Add color variance functions and such

README.md

@@ -14,8 +14,11 @@ as the sum of the different pixel scores.
 For a given pixel(`po` for the original image and `pm` for the mask, same position)
 its score will be calculated as follows:
 
+`v` for variance
+
 ```
-S_p = | po - acolor | x (0.5 - pm)
+||po - acolor|| - ||v - acolor||
+S_p = (|po - acolor| - v) x (0.5 - pm)
 ```
 
 it is assumed that the font mask is of values between `0..1` and made as a

classify.py

@@ -1,6 +1,7 @@
 import cv2 as cv
 import numpy as np
 import h5py as h5
+from rasterizer import text_to_matrix
 
 db = None
 
@@ -30,9 +31,8 @@ def extract_bb(img, bb):
     rot_matrix = cv.getRotationMatrix2D(center, rect[2], 1)
     rot_img = cv.warpAffine(img, rot_matrix, (img.shape[1], img.shape[0]))
     # bounding box is now axis aligned, and we can crop it
-    print(size)
     cropped = cv.getRectSubPix(rot_img, size, center)
-    return cropped
+    return cropped.transpose(1, 0, 2)[::, ::-1, ::]
 
 def get_img(index):
     ''' gets image from database '''
@@ -73,7 +73,25 @@ def get_avg_color(img, mask):
     avg /= count
     return avg
 
-def calc_score(img, mask, avg_color):
+def get_color_variance(img, mask, avg_color):
+    ''' Gets color variance under the mask with given avg_color '''
+    sx, sy, sw = img.shape
+    mx, my = mask.shape
+    if sx != mx or sy != my:
+        print('Image and mask size doesnt match!')
+        return None
+    var = np.zeros(sw, dtype=np.float32)
+    count = 0.0
+    for x in range(sx):
+        for y in range(sy):
+            m = mask[x, y]
+            diff = img[x, y] - avg_color
+            var += diff.dot(diff) * m
+            count += m
+    var /= count
+    return var
+
+def calc_score(img, mask, avg_color, var_mag):
     '''
     Calculates the score for each mask with each color
     '''
@@ -88,8 +106,25 @@ def calc_score(img, mask, avg_color):
     score = 0.0
     for x in range(sx):
         for y in range(sy):
-            m = 0.5 - mask[x, y]
+            m = mask[x, y] - 0.5
             diff = img[x, y] - avg_color
-            mag = np.sqrt(diff.dot(diff)) # calculate magnitude
-            score += mag * m
+            mag = var_mag - np.sqrt(diff.dot(diff)) # calculate magnitude
+            score += mag * m / var_mag
     return score
+
+def score_font(char_img, char, font_name):
+    '''
+    Takes a char_img, the wanted character and a font_name/path
+    and calculates the relevant score
+    '''
+    # default to 128, i think it should be enough and we will probably mostly
+    # reduce the size anyway, also change from rgb to grayscale
+    font_img = text_to_matrix(char, 128, font_name)[::, ::, 1]
+    # resize font_img to match char_img dimensions
+    dim = [char_img.shape[1], char_img.shape[0]]
+    mask = cv.resize(font_img, dim, interpolation=cv.INTER_LINEAR)
+    # get average color
+    ac = get_avg_color(char_img, mask)
+    var = get_color_variance(char_img, mask, ac)
+    var = np.sqrt(var.dot(var))
+    return calc_score(char_img, mask, ac, var)