前言
yolov3是一个很优秀的object-detection模型,其中的anchor box机制在多尺度检测上取得了不错的效果。然而,作者提供的anchor box值是基于voc和coco数据集上的,如果应用到自己数据集可能不完全适用,那么如何基于自己的训练数据聚类anchor box呢?好吧,源代码如下所示。
import numpy as np
def iou(box, clusters):
"""
Calculates the Intersection over Union (IoU) between a box and k clusters.
:param box: tuple or array, shifted to the origin (i. e. width and height)
:param clusters: numpy array of shape (k, 2) where k is the number of clusters
:return: numpy array of shape (k, 0) where k is the number of clusters
"""
x = np.minimum(clusters[:, 0], box[0])
y = np.minimum(clusters[:, 1], box[1])
if np.count_nonzero(x == 0) > 0 or np.count_nonzero(y == 0) > 0:
raise ValueError("Box has no area")
intersection = x * y
box_area = box[0] * box[1]
cluster_area = clusters[:, 0] * clusters[:, 1]
iou_ = intersection / (box_area + cluster_area - intersection)
return iou_
def avg_iou(boxes, clusters):
"""
Calculates the average Intersection over Union (IoU) between a numpy array of boxes and k clusters.
:param boxes: numpy array of shape (r, 2), where r is the number of rows
:param clusters: numpy array of shape (k, 2) where k is the number of clusters
:return: average IoU as a single float
"""
return np.mean([np.max(iou(boxes[i], clusters)) for i in range(boxes.shape[0])])
def translate_boxes(boxes):
"""
Translates all the boxes to the origin.
:param boxes: numpy array of shape (r, 4)
:return: numpy array of shape (r, 2)
"""
new_boxes = boxes.copy()
for row in range(new_boxes.shape[0]):
new_boxes[row][2] = np.abs(new_boxes[row][2] - new_boxes[row][0])
new_boxes[row][3] = np.abs(new_boxes[row][3] - new_boxes[row][1])
return np.delete(new_boxes, [0, 1], axis=1)
def kmeans(boxes, k, dist=np.median):
"""
Calculates k-means clustering with the Intersection over Union (IoU) metric.
:param boxes: numpy array of shape (r, 2), where r is the number of rows
:param k: number of clusters
:param dist: distance function
:return: numpy array of shape (k, 2)
"""
rows = boxes.shape[0]
distances = np.empty((rows, k))
last_clusters = np.zeros((rows,))
np.random.seed()
# the Forgy method will fail if the whole array contains the same rows
clusters = boxes[np.random.choice(rows, k, replace=False)]
while True:
for row in range(rows):
distances[row] = 1 - iou(boxes[row], clusters)
nearest_clusters = np.argmin(distances, axis=1)
if (last_clusters == nearest_clusters).all():
break
for cluster in range(k):
clusters[cluster] = dist(boxes[nearest_clusters == cluster], axis=0)
last_clusters = nearest_clusters
return clusters
import glob
import xml.etree.ElementTree as ET
import numpy as np
from kemans import kmeans, avg_iou
ANNOTATIONS_PATH = "F:/garbage/annotations"
CLUSTERS = 12
def load_dataset(path):
dataset = []
for xml_file in glob.glob("{}/*xml".format(path)):
tree = ET.parse(xml_file)
height = int(tree.findtext("./size/height"))
width = int(tree.findtext("./size/width"))
for obj in tree.iter("object"):
xmin = int(obj.findtext("bndbox/xmin")) / width
ymin = int(obj.findtext("bndbox/ymin")) / height
xmax = int(obj.findtext("bndbox/xmax")) / width
ymax = int(obj.findtext("bndbox/ymax")) / height
xmin = np.float64(xmin)
ymin = np.float64(ymin)
xmax = np.float64(xmax)
ymax = np.float64(ymax)
if xmax == xmin or ymax == ymin:
print(xml_file)
dataset.append([xmax - xmin, ymax - ymin])
return np.array(dataset)
if __name__ == '__main__':
# print(__file__)
data = load_dataset(ANNOTATIONS_PATH)
out = kmeans(data, k=CLUSTERS)
# clusters = [[10,13],[16,30],[33,23],[30,61],[62,45],[59,119],[116,90],[156,198],[373,326]]
# out= np.array(clusters)/416.0
print(out)
print("Accuracy: {:.2f}%".format(avg_iou(data, out) * 100))
print("Boxes:\n {}-{}".format(out[:, 0] * 608, out[:, 1] * 608))
ratios = np.around(out[:, 0] / out[:, 1], decimals=2).tolist()
print("Ratios:\n {}".format(sorted(ratios)))
最终聚类的结果:
[[0.3770724 0.15486111]
[0.03958333 0.0375 ]
[0.20677083 0.13686314]
[0.0703125 0.05347222]
[0.17916667 0.08472222]
[0.47375 0.24791667]
[0.08489583 0.08819444]
[0.125 0.0625 ]
[0.12447917 0.11319444]
[0.28541667 0.1025 ]
[0.2640625 0.21597222]
[0.14942708 0.1875 ]]
Accuracy: 75.99%
Boxes:
[229.26001999 24.06666667 125.71666667 42.75 108.93333333
288.04 51.61666667 76. 75.68333333 173.53333333
160.55 90.85166667]-[ 94.15555556 22.8 83.21278721 32.51111111 51.51111111
150.73333333 53.62222222 38. 68.82222222 62.32
131.31111111 114. ]
Ratios:
[0.8, 0.96, 1.06, 1.1, 1.22, 1.31, 1.51, 1.91, 2.0, 2.11, 2.43, 2.78]