Python image Processing (2): Image display

Happy Shrimp

http://blog.csdn.net/lights_joy/(QQ group: Visual embedlinux Tools 375515651)

Welcome reprint, but please keep the author information

1. OpenCVImage Display

before using Cv2.imshow The image is displayed, but the window cannot be resized in such a way that it cannot be displayed when the displayed image is larger, so we first create the window and then display the image:

Import cv2img = Cv2.imread (' f:\\tmp\\cotton.jpg ') win = Cv2.namedwindow (' Test win ', flags=0) cv2.imshow (' Test win ', IMG) Cv2.waitkey (0)

OpenCV Use the window name to access the window instead of something like a window handle.

Flags to be 0 indicates that the window can be resized with the mouse, and the displayed image changes with the size of the window, and it is important to note that it may cause the image to deform:

Cv2.namedwindow end up using the following C function to accomplish specific functions:

/* Create window */CVAPI (int) Cvnamedwindow (const char* name, int flags Cv_default (cv_window_autosize));

Here's Flags the values that you can accept are:

    These 2 flags is used by Cvnamedwindow and cvset/getwindowproperty    cv_window_normal       = 0x00000000,//the user ca N Resize the window (no constraint)  /also use-Switch a fullscreen window to a normal size    cv_window_autosize
   = 0x00000001,//the user cannot resize the window, the size is constrainted by the image displayed    Cv_window_opengl       = 0x00001000,//window with OpenGL support

2.     matplotlibImage Display

Try it next matplotlib Display Image:

Import Cv2import matplotlib.pyplot as Pltimg = Cv2.imread (' f:\\tmp\\cotton.jpg ') plt.imshow (IMG) plt.show ()

The color of the image is incorrect:

the first 1 Channel and section 3 display after Channel swap:

Import NumPy as Npimport cv2import matplotlib.pyplot as Pltimg = Cv2.imread (' f:\\tmp\\cotton.jpg ') (R, G, b) =cv2.split (img ) Img=cv2.merge ([B,g,r]) Plt.imshow (IMG) plt.show ()

This time normal:

from the front you can see cv2.imshow with plt.imshow > the difference. cv2.imshow plt.imshow You need to swap the first color channel and the third color channel.  

3.      with plt reading Images

compare it again . Plt.imread and the Cv2.imread The difference:

Import NumPy as Npimport cv2import matplotlib.pyplot as Pltimg1 = Cv2.imread (' f:\\tmp\\cotton.jpg ') Img2 = Plt.imread (' f:\ \tmp\\cotton.jpg ') plt.subplot (121) plt.imshow (IMG1) plt.subplot (122) plt.imshow (IMG2) plt.show ()

The above code reads the same image and displays it in the same way, and the difference is on the color channel:

4.     matplotlibdisplaying grayscale graphs

for images with only one color channel, matplotlib You can specify a Map To convert an image of a single color channel to a color image.

matplotlib support the following Map .

In [8]: Import matplotlib.cm as CmIn [9]: cm.cmap_dout[9]: {u ' Accent ': <matplotlib.colors.linearsegmentedcolormap at 0 X22cbf50>, u ' accent_r ': <matplotlib.colors.linearsegmentedcolormap at 0x22d7150>,....... u ' Winter ': < Matplotlib.colors.LinearSegmentedColormap at 0x22d7290>, u ' winter_r ': < Matplotlib.colors.LinearSegmentedColormap at 0x22cb910>}

Select a Map to display:

Import NumPy as Npimport cv2import Matplotlib.pyplot as Pltimport matplotlib.cm as Cmimg = Plt.imread (' f:\\tmp\\cotton.jpg ') img = img[:,:,0]plt.subplot (121) Plt.imshow (IMG) plt.subplot (122) #plt. Colorbar () Plt.imshow (IMG, Cmap=cm.get_cmap ( ' Winter ')) Plt.show ()

The results are as follows:

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Python image Processing (2): Image display