chromakey: opencv make green screen video transparent

I'm using the below script to replace the green screen of the original video with a background image but the result is not what I expected, I've changed u_green array parameters and also l_green but it only gets worst. In the end, I want to make it transparent which mask should I use?

I'd appreciate any help to fix this.

Python script:

import cv2
import numpy as np


video = cv2.VideoCapture("green.mp4")
image = cv2.imread("bg.jpg")

while True:

    ret, frame = video.read()

    frame = cv2.resize(frame, (640, 480))
    image = cv2.resize(image, (640, 480))

    u_green = np.array([104, 153, 70])
    l_green = np.array([30, 30, 0])

    mask = cv2.inRange(frame, l_green, u_green)
    res = cv2.bitwise_and(frame, frame, mask=mask)

    f = frame - res
    f = np.where(f == 0, f, image)

    cv2.imshow("video", frame)
    cv2.imshow("mask", f)

    if cv2.waitKey(25) == 27:
        break

video.release()
cv2.destroyAllWindows()

result :

Update Source video: Link

I did my best to mask out the screen using the HSV color space. There's still some green outline, but I can't increase the color margin any more without chopping out bits of clothes.

Edit: wrapped the code inside of a video loop.

Edit 2: I added a VideoWriter to save the results and swapped over to using the saturation channel since it had better separation.

Output Video:

https://drive.google.com/file/d/1GrECFwFy7JQJT6kUGrfLtlXjcfBsr7fP/view?usp=sharing

import cv2
import numpy as np

# open up video
cap = cv2.VideoCapture("video.mp4");

# grab one frame
scale = 0.5;
_, frame = cap.read();
h,w = frame.shape[:2];
h = int(h*scale);
w = int(w*scale);

# videowriter 
res = (w, h);
fourcc = cv2.VideoWriter_fourcc(*'XVID');
out = cv2.VideoWriter('test_vid.avi',fourcc, 30.0, res);

# loop
done = False;
while not done:
    # get frame
    ret, img = cap.read();
    if not ret:
        done = True;
        continue;

    # resize
    img = cv2.resize(img, res);

    # change to hsv
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV);
    h,s,v = cv2.split(hsv);

    # get uniques
    unique_colors, counts = np.unique(s, return_counts=True);

    # sort through and grab the most abundant unique color
    big_color = None;
    biggest = -1;
    for a in range(len(unique_colors)):
        if counts[a] > biggest:
            biggest = counts[a];
            big_color = int(unique_colors[a]);

    # get the color mask
    margin = 50;
    mask = cv2.inRange(s, big_color - margin, big_color + margin);

    # smooth out the mask and invert
    kernel = np.ones((3,3), np.uint8);
    mask = cv2.dilate(mask, kernel, iterations = 1);
    mask = cv2.medianBlur(mask, 5);
    mask = cv2.bitwise_not(mask);

    # crop out the image
    crop = np.zeros_like(img);
    crop[mask == 255] = img[mask == 255];

    # show
    cv2.imshow("Mask", mask);
    cv2.imshow("Blank", crop);
    cv2.imshow("Image", img);
    done = cv2.waitKey(1) == ord('q');

    # save
    out.write(crop);

# close caps
cap.release();
out.release();