[Feat] Init add post-proc (building regularization) (#44)

* [Style] Update space * [Feat] Init add postproc regularization * [Feat] Init add building regularization * [Docs] Update note about regularization
2 years ago · 9625d958e3
parent 87ecbafa94
commit 9625d958e3
5 changed files with 394 additions and 1 deletions
--- a/paddlers/utils/init.py
+++ b/paddlers/utils/init.py
@ -14,6 +14,7 @@

 from . import logging
 from . import utils
+from . import postprocs
 from .utils import (seconds_to_hms, get_encoding, get_single_card_bs, dict2str,
                    EarlyStop, norm_path, is_pic, MyEncoder, DisablePrint,
                    Timer, to_data_parallel, scheduler_step)
--- a/paddlers/utils/postprocs/init.py
+++ b/paddlers/utils/postprocs/init.py
@ -0,0 +1,15 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .regularization import building_regularization
--- a/paddlers/utils/postprocs/regularization.py
+++ b/paddlers/utils/postprocs/regularization.py
@ -0,0 +1,264 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import cv2
+import numpy as np
+from .utils import (calc_distance, calc_angle, calc_azimuth, rotation, line,
+                    intersection, calc_distance_between_lines,
+                    calc_project_in_line)
+
+S = 20
+TD = 3
+D = TD + 1
+
+ALPHA = math.degrees(math.pi / 6)
+BETA = math.degrees(math.pi * 17 / 18)
+DELTA = math.degrees(math.pi / 12)
+THETA = math.degrees(math.pi / 4)
+
+
+def building_regularization(mask: np.ndarray, W: int=32) -> np.ndarray:
+    """
+    Translate the mask of building into structured mask.
+
+    The original article refers to
+    Wei S, Ji S, Lu M. "Toward Automatic Building Footprint Delineation From Aerial Images Using CNN and Regularization."
+    (https://ieeexplore.ieee.org/document/8933116).
+
+    This algorithm has no public code.
+    The implementation procedure refers to original article and this repo: 
+    https://github.com/niecongchong/RS-building-regularization
+
+    The implementation is not fully consistent with the article.
+
+    Args:
+        mask (np.ndarray): Mask of building.
+        W (int, optional): Minimum threshold in main direction. Default is 32.
+            The larger W, the more regular the image, but the worse the image detail.
+
+    Returns:
+        np.ndarray: Mask of building after regularized.
+    """
+    # check and pro processing
+    mask_shape = mask.shape
+    if len(mask_shape) != 2:
+        mask = mask[..., 0]
+    mask = cv2.medianBlur(mask, 5)
+    class_num = len(np.unique(mask))
+    if class_num != 2:
+        _, mask = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY |
+                                cv2.THRESH_OTSU)
+    mask = np.clip(mask, 0, 1).astype("uint8")  # 0-255 / 0-1 -> 0-1
+    mask_shape = mask.shape
+    # find contours
+    contours, hierarchys = cv2.findContours(mask, cv2.RETR_TREE,
+                                            cv2.CHAIN_APPROX_SIMPLE)
+    if not contours:
+        raise ValueError("There are no contours.")
+    # adjust
+    res_contours = []
+    for contour, hierarchy in zip(contours, hierarchys[0]):
+        contour = _coarse(contour, mask_shape)  # coarse
+        if contour is None:
+            continue
+        contour = _fine(contour, W)  # fine
+        res_contours.append((contour, _get_priority(hierarchy)))
+    result = _fill(mask, res_contours)  # fill
+    result = cv2.morphologyEx(result, cv2.MORPH_OPEN,
+                              cv2.getStructuringElement(cv2.MORPH_RECT,
+                                                        (3, 3)))  # open
+    return result
+
+
+def _coarse(contour, img_shape):
+    def _inline_check(point, shape, eps=5):
+        x, y = point[0]
+        iH, iW = shape
+        if x < eps or x > iH - eps or y < eps or y > iW - eps:
+            return False
+        else:
+            return True
+
+    area = cv2.contourArea(contour)
+    # S = 20
+    if area < S:  # remove polygons whose area is below a threshold S
+        return None
+    # D = 0.3 if area < 200 else 1.0
+    # TD = 0.5 if area < 200 else 0.9
+    epsilon = 0.005 * cv2.arcLength(contour, True)
+    contour = cv2.approxPolyDP(contour, epsilon, True)  # DP
+    p_number = contour.shape[0]
+    idx = 0
+    while idx < p_number:
+        last_point = contour[idx - 1]
+        current_point = contour[idx]
+        next_idx = (idx + 1) % p_number
+        next_point = contour[next_idx]
+        # remove edges whose lengths are below a given side length TD
+        # that varies with the area of a building.
+        distance = calc_distance(current_point, next_point)
+        if distance < TD and not _inline_check(next_point, img_shape):
+            contour = np.delete(contour, next_idx, axis=0)
+            p_number -= 1
+            continue
+        # remove over-sharp angles with threshold α.
+        # remove over-smooth angles with threshold β.
+        angle = calc_angle(last_point, current_point, next_point)
+        if (ALPHA > angle or angle > BETA) and _inline_check(current_point,
+                                                             img_shape):
+            contour = np.delete(contour, idx, axis=0)
+            p_number -= 1
+            continue
+        idx += 1
+    if p_number > 2:
+        return contour
+    else:
+        return None
+
+
+def _fine(contour, W):
+    # area = cv2.contourArea(contour)
+    # W = 6 if area < 200 else 8
+    # TD = 0.5 if area < 200 else 0.9
+    # D = TD + 0.3
+    nW = W
+    p_number = contour.shape[0]
+    distance_list = []
+    azimuth_list = []
+    indexs_list = []
+    for idx in range(p_number):
+        current_point = contour[idx]
+        next_idx = (idx + 1) % p_number
+        next_point = contour[next_idx]
+        distance_list.append(calc_distance(current_point, next_point))
+        azimuth_list.append(calc_azimuth(current_point, next_point))
+        indexs_list.append((idx, next_idx))
+    # add the direction of the longest edge to the list of main direction.
+    longest_distance_idx = np.argmax(distance_list)
+    main_direction_list = [azimuth_list[longest_distance_idx]]
+    max_dis = distance_list[longest_distance_idx]
+    if max_dis <= nW:
+        nW = max_dis - 1e-6
+    # Add other edges’ direction to the list of main directions
+    # according to the angle threshold δ between their directions
+    # and directions in the list.
+    for distance, azimuth in zip(distance_list, azimuth_list):
+        for mdir in main_direction_list:
+            abs_dif_ang = abs(mdir - azimuth)
+            if distance > nW and THETA <= abs_dif_ang <= (180 - THETA):
+                main_direction_list.append(azimuth)
+    contour_by_lines = []
+    md_used_list = [main_direction_list[0]]
+    for distance, azimuth, (idx, next_idx) in zip(distance_list, azimuth_list,
+                                                  indexs_list):
+        p1 = contour[idx]
+        p2 = contour[next_idx]
+        pm = (p1 + p2) / 2
+        # find long edges with threshold W that varies with building’s area.
+        if distance > nW:
+            rotate_ang = main_direction_list[0] - azimuth
+            for main_direction in main_direction_list:
+                r_ang = main_direction - azimuth
+                if abs(r_ang) < abs(rotate_ang):
+                    rotate_ang = r_ang
+                    md_used_list.append(main_direction)
+            abs_rotate_ang = abs(rotate_ang)
+            # adjust long edges according to the list and angles.
+            if abs_rotate_ang < DELTA or abs_rotate_ang > (180 - DELTA):
+                rp1 = rotation(p1, pm, rotate_ang)
+                rp2 = rotation(p2, pm, rotate_ang)
+            elif (90 - DELTA) < abs_rotate_ang < (90 + DELTA):
+                rp1 = rotation(p1, pm, rotate_ang - 90)
+                rp2 = rotation(p2, pm, rotate_ang - 90)
+            else:
+                rp1, rp2 = p1, p2
+        # adjust short edges (judged by a threshold θ) according to the list and angles.
+        else:
+            rotate_ang = md_used_list[-1] - azimuth
+            abs_rotate_ang = abs(rotate_ang)
+            if abs_rotate_ang < THETA or abs_rotate_ang > (180 - THETA):
+                rp1 = rotation(p1, pm, rotate_ang)
+                rp2 = rotation(p2, pm, rotate_ang)
+            else:
+                rp1 = rotation(p1, pm, rotate_ang - 90)
+                rp2 = rotation(p2, pm, rotate_ang - 90)
+        # contour_by_lines.extend([rp1, rp2])
+        contour_by_lines.append([rp1[0], rp2[0]])
+    correct_points = np.array(contour_by_lines)
+    # merge (or connect) parallel lines if the distance between
+    # two lines is less than (or larger than) a threshold D.
+    final_points = []
+    final_points.append(correct_points[0][0].reshape([1, 2]))
+    lp_number = correct_points.shape[0] - 1
+    for idx in range(lp_number):
+        next_idx = (idx + 1) if idx < lp_number else 0
+        cur_edge_p1 = correct_points[idx][0]
+        cur_edge_p2 = correct_points[idx][1]
+        next_edge_p1 = correct_points[next_idx][0]
+        next_edge_p2 = correct_points[next_idx][1]
+        L1 = line(cur_edge_p1, cur_edge_p2)
+        L2 = line(next_edge_p1, next_edge_p2)
+        A1 = calc_azimuth([cur_edge_p1], [cur_edge_p2])
+        A2 = calc_azimuth([next_edge_p1], [next_edge_p2])
+        dif_azi = abs(A1 - A2)
+        # find intersection point if not parallel
+        if (90 - DELTA) < dif_azi < (90 + DELTA):
+            point_intersection = intersection(L1, L2)
+            if point_intersection is not None:
+                final_points.append(point_intersection)
+        # move or add lines when parallel
+        elif dif_azi < 1e-6:
+            marg = calc_distance_between_lines(L1, L2)
+            if marg < D:
+                # move
+                point_move = calc_project_in_line(next_edge_p1, cur_edge_p1,
+                                                  cur_edge_p2)
+                final_points.append(point_move)
+                # update next
+                correct_points[next_idx][0] = point_move
+                correct_points[next_idx][1] = calc_project_in_line(
+                    next_edge_p2, cur_edge_p1, cur_edge_p2)
+            else:
+                # add line
+                add_mid_point = (cur_edge_p2 + next_edge_p1) / 2
+                rp1 = calc_project_in_line(add_mid_point, cur_edge_p1,
+                                           cur_edge_p2)
+                rp2 = calc_project_in_line(add_mid_point, next_edge_p1,
+                                           next_edge_p2)
+                final_points.extend([rp1, rp2])
+        else:
+            final_points.extend(
+                [cur_edge_p1[np.newaxis, :], cur_edge_p2[np.newaxis, :]])
+    final_points = np.array(final_points)
+    return final_points
+
+
+def _get_priority(hierarchy):
+    if hierarchy[3] < 0:
+        return 1
+    if hierarchy[2] < 0:
+        return 2
+    return 3
+
+
+def _fill(img, coarse_conts):
+    result = np.zeros_like(img)
+    sorted(coarse_conts, key=lambda x: x[1])
+    for contour, priority in coarse_conts:
+        if priority == 2:
+            cv2.fillPoly(result, [contour.astype(np.int32)], (0, 0, 0))
+        else:
+            cv2.fillPoly(result, [contour.astype(np.int32)], (255, 255, 255))
+    return result
--- a/paddlers/utils/postprocs/utils.py
+++ b/paddlers/utils/postprocs/utils.py
@ -0,0 +1,113 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import math
+
+
+def calc_distance(p1: np.ndarray, p2: np.ndarray) -> float:
+    return float(np.sqrt(np.sum(np.power((p1[0] - p2[0]), 2))))
+
+
+def calc_angle(p1: np.ndarray, vertex: np.ndarray, p2: np.ndarray) -> float:
+    x1, y1 = p1[0]
+    xv, yv = vertex[0]
+    x2, y2 = p2[0]
+    a = ((xv - x2) * (xv - x2) + (yv - y2) * (yv - y2))**0.5
+    b = ((x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2))**0.5
+    c = ((x1 - xv) * (x1 - xv) + (y1 - yv) * (y1 - yv))**0.5
+    return math.degrees(math.acos((b**2 - a**2 - c**2) / (-2 * a * c)))
+
+
+def calc_azimuth(p1: np.ndarray, p2: np.ndarray) -> float:
+    x1, y1 = p1[0]
+    x2, y2 = p2[0]
+    if y1 == y2:
+        return 0.0
+    if x1 == x2:
+        return 90.0
+    elif x1 < x2:
+        if y1 < y2:
+            ang = math.atan((y2 - y1) / (x2 - x1))
+            return math.degrees(ang)
+        else:
+            ang = math.atan((y1 - y2) / (x2 - x1))
+            return 180 - math.degrees(ang)
+    else:  # x1 > x2
+        if y1 < y2:
+            ang = math.atan((y2 - y1) / (x1 - x2))
+            return 180 - math.degrees(ang)
+        else:
+            ang = math.atan((y1 - y2) / (x1 - x2))
+            return math.degrees(ang)
+
+
+def rotation(point: np.ndarray, center: np.ndarray, angle: float) -> np.ndarray:
+    if angle == 0:
+        return point
+    x, y = point[0]
+    cx, cy = center[0]
+    radian = math.radians(abs(angle))
+    if angle > 0:  # clockwise
+        rx = (x - cx) * math.cos(radian) - (y - cy) * math.sin(radian) + cx
+        ry = (x - cx) * math.sin(radian) + (y - cy) * math.cos(radian) + cy
+    else:
+        rx = (x - cx) * math.cos(radian) + (y - cy) * math.sin(radian) + cx
+        ry = (y - cy) * math.cos(radian) - (x - cx) * math.sin(radian) + cy
+    return np.array([[rx, ry]])
+
+
+def line(p1, p2):
+    A = (p1[1] - p2[1])
+    B = (p2[0] - p1[0])
+    C = (p1[0] * p2[1] - p2[0] * p1[1])
+    return A, B, -C
+
+
+def intersection(L1, L2):
+    D = L1[0] * L2[1] - L1[1] * L2[0]
+    Dx = L1[2] * L2[1] - L1[1] * L2[2]
+    Dy = L1[0] * L2[2] - L1[2] * L2[0]
+    if D != 0:
+        x = Dx / D
+        y = Dy / D
+        return np.array([[x, y]])
+    else:
+        return None
+
+
+def calc_distance_between_lines(L1, L2):
+    eps = 1e-16
+    A1, _, C1 = L1
+    A2, B2, C2 = L2
+    new_C1 = C1 / (A1 + eps)
+    new_A2 = 1
+    new_B2 = B2 / (A2 + eps)
+    new_C2 = C2 / (A2 + eps)
+    dist = (np.abs(new_C1 - new_C2)) / (
+        np.sqrt(new_A2 * new_A2 + new_B2 * new_B2) + eps)
+    return dist
+
+
+def calc_project_in_line(point, line_point1, line_point2):
+    eps = 1e-16
+    m, n = point
+    x1, y1 = line_point1
+    x2, y2 = line_point2
+    F = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1)
+    x = (m * (x2 - x1) * (x2 - x1) + n * (y2 - y1) * (x2 - x1) +
+         (x1 * y2 - x2 * y1) * (y2 - y1)) / (F + eps)
+    y = (m * (x2 - x1) * (y2 - y1) + n * (y2 - y1) * (y2 - y1) +
+         (x2 * y1 - x1 * y2) * (x2 - x1)) / (F + eps)
+    return np.array([[x, y]])
--- a/paddlers/utils/visualize.py
+++ b/paddlers/utils/visualize.py
@ -67,7 +67,7 @@ def map_display(mask_path: str,

        * All tilesets have been corrected through public algorithms from the Internet.
        * Please read the relevant terms of use carefully:
-            - GeoQ [GISUNI] (http://geoq.cn/useragreement.html)
+            - GeoQ [GISUNI]  (http://geoq.cn/useragreement.html)
            - AMap [AutoNavi]  (https://wap.amap.com/doc/serviceitem.html)
            - Tencent Map  (https://ugc.map.qq.com/AppBox/Landlord/serveagreement.html)
            - Baidu Map  (https://map.baidu.com/zt/client/service/index.html)