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utils_nms.py
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utils_nms.py
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"""
apply_nms.py: Wrapper for nms.py
Authors : svp
"""
import numpy as np
'''
nms.py: CPU implementation of non maximal supression modified from Ross's code.
Authors : svp
Modified from https://github.com/rbgirshick/fast-rcnn/blob/master/lib/utils/nms.py
to accommodate a corner case which handles one box lying completely inside another.
'''
def nms(dets, thresh):
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
remove_index_1 = np.where(areas[i] == inter)
remove_index_2 = np.where(areas[order[1:]] == inter)
ovr = inter / (areas[i] + areas[order[1:]] - inter)
ovr[remove_index_1] = 1.0
ovr[remove_index_2] = 1.0
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
'''
Extracts confidence map and box map from N (N=4 here)
channel input.
Parameters:
-----------
confidence_map - (list) list of confidences for N channels
hmap - (list) list of box values for N channels
Returns
-------
nms_conf_map - (HXW) single channel confidence score map
nms_conf_box - (HXW) single channel box map.
'''
def extract_conf_points(confidence_map, hmap):
nms_conf_map = np.zeros_like(confidence_map[0])
nms_conf_box = np.zeros_like(confidence_map[0])
idx_1 = np.where(np.logical_and(confidence_map[0] > 0, confidence_map[1] <= 0))
idx_2 = np.where(np.logical_and(confidence_map[0] <= 0, confidence_map[1] > 0))
idx_common = np.where(np.logical_and(confidence_map[0] > 0, confidence_map[1] > 0))
nms_conf_map[idx_1] = confidence_map[0][idx_1]
nms_conf_map[idx_2] = confidence_map[1][idx_2]
nms_conf_box[idx_1] = hmap[0][idx_1]
nms_conf_box[idx_2] = hmap[1][idx_2]
for ii in range(len(idx_common[0])):
x, y = idx_common[0][ii], idx_common[1][ii]
if confidence_map[0][x, y] > confidence_map[1][x, y]:
nms_conf_map[x, y] = confidence_map[0][x, y]
nms_conf_box[x, y] = hmap[0][x, y]
else:
nms_conf_map[x, y] = confidence_map[1][x, y]
nms_conf_box[x, y] = hmap[1][x, y]
assert (np.sum(nms_conf_map > 0) == len(idx_1[0]) + len(idx_2[0]) + len(idx_common[0]))
return nms_conf_map, nms_conf_box
'''
Wrapper function to perform NMS
Parameters:
-----------
confidence_map - (list) list of confidences for N channels
hmap - (list) list of box values for N channels
wmap - (list) list of box values for N channels
dotmap_pred_downscale -(int) prediction scale
thresh - (float) Threshold for NMS.
Returns
-------
x, y - (list) list of x-coordinates and y-coordinates to keep
after NMS.
h, w - (list) list of height and width of the corresponding x, y
points.
scores - (list) list of confidence for h and w at (x, y) point.
'''
def apply_nms(confidence_map, hmap, wmap, dotmap_pred_downscale=2, thresh=0.3):
nms_conf_map, nms_conf_box = extract_conf_points([confidence_map[0], confidence_map[1]], [hmap[0], hmap[1]])
nms_conf_map, nms_conf_box = extract_conf_points([confidence_map[2], nms_conf_map], [hmap[2], nms_conf_box])
nms_conf_map, nms_conf_box = extract_conf_points([confidence_map[3], nms_conf_map], [hmap[3], nms_conf_box])
confidence_map = nms_conf_map
hmap = nms_conf_box
wmap = nms_conf_box
confidence_map = np.squeeze(confidence_map)
hmap = np.squeeze(hmap)
wmap = np.squeeze(wmap)
dets_idx = np.where(confidence_map > 0)
y, x = dets_idx[-2], dets_idx[-1]
h, w = hmap[dets_idx], wmap[dets_idx]
x1 = x - w / 2
x2 = x + w / 2
y1 = y - h / 2
y2 = y + h / 2
scores = confidence_map[dets_idx]
dets = np.stack([np.array(x1), np.array(y1), np.array(x2), np.array(y2), np.array(scores)], axis=1)
# List of indices to keep
keep = nms(dets, thresh)
y, x = dets_idx[-2], dets_idx[-1]
h, w = hmap[dets_idx], wmap[dets_idx]
x = x[keep]
y = y[keep]
h = h[keep]
w = w[keep]
scores = scores[keep]
return x, y, h, w, scores