I am a beginner and I am trying to apply an outline to the white remote control on the left that shares the same color with the background.
a = cv2.imread(file_name)
imgGray = cv2.cvtColor(a,cv2.COLOR_BGR2GRAY)
imgGray = cv2.GaussianBlur(imgGray,(11,11),20)
k5 = np.array([[-1,-1,-1],[-1,9,-1],[-1,-1,-1]])
imgGray = cv2.filter2D(imgGray,-1,k5)
cv2.namedWindow("Control")
cv2.createTrackbar("blocksize","Control",33,1000,f)
cv2.createTrackbar("c","Control",3,100,f)
while True:
strel = cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))
blocksize = cv2.getTrackbarPos("blocksize","Control")
c = cv2.getTrackbarPos("c","Control")
if blocksize%2==0:
blocksize += 1
thrash = cv2.adaptiveThreshold(imgGray,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV,blockSize=blocksize,C=c)
thrash1 = cv2.adaptiveThreshold(imgGray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,blockSize=blocksize,C=c)
cv2.imshow("mean",thrash)
cv2.imshow("gaussian",thrash1)
#r,thrash = cv2.threshold(imgGray,150,255,cv2.THRESH_BINARY_INV)
key = cv2.waitKey(1000)
if key == 32 or iter == -1:
break
edges = cv2.Canny(thrash,100,200)
cv2.imshow('sharpen',sharpen)
cv2.imshow('edges',edges)
cv2.imshow('grey ',imgGray)
cv2.imshow('thrash ',thrash)
cv2.waitKey(0)
circles = cv2.HoughCircles(imgGray,cv2.HOUGH_GRADIENT,1,60,param1=240,param2=50,minRadius=0,maxRadius=0)
contours,_ = cv2.findContours(thrash,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
putlabel(circles,a,contours)
Those are what I have tried, I have also tried morphological operation such as dilation, erosion, opening and closing too but I am still unable to acquire the result.
Below is my best result but the noise is too severe and the remote controller didn't get fully outlined.
I have thought of a pure image processing approach. But the results are not as accurate as the one depicted by @nathancy
TLDR; I am using Difference of Gaussians (DoG) which is a 2-stage edge detector.
Blurring operation generally acts as a suppressor of high frequencies. By subtracting the result of two different blurring operations we get a band-pass filter. I would like to quote from this blog "Subtracting one blurred image from the other preserves spatial information that lies between the range of frequencies that are preserved in the two blurred images"
I wrote a simple function that returns the difference of two blurred images:
def dog(img, k1, s1, k2, s2):
b1 = cv2.GaussianBlur(img,(k1, k1), s1)
b2 = cv2.GaussianBlur(img,(k2, k2), s2)
return b1 - b2
Note: Extent is a property of a contour which is the ration of the contour area to its corresponding bounding rectangle area. Taken from here
img = cv2.imread('path_to_image', cv2.IMREAD_UNCHANGED)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Function to perform Difference of Gaussians
def difference_of_Gaussians(img, k1, s1, k2, s2):
b1 = cv2.GaussianBlur(img,(k1, k1), s1)
b2 = cv2.GaussianBlur(img,(k2, k2), s2)
return b1 - b2
DoG_img = difference_of_Gaussians(gray, 7, 7, 17, 13)
As you can see, it functions as an edge detector. You can vary the kernel sizes (k1, k2
) and sigma values (s1, s2
)
# Applying Otsu Threshold and finding contours
th = cv2.threshold(DoG_img ,127,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
contours, hierarchy = cv2.findContours(th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Create copy of original image
img1 = img.copy()
# for each contour above certain area and extent, draw minimum bounding box
for c in contours:
area = cv2.contourArea(c)
if area > 1500:
x,y,w,h = cv2.boundingRect(c)
extent = int(area)/(w*h)
if extent > 0.6:
rect = cv2.minAreaRect(c)
box = cv2.boxPoints(rect)
box = np.int0(box)
cv2.drawContours(img1,[box],0,(0,255,0),4)
As you can see, the result is not perfect. The shadows of the objects are also captured during the edge detection process (Difference of Gaussians). You can try varying the parameters to check if the result gets better.