多邊形輪廓點等距離外擴

第三方包安裝

pip install pyclipper

1. 輪廓點等距離外擴

def equidistant_zoom_contour(contour, margin):
    """
    等距離縮放多邊形輪廓點
    :param contour: 一個圖形的輪廓格式[[[x1, x2]],...],shape是(-1, 1, 2)
    :param margin: 輪廓外擴的像素距離，margin正數是外擴，負數是縮小
    :return: 外擴后的輪廓點
    """
    pco = pyclipper.PyclipperOffset()
    ##### 參數限制，默認成2這里設置大一些，主要是用於多邊形的尖角是否用圓角代替
    pco.MiterLimit = 10
    contour = contour[:, 0, :]
    pco.AddPath(contour, pyclipper.JT_MITER, pyclipper.ET_CLOSEDPOLYGON)
    solution = pco.Execute(margin)
    solution = np.array(solution).reshape(-1, 1, 2).astype(int)
    return solution

調試用例

import pyclipper
import math
from shapely.geometry import LineString, Polygon, MultiLineString, Point, MultiPoint

poly = np.array([[[200, 200]], [[200, 300]], [[400, 350]], [[350, 200]], [[300, 200]], [[200, 100]]])
contour1 = equidistant_zoom_contour(poly, 20)
img = np.zeros((500, 500, 3))
cv2.polylines(img, [poly], True, (0, 0, 255), 3)
cv2.polylines(img, [contour1], True, (0, 255, 0), 3)

結果展示：

2. 輪廓點等比例縮放

def perimeter(poly):
    p = 0
    nums = poly.shape[0]
    for i in range(nums):
        p += abs(np.linalg.norm(poly[i % nums] - poly[(i + 1) % nums]))
    return p

def proportional_zoom_contour(contour, ratio):
    """
    多邊形輪廓點按照比例進行縮放
    :param contour: 一個圖形的輪廓格式[[[x1, x2]],...],shape是(-1, 1, 2)
    :param ratio: 縮放的比例，如果大於1是放大小於1是縮小
    :return:
    """
    poly = contour[:, 0, :]
    area_poly = abs(pyclipper.Area(poly))
    perimeter_poly = perimeter(poly)
    poly_s = []
    pco = pyclipper.PyclipperOffset()
    pco.MiterLimit = 10
    if perimeter_poly:
        d = area_poly * (1 - ratio * ratio) / perimeter_poly
        pco.AddPath(poly, pyclipper.JT_MITER, pyclipper.ET_CLOSEDPOLYGON)
        poly_s = pco.Execute(-d)
    poly_s = np.array(poly_s).reshape(-1, 1, 2).astype(int)

    return poly_s

測試用例：

import pyclipper
import math
from shapely.geometry import LineString, Polygon, MultiLineString, Point, MultiPoint
poly = np.array([[[200, 200]], [[200, 300]], [[400, 350]], [[350, 200]], [[300, 200]], [[200, 100]]])
contour1 = proportional_zoom_contour(poly, 1.5)
img = np.zeros((500, 500, 3))
cv2.polylines(img, [contour1], True, (0, 255, 0), 3)
cv2.polylines(img, [poly], True, (0, 0, 255), 3)

其中， pco.MiterLimit = 10這個參數默認是2，如果是默認的值結果圖第一個，改成10的話，結果圖就是第二個，是一個尖角的區別

3. 圖形輪廓的旋轉

# 獲取一個形狀的質心
def get_centroid(coord):
    coord = np.array(coord)
    shape = coord.shape
    if len(shape) == 1 and len(coord) == 2:  # point
        return coord
    if len(shape) == 1 and len(coord) == 4:  # bounding box
        return tuple([(coord[0] + coord[2]) // 2, (coord[1] + coord[3]) // 2])
    elif len(shape) == 2 and shape[-1] == 2:
        if shape[0] == 2:  # 如果是直線
            cen = LineString(coord).centroid
        else:
            cen = Polygon(coord).centroid
        return tuple(map(int, [cen.x, cen.y]))
    elif len(shape) == 3 and shape[1:] == (1, 2):  # contour
        cen = Polygon(coord.squeeze()).centroid
        return tuple(map(int, [cen.x, cen.y]))
    else:
        raise Exception('coordinate error, must be bbox or contour shape:{}'.format(coord))


def point_Srotate(im_w, im_h, angle, spin_point, origin_point):
    """
    :param im_w: 原始點所在的圖片的寬度
    :param im_h: 原始點所在的圖片的高度
    :param angle: 旋轉的角度
    :param spin_point: 旋轉的點
    :param origin_point: 參考點
    :return: 旋轉過后的點
    """
    row, col = im_h, im_w
    # P(x1, y1)，繞某個像素點Q(x2, y2)
    x1, y1 = spin_point
    x2, y2 = origin_point
    y1 = row - y1
    y2 = row - y2
    x = (x1 - x2) * math.cos(math.pi / 180.0 * angle) - (y1 - y2) * math.sin(math.pi / 180.0 * angle) + x2
    y = (x1 - x2) * math.sin(math.pi / 180.0 * angle) + (y1 - y2) * math.cos(math.pi / 180.0 * angle) + y2
    x = x
    y = row - y

    return [x, y]

調用示例：

import pyclipper
import math
from shapely.geometry import LineString, Polygon, MultiLineString, Point, MultiPoint
# 以多邊形輪廓的質心為參照點進行旋轉
poly = np.array([[[200, 200]], [[200, 300]], [[400, 350]], [[350, 200]], [[300, 200]], [[200, 100]]])

origin_point = get_centroid(poly)
spin_list = []
for con in poly:
    print('con', con)
    new = point_Srotate(500, 500, 50, con[0], origin_point)
    spin_list.append(new)
spin_con = np.array(spin_list).reshape(-1, 1, 2).astype(int)
img = np.zeros((500, 500, 3))
cv2.polylines(img, [spin_con], True, (0, 255, 0), 3)
cv2.polylines(img, [poly], True, (0, 0, 255), 3)

結果：

4. 其他外擴函數

def extend_contour2(contour, margin):
    # 每個點相對於質心進行外擴一定的距離
    """
    :param contour: 輪廓點集合
    :param margin: 外擴的距離
    :return: 外擴后的輪廓點集
    """
    #### 求該輪廓的質心 ####
    gravity_point = get_centroid(contour)
    #### 獲取最左下的點 ####
    # min_x = np.minimum(contour)
    #### 計算所有的輪廓點與質心所組成的向量，計算向量的模
    vector_arr = contour - np.array(gravity_point)
    vector_length = np.linalg.norm(vector_arr, axis=2)
    #### 計算所有的點針對對外擴的像素需要放大多少倍
    ratio = 1 + margin / vector_length
    ratio = np.concatenate([ratio, ratio], axis=1)
    #### 進行坐標的縮放
    contour_ext = (vector_arr[:, 0, :] * ratio + np.array(gravity_point)).reshape(-1, 1, 2)
    contour_ext = contour_ext.astype(int)
    return contour_ext

def coordinate_conversion(reference_point, contour, ratio):
    # 對凸多邊形有用，對凹多邊形容易變形，成比例縮放輪廓
    """
    :param reference_point: 參照點的坐標
    :param contour: 圖像的輪廓點
    :param ratio: 縮放的比例
    :return: 以參照點不變將輪廓點獲取縮放后的輪廓點坐標
    """
    contour_trans_array = (contour - np.array(reference_point)) * ratio + np.array(reference_point)
    contour_trans_array = contour_trans_array.astype(int)
    return contour_trans_array