Remove undesired connected pixels from an image with Python

Question

I'm a beginner in image processing with Python so I need help. I'm trying to remove areas of connected pixels from my pictures with the code posted below. Actually, it works but not well. What I desire is the removing of areas of pixels, such as those marked in red in the pictures reported below, from my images, so as to obtain a cleaned picture. Would be also great to set a minimum and a maximum limit for the dimensions of the detected areas of connected pixels. Example of a picture with marked areas 1 Example of a picture with marked areas 2

This is my currently code:

### LOAD MODULES ###
import numpy as np
import imutils
import cv2

def is_contour_bad(c): # Decide what I want to find and its features
    peri=cv2.contourArea(c, True) # Find areas
    approx=cv2.approxPolyDP(c, 0.3*peri, True) # Set areas approximation
    return not len(approx)>2 # Threshold to decide if add an area to the mask for its removing (if>2 remove)


### DATA PROCESSING ###
image=cv2.imread("025.jpg") # Load a picture
gray=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Convert to grayscale
cv2.imshow("Original image", image) # Plot

edged=cv2.Canny(gray, 50, 200, 3) # Edges of areas detection
cnts=cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) # Find contours: a curve joining all the continuous points (along the boundary), having same color or intensity
cnts=imutils.grab_contours(cnts)

mask=np.ones(image.shape[:2], dtype="uint8")*255 # Setup the mask with white background
# Loop over the detected contours
for c in cnts:
    # If the contour satisfies "is_contour_bad", draw it on the mask
    if is_contour_bad(c):
        cv2.drawContours(mask, [c], -1, 0, -1) # (source image, list of contours, with -1 all contours in [c] pass, 0 is the intensity, -1 the thickness)

image_cleaned=cv2.bitwise_and(image, image, mask=mask) # Remove the contours from the original image
cv2.imshow("Adopted mask", mask) # Plot
cv2.imshow("Cleaned image", image_cleaned) # Plot
cv2.imwrite("cleaned_025.jpg", image_cleaned) # Write in a file

Answer 1

You may execute the following processing steps:

• Threshold the image to binary image using cv2.threshold.
  It's not a must, but in your case it looks like shades of gray are not important.
• Use closing morphological operation, for closing small gaps in the binary image.
• Use cv2.findContours with cv2.RETR_EXTERNAL parameter, for getting the contours (perimeter) surrounding the white clusters.
• Modify the logic of "bad contour", to return true, only if area is large (assuming you only want to clean the large three contour).

Here is the updated code:

### LOAD MODULES ###
import numpy as np
import imutils
import cv2

def is_contour_bad(c): # Decide what I want to find and its features
    peri = cv2.contourArea(c) # Find areas
    return peri > 50 # Large area is considered "bad"


### DATA PROCESSING ###
image = cv2.imread("025.jpg") # Load a picture
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Convert to grayscale

# Convert to binary image (all values above 20 are converted to 1 and below to 0)
ret, thresh_gray = cv2.threshold(gray, 20, 255, cv2.THRESH_BINARY)

# Use "close" morphological operation to close the gaps between contours
# https://stackoverflow.com/questions/18339988/implementing-imcloseim-se-in-opencv
thresh_gray = cv2.morphologyEx(thresh_gray, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5)));

#Find contours on thresh_gray, use cv2.RETR_EXTERNAL to get external perimeter
_, cnts, _ = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # Find contours: a curve joining all the continuous points (along the boundary), having same color or intensity

image_cleaned = gray

# Loop over the detected contours
for c in cnts:
    # If the contour satisfies "is_contour_bad", draw it on the mask
    if is_contour_bad(c):
        # Draw black contour on gray image, instead of using a mask
        cv2.drawContours(image_cleaned, [c], -1, 0, -1)


#cv2.imshow("Adopted mask", mask) # Plot
cv2.imshow("Cleaned image", image_cleaned) # Plot
cv2.imwrite("cleaned_025.jpg", image_cleaned) # Write in a file

cv2.waitKey(0)
cv2.destroyAllWindows()

---

Result:

---

Marking contours found for testing:

for c in cnts:
    if is_contour_bad(c):
        # Draw green line for marking the contour
        cv2.drawContours(image, [c], 0, (0, 255, 0), 1)

Result:

There is still work to be done...

---

Update

Two iterations approach:

• First iteration - remove the large contour.
• Second iteration - remove small but bright contours.

Here is the code:

import numpy as np
import imutils
import cv2

def is_contour_bad(c, thrs): # Decide what I want to find and its features
    peri = cv2.contourArea(c) # Find areas
    return peri > thrs # Large area is considered "bad"

image = cv2.imread("025.jpg") # Load a picture
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Convert to grayscale

# First iteration - remove the large contour
###########################################################################
# Convert to binary image (all values above 20 are converted to 1 and below to 0)
ret, thresh_gray = cv2.threshold(gray, 20, 255, cv2.THRESH_BINARY)

# Use "close" morphological operation to close the gaps between contours
# https://stackoverflow.com/questions/18339988/implementing-imcloseim-se-in-opencv
thresh_gray = cv2.morphologyEx(thresh_gray, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5)));

#Find contours on thresh_gray, use cv2.RETR_EXTERNAL to get external perimeter
_, cnts, _ = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # Find contours: a curve joining all the continuous points (along the boundary), having same color or intensity

image_cleaned = gray

# Loop over the detected contours
for c in cnts:
    # If the contour satisfies "is_contour_bad", draw it on the mask
    if is_contour_bad(c, 1000):
        # Draw black contour on gray image, instead of using a mask
        cv2.drawContours(image_cleaned, [c], -1, 0, -1)
###########################################################################


# Second iteration - remove small but bright contours
###########################################################################
# In the second iteration, use high threshold
ret, thresh_gray = cv2.threshold(image_cleaned, 150, 255, cv2.THRESH_BINARY)

# Use "dilate" with small radius
thresh_gray = cv2.morphologyEx(thresh_gray, cv2.MORPH_DILATE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2,2)));

#Find contours on thresh_gray, use cv2.RETR_EXTERNAL to get external perimeter
_, cnts, _ = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # Find contours: a curve joining all the continuous points (along the boundary), having same color or intensity

# Loop over the detected contours
for c in cnts:
    # If the contour satisfies "is_contour_bad", draw it on the mask
    # Remove contour if  area is above 20 pixels
    if is_contour_bad(c, 20):
        # Draw black contour on gray image, instead of using a mask
        cv2.drawContours(image_cleaned, [c], -1, 0, -1)
###########################################################################

---

Marked contours: