如何裁剪图像，就像我以前在PIL中所做的那样，使用OpenCV。
PIL工作示例
im = Image.open('0.png').convert('L')
im = im.crop((1, 1, 98, 33))
im.save('_0.png')
但是我怎么在OpenCV上做呢?
这就是我所尝试的:
im = cv.imread('0.png', cv.CV_LOAD_IMAGE_GRAYSCALE)
(thresh, im_bw) = cv.threshold(im, 128, 255, cv.THRESH_OTSU)
im = cv.getRectSubPix(im_bw, (98, 33), (1, 1))
cv.imshow('Img', im)
cv.waitKey(0)
但这并不奏效。
我想我错误地使用了getRectSubPix。如果是这样，请解释我如何正确使用这个功能。
当前回答
下面是一些更健壮的收割代码(有点像matlab)
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 = np.pad(img, ((np.abs(np.minimum(0, y1)), np.maximum(y2 - img.shape[0], 0)),
(np.abs(np.minimum(0, x1)), np.maximum(x2 - img.shape[1], 0)), (0,0)), mode="constant")
y1 += np.abs(np.minimum(0, y1))
y2 += np.abs(np.minimum(0, y1))
x1 += np.abs(np.minimum(0, x1))
x2 += np.abs(np.minimum(0, x1))
return img, x1, x2, y1, y2
其他回答
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
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。
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
为了让你更容易，这里是我使用的代码:
top=514
right=430
height= 40
width=100
croped_image = image[top : (top + height) , right: (right + width)]
plt.imshow(croped_image, cmap="gray")
plt.show()
# 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()
