refactoring

vidocp/figure_detection.py

@@ -2,7 +2,8 @@ import cv2
 import numpy as np
 from pdf2image import pdf2image
 
-from vidocp.utils import show_mpl, draw_rectangles, remove_included
+from vidocp.utils import show_mpl, draw_rectangles, remove_included, remove_primary_text_regions, \
+    __detect_large_coherent_structures
 
 
 def is_large_enough(cont, min_area=10000):
@@ -18,67 +19,6 @@ def is_likely_figure(cont, min_area=5000, max_width_to_hight_ratio=6):
     return is_large_enough(cont, min_area) and has_acceptable_format(cont, max_width_to_hight_ratio)
 
 
-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.
-
-    References:
-        https://stackoverflow.com/questions/58349726/opencv-how-to-remove-text-from-background
-    """
-
-    def filter_likely_primary_text_segments(cnts):
-        for c in cnts:
-            area = cv2.contourArea(c)
-            if 800 < area < 15000:
-                yield cv2.boundingRect(c)
-
-    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)
-
-    for rect in filter_likely_primary_text_segments(cnts):
-        x, y, w, h = rect
-        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.
-   
-    References:
-         https://stackoverflow.com/questions/60259169/how-to-group-nearby-contours-in-opencv-python-zebra-crossing-detection
-    """
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-
-    thresh = cv2.threshold(gray, 253, 255, cv2.THRESH_BINARY)[1]
-
-    dilate_kernel = cv2.getStructuringElement(cv2.MORPH_OPEN, (5, 5))
-    dilate = cv2.dilate(~thresh, dilate_kernel, iterations=4)
-
-    close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 20))
-    close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, close_kernel, iterations=1)
-
-    cnts, _ = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
-    return cnts
-
-
 def detect_figures(image: np.array):
 
     image = image.copy()

vidocp/utils.py

@@ -2,6 +2,7 @@ from collections import namedtuple
 from functools import partial
 
 import cv2
+import numpy as np
 from matplotlib import pyplot as plt
 
 
@@ -141,3 +142,64 @@ def vec_rect_to_xywh(rect):
     w = x2 - x
     h = y2 - y
     return x, y, w, h
+
+
+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.
+
+    References:
+        https://stackoverflow.com/questions/58349726/opencv-how-to-remove-text-from-background
+    """
+
+    def filter_likely_primary_text_segments(cnts):
+        for c in cnts:
+            area = cv2.contourArea(c)
+            if 800 < area < 15000:
+                yield cv2.boundingRect(c)
+
+    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)
+
+    for rect in filter_likely_primary_text_segments(cnts):
+        x, y, w, h = rect
+        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.
+
+    References:
+         https://stackoverflow.com/questions/60259169/how-to-group-nearby-contours-in-opencv-python-zebra-crossing-detection
+    """
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    thresh = cv2.threshold(gray, 253, 255, cv2.THRESH_BINARY)[1]
+
+    dilate_kernel = cv2.getStructuringElement(cv2.MORPH_OPEN, (5, 5))
+    dilate = cv2.dilate(~thresh, dilate_kernel, iterations=4)
+
+    close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 20))
+    close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, close_kernel, iterations=1)
+
+    cnts, _ = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    return cnts