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def bbox_iou(box1, box2, x1y1x2y2=True):
if x1y1x2y2:
mx = min(box1[0], box2[0])
Mx = max(box1[2], box2[2])
my = min(box1[1], box2[1])
My = max(box1[3], box2[3])
w1 = box1[2] - box1[0]
h1 = box1[3] - box1[1]
w2 = box2[2] - box2[0]
h2 = box2[3] - box2[1]
else:
mx = min(float(box1[0]-box1[2]/2.0), float(box2[0]-box2[2]/2.0))
Mx = max(float(box1[0]+box1[2]/2.0), float(box2[0]+box2[2]/2.0))
my = min(float(box1[1]-box1[3]/2.0), float(box2[1]-box2[3]/2.0))
My = max(float(box1[1]+box1[3]/2.0), float(box2[1]+box2[3]/2.0))
w1 = box1[2]
h1 = box1[3]
w2 = box2[2]
h2 = box2[3]
uw = Mx - mx
uh = My - my
cw = w1 + w2 - uw
ch = h1 + h2 - uh
carea = 0
if cw <= 0 or ch <= 0:
return 0.0
area1 = w1 * h1
area2 = w2 * h2
carea = cw * ch
uarea = area1 + area2 - carea
return carea/uarea
def area2d(b):
return (b[:,2]-b[:,0]+1)*(b[:,3]-b[:,1]+1)
def overlap2d(b1, b2):
xmin = np.maximum( b1[:,0], b2[:,0] )
xmax = np.minimum( b1[:,2]+1, b2[:,2]+1)
width = np.maximum(0, xmax-xmin)
ymin = np.maximum( b1[:,1], b2[:,1] )
ymax = np.minimum( b1[:,3]+1, b2[:,3]+1)
height = np.maximum(0, ymax-ymin)
return width*height
def nms_3d(detections, overlap=0.5):
# detections: [(tube1, score1), (tube2, score2)]
if len(detections) == 0:
return np.array([], dtype=np.int32)
I = np.argsort([d[1] for d in detections])
indices = np.zeros(I.size, dtype=np.int32)
counter = 0
while I.size>0:
i = I[-1]
indices[counter] = i
counter += 1
ious = np.array([ iou3dt(detections[ii][0],detections[i][0]) for ii in I[:-1] ])
I = I[np.where(ious<=overlap)[0]]
return indices[:counter]
def iou3d(b1, b2):
assert b1.shape[0] == b2.shape[0]
assert np.all(b1[:,0] == b2[:,0])
o = overlap2d(b1[:,1:5],b2[:,1:5])
return np.mean( o/(area2d(b1[:,1:5])+area2d(b2[:,1:5])-o) )
def iou3dt(b1, b2):
tmin = max(b1[0,0], b2[0,0])
tmax = min(b1[-1,0], b2[-1,0])
if tmax <= tmin: return 0.0
temporal_inter = tmax-tmin+1
temporal_union = max(b1[-1,0], b2[-1,0]) - min(b1[0,0], b2[0,0]) + 1
return iou3d( b1[np.where(b1[:,0]==tmin)[0][0]:np.where(b1[:,0]==tmax)[0][0]+1,:] , b2[np.where(b2[:,0]==tmin)[0][0]:np.where(b2[:,0]==tmax)[0][0]+1,:] ) * temporal_inter / temporal_union
def voc_ap(pr, use_07_metric=False):
""" ap = voc_ap(rec, prec, [use_07_metric])
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
VOC 07 11 point method (default:False).
"""
rec, prec = pr[:,1], pr[:,0]
if use_07_metric:
# 11 point metric
ap = 0.
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(prec[rec >= t])
ap = ap + p / 11.
else:
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
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