Created
January 2, 2024 05:49
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Sample 2D points from a 2D binary 0-1 mask
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import numpy as np | |
import cv2 | |
import random | |
I = cv2.imread('image.png') | |
mask = np.load('mask.npy') | |
mask = (mask[:,:,0] > 0) # True / False binary matrix | |
visI = np.zeros_like(I) | |
def sample_prompt_pts(mask, h_resol=128, w_resol=128, max_pts=128, K = 20): | |
h_step = max(mask.shape[0] // h_resol, 1) | |
w_step = max(mask.shape[1] // w_resol, 1) | |
valid_pts = [] | |
for i in range(0, mask.shape[0], h_step): | |
for j in range(0, mask.shape[1], w_step): | |
if mask[i][j]: valid_pts.append((i, j)) | |
if len(valid_pts) > max_pts: | |
valid_pts = np.array(valid_pts).reshape(-1, 2) | |
keep = set([np.random.randint(0, len(valid_pts))]) | |
while len(keep) < max_pts: | |
sel = [] | |
while len(sel) < K: | |
i = np.random.randint(0, len(valid_pts)) | |
if i in keep: continue | |
sel.append(i) | |
sel_dists = np.linalg.norm(valid_pts[np.array(sel),None,:] - valid_pts[None,np.array(list(keep)),:],axis=2).min(axis=1) | |
j = sel[np.argmax(sel_dists)] | |
keep.add(j) | |
valid_pts = valid_pts[list(keep),:] | |
return valid_pts | |
valid_pts = sample_prompt_pts(mask) | |
for i in range(len(valid_pts)): | |
visI[valid_pts[i][0]][valid_pts[i][1]] = I[valid_pts[i][0]][valid_pts[i][1]] | |
cv2.imwrite('test.png', visI) |
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