How to augment scanned document image with creases, folds and wrinkles?

Question

I am creating a synthetic dataset to train a model that needs to find documents in an image. the documents will be far from perfect, i.e they were folded, creased and wrinkled crinkled.

I could find a few ways of doing it in photoshop but I was wondering if someone has a better idea of doing this augmentation in opencv without trying to reverse engineer the photoshop process.

for example (from https://www.photoshopessentials.com/photo-effects/folds-creases/): to:

or crinkles (from https://www.myjanee.com/tuts/crumple/crumple.htm):

Answer 1

I have tried to put all your distortions together in one script in Python/Opencv.

Input:

Wrinkles:

import cv2
import numpy as np
import math
import skimage.exposure

# read desert car image and convert to float in range 0 to 1
img = cv2.imread('desert_car.png').astype("float32") / 255.0
hh, ww = img.shape[:2]

# read wrinkle image as grayscale and convert to float in range 0 to 1
wrinkles = cv2.imread('wrinkles.jpg',0).astype("float32") / 255.0

# resize wrinkles to same size as desert car image
wrinkles = cv2.resize(wrinkles, (ww,hh), fx=0, fy=0)

# apply linear transform to stretch wrinkles to make shading darker
#wrinkles = skimage.exposure.rescale_intensity(wrinkles, in_range=(0,1), out_range=(0,1)).astype(np.float32)

# shift image brightness so mean is (near) mid gray
mean = np.mean(wrinkles)
shift = mean - 0.4
wrinkles = cv2.subtract(wrinkles, shift)

# create folds image as diagonal grayscale gradient as float as plus and minus equal amount
hh1 = math.ceil(hh/2)
ww1 = math.ceil(ww/3)
val = math.sqrt(0.2)
grady = np.linspace(-val, val, hh1, dtype=np.float32)
gradx = np.linspace(-val, val, ww1, dtype=np.float32)
grad1 = np.outer(grady, gradx)

# flip grad in different directions
grad2 = cv2.flip(grad1, 0)
grad3 = cv2.flip(grad1, 1)
grad4 = cv2.flip(grad1, -1)

# concatenate to form folds image
foldx1 = np.hstack([grad1-0.1,grad2,grad3])
foldx2 = np.hstack([grad2+0.1,grad3,grad1+0.2])
folds = np.vstack([foldx1,foldx2])
#folds = (1-val)*folds[0:hh, 0:ww]
folds = folds[0:hh, 0:ww]

# add the folds image to the wrinkles image
wrinkle_folds = cv2.add(wrinkles, folds)

# draw creases as blurred lines on black background
creases = np.full((hh,ww), 0, dtype=np.float32)
ww2 = 2*ww1
cv2.line(creases, (0,hh1), (ww-1,hh1), 0.25, 1)
cv2.line(creases, (ww1,0), (ww1,hh-1),  0.25, 1)
cv2.line(creases, (ww2,0), (ww2,hh-1),  0.25, 1)

# blur crease image
creases = cv2.GaussianBlur(creases, (3,3), 0)

# add crease to wrinkles_fold image
wrinkle_folds_creases = cv2.add(wrinkle_folds, creases)

# threshold wrinkles and invert
thresh = cv2.threshold(wrinkle_folds_creases,0.7,1,cv2.THRESH_BINARY)[1]
thresh = cv2.cvtColor(thresh,cv2.COLOR_GRAY2BGR) 
thresh_inv = 1-thresh

# convert from grayscale to bgr 
wrinkle_folds_creases = cv2.cvtColor(wrinkle_folds_creases,cv2.COLOR_GRAY2BGR) 

# do hard light composite and convert to uint8 in range 0 to 255
# see CSS specs at https://www.w3.org/TR/compositing-1/#blendinghardlight
low = 2.0 * img * wrinkle_folds_creases
high = 1 - 2.0 * (1-img) * (1-wrinkle_folds_creases)
result = ( 255 * (low * thresh_inv + high * thresh) ).clip(0, 255).astype(np.uint8)

# save results
cv2.imwrite('desert_car_wrinkles_adjusted.jpg',(255*wrinkles).clip(0,255).astype(np.uint8))
cv2.imwrite('desert_car_wrinkles_folds.jpg', (255*wrinkle_folds).clip(0,255).astype(np.uint8))
cv2.imwrite('wrinkle_folds_creases.jpg', (255*wrinkle_folds_creases).clip(0,255).astype(np.uint8))
cv2.imwrite('desert_car_result.jpg', result)

# show results
cv2.imshow('wrinkles', wrinkles)
cv2.imshow('wrinkle_folds', wrinkle_folds)
cv2.imshow('wrinkle_folds_creases', wrinkle_folds_creases)
cv2.imshow('thresh', thresh)
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

Wrinkles adjusted:

Wrinkles with folds:

Wrinkles with folds and creases:

Result: