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# computer-vision開始使用計算機視覺

## 備註

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

1. 數字圖像處理聖經：RC Gonzalez，RE Woods：數字圖像處理。第三版，Pearson Prentice Hall，Upper Saddle River，2008。

2. 計算機視覺評論（直到深度學習）：R。Szeliski：計算機視覺：算法和應用。施普林格，紐約，2010年。

3. 要了解二進制，灰度，彩色圖像： 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()
``````
``` 如果一切順利，恭喜！您從頭開始創建圖像，並在現有圖像上執行第一個像素級轉換。檢查您的工作文件夾以查看新圖像 安裝或設置 有關設置或安裝計算機視覺的詳細說明。 對於本系列和以下系列的計算機視覺，我將使用Python 2作為編程語言。 Python是科學界的共同選擇，它是免費的，它有許多免費和開源的庫，如果你是新手編程，它是最簡單的學習和開始編程之一。 現在設置，如果你使用Linux，你可能已經有了python，只需打開終端並輸入'python'來檢查一切是否正常。其他人，可以查看此鏈接並下載python 2.7。 其次，我們需要在源代碼中安裝我們將要使用的庫。現在，請注意，此設置是針對此示例設計的，在後續階段，我們將添加不同的庫，當然，不同的計算機視覺應用程序可能需要特定的庫，如OpenCV。我們只需要在我們的系統中安裝一個庫來運行代碼。使用python時，我通常使用'pip'安裝依賴項。這是一個安裝python模塊的簡單工具，您也可以通過此鏈接進行檢查現在，我們已準備好安裝我們需要的庫PyPNG。如果你正在使用pip，你需要做的就是 pip安裝PyPNG 在終端中，如果您使用的是Linux / Mac，請在命令行中使用Windows。 此外，對於本練習，您需要獲取可以在源代碼和ipython筆記本旁邊的github鏈接中找到的圖像。 https://github.com/Skorkmaz88/compvis101 現在，我們應該好好去鍛煉身體 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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