如何掩蓋灰色噪音的圖像？

Question

我有以下我想掩蓋的原始圖像。我只想要圓形（幾乎）橙色/棕色結構被蒙上白色。我該如何去做呢？

http://imgur.com/a/HNmRn

我試過閾值，但我不希望下閾值是一個變量。

Answer 1

您可以嘗試轉換爲HSV顏色空間和顏色閾值。但是，您可能無法將閾值作爲變量移除，因爲每個圖像的光照都有輕微的變化。從經驗中我可以告訴你，有時候你可以慷慨地擴展門檻，以適應大部分你想要的東西。但更通用的解決方案將需要更復雜的算法。

來自OpenCV的文檔：

11  # Convert BGR to HSV 
12  hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) 
13 
14  # define range of blue color in HSV 
15  lower_blue = np.array([110,50,50]) 
16  upper_blue = np.array([130,255,255]) 
17 
18  # Threshold the HSV image to get only blue colors 
19  mask = cv2.inRange(hsv, lower_blue, upper_blue) 

對於你那裏，你將不得不調整過程中的參數暗黃。

Answer 2

使用霍夫圓變換來查找分隔眼睛和灰色區域的圓。

其基本思想是運行Hough circle transfor，然後找到圈子內部與外部之間值最大的圓圈。

結果：

代碼：

import cv2 
import numpy as np 


# Read image 
Irgb = cv2.imread('eye.jpg') 

# Take the first channel (No specifc reason just good contrast between inside the eye and outside) 
Igray = Irgb[:,:,0] 

# Run median filter to reduce noise 
IgrayFilter = cv2.medianBlur(Igray,101) 

# Find circles using hough circles 
minRadius = np.floor(np.min(Igray.shape)/2) 
circles = cv2.HoughCircles(IgrayFilter, cv2.HOUGH_GRADIENT, dp=0.5,param1=100,param2=50,minRadius=int(minRadius),minDist=100) 
circles = np.uint16(np.around(circles)) 
cimg = Irgb 

# For each circle that we found find the intinestiy values inside the circle and outside. 
# We eould take the circle that as the biggest difference between inside and outside 
diff = [] 
for i in circles[0, :]: 

    # Create mask from circel identity 
    mask = np.zeros_like(Igray) 
    maskInverse = np.ones_like(Igray) 

    cv2.circle(mask, (i[0], i[1]), i[2], 1, cv2.FILLED) 
    cv2.circle(maskInverse, (i[0], i[1]), i[2], 0, cv2.FILLED) 

    # Find values inside mask and outside 
    insideMeanValues = np.mean(np.multiply(mask,Igray)) 
    outsideMeanValues = np.mean(np.multiply(maskInverse, Igray)) 

    # Save differnses 
    diff.append(abs(insideMeanValues-outsideMeanValues)) 


# Take the circle with the biggest difference in color as the border circle 
circleID = np.argmax(diff) 
circleInfo = circles[0, circleID] 

# Create mask from final image 
mask = np.zeros_like(Igray) 
cv2.circle(mask, (i[0], i[1]), i[2], 1, cv2.FILLED) 

# Show final image only in the mask 
finalImage = Irgb 
finalImage[:,:,0] = np.multiply(finalImage[:,:,0],mask) 
finalImage[:,:,1] = np.multiply(finalImage[:,:,1],mask) 
finalImage[:,:,2] = np.multiply(finalImage[:,:,2],mask) 

cv2.imwrite('circle.jpg',finalImage)