Refactoring: Move

cv_analysis/figure_detection/text.py

@@ -1,6 +1,6 @@
 import cv2
 
-from cv_analysis.layout_parsing import normalize_to_gray_scale
+from cv_analysis.utils.common import normalize_to_gray_scale
 
 
 def remove_primary_text_regions(image):

cv_analysis/layout_parsing.py

@@ -1,12 +1,19 @@
-from functools import reduce, partial
+from functools import partial
 from typing import Iterable, List
 
 import cv2
 import numpy as np
 from funcy import compose, rcompose, lkeep
 
-from cv_analysis.utils.common import find_contours
-from cv_analysis.utils.conversion import box_to_rectangle, contour_to_rectangle
+from cv_analysis.utils.common import (
+    find_contours,
+    dilate_page_components,
+    normalize_to_gray_scale,
+    threshold_image,
+    invert_image,
+    fill_rectangles,
+)
+from cv_analysis.utils.conversion import contour_to_rectangle
 from cv_analysis.utils.merging import connect_related_rectangles
 from cv_analysis.utils.postprocessing import remove_included, has_no_parent
 from cv_analysis.utils.rectangle import Rectangle
@@ -47,23 +54,6 @@ def __find_segments(image: np.ndarray) -> List[Rectangle]:
     return rectangles
 
 
-def is_likely_segment(rectangle: Rectangle, min_area: float = 100) -> bool:
-    # FIXME: Parameterize via factory
-    return cv2.contourArea(rectangle, False) > min_area
-
-
-def dilate_page_components(image: np.ndarray) -> np.ndarray:
-    # FIXME: Parameterize via factory
-    image = cv2.GaussianBlur(image, (7, 7), 0)
-    # FIXME: Parameterize via factory
-    thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
-    # FIXME: Parameterize via factory
-    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
-    # FIXME: Parameterize via factory
-    dilate = cv2.dilate(thresh, kernel, iterations=4)
-    return dilate
-
-
 def prepare_for_meta_detection(image: np.ndarray, rectangles: Iterable[Rectangle]) -> np.ndarray:
 
     image = fill_rectangles(image, rectangles)
@@ -74,29 +64,6 @@ def prepare_for_meta_detection(image: np.ndarray, rectangles: Iterable[Rectangle
     return image
 
 
-def normalize_to_gray_scale(image: np.ndarray) -> np.ndarray:
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if len(image.shape) > 2 else image
-    return image
-
-
-def threshold_image(image: np.ndarray) -> np.ndarray:
+def is_likely_segment(rectangle: Rectangle, min_area: float = 100) -> bool:
     # FIXME: Parameterize via factory
-    _, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
-    return image
-
-
-def invert_image(image: np.ndarray):
-    return ~image
-
-
-def fill_rectangles(image: np.ndarray, rectangles: Iterable[Rectangle]) -> np.ndarray:
-    image = reduce(fill_in_component_area, rectangles, image)
-    return image
-
-
-def fill_in_component_area(image: np.ndarray, rect: Rectangle) -> np.ndarray:
-    x, y, w, h = rect
-    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 0), -1)
-    cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 255), 7)
-
-    return image
+    return cv2.contourArea(rectangle, False) > min_area

cv_analysis/utils/common.py

@@ -1,7 +1,51 @@
+from functools import reduce
+from typing import Iterable
+
 import cv2
+import numpy as np
 from funcy import first
 
+from cv_analysis.utils.rectangle import Rectangle
+
 
 def find_contours(image):
     contours, hierarchies = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
     return contours, first(hierarchies) if hierarchies is not None else None
+
+
+def dilate_page_components(image: np.ndarray) -> np.ndarray:
+    # FIXME: Parameterize via factory
+    image = cv2.GaussianBlur(image, (7, 7), 0)
+    # FIXME: Parameterize via factory
+    thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
+    # FIXME: Parameterize via factory
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
+    # FIXME: Parameterize via factory
+    dilate = cv2.dilate(thresh, kernel, iterations=4)
+    return dilate
+
+
+def normalize_to_gray_scale(image: np.ndarray) -> np.ndarray:
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if len(image.shape) > 2 else image
+    return image
+
+
+def threshold_image(image: np.ndarray) -> np.ndarray:
+    # FIXME: Parameterize via factory
+    _, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
+    return image
+
+
+def invert_image(image: np.ndarray):
+    return ~image
+
+
+def fill_rectangles(image: np.ndarray, rectangles: Iterable[Rectangle]) -> np.ndarray:
+    image = reduce(fill_in_component_area, rectangles, image)
+    return image
+
+
+def fill_in_component_area(image: np.ndarray, rectangle: Rectangle) -> np.ndarray:
+    cv2.rectangle(image, (rectangle.x1, rectangle.y1), (rectangle.x2, rectangle.y2), (0, 0, 0), -1)
+    cv2.rectangle(image, (rectangle.x1, rectangle.y1), (rectangle.x2, rectangle.y2), (255, 255, 255), 7)
+    return image