Here is a possible solution. You'll have to tweak some parameters, of course...
What my example code does:
- apply
threshold and invert (bitwise_not) the image to get a binary image with black background and white letters
- apply a small
dilate to merge some small elements and decrease the number of detections
- use
findContours to... find contours :)
- calculate
boundingRect and area for each contour, returning rectangles where writings are detected (area can be used to filter small unwanted elements)
- prepare an image overlapping the source image with contours and rectangles (this part is necessary just to debug)
After detection, the code proceed creating the new "texture image" you want:
total_width is the sum of all rectangles widthsmean_height is the mean of all rectagles heightstotal_lines is the number of lines in the new image; calculated from total_width and mean_height, so that the resulting image is approximately square- inside a loop, we will copy each rectangle from the
src image to the newImg
curr_line and curr_width tracks the position where to paste the src rectangle- I've used
cv.min() to blend each new rectangle into newImg; this is similar to "darken" blending mode in photoshop
The image showing detections:
The resulting texture image:
An the code...
import cv2 as cv
import numpy as np
import math
src = cv.imread("handwriting.jpg")
src_gray = cv.cvtColor(src,cv.COLOR_BGR2GRAY)
# apply threshold
threshold = 230
_, img_thresh = cv.threshold(src_gray, threshold, 255, 0)
img_thresh = cv.bitwise_not(img_thresh)
# apply dilate
dilatation_size = 1
dilatation_type = cv.MORPH_ELLIPSE
element = cv.getStructuringElement(dilatation_type, (2*dilatation_size + 1, 2*dilatation_size+1), (dilatation_size, dilatation_size))
img_dilate = cv.dilate(img_thresh, element)
# find contours
contours = cv.findContours(img_dilate, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
# calculate rectangles and areas
boundRect = [None]*len(contours[1])
areas = [None]*len(contours[1])
for i, c in enumerate(contours[1]):
boundRect[i] = cv.boundingRect(c)
areas[i] = cv.contourArea(c)
# set drawing
drawing = np.zeros((src.shape[0], src.shape[1], 3), dtype=np.uint8)
# you can use only contours bigger than some area
for i in range(len(contours[1])):
if areas[i] > 1:
color = (50,50,0)
cv.rectangle(drawing, (int(boundRect[i][0]), int(boundRect[i][1])), \
(int(boundRect[i][0]+boundRect[i][2]), int(boundRect[i][1]+boundRect[i][3])), color, 2)
# set newImg
newImg = np.ones((src.shape[0], src.shape[1], 3), dtype=np.uint8)*255
total_width = 0
mean_height = 0.0
n = len(boundRect)
for r in (boundRect):
total_width += r[2]
mean_height += r[3]/n
total_lines = math.ceil(math.sqrt(total_width/mean_height))
max_line_width = math.floor(total_width/total_lines)
# loop through rectangles and perform a kind of copy paste
curr_line = 0
curr_width = 0
for r in (boundRect):
if curr_width > max_line_width:
curr_line += 1
curr_width = 0
# this is the position in newImg, where to insert source rectangle
pos = [curr_width, \
curr_width + r[2], \
math.floor(curr_line*mean_height), \
math.floor(curr_line*mean_height) + r[3] ]
s = src[r[1]:r[1]+r[3], r[0]:r[0]+r[2], :]
d = newImg[pos[2]:pos[3], pos[0]:pos[1], :]
newImg[pos[2]:pos[3], pos[0]:pos[1], :] = cv.min(d,s)
curr_width += r[2]
cv.imwrite('detection.png',cv.subtract(src,drawing))
cv.imshow('blend',cv.subtract(src,drawing))
crop = int(max_line_width*1.1)
cv.imwrite('texture.png',newImg[:crop, :crop, :])
cv.imshow('newImg',newImg[:crop, :crop, :])
cv.waitKey()
