Rotate image in python and remove the background

Question

Is there a way to rotate these kind of images and remove the background whitespace or any background and get and image like this

I tried to remove the background if the image doesn't have any rotation i am able to remove the background whitespace by using this script but if the image got any rotation it doesn't remove any space i followed this How to crop or remove white background from an image

import cv2
import numpy as np

img = cv2.imread('cheque_img\rotate.PNG')
## (1) Convert to gray, and threshold
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
th, threshed = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY_INV)

## (2) Morph-op to remove noise
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11,11))
morphed = cv2.morphologyEx(threshed, cv2.MORPH_CLOSE, kernel)

## (3) Find the max-area contour
cnts = cv2.findContours(morphed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
cnt = sorted(cnts, key=cv2.contourArea)[-1]

## (4) Crop and save it
x,y,w,h = cv2.boundingRect(cnt)
dst = img[y:y+h, x:x+w]
cv2.imwrite("001.png", dst) 

Please try it with any scanned image and rotate it and try to get rid of the background white space and rotate it to its original dimension for doing computer vision operation

Answer 1

Using cv2.boundingRect will give you the minimum non-rotating rectangle that fit the contour. cv2.boundingRect result :

Instead of cv2.boundingRect, you will need to use cv2.minAreaRect to obtain a rectangle that fit the contour. cv2.minAreaRect result :

After the obtaining the rotated rect information, you will need to find the affine transform matrix between the model points and the current points. Current points are the points found in rotated rect and the model point is the point of the original object. In this case an object with the initial location (0,0) and the width and height of the rotated rect.

Affine might be an overkill here but for generality affine transform is used.

Detailed explanation is located in the code.

import cv2
import numpy as np

img = cv2.imread('Bcm3h.png')

## (1) Convert to gray, and threshold
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
th, threshed = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY_INV)


## (2) Morph-op to remove noise
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11,11))
morphed = cv2.morphologyEx(threshed, cv2.MORPH_CLOSE, kernel)

## (3) Find the max-area contour
cnts = cv2.findContours(morphed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
cnt = sorted(cnts, key=cv2.contourArea)[-1]


## This will extract the rotated rect from the contour
rot_rect = cv2.minAreaRect(cnt)

# Extract useful data
cx,cy = (rot_rect[0][0], rot_rect[0][1]) # rect center
sx,sy = (rot_rect[1][0], rot_rect[1][1]) # rect size
angle = rot_rect[2] # rect angle


# Set model points : The original shape
model_pts = np.array([[0,sy],[0,0],[sx,0],[sx,sy]]).astype('int')
# Set detected points : Points on the image
current_pts = cv2.boxPoints(rot_rect).astype('int')

# sort the points to ensure match between model points and current points
ind_model = np.lexsort((model_pts[:,1],model_pts[:,0]))
ind_current = np.lexsort((current_pts[:,1],current_pts[:,0]))

model_pts = np.array([model_pts[i] for i in ind_model])
current_pts = np.array([current_pts[i] for i in ind_current])


# Estimate the transform betwee points
M = cv2.estimateRigidTransform(current_pts,model_pts,True)

# Wrap the image
wrap_gray = cv2.warpAffine(gray, M, (int(sx),int(sy)))


# for display
cv2.imshow("dst",wrap_gray)
cv2.waitKey(0)

#cv2.imwrite("001.png", dst) 

Result :

Answer 2

Considering you don't know the angle of the rotation and can be different for each scanned image, you need to find it first.

Combine what you already did with accepted answer for this question.

For the image you provided:

Angle is -25.953375702364195

Answer 3

If the background is guaranteed to be saturated white (value 255) and the document mostly unsaturated values, binarize below the threshold 255 and fit a bounding rectangle.

Answer 4

I had some problems running the code presented above, so here is my slightly modified version:

import cv2
import numpy as np

def crop_minAreaRect(img, rect):
    # rotate img
    angle = rect[2]
    print("angle: " + str(angle))
    rows,cols = img.shape[0], img.shape[1]
      
    M = cv2.getRotationMatrix2D((cols/2,rows/2),angle,1)
    img_rot = cv2.warpAffine(img,M,(cols,rows))

    # rotate bounding box
    rect0 = (rect[0], rect[1], angle) 
    box = cv2.boxPoints(rect0)
    pts = np.int0(cv2.transform(np.array([box]), M))[0]    
    pts[pts < 0] = 0

    # crop
    img_crop = img_rot[pts[1][1]:pts[0][1], 
                       pts[1][0]:pts[2][0]]

    return img_crop
    
def ResizeWithAspectRatio(image, width=None, height=None, inter=cv2.INTER_AREA):
    dim = None
    (h, w) = image.shape[:2]

    if width is None and height is None:
        return image
    if width is None:
        r = height / float(h)
        dim = (int(w * r), height)
    else:
        r = width / float(w)
        dim = (width, int(h * r))

    return cv2.resize(image, dim, interpolation=inter)

img = cv2.imread('rotatedCheque.png')
cv2.imshow("orig", img)
img_copy = img.copy()

# (1) Convert to gray, and threshold
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
th, threshed = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY_INV)

# (2) Morph-op to remove noise
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
morphed = cv2.morphologyEx(threshed, cv2.MORPH_CLOSE, kernel)

# (3) Find the max-area contour
cnts = cv2.findContours(morphed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
cnt = sorted(cnts, key=cv2.contourArea)[-1]

# This will extract the rotated rect from the contour
rot_rect = cv2.minAreaRect(cnt)

cropped_img = crop_minAreaRect(img, rot_rect)

width, height = img.shape[0], img.shape[1]
if height > width:
  cropped_img = cv2.rotate(cropped_img, cv2.ROTATE_90_CLOCKWISE)
  
resized_img = ResizeWithAspectRatio(cropped_img, width=800)

cv2.imshow("cropped", resized_img)
cv2.waitKey(0)