python pillow block usage

pillow

Pillow is a derivation branch PIL, but now has evolved into more dynamic than PIL own image processing library. pillow can be said to have replaced the PIL, encapsulates the python library (pip to install), and supports python2 and python3, the latest version is 3.0.0.

Pillow's Github page: https://github.com/python-pillow/Pillow 
Pillow document (corresponding version V3.0.0): 
https://pillow.readthedocs.org/en/latest/handbook/index.html

It is very simple to install pip install pillow

Use:

# Python2 
Import Image 
 # python3 (PIL library because it is derived, so you want to import the PIL Image) 
from PIL Import Image

 

To python3 example,

• open

from PIL import Image
im = Image.open("1.png")
im.show()

 

• format

format attribute defines the format of the image, if the image file from the not open, then the attribute value None; size attribute is a tuple, an image representing the width and height (in pixels); MODE attribute is a schematic of an image, commonly mode as: L to grayscale, RGB true color, CMYK image is pre-press. If the file can not be opened, IOError exception is thrown.

print(im.format, im.size, im.mode)

 

• save

im.save("c:\\")

• convert()

convert () method is an instance of an object image, accepts a mode parameter for designating a color mode, mode values may be summarized as follows: 
·. 1 (. 1 'bit-pixels, Black and White, Stored with One Pixel per byte) 
· L (. 8-'bit pixels, Black and White) 
· P (. 8-' bit pixels, mapped to the any OTHER MODE the using A Color Palette) 
· the RGB (3x8-'bit pixels, to true Color) 
· the RGBA (4x8-' bit pixels, to true Color with Transparency mask) 
· the CMYK (4x8-'bit pixels, Color Separation) 
· the YCbCr (3x8-' bit pixels, Color Video the format) 
· the I (32-'bit Signed Integer pixels) 
· F. (32-' bit Floating Point pixels )

im = Image.open('1.png').convert('L')

　　Filter

from the PIL Import Image, ImageFilter 
IM = Image.open ( '. 1 .png') 
 # Gaussian blur 
im.filter (ImageFilter.GaussianBlur) 
 # ordinary fuzzy 
im.filter (ImageFilter.BLUR) 
 # edge enhancement 
im.filter (ImageFilter.EDGE_ENHANCE ) 
 # find the edge 
im.filter (ImageFilter.FIND_EDGES) 
 # relief 
im.filter (ImageFilter.EMBOSS) 
 # contour 
im.filter (ImageFilter.CONTOUR) 
 # sharpening 
im.filter (ImageFilter.SHARPEN) 
 # smooth 
im.filter (ImageFilter .SMOOTH) 
 # details 
im.filter (ImageFilter.DETAIL)

　　View image histogram

im.histogram()

　　Convert image file format

def img2jpg(imgFile):   
     if type(imgFile)==str and imgFile.endswith(('.bmp', '.gif', '.png')):
          with Image.open(imgFile) as im:
              im.convert('RGB').save(imgFile[:-3]+'jpg')   
 

 img2jpg('1.gif')
 img2jpg('1.bmp')
 img2jpg('1.png')

　　Screenshots

from the PIL Import ImageGrab 
IM = ImageGrab.grab ((0,0,800,200)) # image of the specified area of the screen taken 
IM = ImageGrab.grab () # without parameters indicates full screenshot

　　Cutting and pasting images

= Box (120, 194, 220, 294) # define clipping region 
Region = im.crop (Box) # Crop 
Region = region.transpose (Image.ROTATE_180) 
im.paste (Region, Box) # Paste

Image scaling

im.resize = IM ((100,100)) # parameter indicates the new size of the image, respectively represent the width and height

Image contrast enhancement

from the PIL Import Image  
 from the PIL Import The ImageEnhance   

# original image   
Image Image.open = ( ' lena.jpg ' )   
image.show ()   

# brightness enhancement   
enh_bri = ImageEnhance.Brightness (Image)   
Brightness = for 1.5   
image_brightened = enh_bri.enhance (Brightness)   
image_brightened.show ()   

# chroma enhancement   
enh_col = ImageEnhance.Color (Image)   
Color = for 1.5   
image_colored = enh_col.enhance (Color)   
image_colored.show ()   

#Contrast enhancement   
enh_con = ImageEnhance.Contrast (Image)   
Contrast = for 1.5   
image_contrasted = enh_con.enhance (Contrast)   
image_contrasted.show ()   

# sharpness enhancement   
enh_sha = ImageEnhance.Sharpness (Image)   
the sharpness = 3.0   
image_sharped = enh_sha.enhance (the sharpness)   
image_sharped .show ()  

 

Image module usage presentation

1 Introduction.

    Image processing is a very wide application of technology, and has a very rich library of third-party extensions Python certainly will not miss this feast door. PIL (Python Imaging Library) is the most commonly used Python image processing library, the current version is 1.1.7, we can  here  to download study and find information.

    Image class PIL library is a very important class to create an instance of this class by directly loading image files can be read and processed image grab image obtained by the method of the three methods.

2. Use.

    Image Import module. Then open method Image class to load an image file. If you load a file fails, it will cause a IOError; if no error is returned, the open function returns an Image object. Now, we can check the file contents through a number of object attributes, namely:

>>> import Image
>>> im = Image.open("j.jpg")
>>> im.show()
>>> print im.format, im.size, im.mode
JPEG (440, 330) RGB

There are three attributes, one by one we understand.

        format: identification image source format, if the file is not read from the file, the values ​​were set to None.

        size: returns a tuple, there are two elements, a value of the pixel width and height sense.

        mode: RGB (true color image), in addition, L (luminance), CMTK (pre-press image).

    

from PIL import Image
from PIL import ImageEnhance
import pytesseract
import re
pytesseract.pytesseract.tesseract_cmd = 'D:\\Program Files\\Tesseract-OCR\\tesseract.exe'
tessdata_dir_config = '--tessdata-dir "D:\\Program Files\\Tesseract-OCR\\tessdata"'
im=Image.open("./img/10.jpg")
im=im.convert('L')
im.show()
im=ImageEnhance.Contrast(im)
im=im.enhance(1)
#im = im.resize((300, 90))
ltext = pytesseract.image_to_string(im)
ltext = re.sub("\W", "", ltext)
im.show()
print(ltext)
#print(pytesseract.image_to_string(im))
#print(pytesseract.image_to_boxes(im))
#print(im.format, im.size, im.mode)

convert (): This function can be used to convert the image to a different color mode.
ImageEnhance.Contrast (im): Use ImageEnhance can enhance the recognition rate picture

other

Simple geometric transformation.

OUT = im.resize >>> ((128, 128))                      # resize images 
>>> im.rotate OUT = (45)                              # rotated counterclockwise 45 degrees. 
OUT = im.transpose >>> (Image.FLIP_LEFT_RIGHT)        # left swapped. 
OUT = im.transpose >>> (Image.FLIP_TOP_BOTTOM)        # vertical swapped. 
OUT = im.transpose >>> (Image.ROTATE_90)              # rotation angle of 90 degrees. 
OUT = im.transpose >>> (Image.ROTATE_180)             # rotation angle of 180 degrees. 
OUT = im.transpose >>> (Image.ROTATE_270)             # rotated 270 degrees.

 Sequence of images.

That is, we often see the dynamic map, the most common suffix .gif, in addition to FLI / FLC. PIL library for this movie format chart also provides some basic support. When we open this type of image file, PIL automatically loads the first frame image. We seek and tell methods may be used to move between frames.

import Image
im.seek(1)        # skip to the second frame

try:
    while 1:
        im.seek( im.tell() + 1)
        # do something to im
except EOFError:
    pass