统计图像分割训练集中的类别分布

Posted 2022-12-13 yhl_leo

本系列文章由 @yhl_leo 出品，转载请注明出处。
文章链接： http://blog.csdn.net/yhl_leo/article/details/52225600

---

对于一个语义分割数据集，可以使用如下方法统计样本集ground truth的类别分布情况：

import cv2, os
import numpy as np

#amount of classer
CLASSES_NUM = 21

#find imagee in folder dir
def findImages(dir,topdown=True):
    im_list = []
    if not os.path.exists(dir):
        print "Path for  not exist!".format(dir)
        raise
    else:
        for root, dirs, files in os.walk(dir, topdown):
            for fl in files:
                im_list.append(fl)
    return im_list

# amount of images corresponding to each classes
images_count = [0]*CLASSES_NUM
# amount of pixels corresponding to each class
class_pixels_count = [0]*CLASSES_NUM
# amount of pixels corresponding to the images of each class
image_pixels_count = [0]*CLASSES_NUM

image_folder = '../data/gt'
im_list = findImages(image_folder) 

for im in im_list:
    print im
    cv_img = cv2.imread(os.path.join(image_folder, im), cv2.IMREAD_UNCHANGED)
    size_img = cv_img.shape
    colors = set([])
    for i in range(size_img[0]):
        for j in range(size_img[1]):
            p_value = cv_img.item(i,j)
            if not p_value < CLASSES_NUM: # check
                print p_value
            else:
                class_pixels_count[p_value] = class_pixels_count[p_value] + 1
                colors.add(p_value)
    im_size = size_img[0]*size_img[1]
    for n in range(CLASSES_NUM):
        if n in colors:
            images_count[n] = images_count[n] + 1
            image_pixels_count[n] = image_pixels_count[n] + im_size

print images_count
print class_pixels_count
print image_pixels_count

上述代码，主要统计了每一类别所包含的图像数量（images_count），每一类别的像素数目（class_pixels_count）和每一类别对应的图像的总像素数目（image_pixels_count），有了这三组统计结果，就可以进一步计算训练时每一类别的loss的class_weight。