将yolov5的autoanchor.py里的K-means改为K-means++的具体步骤及代码
时间: 2024-04-24 20:02:43 浏览: 15
K-means++是一种改进的K-means聚类算法,它能够更好地初始化聚类中心,从而提高聚类效果。在yolov5的autoanchor.py中,可以将K-means替换为K-means++,具体步骤如下:
1. 初始化第一个聚类中心,随机选择一个样本点作为第一个聚类中心。
```python
centroids = [boxes[random.randrange(num_boxes)]]
```
2. 对于其他的聚类中心,根据距离上一个聚类中心最远的样本点为下一个聚类中心。
```python
for _ in range(k - 1):
distances = [min([1 - bbox_iou(c, box) for c in centroids]) for box in boxes]
new_centroid_index = np.argmax(distances)
centroids.append(boxes[new_centroid_index])
```
3. 在进行第2步时,需要使用bbox_iou函数计算距离,因此需要将bbox_iou函数添加到代码中。
```python
def bbox_iou(box1, box2):
"""
Calculate the Intersection of Union (IoU) of two bounding boxes.
Args:
box1: Tuple (x1, y1, x2, y2) representing the coordinates of the first bounding box.
box2: Tuple (x1, y1, x2, y2) representing the coordinates of the second bounding box.
Returns:
The IoU of the two bounding boxes.
"""
# Calculate the coordinates of the intersection rectangle.
x1 = max(box1[0], box2[0])
y1 = max(box1[1], box2[1])
x2 = min(box1[2], box2[2])
y2 = min(box1[3], box2[3])
# If the intersection is empty, return 0.
if x2 <= x1 or y2 <= y1:
return 0.0
# Calculate the areas of the bounding boxes and the intersection.
box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1])
intersection_area = (x2 - x1) * (y2 - y1)
# Calculate the IoU and return it.
iou = intersection_area / (box1_area + box2_area - intersection_area)
return iou
```
完整代码如下:
```python
import random
import numpy as np
def bbox_iou(box1, box2):
"""
Calculate the Intersection of Union (IoU) of two bounding boxes.
Args:
box1: Tuple (x1, y1, x2, y2) representing the coordinates of the first bounding box.
box2: Tuple (x1, y1, x2, y2) representing the coordinates of the second bounding box.
Returns:
The IoU of the two bounding boxes.
"""
# Calculate the coordinates of the intersection rectangle.
x1 = max(box1[0], box2[0])
y1 = max(box1[1], box2[1])
x2 = min(box1[2], box2[2])
y2 = min(box1[3], box2[3])
# If the intersection is empty, return 0.
if x2 <= x1 or y2 <= y1:
return 0.0
# Calculate the areas of the bounding boxes and the intersection.
box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1])
intersection_area = (x2 - x1) * (y2 - y1)
# Calculate the IoU and return it.
iou = intersection_area / (box1_area + box2_area - intersection_area)
return iou
def kmeans_plus_plus(boxes, k, dist=np.median):
"""
Perform K-means++ clustering on a set of bounding boxes.
Args:
boxes: Numpy array of shape (num_boxes, 4) representing the bounding boxes.
k: The number of clusters.
dist: The distance function to use (default is median).
Returns:
A list of k centroids (each centroid is of shape (4,)).
"""
# Initialize the first centroid randomly.
num_boxes = boxes.shape[0]
centroids = [boxes[random.randrange(num_boxes)]]
# Initialize the remaining centroids using K-means++.
for _ in range(k - 1):
distances = [min([1 - bbox_iou(c, box) for c in centroids]) for box in boxes]
new_centroid_index = np.argmax(distances)
centroids.append(boxes[new_centroid_index])
# Run K-means clustering on the centroids.
centroids = np.asarray(centroids)
while True:
distances = 1 - np.asarray([[bbox_iou(c, box) for c in centroids] for box in boxes])
nearest_clusters = np.argmin(distances, axis=1)
if (nearest_clusters == np.asarray([nearest_clusters]).T).all():
break
for cluster in range(k):
centroid_boxes = boxes[nearest_clusters == cluster]
if len(centroid_boxes) == 0:
continue
centroids[cluster] = dist(centroid_boxes, axis=0)
return centroids.tolist()
```
在yolov5的autoanchor.py中,将以下代码:
```python
def kmeans(boxes, k, dist=np.median):
"""
Perform K-means clustering on a set of bounding boxes.
Args:
boxes: Numpy array of shape (num_boxes, 4) representing the bounding boxes.
k: The number of clusters.
dist: The distance function to use (default is median).
Returns:
A list of k centroids (each centroid is of shape (4,)).
"""
num_boxes = boxes.shape[0]
distances = np.empty((num_boxes, k))
last_nearest = np.zeros((num_boxes,))
np.random.seed()
centroids = boxes[np.random.choice(num_boxes, k, replace=False)]
while True:
distances = 1 - bbox_iou(centroids, boxes)
current_nearest = np.argmin(distances, axis=1)
if (last_nearest == current_nearest).all():
break
for cluster in range(k):
centroids[cluster] = dist(boxes[current_nearest == cluster], axis=0)
last_nearest = current_nearest
return centroids.tolist()
```
替换为以下代码即可:
```python
def kmeans(boxes, k, dist=np.median):
"""
Perform K-means clustering on a set of bounding boxes.
Args:
boxes: Numpy array of shape (num_boxes, 4) representing the bounding boxes.
k: The number of clusters.
dist: The distance function to use (default is median).
Returns:
A list of k centroids (each centroid is of shape (4,)).
"""
return kmeans_plus_plus(boxes, k, dist)
```