如何用简单的算法生成一个类似『光盘』的彩色圆形图片？
大概就像下图所示的样子
回复内容：
补充一下MATLAB的代码：<span class="n">t</span> <span class="p">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">:.</span><span class="mi">02</span><span class="p">:</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="nb">pi</span><span class="p">;</span> <span class="n">r</span> <span class="p">=</span> <span class="mi">0</span><span class="p">:.</span><span class="mi">02</span><span class="p">:</span><span class="mi">1</span><span class="p">;</span> <span class="n">pcolor</span><span class="p">(</span><span class="nb">cos</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="o">'*</span><span class="n">r</span><span class="p">,</span><span class="nb">sin</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="o">'*</span><span class="n">r</span><span class="p">,</span><span class="n">t</span><span class="o">'*</span><span class="p">(</span><span class="n">r</span><span class="o">==</span><span class="n">r</span><span class="p">))</span> <span class="n">colormap</span><span class="p">(</span><span class="n">hsv</span><span class="p">(</span><span class="mi">256</span><span class="p">)),</span> <span class="n">shading</span> <span class="n">interp</span><span class="p">,</span> <span class="n">axis</span> <span class="n">image</span> <span class="n">off</span>
基本上就是在这个圆上颜色的色相(Hue)，只与该点与圆心连线的夹角有关。知道这一点你就做一幅图像，然后遍历每一个点，把该点的坐标值
转换为极坐标，色相值就是极坐标角度。用 matplotlib 实现的话有个小技巧，把imshow的cmap改为hsv就可以直接按照色相来画。
<span class="c"># -*- coding:utf-8 -*-</span>
<span class="kn">from</span> <span class="nn">pylab</span> <span class="kn">import</span> <span class="o">*</span>
<span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="mi">250</span><span class="p">,</span> <span class="mi">250</span><span class="p">)</span>
<span class="n">radius</span> <span class="o">=</span> <span class="mi">250</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">((</span><span class="mi">500</span><span class="p">,</span><span class="mi">500</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">500</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">500</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">i</span> <span class="o">-</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mf">1.0</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">j</span> <span class="o">-</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mf">1.0</span>
<span class="k">if</span> <span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="o">+</span><span class="n">y</span><span class="o">**</span><span class="mi">2</span> <span class="o"><</span> <span class="n">radius</span><span class="o">**</span><span class="mi">2</span><span class="p">:</span>
<span class="k">if</span> <span class="n">x</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
<span class="n">img</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">arctan</span><span class="p">(</span><span class="n">y</span><span class="o">/</span><span class="n">x</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">x</span><span class="o"><</span><span class="mi">0</span> <span class="ow">and</span> <span class="n">y</span><span class="o">>=</span><span class="mi">0</span><span class="p">:</span>
<span class="n">img</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">arctan</span><span class="p">(</span><span class="n">y</span><span class="o">/</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">pi</span>
<span class="k">elif</span> <span class="n">x</span><span class="o"><</span><span class="mi">0</span> <span class="ow">and</span> <span class="n">y</span><span class="o"><</span><span class="mi">0</span><span class="p">:</span>
<span class="n">img</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">arctan</span><span class="p">(</span><span class="n">y</span><span class="o">/</span><span class="n">x</span><span class="p">)</span> <span class="o">-</span> <span class="n">pi</span>
<span class="k">elif</span> <span class="n">x</span><span class="o">==</span><span class="mi">0</span> <span class="ow">and</span> <span class="n">y</span><span class="o">></span><span class="mi">0</span><span class="p">:</span>
<span class="n">img</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span>
<span class="k">elif</span> <span class="n">x</span><span class="o">==</span><span class="mi">0</span> <span class="ow">and</span> <span class="n">y</span><span class="o"><</span><span class="mi">0</span><span class="p">:</span>
<span class="n">img</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">pi</span> <span class="o">/</span> <span class="o">-</span><span class="mi">2</span>
<span class="k">elif</span> <span class="n">x</span><span class="o">==</span><span class="mi">0</span> <span class="ow">and</span> <span class="n">y</span><span class="o">==</span><span class="mi">0</span><span class="p">:</span>
<span class="n">img</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="k">print</span> <span class="n">img</span>
<span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">hsv</span><span class="p">)</span>
<span class="n">show</span><span class="p">()</span>
<span class="kn">from</span> <span class="nn">pylab</span> <span class="kn">import</span> <span class="o">*</span> <span class="n">n</span><span class="o">=</span><span class="mi">300</span> <span class="n">img</span><span class="o">=</span><span class="p">[[</span><span class="n">arctan2</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span> <span class="k">if</span> <span class="n">x</span><span class="o">*</span><span class="n">x</span><span class="o">+</span><span class="n">y</span><span class="o">*</span><span class="n">y</span><span class="o"><</span><span class="n">n</span><span class="o">*</span><span class="n">n</span> <span class="k">else</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">n</span><span class="p">,</span><span class="n">n</span><span class="p">)]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">n</span><span class="p">,</span><span class="n">n</span><span class="p">)]</span> <span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">hsv</span><span class="p">)</span> <span class="n">show</span><span class="p">()</span>
关于色相是什么（反正这里说的不是可以用来牺牲的那个色相了），可以参看Wiki：HSL和HSV色彩空间，那个H就是色相Hue。
有点像下面这幅图的横截面（摘自Wiki）
只不过明度Chroma没有变化。这样的话，蛮简单的啊。对每个像素算出它所在半径的角度 0~2π 然后映射到Hue的值域上就是了。
具体实现的话，话说可以用OpenCV吗？反正一些图像库里面是有HSR到RGB的转换函数的。
（我一直都不明白所谓邀请是怎么回事，以及为什么大家都爱说谢邀） 补充一下Mathematica代码
n=300;
c=Hue[i/n];
Graphics@Table[{c,EdgeForm@c,Disk[{0,0},1,2Pi/n{i-1,i}]},{i,n}]
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
AI Hentai Generator
Generate AI Hentai for free.
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
How to Use Python to Find the Zipf Distribution of a Text File
Mar 05, 2025 am 09:58 AM
This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th
How Do I Use Beautiful Soup to Parse HTML?
Mar 10, 2025 pm 06:54 PM
This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel
Image Filtering in Python
Mar 03, 2025 am 09:44 AM
Dealing with noisy images is a common problem, especially with mobile phone or low-resolution camera photos. This tutorial explores image filtering techniques in Python using OpenCV to tackle this issue. Image Filtering: A Powerful Tool Image filter
How to Perform Deep Learning with TensorFlow or PyTorch?
Mar 10, 2025 pm 06:52 PM
This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap
Introduction to Parallel and Concurrent Programming in Python
Mar 03, 2025 am 10:32 AM
Python, a favorite for data science and processing, offers a rich ecosystem for high-performance computing. However, parallel programming in Python presents unique challenges. This tutorial explores these challenges, focusing on the Global Interprete
How to Implement Your Own Data Structure in Python
Mar 03, 2025 am 09:28 AM
This tutorial demonstrates creating a custom pipeline data structure in Python 3, leveraging classes and operator overloading for enhanced functionality. The pipeline's flexibility lies in its ability to apply a series of functions to a data set, ge
Serialization and Deserialization of Python Objects: Part 1
Mar 08, 2025 am 09:39 AM
Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program
Mathematical Modules in Python: Statistics
Mar 09, 2025 am 11:40 AM
Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti
