Make ocr_image return/print path of text file

Move the main function to the __init__ file so it can be imported by
other code. Modify it so that it returns the path to the file that
contains the OCR text so that calling code can keep find the results.

pdf_table_extraction_and_ocr.org

@@ -965,11 +965,35 @@ print("\n".join(paths))
 **** table_ocr/ocr_image/__init__.py
 #+BEGIN_SRC python :tangle table_ocr/ocr_image/__init__.py
 import math
+import os
 
 import cv2
 import numpy as np
 import pytesseract
 
+def main(image_file, tess_args):
+    """
+    OCR the image and output the text to a file with an extension that is ready
+    to be used in Tesseract training (.gt.txt).
+
+    Tries to crop the image so that only the relevant text gets passed to Tesseract.
+
+    Returns the name of the text file that contains the text.
+    """
+    directory, filename = os.path.split(image_file)
+    filename_sans_ext, ext = os.path.splitext(filename)
+    image = cv2.imread(image_file, cv2.IMREAD_GRAYSCALE)
+    cropped = crop_to_text(image)
+    ocr_data_dir = os.path.join(directory, "ocr_data")
+    os.makedirs(ocr_data_dir, exist_ok=True)
+    out_imagepath = os.path.join(ocr_data_dir, filename)
+    out_txtpath = os.path.join(ocr_data_dir, "{}.gt.txt".format(filename_sans_ext))
+    cv2.imwrite(out_imagepath, cropped)
+    txt = ocr_image(cropped, " ".join(tess_args))
+    with open(out_txtpath, "w") as txt_file:
+        txt_file.write(txt)
+    return out_txtpath
+
 <<crop-to-text>>
 <<ocr-image>>
 #+END_SRC
@@ -982,13 +1006,8 @@ https://github.com/tesseract-ocr/tesstrain.
 
 #+BEGIN_SRC python :tangle table_ocr/ocr_image/__main__.py :mkdirp yes :results none
 import argparse
-import math
-import os
-import sys
 
-import cv2
-
-from table_ocr.ocr_image import crop_to_text, ocr_image
+from table_ocr.ocr_image import main
 
 description="""Takes a single argument that is the image to OCR.
 Remaining arguments are passed directly to Tesseract.
@@ -999,24 +1018,8 @@ Filenames are of the format for training with tesstrain."""
 parser = argparse.ArgumentParser(description=description)
 parser.add_argument("image", help="filepath of image to perform OCR")
 
-def main(image_file, tess_args):
-    directory, filename = os.path.split(image_file)
-    filename_sans_ext, ext = os.path.splitext(filename)
-    image = cv2.imread(image_file, cv2.IMREAD_GRAYSCALE)
-    cropped = crop_to_text(image)
-    ocr_data_dir = os.path.join(directory, "ocr_data")
-    os.makedirs(ocr_data_dir, exist_ok=True)
-    out_imagepath = os.path.join(ocr_data_dir, filename)
-    out_txtpath = os.path.join(ocr_data_dir, "{}.gt.txt".format(filename_sans_ext))
-    cv2.imwrite(out_imagepath, cropped)
-    txt = ocr_image(cropped, " ".join(tess_args))
-    print(txt)
-    with open(out_txtpath, "w") as txt_file:
-        txt_file.write(txt)
-
-if __name__ == "__main__":
 args, tess_args = parser.parse_known_args()
-    main(args.image, tess_args)
+print(main(args.image, tess_args))
 #+END_SRC
 *** table_ocr/ocr_to_csv/
 **** table_ocr/ocr_to_csv/__init__.py

table_ocr/ocr_image/__init__.py

@@ -1,9 +1,33 @@
 import math
+import os
 
 import cv2
 import numpy as np
 import pytesseract
 
+def main(image_file, tess_args):
+    """
+    OCR the image and output the text to a file with an extension that is ready
+    to be used in Tesseract training (.gt.txt).
+
+    Tries to crop the image so that only the relevant text gets passed to Tesseract.
+
+    Returns the name of the text file that contains the text.
+    """
+    directory, filename = os.path.split(image_file)
+    filename_sans_ext, ext = os.path.splitext(filename)
+    image = cv2.imread(image_file, cv2.IMREAD_GRAYSCALE)
+    cropped = crop_to_text(image)
+    ocr_data_dir = os.path.join(directory, "ocr_data")
+    os.makedirs(ocr_data_dir, exist_ok=True)
+    out_imagepath = os.path.join(ocr_data_dir, filename)
+    out_txtpath = os.path.join(ocr_data_dir, "{}.gt.txt".format(filename_sans_ext))
+    cv2.imwrite(out_imagepath, cropped)
+    txt = ocr_image(cropped, " ".join(tess_args))
+    with open(out_txtpath, "w") as txt_file:
+        txt_file.write(txt)
+    return out_txtpath
+
 def crop_to_text(image):
     MAX_COLOR_VAL = 255
     BLOCK_SIZE = 15

table_ocr/ocr_image/__main__.py

@@ -1,11 +1,6 @@
 import argparse
-import math
-import os
-import sys
 
-import cv2
-
-from table_ocr.ocr_image import crop_to_text, ocr_image
+from table_ocr.ocr_image import main
 
 description="""Takes a single argument that is the image to OCR.
 Remaining arguments are passed directly to Tesseract.
@@ -16,21 +11,5 @@ Filenames are of the format for training with tesstrain."""
 parser = argparse.ArgumentParser(description=description)
 parser.add_argument("image", help="filepath of image to perform OCR")
 
-def main(image_file, tess_args):
-    directory, filename = os.path.split(image_file)
-    filename_sans_ext, ext = os.path.splitext(filename)
-    image = cv2.imread(image_file, cv2.IMREAD_GRAYSCALE)
-    cropped = crop_to_text(image)
-    ocr_data_dir = os.path.join(directory, "ocr_data")
-    os.makedirs(ocr_data_dir, exist_ok=True)
-    out_imagepath = os.path.join(ocr_data_dir, filename)
-    out_txtpath = os.path.join(ocr_data_dir, "{}.gt.txt".format(filename_sans_ext))
-    cv2.imwrite(out_imagepath, cropped)
-    txt = ocr_image(cropped, " ".join(tess_args))
-    print(txt)
-    with open(out_txtpath, "w") as txt_file:
-        txt_file.write(txt)
-
-if __name__ == "__main__":
 args, tess_args = parser.parse_known_args()
-    main(args.image, tess_args)
+print(main(args.image, tess_args))