[WIP] Refactoring meta-detection

cv_analysis/layout_parsing.py

@@ -1,15 +1,12 @@
 from functools import reduce, partial
-from itertools import compress
-from operator import __and__
 from typing import Iterable
 
 import cv2
 import numpy as np
-from funcy import lmap, compose, rcompose
+from funcy import lmap, compose, rcompose, first, lkeep
 
 from cv_analysis.utils.connect_rects import connect_related_rects2
 from cv_analysis.utils.conversion import box_to_rectangle, rectangle_to_box
-from cv_analysis.utils.display import show_image
 from cv_analysis.utils.postprocessing import (
     remove_included,
     has_no_parent,
@@ -45,22 +42,21 @@ def prepare_for_initial_detection(image: np.ndarray):
 
 
 def __find_segments(image):
+    def to_rectangle_if_valid(contour, hierarchy):
+        return (
+            box_to_rectangle(cv2.boundingRect(contour))
+            if is_likely_segment(contour) and has_no_parent(hierarchy)
+            else None
+        )
 
-    contours, hierarchies = find_contours(image)
-    likely_segments = map(is_likely_segment, contours)
-    without_parents = map(has_no_parent, hierarchies[0])
-    valid_contours = map(__and__, likely_segments, without_parents)
-    contours = compress(contours, valid_contours)
-
-    boxes = map(cv2.boundingRect, contours)
-    rectangles = lmap(box_to_rectangle, boxes)
+    rectangles = lkeep(map(to_rectangle_if_valid, *find_contours(image)))
 
     return rectangles
 
 
 def find_contours(image):
     contours, hierarchies = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    return contours, hierarchies
+    return contours, first(hierarchies)
 
 
 def is_likely_segment(rect, min_area=100):