1.標定噪聲的特征，使用cv2.inRange二值化標識噪聲對圖片進行二值化處理，具體代碼：cv2.inRange(img, np.array([200, 200, 240]), np.array([255, 255, 255]))，把[200, 200, 200]~[255, 255, 255]以外的顏色處理為0
import cv2
import numpy as np
from PIL import Image
import os
dir = os.getcwd()
path = "1.jpg"
newPath = "new.jpg"
img=cv2.imread(path,1)
hight,width,depth=img.shape[0:3]
#截取
cropped = img[int(hight*0.8):hight, int(width*0.7):width] # 裁剪坐標為[y0:y1, x0:x1]
cv2.imwrite(newPath, cropped)
imgSY = cv2.imread(newPath,1)
#圖片二值化處理，把[200,200,200]-[250,250,250]以外的顏色變成0
thresh = cv2.inRange(imgSY,np.array([200,200,200]),np.array([250,250,250]))
#創建形狀和尺寸的結構元素
kernel = np.ones((3,3),np.uint8)
#擴展待修復區域
hi_mask = cv2.dilate(thresh,kernel,iterations=10)
specular = cv2.inpaint(imgSY,hi_mask,5,flags=cv2.INPAINT_TELEA)
cv2.imwrite(newPath, specular)
#覆蓋圖片
imgSY = Image.open(newPath)
img = Image.open(path)
img.paste(imgSY, (int(width*0.7),int(hight*0.8),width,hight))
img.save(newPath)
import cv2
import numpy as np
from PIL import Image
import os
dir = os.getcwd()
path = "1.jpg"
newPath = "new.jpg"
img=cv2.imread(path,1)
hight,width,depth=img.shape[0:3]
#截取
cropped = img[int(hight*0.8):hight, int(width*0.7):width] # 裁剪坐標為[y0:y1, x0:x1]
cv2.imwrite(newPath, cropped)
imgSY = cv2.imread(newPath,1)
#圖片二值化處理，把[200,200,200]-[250,250,250]以外的顏色變成0
thresh = cv2.inRange(imgSY,np.array([200,200,200]),np.array([250,250,250]))
#創建形狀和尺寸的結構元素
kernel = np.ones((3,3),np.uint8)
#擴展待修復區域
hi_mask = cv2.dilate(thresh,kernel,iterations=10)
specular = cv2.inpaint(imgSY,hi_mask,5,flags=cv2.INPAINT_TELEA)
cv2.imwrite(newPath, specular)
#覆蓋圖片
imgSY = Image.open(newPath)
img = Image.open(path)
img.paste(imgSY, (int(width*0.7),int(hight*0.8),width,hight))
img.save(newPath)
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