Image restoration is a type of algorithm in computer vision whose goal is to fill the area within an image or video. It is also used to delete small, no need objects in the image.
dst = cv2.inpaint(src, inpaintMask, inpaintRadius, flags)
Parameter Description:
l src: source image(original image)
l inpaintMask: binary mask indicating pixels to be repaired.
l dst: result image
l inpaintRadius: radius of restoration
l flags: restoration algorithm, mainly including INPAINT_NS (Navier-Stokes based method) or INPAINT_TELEA (Fast marching based method)
Code path:
/home/pi/Yahboom_Project/Raspbot/1.OpenCV_course/04image_beautification/03_Image_restoration.ipynb
# Add damage to the original image
import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread('yahboom.jpg',1)
for i in range(200,300):
img[i,200] = (255,255,255)
img[i,200+1] = (255,255,255)
img[i,200-1] = (255,255,255)
for i in range(150,250):
img[250,i] = (255,255,255)
img[250+1,i] = (255,255,255)
img[250-1,i] = (255,255,255)
cv2.imwrite('damaged.jpg',img)
# cv2.imshow('image',img)
# cv2.waitKey(0)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img)
plt.show()
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After running the following program, a pictures will be displayed in the jupyterLab control interface, as shown below.
#1 damaged image 2 mask 3 inpaint
import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread('damaged.jpg',1)
#cv2.imshow('src',img)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
paint = np.zeros((height,width,1),np.uint8)
for i in range(200,300):
paint[i,200] = 255
paint[i,200+1] = 255
paint[i,200-1] = 255
for i in range(150,250):
paint[250,i] = 255
paint[250+1,i] = 255
paint[250-1,i] = 255
#cv2.imshow('paint',paint)
#1 src 2 mask
imgDst = cv2.inpaint(img,paint,3,cv2.INPAINT_TELEA)
# cv2.imshow('image',imgDst)
# cv2.waitKey(0)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
paint = cv2.cvtColor(paint, cv2.COLOR_BGR2RGB)
imgDst = cv2.cvtColor(imgDst, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(14, 9), dpi=100) # Set the size and pixels of the drawing area
plt.subplot(131) # The first in a row and three columns
plt.imshow(img)
plt.subplot(132) # The second in a row and three columns
plt.imshow(paint)
plt.subplot(133) # The third in a row and three columns
plt.imshow(imgDst)
plt.show()
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After running the above program, three pictures will be displayed in the jupyterLab control interface, as shown below.
