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| def bboxesOverRation(bboxesA,bboxesB):
"""
功能等同于matlab的函数bboxesOverRation
bboxesA：M*4 array,形如[x,y,w,h]排布
bboxesB: N*4 array,形如[x,y,w,h]排布
"""
bboxesA = np.array(bboxesA.astype('float'))
bboxesB = np.array(bboxesB.astype('float'))
M = bboxesA.shape[0]
N = bboxesB.shape[0]
areasA = bboxesA[:,2]*bboxesA[:,3]
areasB = bboxesB[:,2]*bboxesB[:,3]
xA = bboxesA[:,0]+bboxesA[:,2]
yA = bboxesA[:,1]+bboxesA[:,3]
xyA = np.stack([xA,yA]).transpose()
xyxyA = np.concatenate((bboxesA[:,:2],xyA),axis=1)
xB = bboxesB[:,0] +bboxesB[:,2]
yB = bboxesB[:,1]+bboxesB[:,3]
xyB = np.stack([xB,yB]).transpose()
xyxyB = np.concatenate((bboxesB[:,:2],xyB),axis=1)
iouRatio = np.zeros((M,N))
for i in range(M):
for j in range(N):
x1 = max(xyxyA[i,0],xyxyB[j,0]);
x2 = min(xyxyA[i,2],xyxyB[j,2]);
y1 = max(xyxyA[i,1],xyxyB[j,1]);
y2 = min(xyxyA[i,3],xyxyB[j,3]);
Intersection = max(0,(x2-x1))*max(0,(y2-y1));
Union = areasA[i]+areasB[j]-Intersection;
iouRatio[i,j] = Intersection/Union;
return iouRatio
def estimateAnchorBoxes(trainingData,numAnchors=9):
'''
功能：kmeans++算法估计anchor，类似于matlab函数estimateAnchorBoxes,当trainingData
数据量较大时候，自写的kmeans迭代循环效率较低，matlab的estimateAnchorBoxes得出
anchors较快，但meanIOU较低，然后乘以实际box的ratio即可。此算法由于优化是局部，易陷入局部最优解，结果不一致属正常
cuixingxing150@gmail.com
Example:
import scipy.io as scipo
data = scipo.loadmat(r'D:\Matlab_files\trainingData.mat')
trainingData = data['temp']
meanIoUList = []
for numAnchor in np.arange(1,16):
anchorBoxes,meanIoU = estimateAnchorBoxes(trainingData,numAnchors=numAnchor)
meanIoUList.append(meanIoU)
plt.plot(np.arange(1,16),meanIoUList,'ro-')
plt.ylabel("Mean IoU")
plt.xlabel("Number of Anchors")
plt.title("Number of Anchors vs. Mean IoU")
Parameters
----------
trainingData : numpy 类型
形如[x,y,w,h]排布，M*4大小二维矩阵
numAnchors : int, optional
估计的anchors数量. The default is 9.
Returns
-------
anchorBoxes : numpy类型
形如[w,h]排布，N*2大小矩阵.
meanIoU : scalar 标量
DESCRIPTION.
'''
numsObver = trainingData.shape[0]
xyArray = np.zeros((numsObver,2))
trainingData[:,0:2] = xyArray
assert(numsObver>=numAnchors)
centroids = []
centroid_index = np.random.choice(numsObver, 1)
centroids.append(trainingData[centroid_index])
while len(centroids)<numAnchors:
minDistList = []
for box in trainingData:
box = box.reshape((-1,4))
minDist = 1
for centroid in centroids:
centroid = centroid.reshape((-1,4))
ratio = (1-bboxesOverRation(box,centroid)).item()
if ratio<minDist:
minDist = ratio
minDistList.append(minDist)
sumDist = np.sum(minDistList)
prob = minDistList/sumDist
idx = np.random.choice(numsObver,1,replace=True,p=prob)
centroids.append(trainingData[idx])
maxIterTimes = 100
iter_times = 0
while True:
minDistList = []
minDistList_ind = []
for box in trainingData:
box = box.reshape((-1,4))
minDist = 1
box_belong = 0
for i,centroid in enumerate(centroids):
centroid = centroid.reshape((-1,4))
ratio = (1-bboxesOverRation(box,centroid)).item()
if ratio<minDist:
minDist = ratio
box_belong = i
minDistList.append(minDist)
minDistList_ind.append(box_belong)
centroids_avg = []
for _ in range(numAnchors):
centroids_avg.append([])
for i,anchor_id in enumerate(minDistList_ind):
centroids_avg[anchor_id].append(trainingData[i])
err = 0
for i in range(numAnchors):
if len(centroids_avg[i]):
temp = np.mean(centroids_avg[i],axis=0)
err += np.sqrt(np.sum(np.power(temp-centroids[i],2)))
centroids[i] = np.mean(centroids_avg[i],axis=0)
iter_times+=1
if iter_times>maxIterTimes or err==0:
break
anchorBoxes = np.array([x[2:] for x in centroids])
meanIoU = 1-np.mean(minDistList)
return anchorBoxes,meanIoU
|
