init + inference.py патч

data/preprocess/sauvola_binarize.py

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+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)