ax.add_patch(rect)
                                plt.text(x, y-17, name, color='black', fontsize=10, ha='center', va='center', fontweight='bold', bbox=dict(facecolor=box[2], edgecolor='none', alpha=1))
                        else:
                                plt.imshow(id_map, cmap='gray')
                                plt.text(cx, cy+10, name, color=box[2], fontsize=10, ha='center', va='center', fontweight='bold')

        for region in regions:
                for side in region:
                        for line in side:
                                line = np.array(line)
                                plt.plot(line[:,1], line[:,0] ,'-', color='lightgrey', marker=None, linewidth=0.5)
                                #plt.ylim(0, 1080)
                                #plt.xlim(0, 1920)
                                plt.gca().invert_yaxis()

        colors = ['yellow','orange','red']
        borders.reverse()
        for i,border in enumerate(borders):
                for side in border:
                        side = np.array(side)
                        if side.size > 0:
                                plt.plot(side[:,0],side[:,1] ,'-', color=colors[i], marker=None, linewidth=0.6) #color=colors[i]
                                #plt.ylim(0, 1080)
                                #plt.xlim(0, 1920)
                                plt.gca().invert_yaxis()
        
        plt.xlim(left=0)  # Ensure only positive X values are displayed
        plt.tight_layout()
        canvas = FigureCanvas(fig)
        canvas.draw()
        width, height = fig.get_size_inches() * fig.get_dpi()
        image = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)
        
        plt.close(fig)  # Close the figure to free memory

        return image

def run(input_image, model_seg, model_det, image_size, target_distances, num_ys = 10):
        
        segmentation_mask, image = segment(input_image, model_seg, image_size)
        
        # Border search
        clues = get_clues(segmentation_mask, num_ys)
        edges = find_edges(segmentation_mask, clues, min_width=0)
        borders, id_map, regions = border_handler(segmentation_mask, image, edges, target_distances)
        
        # Detection
        results, model, image = detect(model_det, input_image)
        boxes_moving, boxes_stationary = manage_detections(results, model)
        
        classification = classify_detections(boxes_moving, boxes_stationary, borders, image.shape, output_dims=segmentation_mask.shape)

        output_image = get_result(classification, id_map, model.names, borders, image, regions)
        cropped_image = output_image[22:output_image.shape[0] - 40, 74:output_image.shape[1] - 33]
        return cropped_image