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Merge branch 'master' into containerization

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Isaac Riley 2022-03-02 11:52:35 +01:00
commit bdd2146330
5 changed files with 294 additions and 3 deletions
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16
.gitignore vendored
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@ -3,3 +3,19 @@ __pycache__/
deskew_model/
/pdfs/
/results/
/pdfs/
/env/
/.idea/
/.idea/.gitignore
/.idea/misc.xml
/.idea/inspectionProfiles/profiles_settings.xml
/.idea/table_parsing.iml
/.idea/vcs.xml
/results/
/data
/table_parsing.egg-info
/tests/VV-313450.pdf
/vidocp.egg-info/dependency_links.txt
/vidocp.egg-info/PKG-INFO
/vidocp.egg-info/SOURCES.txt
/vidocp.egg-info/top_level.txt

29
tests/test_table_parsing.py Normal file
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@ -0,0 +1,29 @@
import pytest
from vidocp.table_parsing import parse_table
import numpy as np
import pdf2image
@pytest.fixture()
def rects():
page_index = 0
pdf_path = "/home/lillian/vidocp/tests/VV-313450.pdf"
page = pdf2image.convert_from_path(pdf_path, first_page=page_index + 1, last_page=page_index + 1)[0]
page = np.array(page)
rectangles = parse_table(page)
return rectangles
def test_num_of_rects(rects):
assert len(rects) == 49
def test_range_of_rects(rects):
expected_range = ((210, 605), (1430, 1620))
topleft = min(rects)
x,y,w,h = max(rects)
bottomright = (x+w, y+h)
assert topleft >= expected_range[0]
assert bottomright <= expected_range[1]

170
vidocp/table_parsig.py Normal file
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@ -0,0 +1,170 @@
from itertools import count
import cv2
import imutils
import numpy as np
import pdf2image
from matplotlib import pyplot as plt
def parse(image: np.array):
gray_scale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#plt.imshow(gray_scale)
blurred = cv2.GaussianBlur(gray_scale, (7, 7), 2) #5 5 1
thresh = cv2.threshold(blurred, 251, 255, cv2.THRESH_BINARY)[1]
#plt.imshow(thresh)
img_bin = ~thresh
line_min_width = 7
kernel_h = np.ones((10, line_min_width), np.uint8)
kernel_v = np.ones((line_min_width, 10), np.uint8)
img_bin_h = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_h)
img_bin_v = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_v)
#plt.imshow(img_bin_h)
#plt.imshow(img_bin_v)
img_bin_final = img_bin_h | img_bin_v
plt.imshow(img_bin_final)
contours = cv2.findContours(img_bin_final, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = imutils.grab_contours(contours)
for c in contours:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.04 * peri, True)
yield cv2.boundingRect(approx)
def parse_tables(image: np.array, rects: list):
parsed_tables = []
for rect in rects:
(x,y,w,h) = rect
region_of_interest = image[x:x+w, y:y+h]
gray = cv2.cvtColor(region_of_interest, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)[1]
img_bin = ~thresh
line_min_width = 5
kernel_h = np.ones((1, line_min_width), np.uint8)
kernel_v = np.ones((line_min_width, 1), np.uint8)
img_bin_h = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_h)
img_bin_v = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_v)
# find_and_close_internal_gaps(img_bin_v)
img_bin_final = img_bin_h | img_bin_v
#plt.imshow(img_bin_final)
# find_and_close_internal_gaps(img_bin_final)
# find_and_close_edges(img_bin_final)
_, labels, stats, _ = cv2.connectedComponentsWithStats(~img_bin_final, connectivity=8, ltype=cv2.CV_32S)
parsed_tables.append([(x,y,w,h), stats])
return parsed_tables
#yield (x,y,w,h), stats, region_of_interest
# return stats
def annotate_table(image, parsed_tables):
for table in parsed_tables:
original_coordinates, stats = table
stats = filter_unconnected_cells(stats)
for stat in stats:
x, y, w, h, area = stat
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 255), 2)
for i, (s, v) in enumerate(zip(["x", "y", "w", "h"], [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, (255, 0, 255), 2)
return image
def filter_unconnected_cells(stats):
filtered_cells = []
# print(stats)
for left, middle, right in zip(stats[0:], stats[1:],
list(stats[2:]) + [np.array([None, None, None, None, None])]):
x, y, w, h, area = middle
if w > 35 and h > 13 and area > 500:
if right[1] is None:
if y == left[1] or x == left[0]:
filtered_cells.append(middle)
else:
if y == left[1] or y == right[1] or x == left[0] or x == right[0]:
filtered_cells.append(middle)
return filtered_cells
def find_and_close_edges(img_bin_final):
contours, hierarchy = cv2.findContours(img_bin_final, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
missing_external_edges = True
left = tuple(cnt[cnt[:, :, 0].argmin()][0])
right = tuple(cnt[cnt[:, :, 0].argmax()][0])
top = tuple(cnt[cnt[:, :, 1].argmin()][0])
bottom = tuple(cnt[cnt[:, :, 1].argmax()][0])
topleft = [left[0], top[1]]
bottomright = [right[0], bottom[1]]
for arr in cnt:
if np.array_equal(arr, np.array([bottomright])) or np.array_equal(arr, np.array([topleft])):
missing_external_edges = False
break
if missing_external_edges and (bottomright[0] - topleft[0]) * (bottomright[1] - topleft[1]) >= 50000:
cv2.rectangle(img_bin_final, tuple(topleft), tuple(bottomright), (255, 255, 255), 2)
# print("missing cell detectet rectangle drawn")
return img_bin_final
def parse_tables_in_pdf(pages):
return zip(map(parse, pages), count())
# def annotate_tables_in_pdf(pdf_path, page_index=1):
# # timeit()
# page = pdf2image.convert_from_path(pdf_path, first_page=page_index + 1, last_page=page_index + 1)[0]
# page = np.array(page)
#
# _, stats = parse(page)
# page = annotate_image(page, stats)
# # print(timeit())
# fig, ax = plt.subplots(1, 1)
# fig.set_size_inches(20, 20)
# ax.imshow(page)
# plt.show()
def annotate_boxes(image, rects):
print(type(rects))
for rect in rects:
(x, y, w, h) = rect
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
return image
def filter_tables_or_images(rects):
filtered = []
for rect in rects:
(x,y,w,h) = rect
print(w*h)
if w * h > 10**6:
filtered.append(rect)
print(filtered)
return filtered
def annotate_tables_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)
layout_boxes = parse(page)
page = annotate_boxes(page, layout_boxes)
parsed_tables = parse_tables(page, filter_tables_or_images(layout_boxes))
page = annotate_table(page, parsed_tables)
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(20, 20)
ax.imshow(page)
plt.show()

8
vidocp/table_parsing.py
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@ -25,6 +25,7 @@ def add_external_contours(image, img):
return image
def isolate_vertical_and_horizontal_components(img_bin, bounding_rects):
line_min_width = 48
kernel_h = np.ones((1, line_min_width), np.uint8)
@ -38,7 +39,7 @@ def isolate_vertical_and_horizontal_components(img_bin, bounding_rects):
kernel_v = np.ones((30, 1), np.uint8)
img_bin_h = cv2.dilate(img_bin_h, kernel_h, iterations=2)
img_bin_v = cv2.dilate(img_bin_v, kernel_v, iterations=2)
show_mpl(img_bin_h | img_bin_v)
# show_mpl(img_bin_h | img_bin_v)
# reduced filtersize from 100 to 80 to minimize splitting narrow cells
img_bin_h = apply_motion_blur(img_bin_h, 80, 0)
@ -49,10 +50,11 @@ def isolate_vertical_and_horizontal_components(img_bin, bounding_rects):
# changed threshold from 110 to 120 to minimize cell splitting
th1, img_bin_final = cv2.threshold(img_bin_final, 120, 255, cv2.THRESH_BINARY)
img_bin_final = cv2.dilate(img_bin_final, np.ones((1, 1), np.uint8), iterations=1)
show_mpl(img_bin_final)
# show_mpl(img_bin_final)
# problem if layout parser detects too big of a layout box as in VV-748542.pdf p.22
img_bin_final = disconnect_non_existing_cells(img_bin_final, bounding_rects)
show_mpl(img_bin_final)
# show_mpl(img_bin_final)
return img_bin_final

74
vidocp/table_parsing_2.py Normal file
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@ -0,0 +1,74 @@
import cv2
import matplotlib.pyplot as plt
import numpy as np
from pdf2image import pdf2image
def add_external_contours(image, img):
contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
cv2.rectangle(image, (x, y), (x + w, y + h), 255, 1)
return image
def isolate_vertical_and_horizontal_components(img_bin):
line_min_width = 30
kernel_h = np.ones((1, line_min_width), np.uint8)
kernel_v = np.ones((line_min_width, 1), np.uint8)
img_bin_h = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_h)
img_bin_v = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_v)
img_bin_final = img_bin_h | img_bin_v
return img_bin_final
def annotate_image(image, stats):
image = image.copy()
for x, y, w, h, area in stats[2:]:
if w > 10 and h > 10:
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 255), 2)
for i, (s, v) in enumerate(zip(["x", "y", "w", "h"], [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, (255, 0, 255), 2)
return image
def parse_table(image: np.array):
gray_scale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
th1, img_bin = cv2.threshold(gray_scale, 150, 255, cv2.THRESH_BINARY)
img_bin = ~img_bin
img_bin = isolate_vertical_and_horizontal_components(img_bin)
img_bin_final = add_external_contours(img_bin, img_bin)
_, labels, stats, _ = cv2.connectedComponentsWithStats(~img_bin_final, connectivity=8, ltype=cv2.CV_32S)
return stats
def annotate_tables_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)
stats = parse_table(page)
page = annotate_image(page, stats)
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(20, 20)
ax.imshow(page)
plt.show()