update

kmeans.py

@@ -0,0 +1,81 @@
+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")                 # 如果报这个错，可以把这行改成pass即可
+
+    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
+
+if __name__ == '__main__':
+    a = np.array([[1, 2, 3, 4], [5, 7, 6, 8]])
+    print(translate_boxes(a))