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Pull request #9: Refactoring

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Merge in RR/vidocp from refactoring to master

Squashed commit of the following:

commit 36a62a13e51148d2420cb12930e84d78629db6b0
Author: Matthias Bisping <matthias.bisping@iqser.com>
Date:   Sun Feb 6 14:54:53 2022 +0100

    refactoring

commit e652da1fa88a048f9a5211b4e8c0b96074fb5849
Author: Matthias Bisping <matthias.bisping@iqser.com>
Date:   Sun Feb 6 14:53:17 2022 +0100

    refactoring

commit d9567da428c81f9cd7971a657281df0a90166810
Author: Matthias Bisping <matthias.bisping@iqser.com>
Date:   Sun Feb 6 14:47:18 2022 +0100

    refactoring

commit 9d30009dceec0357db6499bfaffae8ce97718ee0
Author: Matthias Bisping <matthias.bisping@iqser.com>
Date:   Sun Feb 6 14:45:53 2022 +0100

    refactoring

commit e8863d67aaaff138fb088c4e496a91b6354cc059
Author: Matthias Bisping <matthias.bisping@iqser.com>
Date:   Sun Feb 6 14:42:45 2022 +0100

    refactoring

commit 89a99d3586db4fbafa743a45bdd02eaf0c1f341f
Author: Matthias Bisping <matthias.bisping@iqser.com>
Date:   Sun Feb 6 14:39:49 2022 +0100

    refactoring

commit aa66b6865b00b0490b9e7695a6bae386e6f96723
Author: Matthias Bisping <matthias.bisping@iqser.com>
Date:   Sun Feb 6 14:31:21 2022 +0100

    refactoring

commit 98d77cb522a08821c3a13ae2cffbe7239c654762
Author: Matthias Bisping <matthias.bisping@iqser.com>
Date:   Sun Feb 6 14:27:55 2022 +0100

    refactoring

commit fed3a7e4f1b8b7ca4e14f9e495459c26490fb50b
Author: Matthias Bisping <matthias.bisping@iqser.com>
Date:   Sun Feb 6 14:26:16 2022 +0100

    refactoring

commit 504cafbd5d4bba183d9943b36c60548aae34e402
Author: Matthias Bisping <matthias.bisping@iqser.com>
Date:   Sun Feb 6 14:25:44 2022 +0100

    renaming

commit c9780a57e5a048529d36958ba678eddb11759cef
Author: Matthias Bisping <matthias.bisping@iqser.com>
Date:   Sun Feb 6 14:24:41 2022 +0100

    removed obsolete import

commit d555e86475e82024f8e1a5fc5b0ac70faa091ee1
Author: Matthias Bisping <matthias.bisping@iqser.com>
Date:   Sun Feb 6 14:24:04 2022 +0100

    refactored figure detection once
This commit is contained in:
Matthias Bisping 2022-02-06 14:55:38 +01:00
parent 8432cfe514
commit c9b2f6bf29
13 changed files with 214 additions and 149 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
scripts/annotate.py
View File

@ -1,9 +1,9 @@
import argparse
from vidocp.table_parsing import annotate_tables_in_pdf
from vidocp.redaction_detection import annotate_boxes_in_pdf
from vidocp.redaction_detection import annotate_redactions_in_pdf
from vidocp.layout_parsing import annotate_layout_in_pdf
from vidocp.figure_detection import remove_text_in_pdf
from vidocp.figure_detection import detect_figures_in_pdf
def parse_args():
@ -22,8 +22,8 @@ if __name__ == "__main__":
if args.type == "table":
annotate_tables_in_pdf(args.pdf_path, page_index=args.page_index)
elif args.type == "redaction":
annotate_boxes_in_pdf(args.pdf_path, page_index=args.page_index)
annotate_redactions_in_pdf(args.pdf_path, page_index=args.page_index)
elif args.type == "layout":
annotate_layout_in_pdf(args.pdf_path, page_index=args.page_index)
elif args.type == "figure":
remove_text_in_pdf(args.pdf_path, page_index=args.page_index)
detect_figures_in_pdf(args.pdf_path, page_index=args.page_index)

54
vidocp/figure_detection.py
View File

@ -2,16 +2,12 @@ import cv2
import numpy as np
from pdf2image import pdf2image
from vidocp.utils import draw_contours, show_mpl, draw_rectangles, remove_included, remove_overlapping, show_cv2
def is_large_enough(cont, min_area=10000):
return cv2.contourArea(cont, False) > min_area
def has_acceptable_format(cont, max_width_to_hight_ratio=6):
_, _, w, h = cv2.boundingRect(cont)
return max_width_to_hight_ratio >= w / h >= (1 / max_width_to_hight_ratio)
from vidocp.utils.detection import detect_large_coherent_structures
from vidocp.utils.display import show_mpl
from vidocp.utils.draw import draw_rectangles
from vidocp.utils.post_processing import remove_included
from vidocp.utils.filters import is_large_enough, has_acceptable_format
from vidocp.utils.text import remove_primary_text_regions
def is_likely_figure(cont, min_area=5000, max_width_to_hight_ratio=6):
@ -22,47 +18,17 @@ def detect_figures(image: np.array):
image = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 253, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 3))
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, close_kernel, iterations=1)
dilate_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 3))
dilate = cv2.dilate(close, dilate_kernel, iterations=1)
cnts, _ = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
def filter_rects():
for c in cnts:
area = cv2.contourArea(c)
if area > 800 and area < 15000:
yield cv2.boundingRect(c)
for rect in filter_rects():
x, y, w, h = rect
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 255), -1)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 253, 255, cv2.THRESH_BINARY)[1]
dilate_kernel = cv2.getStructuringElement(cv2.MORPH_OPEN, (5, 5))
dilate = cv2.dilate(~thresh, dilate_kernel, iterations=4)
close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 20))
close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, close_kernel, iterations=1)
cnts, _ = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
image = remove_primary_text_regions(image)
cnts = detect_large_coherent_structures(image)
cnts = filter(is_likely_figure, cnts)
rects = [cv2.boundingRect(c) for c in cnts]
rects = map(cv2.boundingRect, cnts)
rects = remove_included(rects)
return rects
def remove_text_in_pdf(pdf_path, page_index=1):
def detect_figures_in_pdf(pdf_path, page_index=1):
page = pdf2image.convert_from_path(pdf_path, first_page=page_index + 1, last_page=page_index + 1)[0]
page = np.array(page)

4
vidocp/layout_parsing.py
View File

@ -6,7 +6,9 @@ import cv2
import numpy as np
from pdf2image import pdf2image
from vidocp.utils import draw_rectangles, show_mpl, remove_overlapping, remove_included, has_no_parent
from vidocp.utils.display import show_mpl
from vidocp.utils.draw import draw_rectangles
from vidocp.utils.post_processing import remove_overlapping, remove_included, has_no_parent
def is_likely_segment(rect, min_area=100):

21
vidocp/redaction_detection.py
View File

@ -5,22 +5,9 @@ import numpy as np
import pdf2image
from iteration_utilities import starfilter, first
from vidocp.utils import show_mpl, draw_contours
def is_filled(hierarchy):
# See https://stackoverflow.com/questions/60095520/how-to-distinguish-filled-circle-contour-and-unfilled-circle-contour-in-opencv
return hierarchy[3] <= 0 and hierarchy[2] == -1
def is_boxy(contour):
epsilon = 0.01 * cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, epsilon, True)
return len(approx) <= 10
def is_large_enough(contour, min_area):
return cv2.contourArea(contour, False) > min_area
from vidocp.utils.display import show_mpl
from vidocp.utils.draw import draw_contours
from vidocp.utils.filters import is_large_enough, is_filled, is_boxy
def is_likely_redaction(contour, hierarchy, min_area):
@ -43,7 +30,7 @@ def find_redactions(image: np.array, min_normalized_area=200000):
return contours
def annotate_boxes_in_pdf(pdf_path, page_index=1):
def annotate_redactions_in_pdf(pdf_path, page_index=1):
page = pdf2image.convert_from_path(pdf_path, first_page=page_index + 1, last_page=page_index + 1)[0]
page = np.array(page)

5
vidocp/table_parsing.py
View File

@ -2,7 +2,8 @@ import cv2
import numpy as np
from pdf2image import pdf2image
from vidocp.utils import show_cv2, draw_stats
from vidocp.utils.display import show_mpl
from vidocp.utils.draw import draw_stats
def add_external_contours(image, img):
@ -52,4 +53,4 @@ def annotate_tables_in_pdf(pdf_path, page_index=1):
stats = parse_table(page)
page = draw_stats(page, stats)
show_cv2(page)
show_mpl(page)

1
vidocp/utils/__init__.py Normal file
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@ -0,0 +1 @@
from .utils import *

23
vidocp/utils/detection.py Normal file
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@ -0,0 +1,23 @@
import cv2
import numpy as np
def detect_large_coherent_structures(image: np.array):
"""Detects large coherent structures on an image.
References:
https://stackoverflow.com/questions/60259169/how-to-group-nearby-contours-in-opencv-python-zebra-crossing-detection
"""
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 253, 255, cv2.THRESH_BINARY)[1]
dilate_kernel = cv2.getStructuringElement(cv2.MORPH_OPEN, (5, 5))
dilate = cv2.dilate(~thresh, dilate_kernel, iterations=4)
close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 20))
close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, close_kernel, iterations=1)
cnts, _ = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
return cnts

16
vidocp/utils/display.py Normal file
View File

@ -0,0 +1,16 @@
import cv2
from matplotlib import pyplot as plt
def show_mpl(image):
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(20, 20)
ax.imshow(image)
plt.show()
def show_cv2(image):
cv2.imshow("", image)
cv2.waitKey(0)

56
vidocp/utils/draw.py Normal file
View File

@ -0,0 +1,56 @@
import cv2
from vidocp.utils import copy_and_normalize_channels
def draw_contours(image, contours):
image = copy_and_normalize_channels(image)
for cont in contours:
cv2.drawContours(image, cont, -1, (0, 255, 0), 4)
return image
def draw_rectangles(image, rectangles, color=None):
image = copy_and_normalize_channels(image)
if not color:
color = (0, 255, 0)
for rect in rectangles:
x, y, w, h = rect
cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
return image
def draw_stats(image, stats, annotate=False):
image = copy_and_normalize_channels(image)
keys = ["x", "y", "w", "h"]
def annotate_stat(x, y, w, h):
for i, (s, v) in enumerate(zip(keys, [x, y, w, h])):
anno = f"{s} = {v}"
xann = int(x + 5)
yann = int(y + h - (20 * (i + 1)))
cv2.putText(image, anno, (xann, yann), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
def draw_stat(stat):
x, y, w, h, area = stat
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
if annotate:
annotate_stat(x, y, w, h)
for stat in stats[2:]:
draw_stat(stat)
return image

25
vidocp/utils/filters.py Normal file
View File

@ -0,0 +1,25 @@
import cv2
def is_large_enough(cont, min_area):
return cv2.contourArea(cont, False) > min_area
def has_acceptable_format(cont, max_width_to_height_ratio):
_, _, w, h = cv2.boundingRect(cont)
return max_width_to_height_ratio >= w / h >= (1 / max_width_to_height_ratio)
def is_filled(hierarchy):
"""Checks whether a hierarchy is filled.
References:
https://stackoverflow.com/questions/60095520/how-to-distinguish-filled-circle-contour-and-unfilled-circle-contour-in-opencv
"""
return hierarchy[3] <= 0 and hierarchy[2] == -1
def is_boxy(contour):
epsilon = 0.01 * cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, epsilon, True)
return len(approx) <= 10

81
vidocp/utils.py → vidocp/utils/post_processing.py
View File

@ -1,87 +1,6 @@
from collections import namedtuple
from functools import partial
import cv2
from matplotlib import pyplot as plt
def show_mpl(image):
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(20, 20)
ax.imshow(image)
plt.show()
def show_cv2(image):
cv2.imshow("", image)
cv2.waitKey(0)
def copy_and_normalize_channels(image):
image = image.copy()
try:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
except cv2.error:
pass
return image
def draw_contours(image, contours):
image = copy_and_normalize_channels(image)
for cont in contours:
cv2.drawContours(image, cont, -1, (0, 255, 0), 4)
return image
def draw_rectangles(image, rectangles, color=None):
image = copy_and_normalize_channels(image)
if not color:
color = (0, 255, 0)
for rect in rectangles:
x, y, w, h = rect
cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
return image
def draw_stats(image, stats, annotate=False):
image = copy_and_normalize_channels(image)
keys = ["x", "y", "w", "h"]
def annotate_stat(x, y, w, h):
for i, (s, v) in enumerate(zip(keys, [x, y, w, h])):
anno = f"{s} = {v}"
xann = int(x + 5)
yann = int(y + h - (20 * (i + 1)))
cv2.putText(image, anno, (xann, yann), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
def draw_stat(stat):
x, y, w, h, area = stat
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
if annotate:
annotate_stat(x, y, w, h)
for stat in stats[2:]:
draw_stat(stat)
return image
def remove_overlapping(rectangles):
def overlap(a, b):

57
vidocp/utils/text.py Normal file
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@ -0,0 +1,57 @@
import cv2
def remove_primary_text_regions(image):
"""Removes regions of primary text, meaning no figure descriptions for example, but main text body paragraphs.
Args:
image: Image to remove primary text from.
Returns:
Image with primary text removed.
"""
image = image.copy()
cnts = find_primary_text_regions(image)
for cnt in cnts:
x, y, w, h = cv2.boundingRect(cnt)
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 255), -1)
return image
def find_primary_text_regions(image):
"""Finds regions of primary text, meaning no figure descriptions for example, but main text body paragraphs.
Args:
image: Image to remove primary text from.
Returns:
Image with primary text removed.
References:
https://stackoverflow.com/questions/58349726/opencv-how-to-remove-text-from-background
"""
def is_likely_primary_text_segments(cnt):
return 800 < cv2.contourArea(cnt) < 15000
image = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 253, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 3))
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, close_kernel, iterations=1)
dilate_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 3))
dilate = cv2.dilate(close, dilate_kernel, iterations=1)
cnts, _ = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnts = filter(is_likely_primary_text_segments, cnts)
return cnts

12
vidocp/utils/utils.py Normal file
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@ -0,0 +1,12 @@
import cv2
def copy_and_normalize_channels(image):
image = image.copy()
try:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
except cv2.error:
pass
return image