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# -*- coding: UTF-8 -*-
'''=================================================
@Project -> File pram -> geometry
@IDE PyCharm
@Author [email protected]
@Date 07/02/2024 11:08
=================================================='''
import numpy as np
def nms_fast(in_corners, H, W, dist_thresh):
"""
Run a faster approximate Non-Max-Suppression on numpy corners shaped:
3xN [x_i,y_i,conf_i]^T
Algo summary: Create a grid sized HxW. Assign each corner location a 1, rest
are zeros. Iterate through all the 1's and convert them either to -1 or 0.
Suppress points by setting nearby values to 0.
Grid Value Legend:
-1 : Kept.
0 : Empty or suppressed.
1 : To be processed (converted to either kept or supressed).
NOTE: The NMS first rounds points to integers, so NMS distance might not
be exactly dist_thresh. It also assumes points are within image boundaries.
Inputs
in_corners - 3xN numpy array with corners [x_i, y_i, confidence_i]^T.
H - Image height.
W - Image width.
dist_thresh - Distance to suppress, measured as an infinty norm distance.
Returns
nmsed_corners - 3xN numpy matrix with surviving corners.
nmsed_inds - N length numpy vector with surviving corner indices.
"""
grid = np.zeros((H, W)).astype(int) # Track NMS data.
inds = np.zeros((H, W)).astype(int) # Store indices of points.
# Sort by confidence and round to nearest int.
inds1 = np.argsort(-in_corners[2, :])
corners = in_corners[:, inds1]
rcorners = corners[:2, :].round().astype(int) # Rounded corners.
# Check for edge case of 0 or 1 corners.
if rcorners.shape[1] == 0:
return np.zeros((3, 0)).astype(int), np.zeros(0).astype(int)
if rcorners.shape[1] == 1:
out = np.vstack((rcorners, in_corners[2])).reshape(3, 1)
return out, np.zeros((1)).astype(int)
# Initialize the grid.
for i, rc in enumerate(rcorners.T):
grid[rcorners[1, i], rcorners[0, i]] = 1
inds[rcorners[1, i], rcorners[0, i]] = i
# Pad the border of the grid, so that we can NMS points near the border.
pad = dist_thresh
grid = np.pad(grid, ((pad, pad), (pad, pad)), mode='constant')
# Iterate through points, highest to lowest conf, suppress neighborhood.
count = 0
for i, rc in enumerate(rcorners.T):
# Account for top and left padding.
pt = (rc[0] + pad, rc[1] + pad)
if grid[pt[1], pt[0]] == 1: # If not yet suppressed.
grid[pt[1] - pad:pt[1] + pad + 1, pt[0] - pad:pt[0] + pad + 1] = 0
grid[pt[1], pt[0]] = -1
count += 1
# Get all surviving -1's and return sorted array of remaining corners.
keepy, keepx = np.where(grid == -1)
keepy, keepx = keepy - pad, keepx - pad
inds_keep = inds[keepy, keepx]
out = corners[:, inds_keep]
values = out[-1, :]
inds2 = np.argsort(-values)
out = out[:, inds2]
out_inds = inds1[inds_keep[inds2]]
return out_inds
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