细化OpenCV遮罩边缘

Question

I'm in the process of scanning old photographs, and I would like to automate the process of extracting the photograph from the (noisy) solid white background of the scanner so that I have a transparent photograph. This part of the program now works, but I have one more small problem with this.

The photograph can now be accurately detected (and extracted), but it leaves a small and sharp black border from the background around the entire photograph. I've tried to apply a gaussian blur to the transparency mask, but this wouldn't smooth the blackness away (and it made the border of the photograph look 'smudged').

This is the code that I have to extract the photo and generate the transparency mask:

# Load the scan, and convert it to RGBA.
original = cv2.imread('input.jpg')
original = cv2.cvtColor(original, cv2.COLOR_BGR2BGRA)

# Make the scan grayscale, and apply a blur.
image = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)
image = cv2.GaussianBlur(image, (25, 25), 0)

# Binarize the scan.
retval, threshold = cv2.threshold(image, 50, 255, cv2.THRESH_BINARY)

# Find the contour of the object.
contours, hierarchy = cv2.findContours(threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

largestContourArea = -1
largestContour = -1

for contour in contours:
    area = cv2.contourArea(contour)

    if area > largestContourArea:
        largestContourArea = area
        largestContour = contour

# Generate the transparency mask.
mask = numpy.zeros(original.shape, numpy.uint8)

cv2.drawContours(mask, [ largestContour ], -1, (255, 255, 255, 255), -1)

# Apply the transparency mask.
original = cv2.multiply(mask.astype(float) / 255.0, original.astype(float))

cv2.imwrite('output.png', original)

I have a sample scan and the result of the code above using the sample scan. As you can see, there is a slight black border all around the photograph, which I would like to remove.

Answer 1

By using the erode method, you could shrink the contour ( mask ), effectively removing the black edge.

Since this method supports in-place operation, the code would look something like this: cv2.erode(mask, mask, kernel) , where kernel is a kernel acquired using cv2.getStructuringElement . By playing around with the kernel and the iteration count, you can control the amount of shrinking.

This page explains everything in great detail and also provides nice examples: https://docs.opencv.org/2.4/doc/tutorials/imgproc/erosion_dilatation/erosion_dilatation.html