handcrafted_solution.py

# Description: This file contains the handcrafted solution for the task of wireframe reconstruction 

import io
from collections import defaultdict
from typing import Tuple, List

import cv2
import numpy as np
from PIL import Image as PImage
from hoho.color_mappings import gestalt_color_mapping
from hoho.read_write_colmap import read_cameras_binary, read_images_binary, read_points3D_binary
from scipy.spatial import KDTree
from scipy.spatial.distance import cdist

apex_color = gestalt_color_mapping["apex"]
eave_end_point = gestalt_color_mapping["eave_end_point"]
flashing_end_point = gestalt_color_mapping["flashing_end_point"]

apex_color, eave_end_point, flashing_end_point = [np.array(i) for i in [apex_color, eave_end_point, flashing_end_point]]
unclassified = np.array([(215, 62, 138)])
line_classes = ['eave', 'ridge', 'rake', 'valley']


def empty_solution():
    '''Return a minimal valid solution, i.e. 2 vertices and 1 edge.'''
    return np.zeros((2, 3)), [(0, 1)]


def convert_entry_to_human_readable(entry):
    out = {}
    already_good = {'__key__', 'wf_vertices', 'wf_edges', 'edge_semantics', 'mesh_vertices', 'mesh_faces',
                    'face_semantics', 'K', 'R', 't'}
    for k, v in entry.items():
        if k in already_good:
            out[k] = v
            continue
        match k:
            case 'points3d':
                out[k] = read_points3D_binary(fid=io.BytesIO(v))
            case 'cameras':
                out[k] = read_cameras_binary(fid=io.BytesIO(v))
            case 'images':
                out[k] = read_images_binary(fid=io.BytesIO(v))
            case 'ade20k' | 'gestalt':
                out[k] = [PImage.open(io.BytesIO(x)).convert('RGB') for x in v]
            case 'depthcm':
                out[k] = [PImage.open(io.BytesIO(x)) for x in entry['depthcm']]
    return out


def remove_undesired_objects(image):
    image = image.astype('uint8')
    nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(image, connectivity=4)
    sizes = stats[:, -1]
    max_label = 1
    max_size = sizes[1]
    for i in range(2, nb_components):
        if sizes[i] > max_size:
            max_label = i
            max_size = sizes[i]

    img2 = np.zeros(output.shape)
    img2[output == max_label] = 1
    return img2


def clean_image(image_gestalt) -> np.ndarray:
    # clears image in from of unclassified and disconected components
    image_gestalt = np.array(image_gestalt)
    unclassified_mask = cv2.inRange(image_gestalt, unclassified + 0.0, unclassified + 0.8)
    unclassified_mask = cv2.bitwise_not(unclassified_mask)
    mask = remove_undesired_objects(unclassified_mask).astype(np.uint8)
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, np.ones((11, 11), np.uint8), iterations=11)
    mask = cv2.morphologyEx(mask, cv2.MORPH_DILATE, np.ones((11, 11), np.uint8), iterations=2)

    image_gestalt[:, :, 0] *= mask
    image_gestalt[:, :, 1] *= mask
    image_gestalt[:, :, 2] *= mask
    return image_gestalt


def get_vertices(image_gestalt, *, color_range=4., dialations=3, erosions=1, kernel_size=13):
    ### detects the apex and eave end and flashing end points
    apex_mask = cv2.inRange(image_gestalt, apex_color - color_range, apex_color + color_range)
    eave_end_point_mask = cv2.inRange(image_gestalt, eave_end_point - color_range, eave_end_point + color_range)
    flashing_end_point_mask = cv2.inRange(image_gestalt, flashing_end_point - color_range,
                                          flashing_end_point + color_range)
    eave_end_point_mask = cv2.bitwise_or(eave_end_point_mask, flashing_end_point_mask)

    kernel = np.ones((kernel_size, kernel_size), np.uint8)

    apex_mask = cv2.morphologyEx(apex_mask, cv2.MORPH_DILATE, kernel, iterations=dialations)
    apex_mask = cv2.morphologyEx(apex_mask, cv2.MORPH_ERODE, kernel, iterations=erosions)

    eave_end_point_mask = cv2.morphologyEx(eave_end_point_mask, cv2.MORPH_DILATE, kernel, iterations=dialations)
    eave_end_point_mask = cv2.morphologyEx(eave_end_point_mask, cv2.MORPH_ERODE, kernel, iterations=erosions)

    *_, apex_centroids = cv2.connectedComponentsWithStats(apex_mask, connectivity=4, stats=cv2.CV_32S)
    *_, other_centroids = cv2.connectedComponentsWithStats(eave_end_point_mask, connectivity=4, stats=cv2.CV_32S)

    return apex_centroids[1:], other_centroids[1:], apex_mask, eave_end_point_mask


def infer_vertices(image_gestalt, *, color_range=4.):
    ridge_color = np.array(gestalt_color_mapping["ridge"])
    rake_color = np.array(gestalt_color_mapping["rake"])
    ridge_mask = cv2.inRange(image_gestalt,
                             ridge_color - color_range,
                             ridge_color + color_range)
    ridge_mask = cv2.morphologyEx(ridge_mask,
                                  cv2.MORPH_DILATE, np.ones((3, 3)), iterations=4)
    rake_mask = cv2.inRange(image_gestalt,
                            rake_color - color_range,
                            rake_color + color_range)
    rake_mask = cv2.morphologyEx(rake_mask,
                                 cv2.MORPH_DILATE, np.ones((3, 3)), iterations=4)

    intersection_mask = cv2.bitwise_and(ridge_mask, rake_mask)
    intersection_mask = cv2.morphologyEx(intersection_mask, cv2.MORPH_DILATE, np.ones((11, 11)), iterations=3)

    *_, inferred_centroids = cv2.connectedComponentsWithStats(intersection_mask, connectivity=4, stats=cv2.CV_32S)

    return inferred_centroids[1:], intersection_mask


def get_missed_vertices(vertices, inferred_centroids, *, min_missing_distance=200.0, **kwargs):
    vertices = KDTree(vertices)
    closest = vertices.query(inferred_centroids, k=1, distance_upper_bound=min_missing_distance)
    missed_points = inferred_centroids[closest[1] == len(vertices.data)]

    return missed_points


def get_lines_and_directions(gest_seg_np, edge_class, *, color_range=4., rho, theta, threshold, min_line_length,
                             max_line_gap, extend, **kwargs):
    edge_color = np.array(gestalt_color_mapping[edge_class])

    mask = cv2.inRange(gest_seg_np,
                       edge_color - color_range,
                       edge_color + color_range)
    mask = cv2.morphologyEx(mask,
                            cv2.MORPH_DILATE, np.ones((3, 3)), iterations=1)

    if not np.any(mask):
        return [], []

    # Run Hough on edge detected image
    # Output "lines" is an array containing endpoints of detected line segments
    cv2.GaussianBlur(mask, (11, 11), 0, mask)
    lines = cv2.HoughLinesP(mask, rho, theta, threshold, np.array([]),
                            min_line_length, max_line_gap)

    if lines is None:
        return [], []

    line_directions = []
    edges = []
    for line_idx, line in enumerate(lines):
        for x1, y1, x2, y2 in line:
            if x1 < x2:
                x1, y1, x2, y2 = x2, y2, x1, y1
            direction = (np.array([x2 - x1, y2 - y1]))
            direction = direction / np.linalg.norm(direction)
            line_directions.append(direction)

            direction = extend * direction

            x1, y1 = (-direction + (x1, y1)).astype(np.int32)
            x2, y2 = (+ direction + (x2, y2)).astype(np.int32)

            edges.append((x1, y1, x2, y2))
    return edges, line_directions


def infer_missing_vertices(ridge_edges, rake_edges):
    ridge_edges = np.array(ridge_edges)
    rake_edges = np.array(rake_edges)
    ridge_ends = np.concatenate([ridge_edges[:, 2:], ridge_edges[:, :2]])
    rake_ends = np.concatenate([rake_edges[:, 2:], rake_edges[:, :2]])
    ridge_ends = KDTree(ridge_ends)
    rake_ends = KDTree(rake_ends)
    missing_candidates = rake_ends.query_ball_tree(ridge_ends, 5)
    missing_candidates = np.concatenate([*missing_candidates])
    missing_candidates = np.unique(missing_candidates).astype(np.int32)
    return ridge_ends.data[missing_candidates]


def get_vertices_and_edges_from_segmentation(gest_seg_np, *, point_radius=30, max_angle=5.,
                                             **kwargs):
    '''Get the vertices and edges from the gestalt segmentation mask of the house'''
    # Apex
    connections = []
    deviation_threshold = np.cos(np.deg2rad(max_angle))

    apex_centroids, eave_end_point_centroids, apex_mask, eave_end_point_mask = get_vertices(gest_seg_np)

    vertices = np.concatenate([apex_centroids, eave_end_point_centroids])
    # inferred_vertices, inferred_mask = infer_vertices(gest_seg_np)
    # missed_vertices = get_missed_vertices(vertices, inferred_vertices, **kwargs)
    # vertices = np.concatenate([vertices, missed_vertices])

    if len(vertices) < 2:
        return [], []

    # scale = 1
    # vertex_size = np.zeros(vertices.shape[0])
    # for i, coords in enumerate(vertices):
    #     # coords = np.round(coords).astype(np.uint32)
    #     radius = point_radius  # np.clip(int(max_depth//2 + depth_np[coords[1], coords[0]]), 10, 30)#int(np.clip(max_depth - depth_np[coords[1], coords[0]], 10, 20))
    #     vertex_size[i] = (scale * radius) ** 2  # because we are using squared distances

    edges = []
    line_directions = []

    rho = 1  # distance resolution in pixels of the Hough grid
    theta = np.pi / 180  # angular resolution in radians of the Hough grid
    threshold = 20  # minimum number of votes (intersections in Hough grid cell)
    min_line_length = 60  # minimum number of pixels making up a line
    max_line_gap = 40  # maximum gap in pixels between connectable line segments
    ridge_edges, ridge_directions = get_lines_and_directions(gest_seg_np, "ridge",
                                                             rho=rho,
                                                             theta=theta,
                                                             threshold=threshold,
                                                             min_line_length=min_line_length,
                                                             max_line_gap=max_line_gap,
                                                             **kwargs)

    rake_edges, rake_directions = get_lines_and_directions(gest_seg_np, "rake",
                                                           rho=rho,
                                                           theta=theta,
                                                           threshold=threshold,
                                                           min_line_length=min_line_length,
                                                           max_line_gap=max_line_gap,
                                                           **kwargs)

    if len(ridge_edges) > 0:
        edges.append(ridge_edges)
        line_directions.append(ridge_directions)

    if len(rake_edges) > 0:
        edges.append(rake_edges)
        line_directions.append(rake_directions)

    missed_vertices = []
    if len(ridge_edges) > 0 and len(rake_edges) > 0:

        inferred_vertices = infer_missing_vertices(ridge_edges, rake_edges)
        missed_vertices = get_missed_vertices(vertices, inferred_vertices, **kwargs)
        vertices = np.concatenate([vertices, missed_vertices])

    vertices = KDTree(vertices)

    for edge_class in ['eave', 'valley', 'flashing', 'step_flashing', 'hip']:
        class_edges, class_directions = get_lines_and_directions(gest_seg_np, edge_class,
                                                                 rho=rho,
                                                                 theta=theta,
                                                                 threshold=threshold,
                                                                 min_line_length=min_line_length,
                                                                 max_line_gap=max_line_gap,
                                                                 **kwargs)

        if len(class_edges) > 0:
            edges.append(class_edges)
            line_directions.append(class_directions)

    edges = np.concatenate(edges).astype(np.float64)
    line_directions = np.concatenate(line_directions).astype(np.float64)
    if len(edges) < 1:
        return [], []
    # calculate the distances between the vertices and the edge ends

    begin_edges = KDTree(edges[:, :2])
    end_edges = KDTree(edges[:, 2:])

    begin_indices = begin_edges.query_ball_tree(vertices, point_radius)
    end_indices = end_edges.query_ball_tree(vertices, point_radius)

    line_indices = np.where(np.array([len(i) and len(j) for i, j in zip(begin_indices, end_indices)]))[0]

    # create all possible connections between begin and end candidates that correspond to a line
    begin_vertex_list = []
    end_vertex_list = []
    line_idx_list = []
    for line_idx in line_indices:
        begin_vertex, end_vertex = begin_indices[line_idx], end_indices[line_idx]
        begin_vertex, end_vertex = np.meshgrid(begin_vertex, end_vertex)
        begin_vertex_list.extend(begin_vertex.flatten())
        end_vertex_list.extend(end_vertex.flatten())

        line_idx_list.extend([line_idx] * len(begin_vertex.flatten()))

    line_idx_list = np.array(line_idx_list)
    all_connections = np.array([begin_vertex_list, end_vertex_list])

    # decrease the number of possible connections to reduce number of calculations
    possible_connections = np.unique(all_connections, axis=1)
    possible_connections = np.sort(possible_connections, axis=0)
    possible_connections = np.unique(possible_connections, axis=1)
    possible_connections = possible_connections[:, possible_connections[0, :] != possible_connections[1, :]]

    if possible_connections.shape[1] < 1:
        return [], []

    # precalculate the possible direction vectors
    possible_direction_vectors = vertices.data[possible_connections[0]] - vertices.data[possible_connections[1]]
    possible_direction_vectors = possible_direction_vectors / np.linalg.norm(possible_direction_vectors, axis=1)[:,
                                                              np.newaxis]

    owned_lines_per_possible_connections = [list() for i in range(possible_connections.shape[1])]

    # assign lines to possible connections
    for line_idx, i, j in zip(line_idx_list, begin_vertex_list, end_vertex_list):
        if i == j:
            continue
        i, j = min(i, j), max(i, j)
        for connection_idx, connection in enumerate(possible_connections.T):
            if np.all((i, j) == connection):
                owned_lines_per_possible_connections[connection_idx].append(line_idx)
                break

    # check if the lines are in the same direction as the possible connection
    for fitted_line_idx, owned_lines_per_possible_connection in enumerate(owned_lines_per_possible_connections):
        line_deviations = np.abs(
            np.dot(line_directions[owned_lines_per_possible_connection], possible_direction_vectors[fitted_line_idx]))
        if np.any(line_deviations > deviation_threshold):
            connections.append(possible_connections[:, fitted_line_idx])

    vertices = [{"xy": v, "type": "apex"} for v in apex_centroids]
    vertices += [{"xy": v, "type": "apex"} for v in missed_vertices]
    vertices += [{"xy": v, "type": "eave_end_point"} for v in eave_end_point_centroids]
    return vertices, connections


def get_uv_depth(vertices, depth):
    '''Get the depth of the vertices from the depth image'''

    uv = np.array([v['xy'] for v in vertices])
    uv_int = uv.astype(np.int32)
    H, W = depth.shape[:2]
    uv_int[:, 0] = np.clip(uv_int[:, 0], 0, W - 1)
    uv_int[:, 1] = np.clip(uv_int[:, 1], 0, H - 1)
    vertex_depth = depth[(uv_int[:, 1], uv_int[:, 0])]
    return uv, vertex_depth


def merge_vertices_3d(vert_edge_per_image, merge_th=0.1, **kwargs):
    '''Merge vertices that are close to each other in 3D space and are of same types'''
    all_3d_vertices = []
    connections_3d = []
    all_indexes = []
    cur_start = 0
    types = []
    for cimg_idx, (vertices, connections, vertices_3d) in vert_edge_per_image.items():
        types += [int(v['type'] == 'apex') for v in vertices]
        all_3d_vertices.append(vertices_3d)
        connections_3d += [(x + cur_start, y + cur_start) for (x, y) in connections]
        cur_start += len(vertices_3d)
    all_3d_vertices = np.concatenate(all_3d_vertices, axis=0)
    # print (connections_3d)
    distmat = cdist(all_3d_vertices, all_3d_vertices)
    types = np.array(types).reshape(-1, 1)
    same_types = cdist(types, types)
    mask_to_merge = (distmat <= merge_th) & (same_types == 0)
    new_vertices = []
    new_connections = []
    to_merge = sorted(list(set([tuple(a.nonzero()[0].tolist()) for a in mask_to_merge])))
    to_merge_final = defaultdict(list)
    for i in range(len(all_3d_vertices)):
        for j in to_merge:
            if i in j:
                to_merge_final[i] += j
    for k, v in to_merge_final.items():
        to_merge_final[k] = list(set(v))
    already_there = set()
    merged = []
    for k, v in to_merge_final.items():
        if k in already_there:
            continue
        merged.append(v)
        for vv in v:
            already_there.add(vv)
    old_idx_to_new = {}
    count = 0
    for idxs in merged:
        new_vertices.append(all_3d_vertices[idxs].mean(axis=0))
        for idx in idxs:
            old_idx_to_new[idx] = count
        count += 1
    # print (connections_3d)
    new_vertices = np.array(new_vertices)
    # print (connections_3d)
    for conn in connections_3d:
        new_con = sorted((old_idx_to_new[conn[0]], old_idx_to_new[conn[1]]))
        if new_con[0] == new_con[1]:
            continue
        if new_con not in new_connections:
            new_connections.append(new_con)
    # print (f'{len(new_vertices)} left after merging {len(all_3d_vertices)} with {th=}')
    return new_vertices, new_connections


def prune_not_connected(all_3d_vertices, connections_3d):
    '''Prune vertices that are not connected to any other vertex'''
    connected = defaultdict(list)
    for c in connections_3d:
        connected[c[0]].append(c)
        connected[c[1]].append(c)
    new_indexes = {}
    new_verts = []
    connected_out = []
    for k, v in connected.items():
        vert = all_3d_vertices[k]
        if tuple(vert) not in new_verts:
            new_verts.append(tuple(vert))
            new_indexes[k] = len(new_verts) - 1
    for k, v in connected.items():
        for vv in v:
            connected_out.append((new_indexes[vv[0]], new_indexes[vv[1]]))
    connected_out = list(set(connected_out))

    return np.array(new_verts), connected_out


def predict(entry, visualize=False, scale_estimation_coefficient=2.5, **kwargs) -> Tuple[np.ndarray, List[int]]:
    good_entry = convert_entry_to_human_readable(entry)
    if 'gestalt' not in good_entry or 'depthcm' not in good_entry or 'K' not in good_entry or 'R' not in good_entry or 't' not in good_entry:
        print('Missing required fields in the entry')
        return (good_entry['__key__'], *empty_solution())
    vert_edge_per_image = {}
    for i, (gest, depth, K, R, t) in enumerate(zip(good_entry['gestalt'],
                                                   good_entry['depthcm'],
                                                   good_entry['K'],
                                                   good_entry['R'],
                                                   good_entry['t']
                                                   )):
        gest_seg = gest.resize(depth.size)
        gest_seg_np = np.array(gest_seg).astype(np.uint8)
        # Metric3D
        depth_np = np.array(depth) / scale_estimation_coefficient
        vertices, connections = get_vertices_and_edges_from_segmentation(gest_seg_np, **kwargs)
        if (len(vertices) < 2) or (len(connections) < 1):
            print(f'Not enough vertices or connections in image {i}')
            vert_edge_per_image[i] = np.empty((0, 2)), [], np.empty((0, 3))
            continue
        uv, depth_vert = get_uv_depth(vertices, depth_np)
        # Normalize the uv to the camera intrinsics
        xy_local = np.ones((len(uv), 3))
        xy_local[:, 0] = (uv[:, 0] - K[0, 2]) / K[0, 0]
        xy_local[:, 1] = (uv[:, 1] - K[1, 2]) / K[1, 1]
        # Get the 3D vertices
        vertices_3d_local = depth_vert[..., None] * (xy_local / np.linalg.norm(xy_local, axis=1)[..., None])
        world_to_cam = np.eye(4)
        world_to_cam[:3, :3] = R
        world_to_cam[:3, 3] = t.reshape(-1)
        cam_to_world = np.linalg.inv(world_to_cam)
        vertices_3d = cv2.transform(cv2.convertPointsToHomogeneous(vertices_3d_local), cam_to_world)
        vertices_3d = cv2.convertPointsFromHomogeneous(vertices_3d).reshape(-1, 3)
        vert_edge_per_image[i] = vertices, connections, vertices_3d
    all_3d_vertices, connections_3d = merge_vertices_3d(vert_edge_per_image, **kwargs)
    all_3d_vertices_clean, connections_3d_clean = prune_not_connected(all_3d_vertices, connections_3d)
    if (len(all_3d_vertices_clean) < 2) or len(connections_3d_clean) < 1:
        print(f'Not enough vertices or connections in the 3D vertices')
        return (good_entry['__key__'], *empty_solution())
    if visualize:
        from hoho.viz3d import plot_estimate_and_gt
        plot_estimate_and_gt(all_3d_vertices_clean,
                             connections_3d_clean,
                             good_entry['wf_vertices'],
                             good_entry['wf_edges'])
    return good_entry['__key__'], all_3d_vertices_clean, connections_3d_clean