参考：python 简单图像处理（4） 旋转
图片旋转使用pytorch的torchvision 库
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import random
import glob
import numpy as np
import os
import math
import cv2
import torch
import torchvision
from torchvision import transforms as tfs
from torchvision.transforms import RandomRotation
from PIL import Image
img_Lists = []
#dir for rotating image
img_dir = "/data2/myfile/cwb/data"
#dir for rotated image
img_target_dir = "/data2/myfile/cwb/rotate_images"
#dir for rotating label
txt_dir = "/data2/myfile/cwb/txt"
#dir for rotated label
txt_target_dir = "/data2/myfile/cwb/rotate_txts"
img_box_dir = "/data2/myfile/cwb/box_images"
img_Lists = glob.glob(img_dir + '/*.jpg')
img_basenames = [] # e.g. 100.jpg
for item in img_Lists:
img_basenames.append(os.path.basename(item))
img_names = [] # e.g. 100
for item in img_basenames:
temp1, temp2 = os.path.splitext(item)
img_names.append(temp1)
#rotate per image
for img in img_names:
im = Image.open((img_dir + '/' + img + '.jpg'))
#rotate image
rot_im = tfs.RandomRotation((-30,-30),expand=True)(im)
rot_im=rot_im.convert('RGB')
rot_im.save((img_target_dir + '/' + img + '.jpg'))
# open the crospronding txt file
gt = open((txt_dir + '/' + img + '.txt')).read().splitlines()
txt_file = open((txt_target_dir + '/' + img + '.txt') ,'w')
image=np.array(rot_im.convert('RGB'))
for img_each_label in gt:
gt_ind = img_each_label.split(',')
point_coordinates = []
bbox = []
line = ''
for i in xrange(8):
point_coordinates.append(float(gt_ind[i]))
#coordinate rotate
for i in range(0, 8, 2):
x = point_coordinates[i]
y = point_coordinates[i + 1]
angle = 30 * math.pi / 180.0
rotated_x = x * math.cos(angle) - y * math.sin(angle) - 0.5 * im.width * math.cos(angle) + 0.5 * im.height * math.sin(
angle) + 0.5 * rot_im.width
rotated_y = x * math.sin(angle) + y * math.cos(angle) - 0.5 * im.width * math.sin(angle) - 0.5 * im.height * math.cos(
angle) + 0.5 * rot_im.height
line = line + str(rotated_x) + ',' + str(rotated_y) + ','
bbox.append(int(rotated_x))
bbox.append(int(rotated_y))
line = line + str(gt_ind[8])
print(line)
pts = np.array([[bbox[0], bbox[1]], [bbox[2], bbox[3]], [bbox[4], bbox[5]], [bbox[6], bbox[7]]], np.int32)
pts = pts.reshape((-1, 1, 2))
cv2.polylines(image, [pts], True, (0, 255, 255))
txt_file.write(line+'\n')
cv2.imwrite((img_box_dir + '/' + img + '.jpg'),image)
方法二（使用opencv,旋转后的图片更加清晰）
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import random
import glob
import numpy as np
import os
import math
from math import *
import cv2
# import torch
# import torchvision
# from torchvision import transforms as tfs
# from torchvision.transforms import RandomRotation
from PIL import Image
img_Lists = []
#dir for rotating image
img_dir = "/data2/myfile/cwb/data"
#dir for rotated image
img_target_dir = "/data2/myfile/cwb/rotate_images"
#dir for rotating label
txt_dir = "/data2/myfile/cwb/txt"
#dir for rotated label
txt_target_dir = "/data2/myfile/cwb/rotate_txts"
img_box_dir = "/data2/myfile/cwb/box_images"
img_Lists = glob.glob(img_dir + '/*.jpg')
img_basenames = [] # e.g. 100.jpg
for item in img_Lists:
img_basenames.append(os.path.basename(item))
img_names = [] # e.g. 100
for item in img_basenames:
temp1, temp2 = os.path.splitext(item)
img_names.append(temp1)
#rotate per image
for img in img_names:
# im = Image.open((img_dir + '/' + img + '.jpg'))
#rotate image
# rot_im = tfs.RandomRotation((-30,-30),expand=True)(im)
# rot_im=rot_im.convert('RGB')
# rot_im.save((img_target_dir + '/' + img + '.jpg'))
im = cv2.imread((img_dir + '/' + img + '.jpg'))
height,width=im.shape[:2]
degree= -30
#旋转后的尺寸
heightNew=int(width*fabs(sin(radians(degree)))+height*fabs(cos(radians(degree)))) # 这个公式参考之前内容
widthNew=int(height*fabs(sin(radians(degree)))+width*fabs(cos(radians(degree))))
matRotation=cv2.getRotationMatrix2D((width/2,height/2),degree,1)
matRotation[0,2] +=(widthNew-width)/2 # 因为旋转之后,坐标系原点是新图像的左上角,所以需要根据原图做转化
matRotation[1,2] +=(heightNew-height)/2
rot_im=cv2.warpAffine(im,matRotation,(widthNew,heightNew),borderValue=(255,255,255))
cv2.imwrite((img_target_dir + '/' + img + '.jpg'),rot_im)
# open the crospronding txt file
gt = open((txt_dir + '/' + img + '.txt')).read().splitlines()
txt_file = open((txt_target_dir + '/' + img + '.txt') ,'w')
image=rot_im
for img_each_label in gt:
gt_ind = img_each_label.split(',')
point_coordinates = []
bbox = []
line = ''
for i in xrange(8):
point_coordinates.append(float(gt_ind[i]))
#coordinate rotate
for i in range(0, 8, 2):
x = point_coordinates[i]
y = point_coordinates[i + 1]
angle = 30 * math.pi / 180.0
rotated_x = x * math.cos(angle) - y * math.sin(angle) - 0.5 * width * math.cos(angle) + 0.5 * height * math.sin(
angle) + 0.5 * widthNew
rotated_y = x * math.sin(angle) + y * math.cos(angle) - 0.5 * width * math.sin(angle) - 0.5 * height * math.cos(
angle) + 0.5 * heightNew
line = line + str(int(rotated_x)) + ',' + str(int(rotated_y)) + ','
bbox.append(int(rotated_x))
bbox.append(int(rotated_y))
line = line + str(gt_ind[8])
print(line)
pts = np.array([[bbox[0], bbox[1]], [bbox[2], bbox[3]], [bbox[4], bbox[5]], [bbox[6], bbox[7]]], np.int32)
pts = pts.reshape((-1, 1, 2))
#可视化结果
cv2.polylines(image, [pts], True, (0, 255, 255))
#写入txt文件
txt_file.write(line+'\n')
cv2.imwrite((img_box_dir + '/' + img + '.jpg'),image)
