code dump from local scripts

app.py

@@ -2,7 +2,11 @@
 
 from pathlib import Path
 from argparse import ArgumentParser, Namespace, ArgumentDefaultsHelpFormatter
-
+import matplotlib.pyplot as plt
+import keras_ocr
+import cv2
+import math
+import numpy as np
 from chris_plugin import chris_plugin, PathMapper
 
 __version__ = '1.0.0'
@@ -64,6 +68,41 @@ def main(options: Namespace, inputdir: Path, outputdir: Path):
         data = input_file.read_text()
         frequency = data.count(options.word)
         output_file.write_text(str(frequency))
+        pipeline = keras_ocr.pipeline.Pipeline()
+        final_image = inpaint_text(input_file, pipeline)
+        img_rgb = cv2.cvtColor(final_image, cv2.COLOR_BGR2RGB)
+        cv2.imwrite(output_file, img_rgb)
+
+
+
+def midpoint(x1, y1, x2, y2):
+    x_mid = int((x1 + x2) / 2)
+    y_mid = int((y1 + y2) / 2)
+    return x_mid, y_mid
+
+def inpaint_text(img_path, pipeline):
+    # read image
+    img = keras_ocr.tools.read(img_path)
+    # generate (word, box) tuples
+    prediction_groups = pipeline.recognize([img])
+
+    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)
+
+            thickness = int(math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2))
+
+            cv2.line(mask, (x_mid0, y_mid0), (x_mid1, y_mi1), 255,
+                     thickness)
+            img = cv2.inpaint(img, mask, 7, cv2.INPAINT_NS)
+
+    return img
 
 
 if __name__ == '__main__':