kmeans.py

#! /usr/bin/env python
# coding=utf-8
#================================================================
#   Copyright (C) 2019 * Ltd. All rights reserved.
#
#   Editor      : VIM
#   File name   : kmeans.py
#   Author      : YunYang1994
#   Created date: 2019-01-25 11:08:15
#   Description :
#
#================================================================

import cv2
import argparse
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
current_palette = list(sns.xkcd_rgb.values())

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)
        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 kmeans(boxes, k, dist=np.median,seed=1):
    """
    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)) ## N row x N cluster
    last_clusters = np.zeros((rows,))

    np.random.seed(seed)

    # initialize the cluster centers to be k items
    clusters = boxes[np.random.choice(rows, k, replace=False)]

    while True:
        # Step 1: allocate each item to the closest cluster centers
        for icluster in range(k): # I made change to lars76's code here to make the code faster
            distances[:,icluster] = 1 - iou(clusters[icluster], boxes)

        nearest_clusters = np.argmin(distances, axis=1)

        if (last_clusters == nearest_clusters).all():
            break

        # Step 2: calculate the cluster centers as mean (or median) of all the cases in the clusters.
        for cluster in range(k):
            clusters[cluster] = dist(boxes[nearest_clusters == cluster], axis=0)
        last_clusters = nearest_clusters

    return clusters, nearest_clusters, distances


def parse_anno(annotation_path):
    anno = open(annotation_path, 'r')
    result = []
    for line in anno:
        s = line.strip().split(' ')
        image = cv2.imread(s[0])
        image_h, image_w = image.shape[:2]
        s = s[1:]
        box_cnt = len(s) // 5
        for i in range(box_cnt):
            x_min, y_min, x_max, y_max = float(s[i*5+0]), float(s[i*5+1]), float(s[i*5+2]), float(s[i*5+3])
            width  = (x_max - x_min) / image_w
            height = (y_max - y_min) / image_h
            result.append([width, height])
    result = np.asarray(result)
    return result


def plot_cluster_result(clusters,nearest_clusters,WithinClusterSumDist,wh,k):
    for icluster in np.unique(nearest_clusters):
        pick = nearest_clusters==icluster
        c = current_palette[icluster]
        plt.rc('font', size=8)
        plt.plot(wh[pick,0],wh[pick,1],"p",
                 color=c,
                 alpha=0.5,label="cluster = {}, N = {:6.0f}".format(icluster,np.sum(pick)))
        plt.text(clusters[icluster,0],
                 clusters[icluster,1],
                 "c{}".format(icluster),
                 fontsize=20,color="red")
        plt.title("Clusters=%d" %k)
        plt.xlabel("width")
        plt.ylabel("height")
    plt.legend(title="Mean IoU = {:5.4f}".format(WithinClusterSumDist))
    plt.tight_layout()
    plt.savefig("./kmeans.jpg")
    plt.show()

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--dataset_txt", type=str, default="./raccoon_dataset/label_wzp.txt")
    parser.add_argument("--anchors_txt", type=str, default="./data/raccoon_anchors_wzp.txt")
    parser.add_argument("--cluster_num", type=int, default=9)
    args = parser.parse_args()

    anno_result = parse_anno(args.dataset_txt)
    clusters, nearest_clusters, distances = kmeans(anno_result, args.cluster_num)

    # sorted by area
    area = clusters[:, 0] * clusters[:, 1]
    indice = np.argsort(area)
    clusters = clusters[indice]
    with open(args.anchors_txt, "w") as f:
        for i in range(args.cluster_num):
            width, height = clusters[i]
            f.writelines(str(width) + " " + str(height) + " ")

    WithinClusterMeanDist = np.mean(distances[np.arange(distances.shape[0]),nearest_clusters])
    plot_cluster_result(clusters, nearest_clusters, 1-WithinClusterMeanDist, anno_result, args.cluster_num)