训练的标注数据格式如下：
[ { "name": "235_2_t20201127123021723_CAM2.jpg", "image_height": 6000, "image_width": 8192, "category": 5, "bbox": [ 1876.06, 998.04, 1883.06, 1004.04 ] }, { "name": "235_2_t20201127123021723_CAM2.jpg", "image_height": 6000, "image_width": 8192, "category": 5, "bbox": [ 1655.06, 1094.04, 1663.06, 1102.04 ] } ]
聚类anchorbox只需要 bbox 中的左上角与右下角的 x,y 数据
k-means 聚类代码：
import numpy as np
import json
import os
from PIL import Image
def iou(box, clusters):
"""
计算 IOU
param:
box: tuple or array, shifted to the origin (i. e. width and height)
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 + 1e-10)
return iou_
# 计算框的 numpy 数组和 k 个簇之间的平均并集交集（IoU）。
def avg_iou(boxes, clusters):
"""
param:
boxes: numpy array of shape (r, 2), where r is the number of rows
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])])
# 将所有框转换为原点。
def translate_boxes(boxes):
"""
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)
# 使用联合上的交集（IoU）度量计算k均值聚类。
def kmeans(boxes, k, dist=np.median):
"""
param:
boxes: numpy array of shape (r, 2), where r is the number of rows
k: number of clusters
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)] # 初始化k个聚类中心（方法是从原始数据集中随机选k个）
while True:
for row in range(rows):
# 定义的距离度量公式：d(box,centroid)=1-IOU(box,centroid)。到聚类中心的距离越小越好，但IOU值是越大越好，所以使用 1 - IOU，这样就保证距离越小，IOU值越大。
distances[row] = 1 - iou(boxes[row], clusters)
# 将标注框分配给“距离”最近的聚类中心（也就是这里代码就是选出（对于每一个box）距离最小的那个聚类中心）。
nearest_clusters = np.argmin(distances, axis=1)
# 直到聚类中心改变量为0（也就是聚类中心不变了）。
if (last_clusters == nearest_clusters).all():
break
# 更新聚类中心（这里把每一个类的中位数作为新的聚类中心）
for cluster in range(k):
clusters[cluster] = dist(boxes[nearest_clusters == cluster], axis=0)
last_clusters = nearest_clusters
return clusters
# 获取图片宽高
def get_image_width_high(full_image_name):
image = Image.open(full_image_name)
image_width, image_high = image.size[0], image.size[1]
return image_width, image_high
# 读取 json 文件中的标注数据
def parse_label_json(label_path):
with open(label_path, 'r') as f:
label = json.load(f)
result = []
for line in label:
bbox = line['bbox']
x_label_min, y_label_min, x_label_max, y_label_max = bbox[0], bbox[1], bbox[2], bbox[3]
# 计算边框的大小
width = x_label_max - x_label_min
height = y_label_max - y_label_min
assert width > 0
assert height > 0
result.append([width, height])
result = np.asarray(result)
return result
# 读取 txt 标注数据文件
def parse_label_txt(label_path):
all_label = os.listdir(label_path)
result = []
for i in range(len(all_label)):
full_label_name = os.path.join(label_path, all_label[i])
print(full_label_name)
# 分离文件名和文件后缀
label_name, label_extension = os.path.splitext(all_label[i])
full_image_name = os.path.join(label_path.replace('labels', 'images'), label_name + '.jpg')
image_width, image_high = get_image_width_high(full_image_name)
fp = open(full_label_name, mode="r")
lines = fp.readlines()
for line in lines:
array = line.split()
x_label_min = (float(array[1]) - float(array[3]) / 2) * image_width
x_label_max = (float(array[1]) + float(array[3]) / 2) * image_width
y_label_min = (float(array[2]) - float(array[4]) / 2) * image_high
y_label_max = (float(array[2]) + float(array[4]) / 2) * image_high
# 计算边框的大小
width = x_label_max - x_label_min
height = y_label_max - y_label_min
assert width > 0
assert height > 0
result.append([round(width, 2), round(height, 2)])
result = np.asarray(result)
return result
def get_kmeans(label, cluster_num=9):
anchors = kmeans(label, cluster_num)
ave_iou = avg_iou(label, anchors)
anchors = anchors.astype('int').tolist()
anchors = sorted(anchors, key=lambda x: x[0] * x[1])
return anchors, ave_iou
if __name__ == '__main__':
# 读取 json 格式的标注数据
label_path = "tile_round1_train_20201231/train_annos.json"
label_result = parse_label_json(label_path)
# 读取 txt 格式的标注数据
# label_path = "../image_data/seed/labels/" # seed/images/ 内是对应图片文件
# label_result = parse_label_txt(label_path)
anchors, ave_iou = get_kmeans(label_result, 9)
anchor_string = ''
for anchor in anchors:
anchor_string += '{},{}, '.format(anchor[0], anchor[1])
anchor_string = anchor_string[:-2]
print(f'anchors are: {anchor_string}')
print(f'the average iou is: {ave_iou}')
每次运行的结果都会有点不大一样
参考：https://blog.csdn.net/zuliang001/article/details/90551798