1. 旋转图像操作（90°、180°、270°）

# -*- coding: UTF-8 -*-from PIL import Image
import os# 获得文件夹下所有文件
filePath = 'F:/1213bag/test_transforms/0912/2/'
filenames = os.listdir(filePath)# 指定保存的文件夹
outputPath = 'F:/1213bag/test_transforms/0912/3/'# 迭代所有图片
for filename in filenames:# 读取图像im = Image.open(filePath + filename)# 指定逆时针旋转的角度im_rotate = im.transpose(Image.ROTATE_90)# im_rotate = im.transpose(Image.ROTATE_180)# im_rotate = im.transpose(Image.ROTATE_270)# 保存图像im_rotate.save(outputPath + filename)

2. jpg 和png相互转换、resize尺寸大小

import os
import cv2
import sys
import numpy as nppath1 = r'F:\1213bag\test_transforms\0912\3'
path2 = r'F:\1213bag\test_transforms\0912\4'for filename in os.listdir(path1):if os.path.splitext(filename)[1] == '.jpg':# print(filename)sas = os.path.join(path1, filename)img = cv2.imread(sas)tem = cv2.resize(img, (256, 512)) #(w,h)newfilename = filename.replace(".jpg", ".png")# cv2.imshow("Image",img)# cv2.waitKey(0)dst = os.path.join(path2, newfilename)cv2.imwrite(dst, tem)

3. padding 补黑边，长方形保持长宽比最后补黑边padding成正方形

import os
import numpy as np
import cv2
from PIL import Imagedef img_pad(pil_file):# h,w 先后不要写错，不然图片会变形h, w, c = pil_file.shape# print(h, w, c)fixed_size = 512  # 输出正方形图片的尺寸if h >= w:factor = h / float(fixed_size)new_w = int(w / factor)if new_w % 2 != 0:new_w -= 1pil_file = cv2.resize(pil_file, (new_w, fixed_size))pad_w = int((fixed_size - new_w) / 2)array_file = np.array(pil_file)# array_file = np.pad(array_file, ((0, 0), (pad_w, fixed_size-pad_w)), 'constant') #实现黑白图缩放array_file = cv2.copyMakeBorder(array_file, 0, 0, pad_w, fixed_size - new_w - pad_w, cv2.BORDER_CONSTANT,value=(0, 0, 0))  # 255是白色，0是黑色else:factor = w / float(fixed_size)new_h = int(h / factor)if new_h % 2 != 0:new_h -= 1pil_file = cv2.resize(pil_file, (fixed_size, new_h))pad_h = int((fixed_size - new_h) / 2)array_file = np.array(pil_file)# array_file = np.pad(array_file, ((pad_h, fixed_size-pad_h), (0, 0)), 'constant')array_file = cv2.copyMakeBorder(array_file, pad_h, fixed_size - new_h - pad_h, 0, 0, cv2.BORDER_CONSTANT,value=(0, 0, 0))output_file = Image.fromarray(array_file)return output_fileif __name__ == "__main__":dir_image = r'F:\1213bag\test_transforms\0912\4'  # 图片所在文件夹dir_output = r'F:\1213bag\test_transforms\0912\666'  # 输出结果文件夹if not os.path.exists(dir_output):os.makedirs(dir_output)i = 0list_image = os.listdir(dir_image)for file in list_image:path_image = os.path.join(dir_image, file)path_output = os.path.join(dir_output, file)pil_image = cv2.imread(path_image)b, g, r = cv2.split(pil_image)  # 通道分离,再重新合并操作pil_image = cv2.merge([r, g, b])# print(pil_image)# pil_image = pil_image.load()output_image = img_pad(pil_image)output_image.save(path_output)i += 1if i % 1000 == 0:print('The num of processed images:', i)  #

4. 处理过曝数据

import cv2
import os
import numpy as npdef read_path(file_pathname):for filename in os.listdir(file_pathname):print(filename)img = cv2.imread(file_pathname+'/'+filename)image = np.power(img, 0.95)			# 对像素值指数变换cv2.imwrite(r"F:\1213bag\test_transforms\0912\add1" + "/" + filename, image)
read_path(r"F:\1213bag\test_transforms\0912\add")