|
- import numpy as np
-
-
- class YOLO_Kmeans:
-
- 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(
- self.avg_iou(all_boxes, result) * 100))
-
-
- if __name__ == "__main__":
- cluster_number = 9
- filename = "2012_train.txt"
- kmeans = YOLO_Kmeans(cluster_number, filename)
- kmeans.txt2clusters()
|