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NMS.py
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NMS.py
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import numpy as np
# Felzenszwalb et al.
def non_max_suppression_slow(boxes, overlapThresh):
# if there are no boxes, return an empty list
if len(boxes) == 0:
return boxes
# initialize the list of picked indexes
pick = []
# grab the coordinates of the bounding boxes
x1 = boxes[:,0]
y1 = boxes[:,1]
xw = boxes[:,2]
yh = boxes[:,3]
x2 = x1 + xw
y2 = y1 + yh
# compute the area of the bounding boxes and sort the bounding
# boxes by the bottom-right y-coordinate of the bounding box
area = xw * yh
idxs = np.argsort(boxes[:,4])
# keep looping while some indexes still remain in the indexes
# list
while len(idxs) > 0:
# grab the last index in the indexes list, add the index
# value to the list of picked indexes, then initialize
# the suppression list (i.e. indexes that will be deleted)
# using the last index
last = len(idxs) - 1
i = idxs[last]
pick.append(i)
suppress = [last]
# loop over all indexes in the indexes list
for pos in xrange(0, last):
# grab the current index
j = idxs[pos]
# find the largest (x, y) coordinates for the start of
# the bounding box and the smallest (x, y) coordinates
# for the end of the bounding box
xx1 = max(x1[i], x1[j])
yy1 = max(y1[i], y1[j])
xx2 = min(x2[i], x2[j])
yy2 = min(y2[i], y2[j])
# compute the width and height of the bounding box
w = max(0, xx2 - xx1)
h = max(0, yy2 - yy1)
# compute the ratio of overlap between the computed
# bounding box and the bounding box in the area list
overlap = float(w * h) / min(area[j], area[i])
# if there is sufficient overlap, suppress the
# current bounding box
if overlap > overlapThresh:
suppress.append(pos)
# delete all indexes from the index list that are in the
# suppression list
idxs = np.delete(idxs, suppress)
# return only the bounding boxes that were picked
return boxes[pick]
def non_max_suppression_fast(boxes, overlapThresh):
# if there are no boxes, return an empty list
if len(boxes) == 0:
return boxes
# initialize the list of picked indexes
pick = []
# grab the coordinates of the bounding boxes
x1 = boxes[:,0]
y1 = boxes[:,1]
xw = boxes[:,2]
yh = boxes[:,3]
x2 = x1 + xw
y2 = y1 + yh
# compute the area of the bounding boxes and sort the bounding
# boxes by the bottom-right y-coordinate of the bounding box
area = xw * yh
idxs = np.argsort(boxes[:,4])
# keep looping while some indexes still remain in the indexes
# list
while len(idxs) > 0:
# grab the last index in the indexes list and add the
# index value to the list of picked indexes
last = len(idxs) - 1
i = idxs[last]
pick.append(i)
# find the largest (x, y) coordinates for the start of
# the bounding box and the smallest (x, y) coordinates
# for the end of the bounding box
xx1 = np.maximum(x1[i], x1[idxs[:last]])
yy1 = np.maximum(y1[i], y1[idxs[:last]])
xx2 = np.minimum(x2[i], x2[idxs[:last]])
yy2 = np.minimum(y2[i], y2[idxs[:last]])
# compute the width and height of the bounding box
w = np.maximum(0, xx2 - xx1)
h = np.maximum(0, yy2 - yy1)
# compute the ratio of overlap
overlap = (w * h) / np.minimum(area[idxs[:last]], area[i])
# delete all indexes from the index list that have
idxs = np.delete(idxs, np.concatenate(([last], np.where(overlap > overlapThresh)[0])))
return boxes[pick]
def filter_bbox(bbox):
xrng = [0.1, 959.9]
yrng = [0.1, 719.9]
#bbox[:, :4] = bbox[:, :4] / 1.5
x1 = bbox[:, 0]
y1 = bbox[:, 1]
x2 = bbox[:, 0] + bbox[:, 2]
y2 = bbox[:, 1] + bbox[:, 3]
keep = np.where((x1 > xrng[0]) & (x2 < xrng[1]) & (y1 > yrng[0]) & (y2 < yrng[1]))[0]
return bbox[keep, :]