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模型训练需要对图片数据集对样本进行归一化，因此要求均值和标准差 但因为笔记本内存不够，用数组寄存没办法实现，所以写了一个低配版的python脚本
1. 获取图片名称列表
def get_image_list(img_dir, isclasses=False):
"""将图像的绝对路径生成列表
args: img_dir:存放图片的目录
isclasses:图片是否按类别存放标志
return: 图片文件名称列表
"""
img_list = []
# 路径下图像是否按类别分类存放
if isclasses:
img_file = os.listdir(img_dir)
for class_name in img_file:
if not os.path.isfile(os.path.join(img_dir, class_name)):
class_img_list = os.listdir(os.path.join(img_dir, class_name))
img_list.extend(class_img_list)
else:
img_list = os.listdir(img_dir)
# print(img_list)
print('image numbers: {}'.format(len(img_list)))
return img_list
2. 求所有样本的R、G、B均值
def get_image_pixel_mean(img_dir, img_list, img_size):
"""求数据集图像的R、G、B均值
args: img_dir:
img_list:
img_size:
"""
R_sum = 0
G_sum = 0
B_sum = 0
count = 0
# 循环读取所有图片
for img_name in img_list:
img_path = os.path.join(img_dir, img_name)
if not os.path.isdir(img_path):
image = cv2.imread(img_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (img_size, img_size)) #
R_sum += image[:, :, 0].mean()
G_sum += image[:, :, 1].mean()
B_sum += image[:, :, 2].mean()
count += 1
R_mean = R_sum / count
G_mean = G_sum / count
B_mean = B_sum / count
print('R_mean:{}, G_mean:{}, B_mean:{}'.format(R_mean,G_mean,B_mean))
RGB_mean = [R_mean, G_mean, B_mean]
return RGB_mean
3. 求所有样本的R、G、B标准差
def get_image_pixel_std(img_dir, img_mean, img_list, img_size):
R_squared_mean = 0
G_squared_mean = 0
B_squared_mean = 0
count = 0
image_mean = np.array(img_mean)
# 循环读取所有图片
for img_name in img_list:
img_path = os.path.join(img_dir, img_name)
if not os.path.isdir(img_path):
image = cv2.imread(img_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (img_size, img_size)) #
image = image - image_mean # 零均值
# 求单张图片的方差，并累加
R_squared_mean += np.mean(np.square(image[:, :, 0]).flatten())
G_squared_mean += np.mean(np.square(image[:, :, 1]).flatten())
B_squared_mean += np.mean(np.square(image[:, :, 2]).flatten())
count += 1
# 求R、G、B的方差
R_std = math.sqrt(R_squared_mean / count)
G_std = math.sqrt(G_squared_mean / count)
B_std = math.sqrt(B_squared_mean / count)
print('R_std:{}, G_std:{}, B_std:{}'.format(R_std, G_std, B_std))
RGB_std = [R_std, G_std, B_std]
return RGB_std
3. 完整代码
import os
import cv2
import numpy as np
import math
def get_image_list(img_dir, isclasses=False):
"""将图像的名称列表
args: img_dir:存放图片的目录
isclasses:图片是否按类别存放标志
return: 图片文件名称列表
"""
img_list = []
# 路径下图像是否按类别分类存放
if isclasses:
img_file = os.listdir(img_dir)
for class_name in img_file:
if not os.path.isfile(os.path.join(img_dir, class_name)):
class_img_list = os.listdir(os.path.join(img_dir, class_name))
img_list.extend(class_img_list)
else:
img_list = os.listdir(img_dir)
print(img_list)
print('image numbers: {}'.format(len(img_list)))
return img_list
def get_image_pixel_mean(img_dir, img_list, img_size):
"""求数据集图像的R、G、B均值
args: img_dir:
img_list:
img_size:
"""
R_sum = 0
G_sum = 0
B_sum = 0
count = 0
# 循环读取所有图片
for img_name in img_list:
img_path = os.path.join(img_dir, img_name)
if not os.path.isdir(img_path):
image = cv2.imread(img_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (img_size, img_size)) #
R_sum += image[:, :, 0].mean()
G_sum += image[:, :, 1].mean()
B_sum += image[:, :, 2].mean()
count += 1
R_mean = R_sum / count
G_mean = G_sum / count
B_mean = B_sum / count
print('R_mean:{}, G_mean:{}, B_mean:{}'.format(R_mean,G_mean,B_mean))
RGB_mean = [R_mean, G_mean, B_mean]
return RGB_mean
def get_image_pixel_std(img_dir, img_mean, img_list, img_size):
R_squared_mean = 0
G_squared_mean = 0
B_squared_mean = 0
count = 0
image_mean = np.array(img_mean)
# 循环读取所有图片
for img_name in img_list:
img_path = os.path.join(img_dir, img_name)
if not os.path.isdir(img_path):
image = cv2.imread(img_path) # 读取图片
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (img_size, img_size)) #
image = image - image_mean # 零均值
# 求单张图片的方差
R_squared_mean += np.mean(np.square(image[:, :, 0]).flatten())
G_squared_mean += np.mean(np.square(image[:, :, 1]).flatten())
B_squared_mean += np.mean(np.square(image[:, :, 2]).flatten())
count += 1
R_std = math.sqrt(R_squared_mean / count)
G_std = math.sqrt(G_squared_mean / count)
B_std = math.sqrt(B_squared_mean / count)
print('R_std:{}, G_std:{}, B_std:{}'.format(R_std, G_std, B_std))
RGB_std = [R_std, G_std, B_std]
return RGB_std
if __name__ == '__main__':
image_dir = '/图片文件路径'
image_list = get_image_list(image_dir, isclasses=False)
RGB_mean = get_image_pixel_mean(image_dir, image_list, img_size=224)
get_image_pixel_std(image_dir, RGB_mean, image_list, img_size=224)
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