如何在具有轮廓的Numpy 2D数组中找到形状？

Question

我有一个形状轮廓cnt，我需要在2D数组中找到它，我有一个target_index变量，它用于查找所需的区域，但是我需要寻找{{1} }轮廓。

cnt

可以使用其他方法，但首选import numpy as np x = np.linspace(0,1000, int(1000/50)) y = np.linspace(0,1000, int(1000/50)) X,Y = np.meshgrid(x,y) source = np.column_stack([X.ravel(), Y.ravel()]).astype(int) destination = source.copy() cnt = [[550, 42], [600, 42], [690, 273], [640, 273]] # Need to use cnt here target_index = np.where(np.logical_and(destination[:,1]==789,destination[:,0]>=421)) destination[target_index] scope = destination[target_index] scope[:,0] = scope[:,0] + 10 destination[target_index] = scope destination[target_index] # Remap grid_x, grid_y = np.mgrid[0:800, 0:800] grid_z = griddata(source, destination, (grid_x, grid_y), method='cubic') map_x = np.append([], [ar[:,1] for ar in grid_z]).reshape(800,800).astype('float32') map_y = np.append([], [ar[:,0] for ar in grid_z]).reshape(800,800).astype('float32') warped_image = cv2.remap(img, map_x, map_y, cv2.INTER_CUBIC) cv2.drawContours(warped_image,[cnt],0,(0,0,0),2) 。

Answer 1

除非您将自己限制为某些多边形，否则我认为使用np.where很难做到这一点。

以下是使用matplotlib的{​​{1}}对象解决问题（适应this solution）的方法：

Path

然后查看结果：

import numpy as np
from matplotlib.path import Path

x = np.linspace(0,1000, int(1000/50))
y = np.linspace(0,1000, int(1000/50))
X,Y = np.meshgrid(x,y)
source =  np.column_stack([X.ravel(), Y.ravel()]).astype(int)

cnt = [[550,  42],     
       [600,  42],
       [690, 273],
       [640, 273]]

p = Path(cnt)
grid = p.contains_points(source)
mask = grid.reshape(20, 20)

哪个给：

在import matplotlib.pyplot as plt plt.imshow(mask) 中使用更多的点以获得更高分辨率的结果。

Answer 2

根据您的问题判断，您正在申请身体变形，对我而言，此选项最方便，因为您可以为您创建任何轮廓。

    # Left hand contour
    pt1 = (int_12, int_13)
    pt2 = (int_17, int_16)
    pt3 = (int_18, int_19)
    pt4 = (int_14, int_15)
    lh_cnt = np.array([pt1, pt2, pt3, pt4])

    offset = int(hand_lenght / 28)

    for x in destination:
        inside_lh = cv2.pointPolygonTest(lh_cnt, (x[0], x[1]), False)
        elif inside_lh > 0:
            x[0] = x[0] - offset

    # Warping
    grid_x, grid_y = np.mgrid[0:self.width, 0:self.height]
    grid_z = griddata(source, destination, (grid_x, grid_y), method='cubic')
    map_x = np.append([], [ar[:,0] for ar in grid_z]).reshape(self.width, self.height).astype('float32')
    map_y = np.append([], [ar[:,1] for ar in grid_z]).reshape(self.width, self.height).astype('float32')
    warped_image = cv2.transpose(cv2.remap(img, map_x, map_y, cv2.INTER_LANCZOS4))