How to remove noise after Otsu Binarization using OpenCV in Python? - python-3.x

Question

I am using a Raspberry Pi and it's Camera to perform some Image Processing algorithm. So , I am performing a background subtraction on successive frames of the captured stream and trying to find if there is any object present in the image and if yes, print out it's area . The algorithm works fine as expected but there is a problem .
The thresholding function which uses cv2.THRESH_OTSU , results into a grainy image whenever there is no object present , i.e the background and the foreground images are same . However those noises/grain disappear when there is an object present in the foreground image . These are as follows -
Same Background Image and Foreground Image with noise
Different Background and Foreground Image without any noise
As you can see ,if the images are almost same , the noise is present and if any object is introduced in the frame , then the noise vanishes .
I have tried the following to remove the noise but it didn't work .
Tried using only cv2.THRESH_BINARY / cv2.THRESH_BINARY_INV without Otsu binariszation.
I have tried increasing the brightness/contrast/saturation of the captured image to see if the performance varies , but no change .
I have tried to increase/decrease the amount of erosion/dilation preceding the Thresholding step , but this did not make any change either .
This is my code -
from time import sleep
from picamera import PiCamera
from picamera.array import PiRGBArray
import cv2,os
import numpy as np
import threading
def imageSubtract(img):
bilateral_filtered_image = cv2.bilateralFilter(img, 9, 170, 170)
bilateral_filtered_image = cv2.cvtColor(bilateral_filtered_image,cv2.COLOR_BGR2GRAY)
return bilateral_filtered_image
def imageProcessing():
camera = PiCamera()
camera.resolution = (512,512)
camera.awb_mode="fluorescent"
camera.iso = 800
camera.contrast=33
camera.brightness=75
camera.sharpness=100
rawCapture = PiRGBArray(camera, size=(512, 512))
first_time=0
frame_buffer=0
counter=0
camera.start_preview()
sleep(2)
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
if first_time==0:
rawCapture.truncate(0)
if frame_buffer<10:
print("Frame rejected -",str(frame_buffer))
frame_buffer+=1
continue
os.system("clear")
refImg=frame.array
refThresh=imageSubtract(refImg)
first_time=1
image = frame.array
cv2.imshow("Foreground", image)
key = cv2.waitKey(1)
rawCapture.truncate(0)
newThresh=imageSubtract(image)
diff=cv2.absdiff(refThresh,newThresh)
kernel = np.ones((5,5),np.uint8)
diff=cv2.dilate(diff,kernel,iterations = 3)
cv2.imshow("Background",refImg)
_, thresholded = cv2.threshold(diff, 0 , 255, cv2.THRESH_BINARY +cv2.THRESH_OTSU)
_, contours, _= cv2.findContours(thresholded,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
try:
c=max(contours,key=cv2.contourArea)
x,y,w,h = cv2.boundingRect(c)
cv2.rectangle(thresholded,(x,y),(x+w,y+h),(125,125,125),2)
if cv2.contourArea(c)>500:
print("Object detected with area = ",cv2.contourArea(c))
cv2.imshow("Threshold",thresholded)
if key == ord('q'):
camera.close()
cv2.destroyAllWindows()
break
except Exception as e:
pass
if __name__ == "__main__" :
imageProcessing()
Please help me to remove the noise when the background and foreground Images are same .
Thank You !