Python 查找图像内的边（矩形边框）
我在背景上有一张便笺的图像（比如墙壁或笔记本电脑），我想检测便笺的边缘（粗略检测也可以），这样我就可以在上面进行裁剪 我计划使用ImageMagick进行实际裁剪，但我一直在检测边缘 理想情况下，我的输出应该为4个边界点提供4个坐标，以便我可以在其上运行裁剪 我应该如何进行这项工作Python 查找图像内的边（矩形边框）,python,imagemagick,image-manipulation,edge-detection,Python,Imagemagick,Image Manipulation,Edge Detection,我在背景上有一张便笺的图像（比如墙壁或笔记本电脑），我想检测便笺的边缘（粗略检测也可以），这样我就可以在上面进行裁剪 我计划使用ImageMagick进行实际裁剪，但我一直在检测边缘 理想情况下，我的输出应该为4个边界点提供4个坐标，以便我可以在其上运行裁剪 我应该如何进行这项工作 您可以使用ImageMagick实现这一点 人们可以想出不同的即时通讯方法。这是我想到的第一个算法。它假设“便笺”在较大的图像上没有倾斜或旋转： 第一阶段：使用canny边缘检测来显示便笺的边缘 第二阶段：确定边的坐
您可以使用ImageMagick实现这一点 人们可以想出不同的即时通讯方法。这是我想到的第一个算法。它假设“便笺”在较大的图像上没有倾斜或旋转：
convert \
http://i.stack.imgur.com/SxrwG.png \
-canny 0x1+10%+30% \
canny-edges.png
XxY1xYXx14xYXx4identify -format " %W x %H\n" canny-edges.png
400x300
convert canny-edges.png -resize 400x4\! canny-4cols.png
convert canny-edges.png -resize 4x300\! canny-4rows.png
canny-4rows.pngconvert canny-edges.png -resize 400x1\! canny-1col.txt
convert canny-edges.png -resize 1x300\! canny-1row.txt
# ImageMagick pixel enumeration: 400,1,255,gray
0,0: (0,0,0) #000000 gray(0)
1,0: (0,0,0) #000000 gray(0)
2,0: (0,0,0) #000000 gray(0)
[....]
73,0: (0,0,0) #000000 gray(0)
74,0: (0,0,0) #000000 gray(0)
75,0: (10,10,10) #0A0A0A gray(10)
76,0: (159,159,159) #9F9F9F gray(159)
77,0: (21,21,21) #151515 gray(21)
78,0: (156,156,156) #9C9C9C gray(156)
79,0: (14,14,14) #0E0E0E gray(14)
80,0: (3,3,3) #030303 gray(3)
81,0: (3,3,3) #030303 gray(3)
[....]
162,0: (3,3,3) #030303 gray(3)
163,0: (4,4,4) #040404 gray(4)
164,0: (10,10,10) #0A0A0A gray(10)
165,0: (7,7,7) #070707 gray(7)
166,0: (8,8,8) #080808 gray(8)
167,0: (8,8,8) #080808 gray(8)
168,0: (8,8,8) #080808 gray(8)
169,0: (9,9,9) #090909 gray(9)
170,0: (7,7,7) #070707 gray(7)
171,0: (10,10,10) #0A0A0A gray(10)
172,0: (5,5,5) #050505 gray(5)
173,0: (13,13,13) #0D0D0D gray(13)
174,0: (6,6,6) #060606 gray(6)
175,0: (10,10,10) #0A0A0A gray(10)
176,0: (10,10,10) #0A0A0A gray(10)
177,0: (7,7,7) #070707 gray(7)
178,0: (8,8,8) #080808 gray(8)
[....]
319,0: (3,3,3) #030303 gray(3)
320,0: (3,3,3) #030303 gray(3)
321,0: (14,14,14) #0E0E0E gray(14)
322,0: (156,156,156) #9C9C9C gray(156)
323,0: (21,21,21) #151515 gray(21)
324,0: (159,159,159) #9F9F9F gray(159)
325,0: (10,10,10) #0A0A0A gray(10)
326,0: (0,0,0) #000000 gray(0)
327,0: (0,0,0) #000000 gray(0)
[....]
397,0: (0,0,0) #000000 gray(0)
398,0: (0,0,0) #000000 gray(0)
399,0: (0,0,0) #000000 gray(0)
-threshold 50%convert canny-edges.png -resize 400x1\! -threshold 50% canny-1col.txt
convert canny-edges.png -resize 1x300\! -threshold 50% canny-1row.txt
convert canny-edges.png -resize 400x1\! -threshold 50% txt:- \
| grep -v black
# ImageMagick pixel enumeration: 400,1,255,srgb
76,0: (255,255,255) #FFFFFF white
78,0: (255,255,255) #FFFFFF white
322,0: (255,255,255) #FFFFFF white
324,0: (255,255,255) #FFFFFF white
convert canny-edges.png -resize 1x300\! -threshold 50% txt:- \
| grep -v black
# ImageMagick pixel enumeration: 1,300,255,srgb
0,39: (255,255,255) #FFFFFF white
0,41: (255,255,255) #FFFFFF white
0,229: (255,255,255) #FFFFFF white
0,231: (255,255,255) #FFFFFF white
（323 | 40）（77 | 230）convert http://i.stack.imgur.com/SxrwG.png[246x190+77+40] sticky-note.png
autotraceautotraceconvert \
canny-edges.png \
-background black \
-stroke red \
-hough-lines 5x5+20 \
lines.png
-hough lines-hough linescat lines.mvg \
| while read a b c d e ; do \
if [ x${b/0,/} == x${c/400,/} ]; then \
echo "$a $b $c $d $e" ; \
fi; \
done
line 0,39.5 400,39.5 # 249
line 0,62.5 400,62.5 # 48
line 0,71.5 400,71.5 # 52
line 0,231.5 400,231.5 # 249
cat lines.mvg \
| while read a b c d e; do \
if [ x${b/,0/} == x${c/,300} ]; then \
echo "$a $b $c $d $e" ; \
fi; \
done
line 76.5,0 76.5,300 # 193
line 324.5,0 324.5,300 # 193
上周我遇到了检测图像边界（空白）的类似问题，花了很多时间尝试各种方法和工具，之后最终使用熵差计算方法解决了这个问题，所以这里是JFYI算法 假设要检测200x100px图像的顶部是否有边框：
import numpy as np
MEDIAN = 0.5
def scan(im):
w, h = im.size
array = np.array(im)
center_ = None
diff_ = None
for center in reversed(range(1, h // 4 + 1)):
upper = entropy(array[0: center, 0: w].flatten())
lower = entropy(array[center: 2 * center, 0: w].flatten())
diff = upper / lower if lower != 0.0 else MEDIAN
if center_ is None or diff_ is None:
center_ = center
diff_ = diff
if diff < diff_:
center_ = center
diff_ = diff
top = diff_ < MEDIAN and center_ < h // 4, center_, diff_
将numpy导入为np
中位数=0.5
def扫描（im）：
w、 h=im尺寸
数组=np.数组（im）
中心=无
差异=无
对于反向中心（范围（1，h//4+1））：
上限=熵（数组[0:cent
import numpy as np
MEDIAN = 0.5
def scan(im):
w, h = im.size
array = np.array(im)
center_ = None
diff_ = None
for center in reversed(range(1, h // 4 + 1)):
upper = entropy(array[0: center, 0: w].flatten())
lower = entropy(array[center: 2 * center, 0: w].flatten())
diff = upper / lower if lower != 0.0 else MEDIAN
if center_ is None or diff_ is None:
center_ = center
diff_ = diff
if diff < diff_:
center_ = center
diff_ = diff
top = diff_ < MEDIAN and center_ < h // 4, center_, diff_