如何从 Python 中的二维点云在方形网格上创建二进制掩码？

Question

I have a the X and Y coordinates of a 2D cloud of points that I want to map onto a 2D uniform grid with a resolution of imageResolution of initially all zeros. I want all pixels in the grid which overlay the 2D cloud of points to contain ones, to produce a binary image.

Please note, there are a very large number of points both in my 2D cloud of points and in the uniform grid, and so loops are not an effective solution here.

I have looked at convex hulls but my points are not necessarily in a convex set.

I have tried this following code, but its not giving me the correct binary map, since its only assigning 1s to the nearest grid points closest to the points in the point cloud (see image below):

X = points[:,0] #1D array of X coordinates
Y = points[:,1] #1D array of Y coordinates

imageResolution = 256
xVec = np.linspace(0,800,imageResolution)
yVec = xVec

def find_index(x,y):
    xi=np.searchsorted(xVec,x)
    yi=np.searchsorted(yVec,y)
    return xi,yi

xIndex, yIndex = find_index(X,Y)

binaryMap = np.zeros((imageResolution,imageResolution))

binaryMap[xIndex,yIndex] = 1

fig = plt.figure(1)
plt.imshow(binaryMap, cmap='jet')
plt.colorbar()

Please see this image which shows my 2D cloud of points, the desired binary map I want, and the current binary map I am getting from the code above. Please note the red pixels are difficult to see in the last image.

How do I create a binary mask on a square grid from a 2D cloud of points in Python?

Thank you

Answer 1

DISCLAIMER :: Untested suggestion

If I've understood correctly, rather than mark an individual pixel with 1 , you should be marking a neighborhood of pixels.

You could try inserting the following lines just before binaryMap[xIndex,yIndex] = 1 :

DELTA_UPPER=2                                # Param. Needs fine-tuning
delta = np.arange(DELTA_UPPER).reshape(-1,1)
xIndex = xIndex + delta
xIndex [xIndex >= imageResolution] = imageResolution-1
yIndex = yIndex + delta
yIndex [yIndex >= imageResolution] = imageResolution-1

x_many, y_many = np.broadcast_arrays (xIndex[:,None], yIndex)

xIndex = x_many.reshape(-1)
yIndex = y_many.reshape(-1)

Note:

DELTA_UPPER is a parameter that you will have to fine-tune by playing around with. (Maybe start with DELTA_UPPER=3 )

Answer 2

UNTESTED CODE

Based on further clarifications, posting this second answer, to better index the binaryMap , given that points contains floats.

imageResoluton = 256
MAX_X = # Fill in here the max x value ever possible in `points`
MIN_X = # Fill in here the min x value ever possible in `points`
MAX_Y = # Fill in here the max y value ever possible in `points`
MIN_Y = # Fill in here the min y value ever possible in `points`

SCALE_FAC = imageResolution / max(MAX_X-MIN_X, MAX_Y-MIN_Y)

X = np.around(SCALE_FAC * points[:,0]).astype(np.int64)
Y = np.around(SCALE_FAC * points[:,1]).astype(np.int64)
X [X >= imageResolution] = imageResolution-1
Y [Y >= imageResolution] = imageResolution-1

binaryMap[X, Y] = 1

(There's no need for find_index() )