refactoring

vidocp/figure_detection.py

@@ -6,11 +6,11 @@ from vidocp.utils import (
     show_mpl,
     draw_rectangles,
     remove_included,
-    remove_primary_text_regions,
     detect_large_coherent_structures,
     is_large_enough,
     has_acceptable_format,
 )
+from vidocp.utils.text import remove_primary_text_regions
 
 
 def is_likely_figure(cont, min_area=5000, max_width_to_hight_ratio=6):

vidocp/utils/text.py

@@ -0,0 +1,57 @@
+import cv2
+
+
+def remove_primary_text_regions(image):
+    """Removes regions of primary text, meaning no figure descriptions for example, but main text body paragraphs.
+
+    Args:
+        image: Image to remove primary text from.
+
+    Returns:
+        Image with primary text removed.
+    """
+
+    image = image.copy()
+
+    cnts = find_primary_text_regions(image)
+
+    for cnt in cnts:
+        x, y, w, h = cv2.boundingRect(cnt)
+        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 255), -1)
+
+    return image
+
+
+def find_primary_text_regions(image):
+    """Finds regions of primary text, meaning no figure descriptions for example, but main text body paragraphs.
+
+    Args:
+        image: Image to remove primary text from.
+
+    Returns:
+        Image with primary text removed.
+
+    References:
+        https://stackoverflow.com/questions/58349726/opencv-how-to-remove-text-from-background
+    """
+
+    def is_likely_primary_text_segments(cnt):
+        return 800 < cv2.contourArea(cnt) < 15000
+
+    image = image.copy()
+
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    thresh = cv2.threshold(gray, 253, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
+
+    close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 3))
+    close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, close_kernel, iterations=1)
+
+    dilate_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 3))
+    dilate = cv2.dilate(close, dilate_kernel, iterations=1)
+
+    cnts, _ = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+
+    cnts = filter(is_likely_primary_text_segments, cnts)
+
+    return cnts

vidocp/utils/utils.py

@@ -144,62 +144,6 @@ def vec_rect_to_xywh(rect):
     return x, y, w, h
 
 
-def find_primary_text_regions(image):
-    """Finds regions of primary text, meaning no figure descriptions for example, but main text body paragraphs.
-
-    Args:
-        image: Image to remove primary text from.
-
-    Returns:
-        Image with primary text removed.
-
-    References:
-        https://stackoverflow.com/questions/58349726/opencv-how-to-remove-text-from-background
-    """
-
-    def is_likely_primary_text_segments(cnt):
-        return 800 < cv2.contourArea(cnt) < 15000
-
-    image = image.copy()
-
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-
-    thresh = cv2.threshold(gray, 253, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
-
-    close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 3))
-    close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, close_kernel, iterations=1)
-
-    dilate_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 3))
-    dilate = cv2.dilate(close, dilate_kernel, iterations=1)
-
-    cnts, _ = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
-
-    cnts = filter(is_likely_primary_text_segments, cnts)
-
-    return cnts
-
-
-def remove_primary_text_regions(image):
-    """Removes regions of primary text, meaning no figure descriptions for example, but main text body paragraphs.
-
-    Args:
-        image: Image to remove primary text from.
-
-    Returns:
-        Image with primary text removed.
-    """
-
-    image = image.copy()
-
-    cnts = find_primary_text_regions(image)
-
-    for cnt in cnts:
-        x, y, w, h = cv2.boundingRect(cnt)
-        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 255), -1)
-
-    return image
-
-
 def detect_large_coherent_structures(image: np.array):
     """Detects large coherent structures on an image.