Marmelator/cv-analysis-service
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[WIP] Refactoring meta-detection

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This commit is contained in:
Matthias Bisping 2023-01-09 11:38:55 +01:00
parent 8824c5c3ea
commit b540cfd0f2
1 changed files with 12 additions and 10 deletions
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22
cv_analysis/layout_parsing.py
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@ -13,18 +13,12 @@ from cv_analysis.utils.postprocessing import (
remove_included, remove_included,
has_no_parent, has_no_parent,
) )
from cv_analysis.utils.rectangle import Rectangle from cv_analysis.utils.rectangle import Rectangle
def parse_layout(image: np.array): def parse_layout(image: np.array):
original = image.copy()
image = normalize_to_gray_scale(image)
image = dilate_page_components(image)
rectangles = find_segments(image) rectangles = find_segments(image)
rectangles = meta_detection(original, rectangles)
rectangles = remove_included(rectangles) rectangles = remove_included(rectangles)
rectangles = lmap(rectangle_to_box, rectangles) rectangles = lmap(rectangle_to_box, rectangles)
@ -35,7 +29,13 @@ def parse_layout(image: np.array):
return rectangles return rectangles
def find_segments(image): def find_segments(image, meta=1):
if meta:
original = image.copy()
image = normalize_to_gray_scale(image)
image = dilate_page_components(image)
contours, hierarchies = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) contours, hierarchies = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
mask1 = map(is_likely_segment, contours) mask1 = map(is_likely_segment, contours)
mask2 = map(has_no_parent, hierarchies[0]) mask2 = map(has_no_parent, hierarchies[0])
@ -43,6 +43,10 @@ def find_segments(image):
contours = compress(contours, mask) contours = compress(contours, mask)
rectangles = lmap(compose(box_to_rectangle, cv2.boundingRect), contours) rectangles = lmap(compose(box_to_rectangle, cv2.boundingRect), contours)
if meta:
image = meta_detection(original, rectangles)
rectangles = find_segments(image, 0)
return rectangles return rectangles
@ -67,9 +71,7 @@ def meta_detection(image: np.ndarray, rectangles: Iterable[Rectangle]):
image = invert_image(image) image = invert_image(image)
image = normalize_to_gray_scale(image) image = normalize_to_gray_scale(image)
rectangles = find_segments(image) return image
return rectangles
def normalize_to_gray_scale(image): def normalize_to_gray_scale(image):