[FIXED] how to generate company logo image mask using OpenCV?

Issue

I am working on removing the company logo background image mask but I'm not getting the exact output. I try many ways to remove the company logo background but every model is trained in human categories just like model name mode net, u2net, etc. I plan to train the custom model so I need an image and mask I'm generating an image mask but not getting exact output here is my code.

import numpy as np 

# Reading image
image = cv2.imread('image_path')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Find Canny edges
edged = cv2.Canny(gray, 30, 200)
cv2.waitKey(0)

# creating canvas
canvas = np.zeros(image.shape, np.uint8)
canvas.fill(255)

#creating image background mask
mask = np.ones(image.shape, dtype=np.uint8) * 255

contours, hierarchy = cv2.findContours(edged,
   cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

cnts = contours[0]
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)

    ROI = image[y:y+h, x:x+w]

#finding controus
contours_mask, hierarchy = cv2.findContours(edged, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

for contour in range(len(contours_mask)):
   cv2.fillConvexPoly(mask, contours_mask[contour], (0,0,255))

# fill color in background image
cv2.floodFill(mask, None, seedPoint=(0, 0), newVal=128, loDiff=1, upDiff=1)

r=cv2.selectROI('ROI', edged,False,False)
imROI = edged[int(r[1]):int(r[1]+r[3]), int(r[0]):int(r[0]+r[2])]

cv2.imshow('Canny Edges After Contouring', ROI)
cv2.waitKey(0)

print("Number of Contours found = " + str(len(contours)))

# Draw all contours
# -1 signifies drawing all contours
cv2.drawContours(image, contours, -1, (0, 255, 0), 3)

cv2.imshow('Contours', image)
cv2.imshow('background mask', mask)
cv2.imshow('canvas', canvas)
cv2.waitKey(0)
cv2.destroyAllWindows()```

**>I am getting that kind of output in background mask image output.**
[enter image description here][1]


[enter image description here][2]


  [1]: https://i.stack.imgur.com/6B5Mx.png

Solution

Here is my code for remove company logo background using opencv it will be work for me. Here is my input image and output image of mask in below.

# import library
import cv2
import numpy as np

# Reading image
img = cv2.imread("sample_image/logo_1.png", cv2.IMREAD_GRAYSCALE)

# Cannny edge detection
img_canny = cv2.Canny(img, 50, 50)

# Applies a morphological filter to image
img_dilate = cv2.dilate(img_canny, None, iterations=1)

# Perform erosion on the image
img_erode = cv2.erode(img_dilate, None, iterations=1)

# creating new image of given shape 
mask = np.full(img.shape, 0, "uint8")

# Finding Contours
contours, hierarchies = cv2.findContours(img_erode, cv2.RETR_TREE, 
cv2.CHAIN_APPROX_NONE)

# draw contours
for cnt in contours:
  cv2.drawContours(mask, [cnt], -1, 255, -1)

# write image
cv2.imwrite("output_of_mask.png", mask)

# show image
cv2.imshow("result", mask)
cv2.waitKey(0)

Input image:

Output image mask:

Answered By - Ghanshyam