Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,969 Bytes
c509e76 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
import cv2
# importing required libraries
import numpy as np
import cv2
from skimage.filters import threshold_sauvola
import glob
from tqdm import tqdm
import os
from skimage import io
def SauvolaModBinarization(image,n1=51,n2=51,k1=0.3,k2=0.3,default=True):
'''
Binarization using Sauvola's algorithm
@name : SauvolaModBinarization
parameters
@param image (numpy array of shape (3/1) of type np.uint8): color or gray scale image
optional parameters
@param n1 (int) : window size for running sauvola during the first pass
@param n2 (int): window size for running sauvola during the second pass
@param k1 (float): k value corresponding to sauvola during the first pass
@param k2 (float): k value corresponding to sauvola during the second pass
@param default (bool) : bollean variable to set the above parameter as default.
@param default is set to True : thus default values of the above optional parameters (n1,n2,k1,k2) are set to
n1 = 5 % of min(image height, image width)
n2 = 10 % of min(image height, image width)
k1 = 0.5
k2 = 0.5
Returns
@return A binary image of same size as @param image
@cite https://drive.google.com/file/d/1D3CyI5vtodPJeZaD2UV5wdcaIMtkBbdZ/view?usp=sharing
'''
if(default):
n1 = int(0.05*min(image.shape[0],image.shape[1]))
if (n1%2==0):
n1 = n1+1
n2 = int(0.1*min(image.shape[0],image.shape[1]))
if (n2%2==0):
n2 = n2+1
k1 = 0.5
k2 = 0.5
if(image.ndim==3):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
gray = np.copy(image)
T1 = threshold_sauvola(gray, window_size=n1,k=k1)
max_val = np.amax(gray)
min_val = np.amin(gray)
C = np.copy(T1)
C = C.astype(np.float32)
C[gray > T1] = (gray[gray > T1] - T1[gray > T1])/(max_val - T1[gray > T1])
C[gray <= T1] = 0
C = C * 255.0
new_in = np.copy(C.astype(np.uint8))
T2 = threshold_sauvola(new_in, window_size=n2,k=k2)
binary = np.copy(gray)
binary[new_in <= T2] = 0
binary[new_in > T2] = 255
return binary,T2
def dtprompt(img):
x = cv2.Sobel(img,cv2.CV_16S,1,0)
y = cv2.Sobel(img,cv2.CV_16S,0,1)
absX = cv2.convertScaleAbs(x) # 转回uint8
absY = cv2.convertScaleAbs(y)
high_frequency = cv2.addWeighted(absX,0.5,absY,0.5,0)
high_frequency = cv2.cvtColor(high_frequency,cv2.COLOR_BGR2GRAY)
return high_frequency
im_paths = glob.glob('imgs/*')
for im_path in tqdm(im_paths):
if '_bin.' in im_path:
continue
if '_thr.' in im_path:
continue
if '_gradient.' in im_path:
continue
im = cv2.imread(im_path)
result,thresh = SauvolaModBinarization(im)
gradient = dtprompt(im)
thresh = thresh.astype(np.uint8)
cv2.imwrite(im_path.replace('.','_bin.'),result)
cv2.imwrite(im_path.replace('.','_thr.'),thresh)
cv2.imwrite(im_path.replace('.','_gradient.'),gradient) |