通过使用这个函数，你可以很容易地裁剪图像

def cropImage(Image, XY: tuple, WH: tuple, returnGrayscale=False):
    # Extract the x,y and w,h values
    (x, y) = XY
    (w, h) = WH
    # Crop Image with numpy splitting
    crop = Image[y:y + h, x:x + w]
    # Check if returnGrayscale Var is true if is then convert image to grayscale
    if returnGrayscale:
        crop = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)
    # Return cropped image
    return crop

希望这能有所帮助

下面是裁剪图像的方法。

image_path:要编辑的图像的路径

坐标:x/y坐标(x1, y1, x2, y2)的元组[打开图像在 Mspaint和检查“标尺”在视图选项卡查看坐标]

saved_location:保存裁剪图像的路径

from PIL import Image
    def crop(image_path, coords, saved_location:
        image_obj = Image.open("Path of the image to be cropped")
            cropped_image = image_obj.crop(coords)
            cropped_image.save(saved_location)
            cropped_image.show()


if __name__ == '__main__':
    image = "image.jpg"
    crop(image, (100, 210, 710,380 ), 'cropped.jpg')

注意，图像切片不是创建裁剪图像的副本，而是创建一个指向roi的指针。如果加载这么多图像，用切片裁剪图像的相关部分，然后追加到一个列表中，这可能是巨大的内存浪费。

假设你加载N张图片，每张图片为>1MP，你只需要左上角100x100的区域。

切片:

X = []
for i in range(N):
    im = imread('image_i')
    X.append(im[0:100,0:100]) # This will keep all N images in the memory. 
                              # Because they are still used.

或者，你可以通过.copy()复制相关部分，这样垃圾收集器就会删除im。

X = []
for i in range(N):
    im = imread('image_i')
    X.append(im[0:100,0:100].copy()) # This will keep only the crops in the memory. 
                                     # im's will be deleted by gc.

在发现这一点后，我意识到user1270710的一个评论提到了这一点，但我花了很长时间才发现(即调试等)。所以，我认为值得一提。

健壮的农作物与opencv复制边界功能:

def imcrop(img, bbox):
   x1, y1, x2, y2 = bbox
   if x1 < 0 or y1 < 0 or x2 > img.shape[1] or y2 > img.shape[0]:
        img, x1, x2, y1, y2 = pad_img_to_fit_bbox(img, x1, x2, y1, y2)
   return img[y1:y2, x1:x2, :]

def pad_img_to_fit_bbox(img, x1, x2, y1, y2):
    img = cv2.copyMakeBorder(img, - min(0, y1), max(y2 - img.shape[0], 0),
                            -min(0, x1), max(x2 - img.shape[1], 0),cv2.BORDER_REPLICATE)
   y2 += -min(0, y1)
   y1 += -min(0, y1)
   x2 += -min(0, x1)
   x1 += -min(0, x1)
   return img, x1, x2, y1, y2

# Import packages
import cv2

import numpy as np
img = cv2.imread('skewness.png')
print(img.shape) # Print image shape

cv2.imshow("original", img)

# Cropping an image
cropped_image = img[80:280, 150:330]
 
# Display cropped image
cv2.imshow("cropped", cropped_image)

# Save the cropped image
cv2.imwrite("Cropped Image.jpg", cropped_image)

#The function waitKey waits for a key event infinitely (when \f$\texttt{delay}\leq 0\f$ ) or for delay milliseconds, when it is positive
cv2.waitKey(0)

#The function destroyAllWindows destroys all of the opened HighGUI windows.
cv2.destroyAllWindows()

或者，你可以使用tensorflow进行裁剪，使用openCV从图像中生成数组。

import cv2
img = cv2.imread('YOURIMAGE.png')

img是一个(imageheight, imagewidth, 3)形状数组。用tensorflow裁剪数组:

import tensorflow as tf
offset_height=0
offset_width=0
target_height=500
target_width=500
x = tf.image.crop_to_bounding_box(
    img, offset_height, offset_width, target_height, target_width
)

用tf重新组装图像。Keras，所以我们可以看看它是否有效:

tf.keras.preprocessing.image.array_to_img(
    x, data_format=None, scale=True, dtype=None
)

这将在笔记本中打印出图片(在谷歌Colab中测试)。

整个代码放在一起:

import cv2
img = cv2.imread('YOURIMAGE.png')

import tensorflow as tf
offset_height=0
offset_width=0
target_height=500
target_width=500
x = tf.image.crop_to_bounding_box(
    img, offset_height, offset_width, target_height, target_width
)

tf.keras.preprocessing.image.array_to_img(
    x, data_format=None, scale=True, dtype=None
)