détection de cible pytorch détection de cible yolov4 divers types de statistiques comptage statistiques de piétons et de véhicules comptage

Détection de cible Pytorch Détection de cible yolov4

import numpy as np
import xml.etree.ElementTree as ET
import glob
import random

def cas_iou(box,cluster):
    x = np.minimum(cluster[:,0],box[0])
    y = np.minimum(cluster[:,1],box[1])

    intersection = x * y
    area1 = box[0] * box[1]

    area2 = cluster[:,0] * cluster[:,1]
    iou = intersection / (area1 + area2 -intersection)

    return iou

def avg_iou(box,cluster):
    return np.mean([np.max(cas_iou(box[i],cluster)) for i in range(box.shape[0])])


def kmeans(box,k):
    # 取出一共有多少框
    row = box.shape[0]
    
    # 每个框各个点的位置
    distance = np.empty((row,k))
    
    # 最后的聚类位置
    last_clu = np.zeros((row,))

    np.random.seed()

    # 随机选5个当聚类中心
    cluster = box[np.random.choice(row,k,replace = False)]
    # cluster = random.sample(row, k)
    while True:
        # 计算每一行距离五个点的iou情况。
        for i in range(row):
            distance[i] = 1 - cas_iou(box[i],cluster)
        
        # 取出最小点
        near = np.argmin(distance,axis=1)

        if (last_clu == near).all():
            break
        
        # 求每一个类的中位点
        for j in range(k):
            cluster[j] = np.median(
                box[near == j],axis=0)

        last_clu = near

    return cluster

def load_data(path):
    data = []
    # 对于每一个xml都寻找box
    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'))
        # 对于每一个目标都获得它的宽高
        for obj in tree.iter('object'):
            xmin = int(float(obj.findtext('bndbox/xmin'))) / width
            ymin = int(float(obj.findtext('bndbox/ymin'))) / height
            xmax = int(float(obj.findtext('bndbox/xmax'))) / width
            ymax = int(float(obj.findtext('bndbox/ymax'))) / height

            xmin = np.float64(xmin)
            ymin = np.float64(ymin)
            xmax = np.float64(xmax)
            ymax = np.float64(ymax)
            # 得到宽高
            data.append([xmax-xmin,ymax-ymin])
    return np.array(data)


if __name__ == '__main__':
    # 运行该程序会计算'./VOCdevkit/VOC2007/Annotations'的xml
    # 会生成yolo_anchors.txt
    SIZE = 448

    # for anchors_num in range(1,20):
    anchors_num = 6
    # 载入数据集，可以使用VOC的xml
    path = r'data\\Annotations'
    # 载入所有的xml
    # 存储格式为转化为比例后的width,height
    data = load_data(path)


    # 使用k聚类算法
    out = kmeans(data,anchors_num)
    out = out[np.argsort(out[:,0])]
    print('acc:{:.2f}%'.format(avg_iou(data,out) * 100))
    print(out*SIZE)
    data = out*SIZE
    f = open("Kmeans_anchors.txt", 'w')
    row = np.shape(data)[0]
    for i in range(row):
        if i == 0:
            x_y = "%d,%d" % (data[i][0], data[i][1])
        else:
            x_y = ", %d,%d" % (data[i][0], data[i][1])
        f.write(x_y)
    f.close()

 

détection de cible pytorch yolov4 détection de cible comptage statistique du nombre de différents types

https://download.csdn.net/download/babyai996/85077375