我正在制作一个程序，通过将图像变成二维numpy数组并旋转它，在给定文件路径的情况下顺时针或逆时针旋转图像。我可以顺时针旋转图像(重复工作(；然而，逆时针旋转(rotate_counter(((仅在第一次运行时起作用，任何未来调用rotate_Ccounter((都将顺时针旋转图像(或展开图像(。我已经从PIL和numpy导入了图像作为np。图像保存到与原始图像相同的位置(路径用于导入图像和保存图像(。
我曾尝试更改每个旋转方法中的变量名，认为这可能是关于我在两个方法中的new_image变量的内存错误(更改为image2和image3(，我曾多次尝试重置widthitor变量，以防出现错误。
我的一个朋友提到，可能是在第一次逆时针旋转图像后，图像数组的迭代起点没有重置，但如果我在同一个运行实例中运行两次该方法，运行整个程序两次(每次只调用一次旋转(，并在两次运行之间重新启动IDE，就会出现同样的错误，这应该涵盖这种可能性。
我的理论是，这与图像的路径有关，在该路径中，系统内存记住了原始图像，当调用第二次逆时针旋转时，将导入未旋转的图像，但这将导致第二次运行保存与第一次运行相同的图像。
我可能还缺少什么吗？
这是我在这个论坛上的第一篇帖子，所以如果有任何手续我错过了，我很抱歉，并随时提出问题。
path = "Insert_Path_Here"
image = Image.open(path)
class Image_Processer:
def __init__(self, image, path):
self.image = image
self.data = np.asarray(image)
self.data_type = type(self.data)
self.data_shape = self.data.shape
self.path = path
#Rotates the image Clockwise
def rotate_Clockwise(self):
print("Rotating Clockwise")
size = [len(self.data[0]), len(self.data)] #Width, Height
image2 = [] #Creates the new image list
for i in range(0,size[0]): #Creates the rows in the new image based on the height of the given image
image2.append([])
heightiterator = size[1]-1 #Starting at the last row
widthiterator = size[0]-1 #Start at last element of each row
while heightiterator >= 0: #For each row in the image
widthiterator = size[0]-1 #Start at last element of each row
while widthiterator >= 0: #While in the row
image2[widthiterator].append(self.data[heightiterator][widthiterator]) #Each column should go in one row
widthiterator -= 1 #Move one element
heightiterator -= 1 #Moves up one row
return np.asarray(image2) #Returns the new image as a numpy array
#Rotates the image Counter-Clockwise
def rotate_Counter(self):
print("Rotating Counter-Clockwise")
size = [len(self.data[0]), len(self.data)] #Width, Height
image3 = [] #Creates the new image list
for i in range(0,size[0]): #Creates the rows in the new image based on the height of the given image
image3.append([])
heightiterator = 0 #Starting at the first row
widthiterator = 0 #Start at the first element of each row
while heightiterator < size[1]: #For each row in the image
widthiterator = 0 #Start at the first element of each row
while widthiterator < size[0]: #While in the row
image3[widthiterator].append(self.data[heightiterator][widthiterator]) #Each column should go in one row
widthiterator += 1 #Move one element
heightiterator += 1 #Moves down one row
return np.asarray(image3) #Returns the new image as a numpy array
def save_image(self, new_image): #Saves the new image in place of the old image
self.image = Image.fromarray(new_image)
self.image.save(path)
time.sleep(1) #Waits 1 second
image1 = Image_Processer(image, path)
#image1.save_image(image1.rotate_Clockwise())
image1.save_image(image1.rotate_Counter())
您在错误的位置分配值，得到的是transpose而不是rotate。
当你运行两次transponse时，你会得到相同的图像。
您必须使用[width-widthiterator-1]而不是[widthiterator]，
或更短的[-widthiterator]
第一个[widthiterator]中的minus
image3[-widthiterator].append(self.data[heightiterator][widthiterator])
或第二个CCD_ 10 中的CCD_
image3[widthiterator].append(self.data[heightiterator][-widthiterator])
def rotate_Counter(self):
print("Rotating Counter-Clockwise")
size = [len(self.data[0]), len(self.data)] #Width, Height
image3 = [] #Creates the new image list
for i in range(0,size[0]): #Creates the rows in the new image based on the height of the given image
image3.append([])
heightiterator = 0 #Starting at the first row
widthiterator = 0 #Start at the first element of each row
while heightiterator < size[1]: #For each row in the image
widthiterator = 0 #Start at the first element of each row
while widthiterator < size[0]: #While in the row
image3[-widthiterator].append(self.data[heightiterator][widthiterator]) #Each column should go in one row
widthiterator += 1 #Move one element
heightiterator += 1 #Moves down one row
return np.asarray(image3) #Returns the new image as a numpy array
编辑：
与for相同-环路
def rotate_Clockwise(self):
print("Rotating Counter-Clockwise")
height = self.data_shape[0]
width = self.data_shape[1]
new_image = []
for x in range(width):
new_image.append([])
for y in range(height):
for x in range(width):
new_image[x].append(self.data[-y][x]) # [-y]
return np.asarray(new_image)
def rotate_Counter(self):
print("Rotating Counter-Clockwise")
height = self.data_shape[0]
width = self.data_shape[1]
new_image = []
for x in range(width):
new_image.append([])
for y in range(height):
for x in range(width):
new_image[x].append(self.data[y][-x]) # [-x]
return np.asarray(new_image)
如果使用self.data[y][x]，则在点(left,top)周围得到标准transposition。
如果使用self.data[-y][-x]，则在点(right, top)周围得到非标准transposition。
如果使用self.data[-x][y]，则可以垂直翻转。
如果使用self.data[x][-y]，则可以进行水平翻转。
有趣的结果也给出了data[ (y+100) % height ][x]或data[y][(x + 100) % width]——它向上或向左移动图像。
但这些函数需要不同的行数——height而不是width
for y in range(height):
new_image.append([])
编辑：
我用于测试的完整代码：
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
class ImageProcesser: # PEP8: `CamelCaseNames` for classes
def __init__(self, image, path):
self.image = image
self.data = np.asarray(image)
self.data_type = type(self.data)
self.data_shape = self.data.shape
self.path = path
def rotate_clockwise(self): # PEP8: `lower_case_names` for functions
print("Rotating Clockwise")
height = self.data_shape[0]
width = self.data_shape[1]
new_image = []
for _ in range(width):
new_image.append([])
for y in range(height):
for x in range(width):
new_image[x].append(self.data[-y][x])
return np.asarray(new_image)
def rotate_counter(self):
print("Rotating Counter-Clockwise")
height = self.data_shape[0]
width = self.data_shape[1]
new_image = []
for _ in range(width):
new_image.append([])
for y in range(height):
for x in range(width):
new_image[x].append(self.data[y][-x])
return np.asarray(new_image)
def transpose(self):
print("Transpose")
height = self.data_shape[0]
width = self.data_shape[1]
new_image = []
for _ in range(width):
new_image.append([])
for y in range(height):
for x in range(width):
new_image[x].append(self.data[y][x])
return np.asarray(new_image)
def flip_horizontal(self):
print("Flip Horizontal")
height = self.data_shape[0]
width = self.data_shape[1]
new_image = []
for _ in range(height):
new_image.append([])
for y in range(height):
for x in range(width):
new_image[y].append(self.data[y][-x])
return np.asarray(new_image)
def flip_vertical(self):
print("Flip Vertical")
height = self.data_shape[0]
width = self.data_shape[1]
new_image = []
for _ in range(height):
new_image.append([])
for y in range(height):
for x in range(width):
new_image[y].append(self.data[-y][x])
return np.asarray(new_image)
def move_horizontal(self, step=0):
print("Move Horizontal")
height = self.data_shape[0]
width = self.data_shape[1]
new_image = []
for _ in range(height):
new_image.append([])
for y in range(height):
for x in range(width):
new_image[y].append(self.data[y][(x-step)%width])
return np.asarray(new_image)
def move_vertical(self, step=0):
print("Move Vertical")
height = self.data_shape[0]
width = self.data_shape[1]
new_image = []
for _ in range(height):
new_image.append([])
for y in range(height):
for x in range(width):
new_image[y].append(self.data[(y-step)%height][x])
return np.asarray(new_image)
def save_image(self, new_image):
self.image = Image.fromarray(new_image)
self.data = np.asarray(self.image)
self.data_shape = self.data.shape
#self.image.save(path)
plt.imshow(self.image)
plt.show()
# --- main ---
path = "/home/furas/test/image.jpg"
image = Image.open(path)
image = ImageProcesser(image, path)
image.save_image(image.rotate_clockwise())
image.save_image(image.rotate_clockwise())
image.save_image(image.rotate_counter())
image.save_image(image.rotate_counter())
image.save_image(image.transpose())
image.save_image(image.transpose())
image.save_image(image.flip_horizontal())
image.save_image(image.flip_horizontal())
image.save_image(image.flip_vertical())
image.save_image(image.flip_vertical())
image.save_image(image.move_horizontal(100))
image.save_image(image.move_horizontal(-200))
image.save_image(image.move_horizontal(100))
image.save_image(image.move_vertical(50))
image.save_image(image.move_vertical(-100))
image.save_image(image.move_vertical(50))
PEP 8——Python代码的样式指南