Question

我正在构建算法，以从相同大小的瓦片数量自动解决/重建QR码。

我的方法：

获取3个正方形之一：位置正方形
使用ImageMagic以某种方式获取外部黑色边框的宽度

enter image description here

给出的例子是14px x 14px （更新：可能是13px×13px）

将其设为常量：BLOCK_SIZE
开始循环匹配VALID磁贴和已编译的QR码块

VALID是：不匹配且不是任何3个角落瓷砖（大黑方块）

4.1。渲染已编译的qr块和1个随机有效磁贴的所有可能组合，直到找到匹配为止

4.2如果找到匹配（可以通过已编译/组合的QR码块的一侧放置图块），则记录匹配

4.3匹配条件：

没有黑色区域的宽度和高度符合这个等式：

AREA_HEIGHT % BLOCK_SIZE  == 0  && AREA_WIDTH % BLOCK_SIZE  == 0

现在出现了一些实施问题：

如何实现匹配tile到tile的算法（或者已编译/组合的QR码块）？

我认为ImageMagic可以解决它。 目标是获取原始二维码。

Example QR tiles in ZIP

更新

正如我所看到的那样，解决方案会缩小以进行ImageMagick扫描并查找是否可以将两个图块放在一起而不会破坏QR码的规则。算法应该尝试并排放置2个图块并扫描边框区域。诀窍是测量连接瓷砖的黑线/块的宽度和高度。怎么做？

UPDATE2

在某些情况下，如果使用2列宽度为1px的瓷砖匹配，则进行比较是不够的。例如，两种算法都将这2个图块连接到：

VALID **VALID**

和

INVALID **INVALID**

正如您在 INVALID 中看到的那样，第二个瓷砖的一部分（右边的一个）被红色箭头取消了，但由于通过1px进行比较 - 算法不关心abuot这个案例。 Hance无效QR。

UPDATE3

http://dropcanvas.com/6bl6v

存档包含：

无效的join.png
有效的join.png
src1.png
SRC2-invalid.png
SRC2-valid.png
invalid.png

如果您的算法将加入src1.png和src2-invalid.png，则会失败。这是关于连接的瓷砖最终会产生有效的QR。我希望你明白我的观点。

UPDATE4

我在测试并获得正确的解决方案后会接受答案。可能需要一些时间......

更新5：测试

Mark的解决方案似乎都是正确的。

Answer 1

我试过这个如下。

浏览所有PNG并提取其北，东，南和西边缘。如果任何PNG有两个白边，告诉用户它是一个角落。如果任何PNG有一个白边，告诉用户它是一个边缘。
浏览所有边缘部分。将每个转换为PBM格式文件并剥离标题和新行，使其只是一个80位长的字符串，每个数字为零或一个为黑色或白色。计算字符串的校验和，然后反转字符串以考虑正在翻转或旋转的图像，然后重新计算校验和。打印出校验和并输入sort，以便匹配的边缘一起出现。

以下是代码：

#!/bin/bash

# Remove any edges generated in previous runs of this script
rm *_[NESW]*png 2> /dev/null
rm *_[NESW]*txt 2> /dev/null

DEBUG=1

# Process all PNGs
for f in *.png; do

   echo "Processing $f ..."

   # Get basename of image
   base=$(basename -s .png "$f")

   # Get width and height - not actually used at the moment
   read w h <<< $(identify -format "%w %h" "$f")
   [ $DEBUG -gt 0 ] && echo "   width:$w"
   [ $DEBUG -gt 0 ] && echo "   height:$h"

   # Extract North edge
   convert "$f" +repage -crop x1+0+0 +repage "${base}_N.png"
   [ $DEBUG -gt 0 ] && echo "   North edge extracted"

   # Extract East edge
   convert "$f" +repage -gravity east -crop 1x+0+0 -rotate 90 +repage "${base}_E.png"
   [ $DEBUG -gt 0 ] && echo "   East edge extracted"

   # Extract South edge
   convert "$f" +repage -gravity south -crop x1+0+0 +repage "${base}_S.png"
   [ $DEBUG -gt 0 ] && echo "   South edge extracted"

   # Extract West edge
   convert "$f" +repage -gravity west -crop 1x+0+0 -rotate 90 +repage "${base}_W.png"
   [ $DEBUG -gt 0 ] && echo "   West edge extracted"

   # Test if corner or edge piece
   n=0
   for edge in N E S W; do
      name="${base}_${edge}.png"
      min=$(identify -format "%[min]" "$name")
      if [ $min -gt 0 ]; then
         ((n++))
         e=$name
      fi
   done
   [ $n -eq 1 ] && echo "   $e is edge-piece"
   [ $n -eq 2 ] && echo "   $name is corner-piece"
done

EDITED FROM HERE ---
# Now convert all edges to text, forwards and backwards to allow rotation
for f in *_[NESW].png; do
   base=$(basename -s png "$f")
   # Convert to PBM format, remove 2 header lines and make into one line string
   str=$(convert "$f" -compress none pbm: | sed "1,2d" | tr -d "\n ")
   echo "$str:$f"
   str=$(rev <<< $str)
   echo "$str:$f (flipped)"
done | sort

部分输出（以节省空间）

Processing 4555-18116-29.png ...
   width:80
   height:80
   North edge extracted
   East edge extracted
   South edge extracted
   West edge extracted
   4555-18116-29_S.png is edge-piece
Processing 5004-10810-17642.png ...
   width:80
   height:80
   North edge extracted
   East edge extracted
   South edge extracted
   West edge extracted
   5004-10810-17642_W.png is corner-piece
Processing 5167-27533-24066.png ...
   width:80
   height:80
   North edge extracted
   East edge extracted
   South edge extracted
   West edge extracted
Processing 5774-30645-16062.png ...
   width:80
   height:80
   North edge extracted
   East edge extracted
   South edge extracted
   West edge extracted
0a7bb6f610c0f1a6da4794ea7ae00f00:10297-13918-3702_W.png (flipped)
0a7bb6f610c0f1a6da4794ea7ae00f00:11976-7751-26756_E.png (flipped)   <-- this image is identical to the one above as the md5 checksum on the left is the same
0ce419e072c7ea5d14e3525d4afe150e:11976-7751-26756_W.png (flipped)
0ce419e072c7ea5d14e3525d4afe150e:13858-18007-13070_E.png (flipped)  <-- this image is identical to the one above as the md5 checksum on the left is the same
0ce419e072c7ea5d14e3525d4afe150e:20056-20936-29071_S.png
0ce419e072c7ea5d14e3525d4afe150e:24658-20374-23042_E.png (flipped)
0ce419e072c7ea5d14e3525d4afe150e:24658-20374-23042_S.png
0ce419e072c7ea5d14e3525d4afe150e:27206-10104-18226_N.png
0ce419e072c7ea5d14e3525d4afe150e:30261-16558-25650_N.png

备注

请注意，这假设您没有在各个＆＃34;字段之间完全平铺图像＆＃34;由@YvesDaoust确定的QR码。

此外，代码的某些部分是冗余的，因为我在编写代码的同时开发算法 - 我认为-rotates和+repages是不必要的，事情可以更优化但是这个问题没有明确的速度需求。北，东，南和西边缘也可以在单个IM命令中提取。我获得的宽度和高度未使用，因此可以从代码中删除。

此外，md5校验和不是必需的，sort将0001110001010110放在另一个字符串旁边，无需校正它们。

根据请求，我正在上传完整的解决方案。我删除了md5校验和的东西，因为它是不必要的。您需要向右滚动才能看到下面的文件名：

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Answer 2

如果使用单个图块PNG中的所有可用信息，这个很容易：

QR code (solution)

QR读者说：http://www.oracle.com/javaone/index.html

我花了不到3分钟的时间来提出解决方案 - 主要是因为我必须用一只手同时输入相应的命令同时在手机上....

你是如何制作瓷砖的？

更新

现在@RCola终于回答了我的问题（＆＃34;你是如何制作瓷砖的？＆＃34; ），我将揭示我是如何快速地提出来的正确的二维码图像。

我没有像他要求的那样使用算法。我使用了一个＆＃34; side-channel＆＃34;攻击，绕过任何需要考虑这样的算法。

这个旁道使用了＆＃34;信息泄漏＆＃34;它包含在@ RCola的ZIP文件中提供的图块图像中。运行简单的identify命令时，此信息会泄露。在这里，我运行它不是一个简单的＆＃39;命令，但是 - 使泄露的信息更加明显 - 作为强制特定输出格式的命令：

identify -format "%f :  %g\n" {1,2,3,4,5}*.png

  10297-13918-3702.png :  400x400+160+0
  11976-7751-26756.png :  400x400+80+0
  13789-10513-4721.png :  400x400+80+160
 13858-18007-13070.png :  400x400+240+0
  15816-4564-31665.png :  400x400+160+240
  16369-21469-8252.png :  400x400+320+80
  17636-24599-1877.png :  400x400+80+240
 18056-16294-30425.png :  400x400+80+320
 20021-11440-20836.png :  400x400+240+320
 20056-20936-29071.png :  400x400+0+240
  21875-14159-1067.png :  400x400+320+240
 22161-18187-20222.png :  400x400+240+80
  22806-3380-17484.png :  400x400+0+160
  24426-18830-5627.png :  400x400+320+160
 24658-20374-23042.png :  400x400+0+0
 26507-21853-11958.png :  400x400+240+240
 27206-10104-18226.png :  400x400+0+320
 28824-13023-24184.png :  400x400+160+80
 30261-16558-25650.png :  400x400+0+80
   31250-3578-9750.png :  400x400+320+0
  32078-14314-1511.png :  400x400+160+160
     4555-18116-29.png :  400x400+160+320
  5004-10810-17642.png :  400x400+320+320
  5167-27533-24066.png :  400x400+240+160
  5774-30645-16062.png :  400x400+80+80

（相同的信息将包含在identify {1,2,3,4,5}*.png的输出中 - 它不会如此明显地跳出来。）

这表明了什么？ %g percent-escape打印有关与PNG图块关联的画布的 geometry 信息：

400x400是此图块画布的整体尺寸。很明显，这是原始PNG的大小，代表完整的QR码。
+X+Y是与整个画布上此图块关联的偏移。

使用此信息可以非常轻松地直接了解哪个PNG图块必须放置在拼图的哪个位置：

+0+0显然必须转到左上角
+0+80显然就在
+80+0显然恰好位于左上角
......等等，直到
+320+320进入右下角

OP可以通过将+repage参数添加到用于生成切片的IM命令中来避免侧通道信息泄漏（在这种情况下可以完成游戏）。

为了修复＆＃34;他提供的漏水瓷砖产生了更具挑战性的瓷砖我做了这个：

 mkdir challenge

 for i in {1,2,3,4,5}*.png; do
     convert $i +repage challenge/$i
 done

更新2

（回答关于未能及＃34;在十六进制编辑器的帮助下查看＆＃34;信息的绝望评论......）

identify显示的相同信息也可以在exiftool的帮助下获得（尽管以较少的＃34;可访问的＆＃34;形式）：

exiftool 11976-7751-26756.png  | grep -E '(Virtual Image|Width|Height|Offset)'
 Image Width                     : 80
 Image Height                    : 80
 Image Offset                    : 80, 0 (pixels)
 Virtual Image Width             : 400
 Virtual Image Height            : 400

Answer 3

这是一个更好的算法，我已编写脚本：

从每个图块中删除每个边缘的1像素行或列，并将其输出为ImageMagick的特殊*.text格式，使用指示其各自的左/右或上/下边缘原点的名称
转换每个此类列/行，创建其md5sum的{{1}}并按字母顺序对其进行排序。
确定哪些MD5总和相同。
从相同的MD5总和中确定适合各自边界的候选人。

代码段

*.txt

这样一个文本文件的外观如下：

for i in {1,2,3,4,5}*.png; do
  convert ${i}[1x80+0+0]  +repage left---edge-${i/.png/.text}
  convert ${i}[1x80+79+0] +repage right--edge-${i/.png/.text}
  convert ${i}[80x1+0+0]  +repage top----edge-${i/.png/.text}
  convert ${i}[80x1+0+79] +repage bottom-edge-${i/.png/.text}
done

如您所见，它是提取列的每个像素颜色的文本描述（其中像素坐标在每行文本的第一个字段中给出）。第一行表示使用了哪个颜色空间。

*按字母顺序排序MD5

按字母顺序对这些文本文件的MD5总和进行排序时，如果有相同的像素颜色行和列，应该立即明显：

cat right--edge-5167-27533-24066.text

# ImageMagick pixel enumeration: 1,80,255,gray
0,0: (0,0,0)  #000000  gray(0)
0,1: (0,0,0)  #000000  gray(0)
0,2: (0,0,0)  #000000  gray(0)
0,3: (0,0,0)  #000000  gray(0)
0,4: (0,0,0)  #000000  gray(0)
0,5: (0,0,0)  #000000  gray(0)
0,6: (0,0,0)  #000000  gray(0)
0,7: (255,255,255)  #FFFFFF  gray(255)
0,8: (255,255,255)  #FFFFFF  gray(255)
0,9: (255,255,255)  #FFFFFF  gray(255)
0,10: (255,255,255)  #FFFFFF  gray(255)
0,11: (255,255,255)  #FFFFFF  gray(255)
0,12: (255,255,255)  #FFFFFF  gray(255)
0,13: (255,255,255)  #FFFFFF  gray(255)
0,14: (255,255,255)  #FFFFFF  gray(255)
0,15: (255,255,255)  #FFFFFF  gray(255)
0,16: (255,255,255)  #FFFFFF  gray(255)
0,17: (255,255,255)  #FFFFFF  gray(255)
0,18: (255,255,255)  #FFFFFF  gray(255)
0,19: (255,255,255)  #FFFFFF  gray(255)
0,20: (255,255,255)  #FFFFFF  gray(255)
0,21: (255,255,255)  #FFFFFF  gray(255)
0,22: (255,255,255)  #FFFFFF  gray(255)
0,23: (255,255,255)  #FFFFFF  gray(255)
0,24: (255,255,255)  #FFFFFF  gray(255)
0,25: (255,255,255)  #FFFFFF  gray(255)
0,26: (255,255,255)  #FFFFFF  gray(255)
0,27: (255,255,255)  #FFFFFF  gray(255)
0,28: (255,255,255)  #FFFFFF  gray(255)
0,29: (255,255,255)  #FFFFFF  gray(255)
0,30: (255,255,255)  #FFFFFF  gray(255)
0,31: (255,255,255)  #FFFFFF  gray(255)
0,32: (255,255,255)  #FFFFFF  gray(255)
0,33: (0,0,0)  #000000  gray(0)
0,34: (0,0,0)  #000000  gray(0)
0,35: (0,0,0)  #000000  gray(0)
0,36: (0,0,0)  #000000  gray(0)
0,37: (0,0,0)  #000000  gray(0)
0,38: (0,0,0)  #000000  gray(0)
0,39: (0,0,0)  #000000  gray(0)
0,40: (0,0,0)  #000000  gray(0)
0,41: (0,0,0)  #000000  gray(0)
0,42: (0,0,0)  #000000  gray(0)
0,43: (0,0,0)  #000000  gray(0)
0,44: (0,0,0)  #000000  gray(0)
0,45: (0,0,0)  #000000  gray(0)
0,46: (0,0,0)  #000000  gray(0)
0,47: (0,0,0)  #000000  gray(0)
0,48: (0,0,0)  #000000  gray(0)
0,49: (0,0,0)  #000000  gray(0)
0,50: (0,0,0)  #000000  gray(0)
0,51: (0,0,0)  #000000  gray(0)
0,52: (0,0,0)  #000000  gray(0)
0,53: (0,0,0)  #000000  gray(0)
0,54: (0,0,0)  #000000  gray(0)
0,55: (0,0,0)  #000000  gray(0)
0,56: (0,0,0)  #000000  gray(0)
0,57: (0,0,0)  #000000  gray(0)
0,58: (0,0,0)  #000000  gray(0)
0,59: (0,0,0)  #000000  gray(0)
0,60: (0,0,0)  #000000  gray(0)
0,61: (0,0,0)  #000000  gray(0)
0,62: (0,0,0)  #000000  gray(0)
0,63: (0,0,0)  #000000  gray(0)
0,64: (0,0,0)  #000000  gray(0)
0,65: (0,0,0)  #000000  gray(0)
0,66: (0,0,0)  #000000  gray(0)
0,67: (0,0,0)  #000000  gray(0)
0,68: (0,0,0)  #000000  gray(0)
0,69: (0,0,0)  #000000  gray(0)
0,70: (0,0,0)  #000000  gray(0)
0,71: (0,0,0)  #000000  gray(0)
0,72: (0,0,0)  #000000  gray(0)
0,73: (0,0,0)  #000000  gray(0)
0,74: (0,0,0)  #000000  gray(0)
0,75: (0,0,0)  #000000  gray(0)
0,76: (0,0,0)  #000000  gray(0)
0,77: (0,0,0)  #000000  gray(0)
0,78: (0,0,0)  #000000  gray(0)
0,79: (0,0,0)  #000000  gray(0)

<强> 结果：

（我在某些行的末尾手动添加了一些注释。）

md5sum *.text | sort

可以立即识别以下模式：

10个相同的MD5总和09c0670b59c03fb3d6f8116ee0fe35a2 bottom-edge-10297-13918-3702.text 09c0670b59c03fb3d6f8116ee0fe35a2 top----edge-28824-13023-24184.text 10be914aa8b6aaa8c0e35a4a93421f3a left---edge-20056-20936-29071.text #left edge of QR 10be914aa8b6aaa8c0e35a4a93421f3a left---edge-22806-3380-17484.text #left edge of QR 10be914aa8b6aaa8c0e35a4a93421f3a left---edge-24658-20374-23042.text #left edge of QR 10be914aa8b6aaa8c0e35a4a93421f3a left---edge-27206-10104-18226.text #left edge of QR 10be914aa8b6aaa8c0e35a4a93421f3a left---edge-30261-16558-25650.text #left edge of QR 10be914aa8b6aaa8c0e35a4a93421f3a right--edge-16369-21469-8252.text #right edge of QR 10be914aa8b6aaa8c0e35a4a93421f3a right--edge-21875-14159-1067.text #right edge of QR 10be914aa8b6aaa8c0e35a4a93421f3a right--edge-24426-18830-5627.text #right edge of QR 10be914aa8b6aaa8c0e35a4a93421f3a right--edge-31250-3578-9750.text #right edge of QR 10be914aa8b6aaa8c0e35a4a93421f3a right--edge-5004-10810-17642.text #right edge of QR 217c799ce772d99e11b7ba2a04f42bb7 bottom-edge-11976-7751-26756.text 217c799ce772d99e11b7ba2a04f42bb7 top----edge-5774-30645-16062.text 2c96cebc6cc175b54cae54f1a27771fa bottom-edge-24426-18830-5627.text 2c96cebc6cc175b54cae54f1a27771fa top----edge-21875-14159-1067.text 3088414b0fe1190bbd4ed9315d86a0ac left---edge-13789-10513-4721.text 3088414b0fe1190bbd4ed9315d86a0ac right--edge-22806-3380-17484.text 3755d45bb6ad21b70f545f39c5550eda left---edge-17636-24599-1877.text 3755d45bb6ad21b70f545f39c5550eda right--edge-20056-20936-29071.text 41fc32b4b70622b4aff7d9e81f81daad left---edge-13858-18007-13070.text 41fc32b4b70622b4aff7d9e81f81daad right--edge-10297-13918-3702.text 480bed740a716fa10593ba061c5b6df5 left---edge-5004-10810-17642.text 480bed740a716fa10593ba061c5b6df5 right--edge-20021-11440-20836.text 55cc4713ee13ebbeb5c2e212e430da94 bottom-edge-28824-13023-24184.text 55cc4713ee13ebbeb5c2e212e430da94 top----edge-32078-14314-1511.text 62fecd6971445d4176a842727767d3f7 left---edge-4555-18116-29.text 62fecd6971445d4176a842727767d3f7 right--edge-18056-16294-30425.text 69565761226f122f98267862108119a6 left---edge-21875-14159-1067.text 69565761226f122f98267862108119a6 right--edge-26507-21853-11958.text 72cec2b0a518c2cce7204188923d9f79 left---edge-26507-21853-11958.text 72cec2b0a518c2cce7204188923d9f79 right--edge-15816-4564-31665.text 7343348f1b10e47b33e0bde47b455c2b left---edge-16369-21469-8252.text 7343348f1b10e47b33e0bde47b455c2b right--edge-22161-18187-20222.text 7661c257d28e1916208ed2a70989e42d bottom-edge-13789-10513-4721.text 7661c257d28e1916208ed2a70989e42d top----edge-17636-24599-1877.text 7d2a0d7aae6dc017ffa7af7180b1c017 left---edge-5167-27533-24066.text 7d2a0d7aae6dc017ffa7af7180b1c017 right--edge-32078-14314-1511.text 85b744d1774cd4ebe50f0cc40d10b491 left---edge-18056-16294-30425.text 85b744d1774cd4ebe50f0cc40d10b491 right--edge-27206-10104-18226.text 88942ff37ce0e884d1995a6dd11708d6 left---edge-10297-13918-3702.text 88942ff37ce0e884d1995a6dd11708d6 right--edge-11976-7751-26756.text 9185daee4fdcea482c9fa805bba40346 bottom-edge-31250-3578-9750.text 9185daee4fdcea482c9fa805bba40346 top----edge-16369-21469-8252.text 9b8a7f18e4ebbc7249e5f26ac18190a8 left---edge-28824-13023-24184.text 9b8a7f18e4ebbc7249e5f26ac18190a8 right--edge-5774-30645-16062.text 9bd8f7a474229aefd988c4231f3b26f4 bottom-edge-21875-14159-1067.text 9bd8f7a474229aefd988c4231f3b26f4 top----edge-5004-10810-17642.text a1778c1a468c122bf408a9c2bec9135c bottom-edge-13858-18007-13070.text a1778c1a468c122bf408a9c2bec9135c top----edge-22161-18187-20222.text a4f3eee0e523f1b343d292987da297b3 bottom-edge-20056-20936-29071.text a4f3eee0e523f1b343d292987da297b3 bottom-edge-24658-20374-23042.text a4f3eee0e523f1b343d292987da297b3 top----edge-27206-10104-18226.text a4f3eee0e523f1b343d292987da297b3 top----edge-30261-16558-25650.text af851da184ea6d6586c64aaefdc6875b bottom-edge-22806-3380-17484.text af851da184ea6d6586c64aaefdc6875b top----edge-20056-20936-29071.text b4047de9fb6458ebfde5b3a11b40241a left---edge-5774-30645-16062.text b4047de9fb6458ebfde5b3a11b40241a right--edge-30261-16558-25650.text b58fe9214967f6d2311e11cb0bf1acc4 left---edge-15816-4564-31665.text b58fe9214967f6d2311e11cb0bf1acc4 right--edge-17636-24599-1877.text bef4d7004da4f5de4ca5beeabc1222e8 bottom-edge-5774-30645-16062.text bef4d7004da4f5de4ca5beeabc1222e8 top----edge-13789-10513-4721.text c042d1cd3dfb339177e8da3c2907d58e bottom-edge-15816-4564-31665.text c042d1cd3dfb339177e8da3c2907d58e top----edge-4555-18116-29.text c5f38ebda77190495d77df1fb7002d1e bottom-edge-32078-14314-1511.text c5f38ebda77190495d77df1fb7002d1e top----edge-15816-4564-31665.text c61f738453fa3a86b52b2de5c0d43530 left---edge-20021-11440-20836.text c61f738453fa3a86b52b2de5c0d43530 right--edge-4555-18116-29.text c772762652d22cbf228e57f0dcf1a3b4 bottom-edge-22161-18187-20222.text c772762652d22cbf228e57f0dcf1a3b4 top----edge-5167-27533-24066.text cfaa399406e0be3d07297f23c684e7c3 left---edge-11976-7751-26756.text cfaa399406e0be3d07297f23c684e7c3 left---edge-31250-3578-9750.text cfaa399406e0be3d07297f23c684e7c3 right--edge-13858-18007-13070.text cfaa399406e0be3d07297f23c684e7c3 right--edge-24658-20374-23042.text d39c2a0c14c20b66b441d368c164171d bottom-edge-18056-16294-30425.text #bottom edge of QR d39c2a0c14c20b66b441d368c164171d bottom-edge-20021-11440-20836.text #bottom edge of QR d39c2a0c14c20b66b441d368c164171d bottom-edge-27206-10104-18226.text #bottom edge of QR d39c2a0c14c20b66b441d368c164171d bottom-edge-4555-18116-29.text #bottom edge of QR d39c2a0c14c20b66b441d368c164171d bottom-edge-5004-10810-17642.text #bottom edge of QR d39c2a0c14c20b66b441d368c164171d top----edge-10297-13918-3702.text #top edge of QR d39c2a0c14c20b66b441d368c164171d top----edge-11976-7751-26756.text #top edge of QR d39c2a0c14c20b66b441d368c164171d top----edge-13858-18007-13070.text #top edge of QR d39c2a0c14c20b66b441d368c164171d top----edge-24658-20374-23042.text #top edge of QR d39c2a0c14c20b66b441d368c164171d top----edge-31250-3578-9750.text #top edge of QR d71e7b561d2cc251c962a843daff53bb bottom-edge-5167-27533-24066.text d71e7b561d2cc251c962a843daff53bb top----edge-26507-21853-11958.text d7ffe4aaf744387e7fa9db32271247e0 bottom-edge-26507-21853-11958.text d7ffe4aaf744387e7fa9db32271247e0 top----edge-20021-11440-20836.text da2aa01201b3da20a970928bc2bdef36 bottom-edge-30261-16558-25650.text da2aa01201b3da20a970928bc2bdef36 top----edge-22806-3380-17484.text dd86f02cfc0e260463c75ae5b8709f33 bottom-edge-16369-21469-8252.text dd86f02cfc0e260463c75ae5b8709f33 top----edge-24426-18830-5627.text dfdaa88ebf33284ac0367f92060cd060 bottom-edge-17636-24599-1877.text dfdaa88ebf33284ac0367f92060cd060 top----edge-18056-16294-30425.text f8e2f384f2269e086dc00fb6ff0b3f41 left---edge-32078-14314-1511.text f8e2f384f2269e086dc00fb6ff0b3f41 right--edge-13789-10513-4721.text fa59372704f3b48912a33f3780e9dce7 left---edge-22161-18187-20222.text fa59372704f3b48912a33f3780e9dce7 right--edge-28824-13023-24184.text fff7e26c6f9852fcfa51e79d154dc58a left---edge-24426-18830-5627.text fff7e26c6f9852fcfa51e79d154dc58a right--edge-5167-27533-24066.text
10个相同的MD5总和d39c2a0c14c20b66b441d368c164171d
左/右边缘的20对相同的MD5总和
20对相同的MD5总和（上/下边缘）

在'1'的情况下和'2.'很容易看出这些是全白边缘，代表完整QR的左/右/上/下部分。

剩下的比赛，'3'和'4.'使每个单独的瓷砖以确定的方式容易地适合左或右“伙伴”。

更新

直到现在我才看到马克的答案。但我看到他的方法与我的相似。

我不愿意添加完整的脚本来制作最终重新编写的QR码。

因此，如果马克的解决方案包括这一点，他应该得到正确答案的批准。

Answer 4

我对你的解释一无所知，但对于匹配的瓷砖，它足以比较沿公共边缘的像素。这是因为分裂是在QR细胞上进行的，而不是在它们之间。

每次尝试所有未使用的块时，您可以逐行填充数组，直到找到匹配项。实际上，巧合会产生几场比赛，因此建议在找到比赛后继续搜索。这将导致递归实现。

角落及其邻居更容易手工放置。通过一点点仔细检查，您还可以找到具有时序模式的块。

解决QR拼图的算法

4 个答案:

更新

更新2

更新