Parameterize thresholding blocksize and constant

camelot/lattice.py

@@ -139,6 +139,17 @@ class Lattice:
         List of ints specifying m-tolerance parameters.
         (optional, default: [2])
 
+    blocksize: int
+        Size of a pixel neighborhood that is used to calculate a
+        threshold value for the pixel: 3, 5, 7, and so on.
+        (optional, default: 15)
+
+    threshold_constant: float
+        Constant subtracted from the mean or weighted mean
+        (see the details below). Normally, it is positive but may be
+        zero or negative as well.
+        (optional, default: -2)
+
     scale : int
         Used to divide the height/width of a pdf to get a structuring
         element for image processing.
@@ -177,15 +188,17 @@ class Lattice:
         (optional, default: None)
     """
     def __init__(self, table_area=None, fill=None, headers=None, mtol=[2],
-                 scale=15, invert=False, margins=(1.0, 0.5, 0.1),
-                 split_text=False, flag_size=True, shift_text=['l', 't'],
-                 debug=None):
+                 blocksize=15, threshold_constant=-2, scale=15, invert=False,
+                 margins=(1.0, 0.5, 0.1), split_text=False, flag_size=True,
+                 shift_text=['l', 't'], debug=None):
 
         self.method = 'lattice'
         self.table_area = table_area
         self.fill = fill
         self.headers = headers
         self.mtol = mtol
+        self.blocksize = blocksize
+        self.threshold_constant = threshold_constant
         self.scale = scale
         self.invert = invert
         self.char_margin, self.line_margin, self.word_margin = margins
@@ -230,7 +243,8 @@ class Lattice:
         subprocess.call(gs_call, stdout=open(os.devnull, 'w'),
             stderr=subprocess.STDOUT)
 
-        img, threshold = adaptive_threshold(imagename, invert=self.invert)
+        img, threshold = adaptive_threshold(imagename, invert=self.invert,
+            blocksize=self.blocksize, c=self.threshold_constant)
         pdf_x = width
         pdf_y = height
         img_x = img.shape[1]

camelot/ocr.py

@@ -27,6 +27,17 @@ class OCR:
         List of ints specifying m-tolerance parameters.
         (optional, default: [2])
 
+    blocksize: int
+        Size of a pixel neighborhood that is used to calculate a
+        threshold value for the pixel: 3, 5, 7, and so on.
+        (optional, default: 15)
+
+    threshold_constant: float
+        Constant subtracted from the mean or weighted mean
+        (see the details below). Normally, it is positive but may be
+        zero or negative as well.
+        (optional, default: -2)
+
     dpi : int
         Dots per inch.
         (optional, default: 300)
@@ -46,12 +57,14 @@ class OCR:
         of detected contours, lines, joints and the table generated.
         (optional, default: None)
     """
-    def __init__(self, table_area=None, mtol=[2], dpi=300, lang="eng", scale=15,
-                 debug=None):
+    def __init__(self, table_area=None, mtol=[2], blocksize=15, threshold_constant=-2,
+                 dpi=300, lang="eng", scale=15, debug=None):
 
         self.method = 'ocr'
         self.table_area = table_area
         self.mtol = mtol
+        self.blocksize = blocksize
+        self.threshold_constant = threshold_constant
         self.tool = pyocr.get_available_tools()[0] # fix this
         self.dpi = dpi
         self.lang = lang
@@ -75,7 +88,8 @@ class OCR:
         subprocess.call(gs_call, stdout=open(os.devnull, 'w'),
             stderr=subprocess.STDOUT)
 
-        img, threshold = adaptive_threshold(imagename)
+        img, threshold = adaptive_threshold(imagename, blocksize=self.blocksize,
+            c=self.threshold_constant)
         vmask, v_segments = find_lines(threshold, direction='vertical',
             scale=self.scale)
         hmask, h_segments = find_lines(threshold, direction='horizontal',

camelot/utils.py

@@ -570,14 +570,17 @@ def get_score(error_weights):
     score : float
     """
     SCORE_VAL = 100
-    score = 0
-    if sum([ew[0] for ew in error_weights]) != SCORE_VAL:
-        raise ValueError("Please assign a valid weightage to each parameter"
-                         " such that their sum is equal to 100")
-    for ew in error_weights:
-        weight = ew[0] / len(ew[1])
-        for error_percentage in ew[1]:
-            score += weight * (1 - error_percentage)
+    try:
+        score = 0
+        if sum([ew[0] for ew in error_weights]) != SCORE_VAL:
+            raise ValueError("Please assign a valid weightage to each parameter"
+                             " such that their sum is equal to 100")
+        for ew in error_weights:
+            weight = ew[0] / len(ew[1])
+            for error_percentage in ew[1]:
+                score += weight * (1 - error_percentage)
+    except ZeroDivisionError:
+        score = 0
     return score