First, let's take a look at the verification form of the target

It is a verification method through verification reasoning , It's really effective to prevent man-machine cracking , however ,But, There are already some flaws in this , such as ：

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（ The above is the result of the original drawing and binarization ）

（ This is a normal picture ）

Places like red lines , You can see obvious mutations , And after binarization, the edge tends to be straight , But the normal image will not have such obvious mutation phenomenon .

First met Pandora

later , I went to read the articles and papers related to machine vision , Found a powerful algorithm , This algorithm is ——Genetic Algorithm Genetic algorithm (ga) , The most intimate thing is , The author uses this algorithm to realize a function ,“ The puzzle is automatically restored ”（ It's not like A* Algorithm to find the optimal route solution , It's a simple puzzle ） Project warehouse address
First of all, let's introduce how to use the running project , There are really many pits , Now feel pyCham Report mistakes all the way ！
So here I'm going to use theta python3.10 Version of , It's the latest version
This step in the document will report an error

pip3 install -r requirements.txt

Solution ： Alone requirements.txt Each package under the file is downloaded separately , Then replace the old version number according to the latest version of the currently downloaded package .

I currently use these version numbers for each package

After all the replacement , Execute the following code again , He won't report wrong

pip3 install -r requirements.txt

And then the next step , Execute the following code

pip3 install -e .

Enter Pandora

Then we follow the tips on the official website , First create a puzzle , This is the order （ I changed the file name here ）

create_puzzle images/starry.jpg --size=60 --destination=puzzle.jpg

Will find , I don 't think I can , Because we didn't execute in the right place , His script is located in bin Under the folder , You may encounter the following problems

After success , Will be in bin Generate a puzzle picture under the directory

The above is how to generate pictures , Next is the play , How to restore pictures

gaps --image=puzzle.jpg --generations=20 --population=600

For the interpretation of parameters, the official website is like this ：

Option
: --image Path to puzzle（ Pictures that need to be restored ）
--size Puzzle piece size in pixels （ The size of the puzzle ）
--generations Number of generations for genetic algorithm （ Algebra of genetic algorithm ）
--population Number of individuals in population
--verbose Show best solution after each generation （ Show the best solution after each generation ）
--save Save puzzle solution as image （ Save the puzzle result as an image ）

Let's go through it according to the official

very good , Very comfortable , Continue to report mistakes , And we haven't misspelled in grammar and spelling , No problem ！ I've helped you find a solution .

 python gaps --image=puzzle.jpg --generations=20 --population=600 --size=60 --save

Or you don't specify genetic algebra and population

 python gaps --image=puzzle.jpg --size=60 --save

This is the result of complete restoration , thus , We cut through difficulties to report mistakes from solving the project , Dig and fill all the way to demo Running successfully , Here we have made it 60%, The next step is how to use this project , To crack Netease's reasoning puzzle verification .

Explore Pandora's Secret

First look at the project directory ：

gaps Below the folder is the source code of all modules , The author encapsulates the basic functions , But the function we want doesn't exist , You need to read and understand the source code by yourself , Then the second switch realizes the function you want .

We mainly focus on this document individual.py：

Let me translate it for you in vernacular ,“ In all permutations , Help you find the optimal solution ”

Output this mapping , It turns out like this , Just what we want

Here's a way to create an image , This is the image mosaic function that finally obtains the optimal solution

that , We map the final index to OK 了 ！

The following is an example of the mapping value output of the optimal solution ：

When initializing , Add a variable to store the final result

self.pieceMapping = None

Customize a function

# Return the mapping value def getPieceMapping(self): return self.pieceMapping

Every time it's executed , Pass the optimal solution out

def to_image(self): """Converts individual to showable image""" pieces = [piece.image for piece in self.pieces] self.pieceMapping = self._piece_mapping return image_helpers.assemble_image(pieces, self.rows, self.columns)

above , Just output the results we want

call ：

key Mark the final result ,value Is the original logo .

PS： For extremely complex puzzles created from original images , After restore , The error will be big .