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RemoveText.py

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+
+
+from cv2 import threshold
+import matplotlib.pyplot as plt
+import keras_ocr
+import cv2
+import math
+import numpy as np
+
+
+def midpoint(x1, y1, x2, y2):
+    x_mid = int((x1 + x2)/2)
+    y_mid = int((y1 + y2)/2)
+    return (x_mid, y_mid)
+
+def segment_img(img):
+    hsv=cv2.cvtColor(img,cv2.COLOR_RGB2HSV)
+    #mask
+    mask=cv2.inRange(hsv,(40,25,25),(70,255,255))
+    
+    imask=mask>0
+    threshold=np.zeros_like(img,np.uint8)
+    threshold[imask]=img[imask]
+    
+    return threshold
+#Main function that detects text and inpaints. 
+#Inputs are the image path and kreas_ocr pipeline
+def inpaint_text(img_path, pipeline):
+    # read the image 
+    img = keras_ocr.tools.read(img_path) 
+    
+    #img=segment_img(img)
+    
+    
+    # Recogize text (and corresponding regions)
+    # Each list of predictions in prediction_groups is a list of
+    # (word, box) tuples. 
+    prediction_groups = pipeline.recognize([img])
+    
+    #Define the mask for inpainting
+    mask = np.zeros(img.shape[:2], dtype="uint8")
+    for box in prediction_groups[0]:
+        x0, y0 = box[1][0]
+        x1, y1 = box[1][1] 
+        x2, y2 = box[1][2]
+        x3, y3 = box[1][3] 
+        
+        x_mid0, y_mid0 = midpoint(x1, y1, x2, y2)
+        x_mid1, y_mi1 = midpoint(x0, y0, x3, y3)
+        
+        #For the line thickness, we will calculate the length of the line between 
+        #the top-left corner and the bottom-left corner.
+        thickness = int(math.sqrt( (x2 - x1)**2 + (y2 - y1)**2 ))
+        
+        #Define the line and inpaint
+        cv2.line(mask, (x_mid0, y_mid0), (x_mid1, y_mi1), 255,    
+        thickness)
+        inpainted_img = cv2.inpaint(img, mask, 7, cv2.INPAINT_NS)
+                 
+    return (inpainted_img)
+
+# keras-ocr will automatically download pretrained
+# weights for the detector and recognizer.
+pipeline = keras_ocr.pipeline.Pipeline()
+
+img_text_removed = inpaint_text('1.jpg', pipeline)
+
+plt.imshow(img_text_removed)
+
+cv2.imwrite('text_removed_image.jpg', cv2.cvtColor(img_text_removed, cv2.COLOR_BGR2RGB))