We know that the background is green and is distinguishable from the object by its color, so I suggest using color thresholding. For this, I have written a simple OpenCV Python code to demonstrate the results.

First, we need to install OpenCV.

sudo apt update
pip3 install opencv-python
# verify installation
python3 -c "import cv2; print(cv2.__version__)"

Then, we create a script named skull.py in the same directory with the images.

import cv2
import numpy as np

def show_result(winname, img, wait_time):
    scale = 0.2
    disp_img = cv2.resize(img, None, fx=scale, fy=scale)
    cv2.imshow(winname, disp_img)
    cv2.waitKey(wait_time)

img = cv2.imread('skull.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# define range of green color in HSV
lower_green = np.array([70, 200, 100])
upper_green = np.array([90, 255, 255])
# Threshold the HSV image to extract green color
mask = cv2.inRange(hsv, lower_green, upper_green)
mask = cv2.bitwise_not(mask)

#cv2.imwrite('mask.png', mask)
show_result('mask', mask, 0)
cv2.destroyAllWindows()

You can easily find a tutorial about HSV color operations using OpenCV. I will not go over the functions used here, but one part is important. Image operations are generally done in RGB color space, which holds red, green and blue components. However, HSV is more like human vision system which holds hue, saturation and value components. You can find the conversion here. Since we seperate color based on our perception, HSV is more suitable for this task.

The essential part is to choose the threshold values appropriately. I chose by inspection around 80 for hue (which is max. 180), and above 200 and 100 for saturation and value (max. 255), respectively. You can print the values of a particular pixel by the following lines:

rows,cols,channels = hsv.shape
print(hsv[row, column])

Note that the origin is left upper corner.

Here is the result:

Two things may be needed. One is doing the operation for a set of images, which is trivial using for loops. The other is that if you do not like some portion of the result, you may want to know the pixel location and change the threshold accordingly. This is possible using mouse events.

for i in range(1, 100):
    img = imread(str(i) + '.jpg')

def mouse_callback(event, x, y, flags, params):
    if event == cv2.EVENT_LBUTTONDOWN:
        row = y
        column = x
        print(row, column)

winname = 'img'
cv2.namedWindow(winname)
cv2.setMouseCallback(winname, mouse_callback)

Keep in mind that show_result function resizes the image by scale factor.

If you do not want to deal with pixel positions, rather you want smooth results, you can apply morphological transformations. Especially opening and closing will get the work done.

kernel = np.ones((11,11), np.uint8)
opened = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
closed = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)

Result with opening (kernel=11×11):