blocks|key|351716|text|这很简单。使用numpy切片。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|351717|import+cv2
img+=+cv2.imread("lenna.png")
crop_img+=+img[y:y%2Bh,+x:x%2Bw]
cv2.imshow("cropped",+crop_img)
cv2.waitKey(0)|code-block|syntax|javascript|351718|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

It's very simple. Use numpy slicing.

<pre><code>import cv2
img = cv2.imread("lenna.png")
crop_img = img[y:y+h, x:x+w]
cv2.imshow("cropped", crop_img)
cv2.waitKey(0)
</code></pre>

blocks|key|351761|text|我有这个问题，并在这里找到了另一个答案：copy+region+of+interest|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|351762|如果我们考虑(0,0)作为图像的左上角，称为im，从左到右是x方向，从上到下是y方向。我们将(x1，y1)作为该图像中矩形区域的左上角顶点和(x2，y2)作为右下角顶点，然后：|style|CODE|351763|roi+=+im[y1:y2,+x1:x2]|code-block|syntax|javascript|351764|这里有一个关于numpy+array+indexing+and+slicing的综合资源，它可以告诉你更多关于裁剪图像部分的事情。图像将被存储为opencv2中的numpy数组。|351765|:)|351766|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/9084609/how-to-copy-a-image-region-using-opencv-in-python|1|https://web.archive.org/web/20171229114756/https://docs.scipy.org/doc/numpy-dev/user/quickstart.html^0|K|N|0|0|M|2|0|0|7|W|1|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@$A|11|B|12|1|13]]|C|$]]|$1|D|3|E|5|6|7|14|8|@$A|15|B|16|F|G]]|9|@]|C|$]]|$1|H|3|I|5|J|7|17|8|@]|9|@]|C|$K|L]]|$1|M|3|N|5|6|7|18|8|@]|9|@$A|19|B|1A|1|1B]]|C|$]]|$1|O|3|P|5|6|7|1C|8|@]|9|@]|C|$]]|$1|Q|3|-4|5|6|7|1D|8|@]|9|@]|C|$]]]|R|$S|$5|T|U|V|C|$W|X]]|Y|$5|T|U|V|C|$W|Z]]]]

i had this question and found another answer here: <a href="https://stackoverflow.com/questions/9084609/how-to-copy-a-image-region-using-opencv-in-python">copy region of interest</a>

If we consider (0,0) as top left corner of image called <code>im</code> with left-to-right as x direction and top-to-bottom as y direction. and we have (x1,y1) as the top-left vertex and (x2,y2) as the bottom-right vertex of a rectangle region within that image, then:

<pre><code>roi = im[y1:y2, x1:x2]
</code></pre>

here is a comprehensive resource on <a href="https://web.archive.org/web/20171229114756/https://docs.scipy.org/doc/numpy-dev/user/quickstart.html" rel="noreferrer">numpy array indexing and slicing</a> which can tell you more about things like cropping a part of an image. images would be stored as a numpy array in opencv2.

:)

blocks|key|133944|text|这段代码将图像从x=0，y=0裁剪到h=100，w=200。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|133945|import+numpy+as+np
import+cv2

image+=+cv2.imread('download.jpg')
y=0
x=0
h=100
w=200
crop+=+image[y:y%2Bh,+x:x%2Bw]
cv2.imshow('Image',+crop)
cv2.waitKey(0)+|code-block|syntax|javascript|133946|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

This code crops an image from x=0,y=0 to h=100,w=200.
<pre><code>import numpy as np
import cv2

image = cv2.imread('download.jpg')
y=0
x=0
h=100
w=200
crop = image[y:y+h, x:x+w]
cv2.imshow('Image', crop)
cv2.waitKey(0) 
</code></pre>

blocks|key|351821|text|请注意，图像切片不是创建cropped+image的副本，而是创建roi的pointer。如果您加载了如此多的图像，使用切片来裁剪图像的相关部分，然后将其添加到列表中，这可能是巨大的内存浪费。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|351822|假设您加载了N个图像，每个图像都是>1MP，并且您只需要从左上角开始使用100x100区域。|351823|Slicing|351824|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.|code-block|syntax|javascript|351825|或者，您可以通过.copy()复制相关部分，这样垃圾收集器将删除im。|351826|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.|351827|在发现这一点后，我意识到user1270710提到了one+of+the+comments，但我花了相当长的时间才找到(即调试等)。所以，我认为值得一提的是。|351828|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/users/1270710/user1270710|1|https://stackoverflow.com/questions/15589517/how-to-crop-an-image-in-opencv-using-python#comment92938795_15589825^0|C|D|X|3|11|7|0|H|4|10|7|0|0|7|0|0|8|7|W|2|0|0|C|B|0|Q|J|1|0^^$0|@$1|2|3|4|5|6|7|14|8|@$9|15|A|16|B|C]|$9|17|A|18|B|C]|$9|19|A|1A|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|1B|8|@$9|1C|A|1D|B|C]|$9|1E|A|1F|B|C]]|D|@]|E|$]]|$1|H|3|I|5|6|7|1G|8|@$9|1H|A|1I|B|C]]|D|@]|E|$]]|$1|J|3|K|5|L|7|1J|8|@]|D|@]|E|$M|N]]|$1|O|3|P|5|6|7|1K|8|@$9|1L|A|1M|B|C]|$9|1N|A|1O|B|C]]|D|@]|E|$]]|$1|Q|3|R|5|L|7|1P|8|@]|D|@]|E|$M|N]]|$1|S|3|T|5|6|7|1Q|8|@]|D|@$9|1R|A|1S|1|1T]|$9|1U|A|1V|1|1W]]|E|$]]|$1|U|3|-4|5|6|7|1X|8|@]|D|@]|E|$]]]|V|$W|$5|X|Y|Z|E|$10|11]]|12|$5|X|Y|Z|E|$10|13]]]]

Note that, image slicing is not creating a copy of the <code>cropped image</code> but creating a <code>pointer</code> to the <code>roi</code>. If you are loading so many images, cropping the relevant parts of the images with slicing, then appending into a list, this might be a huge memory waste. 

Suppose you load N images each is <code>&gt;1MP</code> and you need only <code>100x100</code> region from the upper left corner. 

<code>Slicing</code>:

<pre><code>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.
</code></pre>

Alternatively, you can copy the relevant part by <code>.copy()</code>, so garbage collector will remove <code>im</code>.

<pre><code>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.
</code></pre>

After finding out this, I realized <a href="https://stackoverflow.com/questions/15589517/how-to-crop-an-image-in-opencv-using-python#comment92938795_15589825">one of the comments</a> by <a href="https://stackoverflow.com/users/1270710/user1270710">user1270710</a> mentioned that but it took me quite some time to find out (i.e., debugging etc). So, I think it worths mentioning.

blocks|key|351807|text|具有opencv复制边框功能的强大裁剪功能：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|351808|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+%2B=+-min(0,+y1)
+++y1+%2B=+-min(0,+y1)
+++x2+%2B=+-min(0,+x1)
+++x1+%2B=+-min(0,+x1)
+++return+img,+x1,+x2,+y1,+y2|code-block|syntax|javascript|351809|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Robust crop with opencv copy border function:

<pre><code>def imcrop(img, bbox):
 x1, y1, x2, y2 = bbox
 if x1 &lt; 0 or y1 &lt; 0 or x2 &gt; img.shape[1] or y2 &gt; 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
</code></pre>

blocks|key|133874|text|下面是一些更健壮的imcrop代码(有点像matlab中的代码)|type|unstyled|depth|inlineStyleRanges|entityRanges|data|133875|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+%2B=+np.abs(np.minimum(0,+y1))
++++y2+%2B=+np.abs(np.minimum(0,+y1))
++++x1+%2B=+np.abs(np.minimum(0,+x1))
++++x2+%2B=+np.abs(np.minimum(0,+x1))
++++return+img,+x1,+x2,+y1,+y2|code-block|syntax|javascript|133876|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

here is some code for more robust imcrop ( a bit like in matlab )

<pre><code>def imcrop(img, bbox): 
 x1,y1,x2,y2 = bbox
 if x1 &lt; 0 or y1 &lt; 0 or x2 &gt; img.shape[1] or y2 &gt; 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
</code></pre>

blocks|key|133960|text|为了让您更容易，这里是我使用的代码：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|133961|w,+h+=+image.shape
top=10
right=50
down=15
left=80
croped_image+=+image[top:((w-down)%2Btop),+right:((h-left)%2Bright)]
plt.imshow(croped_image,+cmap="gray")
plt.show()|code-block|syntax|javascript|133962|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

to make it easier for you here is the code that i use :
<pre><code>w, h = image.shape
top=10
right=50
down=15
left=80
croped_image = image[top:((w-down)+top), right:((h-left)+right)]
plt.imshow(croped_image, cmap=&quot;gray&quot;)
plt.show()
</code></pre>

blocks|key|351879|text|裁剪或感兴趣的区域(ROI)以供以下代码的面部使用|type|unstyled|depth|inlineStyleRanges|entityRanges|data|351880|import+cv2+
face_cascade+=+cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
image=cv2.imread("ronaldo.jpg")
gray+=+cv2.cvtColor(image,+cv2.COLOR_BGR2GRAY)
faces+=+face_cascade.detectMultiScale(gray,+1.3,+5)
for+(x,y,w,h)+in+faces:
+++++cv2.rectangle(img,(x,y),(x%2Bw,y%2Bh),(255,255,0),2)+
+++++roi_image+=+gray[y:y%2Bh,+x:x%2Bw]
cv2.imshow("crop/region+of+interset+image",roi_image)+
cv2.waitKey(0)
cv2.destroyAllWindows()|code-block|syntax|javascript|351881|check+for+reference|offset|length|351882|entityMap|0|LINK|mutability|MUTABLE|url|https://www.geeksforgeeks.org/opencv-python-program-face-detection/^0|0|0|0|J|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@]|9|@$I|V|J|W|1|X]]|A|$]]|$1|K|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|L|$M|$5|N|O|P|A|$Q|R]]]]

to crop or region of interest(ROI) for face use below code
<pre><code>import cv2 
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
image=cv2.imread(&quot;ronaldo.jpg&quot;)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in faces:
 cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2) 
 roi_image = gray[y:y+h, x:x+w]
cv2.imshow(&quot;crop/region of interset image&quot;,roi_image) 
cv2.waitKey(0)
cv2.destroyAllWindows()
</code></pre>
<a href="https://www.geeksforgeeks.org/opencv-python-program-face-detection/" rel="nofollow noreferrer">check for reference</a>

blocks|key|351884|text|#+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()|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|351885|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code># Import packages
import cv2

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

cv2.imshow(&quot;original&quot;, img)

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

# Save the cropped image
cv2.imwrite(&quot;Cropped Image.jpg&quot;, 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()
</code></pre>

How can I crop images, like I've done before in PIL, using OpenCV. 

Working example on PIL

<pre><code>im = Image.open('0.png').convert('L')
im = im.crop((1, 1, 98, 33))
im.save('_0.png')
</code></pre>

But how I can do it on OpenCV? 

This is what I tried:

<pre><code>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)
</code></pre>

But it doesn't work.

I think I incorrectly used <code>getRectSubPix</code>. If this is the case, please explain how I can correctly use this function.

How to crop an image in OpenCV using Python

如何使用OpenCV裁剪图像，就像我以前在PIL中所做的那样。PIL上的工作示例im = Image.open('0.png').convert('L')im = im.crop((1, 1, 98, 33))im.save('_0.png')但是我如何在OpenCV上做到这一点呢？这是我尝试过的：im = cv.imread('0.png', cv.CV_LOAD_IMAGE_GRAYSCALE

问如何使用Python在OpenCV中裁剪图像
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回答 9

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问如何使用Python在OpenCV中裁剪图像
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