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# computer-vision开始使用计算机视觉

## 备注

``````                                        f : R² ⊃ Ω → R
``````

1. 数字图像处理圣经：RC Gonzalez，RE Woods：数字图像处理。第三版，Pearson Prentice Hall，Upper Saddle River，2008。

2. 计算机视觉评论（直到深度学习）：R。Szeliski：计算机视觉：算法和应用。施普林格，纽约，2010年。

3. 要了解二进制，灰度，彩色图像： https//en.wikipedia.org/wiki/Grayscale

## 例子

``````git clone https://github.com/Skorkmaz88/compvis101
``` ```

``````
# libs
import png

# We create a greyscale image as described in our text.
# To do that simply, we create a 2D array in python.
# x and y, x being horizontal and y being vertical directions.

x  = []
y = []
# Play around with these pixels values to get different grayscale images, they shoud be
# in range of 0 - 255.
white = 255
gray = 128
black = 0
width  = 100
height = 300

# Add 100 x 100 rectangle as just white(255) valued pixels
for i in range(0, 100):
for j in range(0,100):
y.append(white); # Pixel (i,j) is being set to a value, rest is coding trick to nest two lists
x.append(y)
y = []

# Add 100 x 100 rectangle as just mid-gray(128) valued pixels
for i in range(0, 100):
for j in range(0,100):
y.append(gray);
x.append(y)
y = []

# Add 100 x 100 rectangle as just black(0) valued pixels
for i in range(0, 100):
for j in range(0,100):
y.append(black);
x.append(y)
y = []

# output image file
f = open('out.png', 'wb')
w = png.Writer(width, height , greyscale=True, bitdepth=8)
w.write(f, x)
f.close()
# If everything went well, you should have 3 vertically aligned rectangles white, gray and black
# Check your working folder

# PART 2
# Read a grayscale image and convert it to binary

# This time we will binarize a grayscale image, to do that we will read pixels and according to threshold we set
# we will decide if that pixel should be white or black

# This file is originally 8 bit png image, can be found in github repository, you should use only this type of
# images if you want to change the image.
f = open('./img/lenaG.png', 'r')

# You will the details about the image, for now pay attention to size and bitdepth only.

width = img[0]
height = img[1]
# Threshold value for binarizing images,
threshold = 128
print "Input image size is: "+ str(width)+ " pixels as  width, " + str(height) + " pixels as height"

f_out = open('lenaBinary.png', 'wb')
w = png.Writer(width, height , greyscale=True, bitdepth=1)

pixels = img[2]

x = []
y = []

# Let's traverse the Lena image
for row in pixels:
for pixel in row:
p_value =  pixel
# Now here we binarize image in pixel level
if p_value > threshold:
p_value = 1
else:
p_value = 0

y.append(p_value);
x.append(y)
y = []

w.write(f_out, x)
f_out.close()
``````
``` 如果一切顺利，恭喜！您从头开始创建图像，并在现有图像上执行第一个像素级转换。检查您的工作文件夹以查看新图像 安装或设置 有关设置或安装计算机视觉的详细说明。 对于本系列和以下系列的计算机视觉，我将使用Python 2作为编程语言。 Python是科学界的共同选择，它是免费的，它有许多免费和开源的库，如果你是新手编程，它是最简单的学习和开始编程之一。 现在设置，如果你使用Linux，你可能已经有了python，只需打开终端并输入'python'来检查一切是否正常。其他人，可以查看此链接并下载python 2.7。 其次，我们需要在源代码中安装我们将要使用的库。现在，请注意，此设置是针对此示例设计的，在后续阶段，我们将添加不同的库，当然，不同的计算机视觉应用程序可能需要特定的库，如OpenCV。我们只需要在我们的系统中安装一个库来运行代码。使用python时，我通常使用'pip'安装依赖项。这是一个安装python模块的简单工具，您也可以通过此链接进行检查现在，我们已准备好安装我们需要的库PyPNG。如果你正在使用pip，你需要做的就是 pip安装PyPNG 在终端中，如果您使用的是Linux / Mac，请在命令行中使用Windows。 此外，对于本练习，您需要获取可以在源代码和ipython笔记本旁边的github链接中找到的图像。 https://github.com/Skorkmaz88/compvis101 现在，我们应该好好去锻炼身体 PDF - Download computer-vision for free Previous Next ```
``` Related Tags algorithm Android C Language C# Language C++ Java Language MATLAB Language opencv Python Language tensorflow This modified text is an extract of the original Stack Overflow Documentation created by following contributors and released under CC BY-SA 3.0 This website is not affiliated with Stack Overflow Email: tutorialpedia@outlook.com ```
``` ```
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