black

.gitignore

@@ -25,3 +25,4 @@ build_venv/
 /data/metadata_testing_files.csv
 .coverage
 /data/
+/venv/

cv_analysis/layout_parsing.py

@@ -1,4 +1,3 @@
-import itertools
 from itertools import compress
 from itertools import starmap
 from operator import __and__
@@ -6,17 +5,15 @@ from operator import __and__
 import cv2
 import numpy as np
 
-
 from cv_analysis.utils.connect_rects import connect_related_rects2
-from cv_analysis.utils.structures import Rectangle
 from cv_analysis.utils.postprocessing import (
-    remove_overlapping,
     remove_included,
     has_no_parent,
 )
-from cv_analysis.utils.visual_logging import vizlogger
+from cv_analysis.utils.structures import Rectangle
 
-#could be dynamic parameter is the scan is noisy
+
+# could be dynamic parameter is the scan is noisy
 def is_likely_segment(rect, min_area=100):
     return cv2.contourArea(rect, False) > min_area
 
@@ -34,7 +31,7 @@ def find_segments(image):
 
 
 def dilate_page_components(image):
-    #if text is detected in words make kernel bigger
+    # if text is detected in words make kernel bigger
     image = cv2.GaussianBlur(image, (7, 7), 0)
     thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
     kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
@@ -49,7 +46,6 @@ def fill_in_component_area(image, rect):
     return ~image
 
 
-
 def parse_layout(image: np.array):
     image = image.copy()
     image_ = image.copy()

cv_analysis/redaction_detection.py

@@ -2,7 +2,8 @@ from functools import partial
 
 import cv2
 import numpy as np
-from iteration_utilities import starfilter, first
+from iteration_utilities import first
+from iteration_utilities._iteration_utilities import starfilter
 
 from cv_analysis.utils.filters import is_large_enough, is_filled, is_boxy
 from cv_analysis.utils.visual_logging import vizlogger

cv_analysis/table_parsing.py

@@ -1,7 +1,3 @@
-from functools import partial
-from itertools import chain, starmap
-from operator import attrgetter
-
 import cv2
 import numpy as np
 from funcy import lmap
@@ -127,10 +123,10 @@ def parse_tables(image: np.array, show=False):
     image = preprocess(image)
     image = isolate_vertical_and_horizontal_components(image)
     rects = turn_connected_components_into_rects(image)
-    #print(rects, "\n\n")
+    # print(rects, "\n\n")
     rects = list(map(Rectangle.from_xywh, rects))
-    #print(rects, "\n\n")
+    # print(rects, "\n\n")
     rects = remove_isolated(rects)
-    #print(rects, "\n\n")
-    
+    # print(rects, "\n\n")
+
     return rects

scripts/annotate.py

@@ -46,5 +46,6 @@ if __name__ == "__main__":
         from cv_analysis.layout_parsing import parse_layout as analyze
     elif args.type == "figure":
         from cv_analysis.figure_detection.figure_detection import detect_figures
+
         analyze = detect_figures
     annotate_page(page, analyze, draw, name=name, show=args.show)

scripts/annotate_pdf.py

@@ -1,6 +1,5 @@
 import argparse
 import timeit
-from time import process_time
 from itertools import starmap
 from pathlib import Path
 
@@ -57,7 +56,6 @@ if __name__ == "__main__":
     t2 = timeit.default_timer()
     save_as_pdf(annotated_pages, args.output_folder, Path(args.pdf_path).stem, args.type)
     t3 = timeit.default_timer()
-    print("[s] opening file and convert pdf pages to images: ", t1-t0)
-    print("[s] analyse and annotate images: ", t2-t1)
-    print("[s] save images as pdf: ", t3-t2)
-
+    print("[s] opening file and convert pdf pages to images: ", t1 - t0)
+    print("[s] analyse and annotate images: ", t2 - t1)
+    print("[s] save images as pdf: ", t3 - t2)