参考文章：YUV格式的图片查看工具YUView 2.13
亲测可用
在windows上解压打开即可
需要输入一下宽高，如果格式不对，需要设置下格式
还可以看像素分量值，爽歪歪
YUV查看器和YUV测试文件
文章目录
- 20230816
- YUV图片查看器及其测试文件（YUV420）
- 一、YUV格式：理解色彩空间
- 二、YUV420：色度子采样
- 三、YUV图片查看器：构建基础应用
- 3.1 安装依赖
- 3.2 加载和显示YUV420图像
- 结束语
20230816
YUV图片查看器及其测试文件（YUV420）
本文将深入探讨YUV图片查看器以及与之相关的测试文件（特别是YUV420格式）。我们将首先理解YUV格式，然后详细讨论YUV420子采样。最后，我们将介绍如何构建一个基础的YUV图片查看器，并给出示例代码。
一、YUV格式：理解色彩空间
YUV是一种颜色编码系统，常用于视频系统，包括电视广播和模拟电视。在这种格式中，Y表示亮度（灰阶），而U和V表示色差，即从灰度色彩中减去的颜色部分。
参考：YUV - Wikipedia
二、YUV420：色度子采样
YUV420是YUV格式的一个变种，其中4:2:0表示色度子采样。这意味着每四个像素共享一个色度（U、V）值，从而节省存储空间和传输带宽。此外，人眼对亮度的敏感度远高于色度，因此，通过压缩色度信息，可以在保持图像质量的同时，实现高效压缩。
参考：Chroma subsampling - Wikipedia
// YUV420到RGB的转换公式
int Y = ...;
int U = ...;
int V = ...;int R = Y + 1.13983 * V;
int G = Y - 0.39465 * U - 0.58060 * V;
int B = Y + 2.03211 * U;
三、YUV图片查看器：构建基础应用
下面我们将探讨如何构建一个基础的YUV图片查看器。为了简化说明，我们将使用Python和OpenCV库来完成这项任务。OpenCV是一个广泛使用的计算机视觉库，支持多种图像格式和操作。
3.1 安装依赖
在开始之前，确保已经安装了必要的依赖。以下是安装Python和OpenCV的基本步骤：
# 安装Python
sudo apt-get install python3.8# 安装pip
sudo apt-get install python3-pip# 使用pip安装opencv-python
pip3 install opencv-python
3.2 加载和显示YUV420图像
以下是一个基本的Python脚本，用于加载YUV420图像并将其转换为RGB格式以进行显示：
import numpy as np
import cv2def load_YUV420_image(filename, width, height):# 打开文件with open(filename, 'rb') as f:# 读取Y, U, V通道Y = np.fromfile(f, dtype=np.uint8, count=width*height).reshape((height, width))U = np.fromfile(f, dtype=np.uint8, count=(width//2)*(height//2)).reshape((height//2, width//2))V = np.fromfile(f, dtype=np.uint8, count=(width//2)*(height//2)).reshape((height//2, width//2))# 对U, V通道进行上采样U = cv2.resize(U, (width, height), interpolation=cv2.INTER_CUBIC)V = cv2.resize(V, (width, height), interpolation=cv2.INTER_CUBIC)# 返回合并后的YUV图像return cv2.merge([Y, U, V])# 测试函数
image = load_YUV420_image('test.yuv', 640, 480)# 转换YUV图像为RGB格式
rgb_image = cv2.cvtColor(image, cv2.COLOR_YUV2BGR)# 显示图像
cv2.imshow('Image', rgb_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
至此，我们已经成功创建了一个基础的YUV图片查看器，它可以加载YUV420格式的图像，将其转换为RGB格式，并在窗口中显示出来。
结束语
虽然本文只涵盖了基本的YUV420图像处理和查看技术，但这些内容可以作为进一步探索更复杂应用的基础，例如视频流处理、实时图像转换等。
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