Two ways Python reads and displays pictures

In Python, in addition to using OPENCV, you can also use the matplotlib and PIL the two libraries to manipulate the picture. I prefer matpoltlib, because its syntax is more like MATLAB.

First, MATPLOTLIB1. Show pictures

ImportMatplotlib.pyplot as Plt#plt for displaying picturesImportMatplotlib.image as Mpimg#mpimg for reading picturesImportNumPy as NPLena= Mpimg.imread ('Lena.png')#Lena.png of Reading and code in the same directory#at this time Lena is already a np.array, you can handle it arbitrarilyLena.shape#(3)Plt.imshow (Lena)#Show PicturesPlt.axis ('off')#do not display axesPlt.show ()

2. Show a channel

# display the first channel of a picture lena_1 = lena[:,:,0]plt.imshow ('lena_1') plt.show ()  #  At this point you will find that the thermal map is displayed, not our expected grayscale image, you can add the CMAP parameter, there are several ways to add:plt.imshow ('lena_1 ', cmap='greys_r')
Plt.show ()

img = plt.imshow (' lena_1 ')
Img.set_cmap (' Gray ') # ' hot ' is the Heat map plt.show ()

3. Convert RGB to Grayscale

There is no proper function in matplotlib to convert an RGB graph to a grayscale graph, which can be customized according to the formula:

def Rgb2gray (RGB):     return Np.dot (Rgb[...,:3], [0.299, 0.587, 0.114= Rgb2gray (Lena)    #  can also be used Plt.imshow (Gray, CMap = Plt.get_cmap (' Gray '))plt.imshow (Gray, cmap='greys_r' ) )
Plt.axis (' Off ') plt.show ()

4. Zoom in on the image

We're going to use scipy here.

 from Import  # The second parameter, if it is an integer, is a percentage, or, if it is a tuple, the size of the output image plt.imshow (LENA_NEW_SZ) plt.axis ('  off') plt.show ()

5. Save the image

5.1 Save the image drawn by matplotlib

This method is suitable for saving any matplotlib-drawn image, which is equivalent to a screencapture.

plt.imshow (LENA_NEW_SZ) plt.axis ('off') plt.savefig ('  Lena_new_sz.png')

5.2 Saving an array as an image

 from Import miscmisc.imsave ('lena_new_sz.png', LENA_NEW_SZ)

5.3 Save Array directly

After reading, the image can be displayed according to the previous array method, which does not lose the image quality at all

Np.save ('lena_new_sz'#  is automatically appended with the saved name. NPY= np.load ('  lena_new_sz.npy'#  Read the previously saved array

Second, PIL 1. Show pictures

 from Import  = Image.open ('lena.png') im.show ()

2. Convert PIL image image to NumPy array

Im_array = Np.array (IM)
# can also be used Np.asarray (IM) difference is np.array () is a deep copy, Np.asarray () is a shallow copy

3. Save PIL Pictures

Call the Save method of the Image class directly

 from Import  = Image.open ('lena.png') i.save ('new_lena.png  ')

4. Convert an NumPy array to a PIL picture

Here the matplotlib.image is read into the image array, note that the array read in here is float32 type, the range is 0-1, and PIL. The Image data is of type UINIT8, the range is 0-255, so the conversion is done:

Import Matplotlib.image as Mpimg  from Import  = mpimg.imread ('lena.png'# The data read in here is float32 type, the range is 0-1im = Image.fromarray (Np.uinit8 (lena*255)) im.show ()

Two ways Python reads and displays pictures