def __init__(self, cluster_number, filename):

self.cluster_number = cluster_number

self.filename = "2012_train.txt"

def iou(self, boxes, clusters): # 1 box -> k clusters

n = boxes.shape[0]

k = self.cluster_number

box_area = boxes[:, 0] * boxes[:, 1]

box_area = box_area.repeat(k)

box_area = np.reshape(box_area, (n, k))

cluster_area = clusters[:, 0] * clusters[:, 1]

cluster_area = np.tile(cluster_area, [1, n])

cluster_area = np.reshape(cluster_area, (n, k))

box_w_matrix = np.reshape(boxes[:, 0].repeat(k), (n, k))

cluster_w_matrix = np.reshape(np.tile(clusters[:, 0], (1, n)), (n, k))

min_w_matrix = np.minimum(cluster_w_matrix, box_w_matrix)

box_h_matrix = np.reshape(boxes[:, 1].repeat(k), (n, k))

cluster_h_matrix = np.reshape(np.tile(clusters[:, 1], (1, n)), (n, k))

min_h_matrix = np.minimum(cluster_h_matrix, box_h_matrix)

inter_area = np.multiply(min_w_matrix, min_h_matrix)

result = inter_area / (box_area + cluster_area - inter_area)

return result

def avg_iou(self, boxes, clusters):

accuracy = np.mean([np.max(self.iou(boxes, clusters), axis=1)])

return accuracy

def kmeans(self, boxes, k, dist=np.median):

box_number = boxes.shape[0]

distances = np.empty((box_number, k))

last_nearest = np.zeros((box_number,))

np.random.seed()

clusters = boxes[np.random.choice(

box_number, k, replace=False)] # init k clusters

while True:

distances = 1 - self.iou(boxes, clusters)

current_nearest = np.argmin(distances, axis=1)

if (last_nearest == current_nearest).all():

break # clusters won't change

for cluster in range(k):

clusters[cluster] = dist( # update clusters

boxes[current_nearest == cluster], axis=0)

last_nearest = current_nearest

return clusters

def result2txt(self, data):

f = open("yolo_anchors.txt", 'w')

row = np.shape(data)[0]

for i in range(row):

if i == 0:

x_y = "%d,%d" % (data[i][0], data[i][1])

else:

x_y = ", %d,%d" % (data[i][0], data[i][1])

f.write(x_y)

f.close()

def txt2boxes(self):

f = open(self.filename, 'r')

dataSet = []

for line in f:

infos = line.split(" ")

length = len(infos)

for i in range(1, length):

width = int(infos[i].split(",")[2]) - \

int(infos[i].split(",")[0])

height = int(infos[i].split(",")[3]) - \

int(infos[i].split(",")[1])

dataSet.append([width, height])

result = np.array(dataSet)

f.close()

return result

def txt2clusters(self):

all_boxes = self.txt2boxes()

result = self.kmeans(all_boxes, k=self.cluster_number)

result = result[np.lexsort(result.T[0, None])]

self.result2txt(result)

print("K anchors:\n {}".format(result))

print("Accuracy: {:.2f}%".format(