Marmelator/cv-analysis-service
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases 37 Wiki Activity

partial clean up

Browse Source
This commit is contained in:
llocarnini 2022-09-20 17:25:03 +02:00
parent 5a7b756fc1
commit ba33417166
6 changed files with 45 additions and 65 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
cv_analysis/layout_parsing.py
View File

@ -84,6 +84,6 @@ def parse_layout(image: np.array):
rects = map(lambda r: r.xywh(), rects)
rects = connect_related_rects(rects)
# rects = list(map(Rectangle.from_xywh, rects))
rects = list(map(Rectangle.from_xywh, rects))
# rects = remove_included(rects)
return rects

37
cv_analysis/table_parsing.py
View File

@ -6,7 +6,6 @@ import cv2
import numpy as np
from funcy import lmap
from cv_analysis.layout_parsing import parse_layout
from cv_analysis.utils.postprocessing import remove_isolated # xywh_to_vecs, xywh_to_vec_rect, adjacent1d
from cv_analysis.utils.structures import Rectangle
from cv_analysis.utils.visual_logging import vizlogger
@ -86,32 +85,36 @@ def isolate_vertical_and_horizontal_components(img_bin):
return img_bin_final
def find_table_layout_boxes(image: np.array):
def is_large_enough(box):
(x, y, w, h) = box
if w * h >= 100000:
return Rectangle.from_xywh(box)
layout_boxes = parse_layout(image)
a = lmap(is_large_enough, layout_boxes)
return lmap(is_large_enough, layout_boxes)
def preprocess(image: np.array):
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if len(image.shape) > 2 else image
_, image = cv2.threshold(image, 195, 255, cv2.THRESH_BINARY)
return ~image
# def turn_connected_components_into_rects(image: np.array):
# def is_large_enough(stat):
# x1, y1, w, h, area = stat
# return area > 2000 and w > 35 and h > 25
#
# _, _, stats, _ = cv2.connectedComponentsWithStats(~image, connectivity=8, ltype=cv2.CV_32S)
#
# stats = np.vstack(list(filter(is_large_enough, stats)))
# rects = list(map(Rectangle.from_xywh, stats[:, :-1][2:]))
# return remove_isolated(rects)
def turn_connected_components_into_rects(image: np.array):
def is_large_enough(stat):
x1, y1, w, h, area = stat
return area > 2000 and w > 35 and h > 25
_, _, stats, _ = cv2.connectedComponentsWithStats(~image, connectivity=8, ltype=cv2.CV_32S)
stats = np.vstack(list(filter(is_large_enough, stats)))
return stats[:, :-1][2:]
try:
stats = np.vstack(list(filter(is_large_enough, stats)))
rects = list(map(Rectangle.from_xywh, stats[:, :-1][2:]))
return remove_isolated(rects)
except ValueError:
return []
def parse_tables(image: np.array, show=False):
@ -128,9 +131,9 @@ def parse_tables(image: np.array, show=False):
image = isolate_vertical_and_horizontal_components(image)
rects = turn_connected_components_into_rects(image)
#print(rects, "\n\n")
rects = list(map(Rectangle.from_xywh, rects))
#rects = list(map(Rectangle.from_xywh, rects))
#print(rects, "\n\n")
rects = remove_isolated(rects)
#rects = remove_isolated(rects)
#print(rects, "\n\n")
return rects

10
cv_analysis/utils/connect_rects.py
View File

@ -65,16 +65,6 @@ def rects_not_the_same(r):
return r[0] != r[1]
def find_related_rects(rects):
rect_pairs = list(filter(is_related, combinations(rects, 2)))
rect_pairs = list(filter(rects_not_the_same, rect_pairs))
if not rect_pairs:
return [], rects
rel_rects = list(set([rect for pair in rect_pairs for rect in pair]))
unrel_rects = [rect for rect in rects if rect not in rel_rects]
return rect_pairs, unrel_rects
def connect_related_rects(rects: Iterable[tuple]):
rects = list(rects)

38
cv_analysis/utils/label_rects.py
View File

@ -32,37 +32,33 @@ def define_rect(rect_img, original_position):
w_img, h_img = rect_img.shape
if w * h / (w_img * h_img) >= 0.75:
# print("is table")
return (255, 255, 0)
return "table"
else:
# show_image_mpl(rect_img)
# print(" table detected but to small for layout rect, so cant be table, maybe figure?")
return (0, 255, 255)
return "other"
else:
if is_header(yo + ho):
# print("is header component")
return (255, 0, 0)
return "header component"
elif is_footer(yo):
# print("is footer component")
return (0, 255, 0)
return "footer component"
else:
# print("img-inv",pixel_density(rect_img_inv))
# show_image_mpl(rect_img)
# print("grid_in", pixel_density(grid_inv))
# show_image_mpl(grid_inv)
# print("single cell or no connected components, maybe figure?")
return (0, 255, 255)
return "other"
else:
if is_header(yo + ho):
# print("is header text")
return (255, 0, 0)
return "header text"
elif is_footer(yo):
# print("is footer text")
return (0, 255, 0)
return "footer text"
else:
# print("is text")
return (0, 0, 255)
return "text"
def is_header(y):
@ -86,24 +82,6 @@ def pixel_density(img):
return density
def annotate_rect(image, rects, rect_labels):
def annotate_rect(x, y, w, h):
cv2.putText(
image,
"+",
(x + (w // 2) - 12, y + (h // 2) + 9),
cv2.FONT_HERSHEY_SIMPLEX,
1,
(0, 255, 0),
2,
)
for rect, label in zip(rects, rect_labels):
x, y, w, h = rect
cv2.rectangle(image, (x, y), (x + w, y + h), label, 2)
return image
def label_rects(image: np.array, rects: Iterable[tuple]):
def crop_image_rects(rect):

21
cv_analysis/utils/sort_rects.py
View File

@ -1,3 +1,4 @@
from itertools import starmap
from typing import Iterable
import cv2
@ -16,11 +17,11 @@ def area_is_bigger_than(rect: tuple, maxarea=100000):
def define_rect(rect_img, original_position):
show_image_mpl(rect_img)
xo,yo,wo,ho = original_position
if is_header(yo+ho):
x,y,w,h = original_position
if is_header(y+h):
print(original_position, " is header")
return "header"
elif is_footer(yo):
elif is_footer(y):
print(original_position, " is footer")
return "footer"
elif is_table(rect_img):
@ -29,6 +30,8 @@ def define_rect(rect_img, original_position):
elif is_text(rect_img):
print(original_position, " is text")
return "text"
else:
return "other"
def is_table(rect_img):
@ -43,7 +46,7 @@ def is_table(rect_img):
x, y, w, h = bbox[0]
w_img, h_img = rect_img.shape
if w * h / (w_img * h_img) >= 0.75:
print("is table")
#print("is table")
return True
else:
print(" table detected but to small for layout rect, so cant be table, maybe figure?")
@ -83,7 +86,11 @@ def annotate_rect(rect, rect_img):
def label_rects(rects: Iterable[tuple], image: np.array):
labeled_rects = {}
for rect in rects:
def crop_image_rects(rect):
x, y, w, h = rect
labeled_rects[rect] = define_rect(image[y:y + h, x:x + w], rect)
return image[y:y + h, x:x + w]
rect_images = map(crop_image_rects, rects)
rect_labels = starmap(define_rect, zip(rect_images, rects))
print(rect_labels)
return rect_labels

2
scripts/annotate_pdf.py
View File

@ -13,6 +13,8 @@ from cv_analysis.table_parsing import parse_tables
from cv_analysis.utils.draw import draw_rectangles
from pdf2img.conversion import convert_pages_to_images
from cv_analysis.utils.sort_rects import label_rects
def parse_args():
parser = argparse.ArgumentParser()