在 python 中使用 PIL 修剪图像中的白色 space
Trimming the white space in an image using PIL in python
我正在使用 SciKit-learn 进行手写数字识别 所以我需要裁剪点击的图片所以我准备了一个Word 上的模板。
现在我想要的是沿着边界裁剪图像,以便我可以进一步裁剪它以提取数字。
示例图片如下:
为了裁剪图像,我正在使用 this 代码。
下面是裁剪了上述矩形的父图像:
Note: The parent image has a border too(which is not visible in the image) so trimming the white space might help in getting a modified parent image so that predefined (height, width) would be almost same for various crops to be done on the image.
您可以应用此管道:转换为灰度 -> 应用阈值(转换为白色和黑色)-> 查找轮廓 -> 选择正确形状的轮廓。
示例代码如下:
#!/usr/bin/env python
import cv2
BLACK_THRESHOLD = 200
THIN_THRESHOLD = 10
ANNOTATION_COLOUR = (222,0,222)
img = cv2.imread('template.png')
orig = img.copy()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, thresh=BLACK_THRESHOLD, maxval=255, type=cv2.THRESH_BINARY_INV)[1]
# Optional: save thesholded image
cv2.imwrite("temp_thres.png", thresh)
# Find contours on the thresholded image
contours = cv2.findContours(thresh,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[1]
for cont in contours:
# Find bounding rectangle of a contour
x,y,w,h = cv2.boundingRect(cont)
# Skip thin contours (vertical and horizontal lines)
if h<THIN_THRESHOLD or w<THIN_THRESHOLD:
continue
# Does the countour has the right shape (roughly four times longer than high)?
if 3*h<w<5*h:
roi = orig[y:y+h,x:x+w]
cv2.imwrite("four_letters.png",roi)
# Optional: draw annotations
cv2.rectangle(img,(x,y),(x+w,y+h),ANNOTATION_COLOUR,3)
# Optional: save annotated image
cv2.imwrite("temp_cont.png",img)
(您可以删除三个可选步骤。它们仅用于生成图像 temp_thres.png
和 temp_cont.png
。)
输入图片template.png
:
阈值图像 temp_thres.png
:
找到等高线temp_cont.png
:
四个字母 space four_letters.png
:
我正在使用 SciKit-learn 进行手写数字识别 所以我需要裁剪点击的图片所以我准备了一个Word 上的模板。
现在我想要的是沿着边界裁剪图像,以便我可以进一步裁剪它以提取数字。
示例图片如下:
为了裁剪图像,我正在使用 this 代码。
下面是裁剪了上述矩形的父图像:
Note: The parent image has a border too(which is not visible in the image) so trimming the white space might help in getting a modified parent image so that predefined (height, width) would be almost same for various crops to be done on the image.
您可以应用此管道:转换为灰度 -> 应用阈值(转换为白色和黑色)-> 查找轮廓 -> 选择正确形状的轮廓。
示例代码如下:
#!/usr/bin/env python
import cv2
BLACK_THRESHOLD = 200
THIN_THRESHOLD = 10
ANNOTATION_COLOUR = (222,0,222)
img = cv2.imread('template.png')
orig = img.copy()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, thresh=BLACK_THRESHOLD, maxval=255, type=cv2.THRESH_BINARY_INV)[1]
# Optional: save thesholded image
cv2.imwrite("temp_thres.png", thresh)
# Find contours on the thresholded image
contours = cv2.findContours(thresh,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[1]
for cont in contours:
# Find bounding rectangle of a contour
x,y,w,h = cv2.boundingRect(cont)
# Skip thin contours (vertical and horizontal lines)
if h<THIN_THRESHOLD or w<THIN_THRESHOLD:
continue
# Does the countour has the right shape (roughly four times longer than high)?
if 3*h<w<5*h:
roi = orig[y:y+h,x:x+w]
cv2.imwrite("four_letters.png",roi)
# Optional: draw annotations
cv2.rectangle(img,(x,y),(x+w,y+h),ANNOTATION_COLOUR,3)
# Optional: save annotated image
cv2.imwrite("temp_cont.png",img)
(您可以删除三个可选步骤。它们仅用于生成图像 temp_thres.png
和 temp_cont.png
。)
输入图片template.png
:
阈值图像 temp_thres.png
:
找到等高线temp_cont.png
:
四个字母 space four_letters.png
: