How to remove white background from an image in Python with opencv?

I have improved my code considerably

• in addition to removing the background and painting it completely white

• I have put a forto take the number of photos you want, in this case it would be changed in the range for acum in range(40):

• and immediately organize the completely white background or the color that I want in the Parameter part, it will save the photos in a folder that I defined img.

  MASK_COLOR = (1.0,1.0,1.0) # In BGR format

I attach the code in case someone else needs it in a project.

import cv2
import numpy as np
import time
from matplotlib import pyplot as plt

#== Parameters ======================================================================= 
BLUR = 21 
CANNY_THRESH_1 = 10 
CANNY_THRESH_2 = 200 
MASK_DILATE_ITER = 10 
MASK_ERODE_ITER = 10 
MASK_COLOR = (1.0,1.0,1.0) # In BGR format 

#== Processing ======================================================================= 

#== Camera 1, 2 or n ================================================================= 
for acum in range(40):
    cap = cv2.VideoCapture(0)
    read, frame = cap.read()

    if read == True:
    
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2BGRA)
        frame[np.all(frame == [255, 255, 255, 255], axis=2)] = [0, 0, 0, 0]
        cv2.imwrite("img/foto"+str(acum)+".png", frame)
        print("Photo taken correctly")
        #== Processing ======================================================================= 

        #-- Read image ----------------------------------------------------------------------- 
        img = cv2.imread("img/foto"+str(acum)+".png") 
        gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 

        #-- Edge detection ------------------------------------------------------------------- 
        edges = cv2.Canny(gray, CANNY_THRESH_1, CANNY_THRESH_2) 
        edges = cv2.dilate(edges, None) 
        edges = cv2.erode(edges, None) 

        #-- Find contours in edges, sort by area --------------------------------------------- 
        contour_info = [] 
        contours, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) 
        for c in contours: 
            contour_info.append((
            c, 
            cv2.isContourConvex(c), 
            cv2.contourArea(c), 
            )) 
        contour_info = sorted(contour_info, key=lambda c: c[2], reverse=True) 
        max_contour = contour_info[0] 

        #-- Create empty mask, draw filled polygon on it corresponding to largest contour ---- 
        # Mask is black, polygon is white 
        mask = np.zeros(edges.shape) 
        cv2.fillConvexPoly(mask, max_contour[0], (255)) 

        #-- Smooth mask, then blur it -------------------------------------------------------- 
        mask = cv2.dilate(mask, None, iterations=MASK_DILATE_ITER) 
        mask = cv2.erode(mask, None, iterations=MASK_ERODE_ITER) 
        mask = cv2.GaussianBlur(mask, (BLUR, BLUR), 0) 
        mask_stack = np.dstack([mask]*3) # Create 3-channel alpha mask 

        #-- Blend masked img into MASK_COLOR background -------------------------------------- 
        mask_stack = mask_stack.astype('float32')/255.0   # Use float matrices, 
        img   = img.astype('float32')/255.0     # for easy blending 

        masked = (mask_stack * img) + ((1-mask_stack) * MASK_COLOR) # Blend 
        masked = (masked * 255).astype('uint8')      # Convert back to 8-bit 

        cv2.imwrite("img/foto"+str(acum)+".png", masked)   # Save
    else:
        print("Error accessing camera")
        #We finally released the camera
    cap.release()

Post I relied on

I bring you the answer from http://benjamintan.io/blog/2018/05/24/making-transparent-backgrounds-with-numpy-and-opencv-in-python/

To your code you must add:

frame = cv2.cvtColor(frame, cv2.COLOR_BGR2BGRA)

to add the alpha channel to the image (pay attention to the color order: BGR and not RGB), and

frame[np.all(frame == [255, 255, 255, 255], axis=2)] = [0, 0, 0, 0]

to find the pixels that are white , i.e. (255, 255, 255) in BGR. On the same line you replace the colors.

To change pixels that are not completely white, you can change the line above to

frame[np.all(np.isclose(frame, [255, 255, 255, 255], atol=200), axis=2)] = [0, 0, 0, 0]

where the value of atol ( atol=200) indicates how close the value is, with 200 being a fairly high value.

I leave you the complete code:

import cv2
import numpy as np
from matplotlib import pyplot as plt
"""
    En este caso, 0 quiere decir que queremos acceder
    a la cámara 0. Si hay más cámaras, puedes ir probando
    con 1, 2, 3...
"""
cap = cv2.VideoCapture(0)
read, frame = cap.read()

if read == True:
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2BGRA)
    #frame[np.all(frame == [255, 255, 255, 255], axis=2)] = [0, 0, 0, 0]
    frame[np.all(np.isclose(frame, [255, 255, 255, 255], atol=200), axis=2)] = [0, 0, 0, 0]
    cv2.imwrite("foto.png", frame)
    print("Foto tomada correctamente")
else:
    print("Error al acceder a la cámara")
    #Finalmente liberamos o soltamos la cámara
cap.release()