TODO.md

@@ -1,15 +0,0 @@
-# TODOO
-
-[x] fix get_avg_color
-[ ] make rasterizer function return a grayscale image rather than rgb
-[x] implement score function: (image, mask: grayscale) -> score number
-[ ] resize font to be size of character bounding box
-[ ] implement a final function check: (image) -> which font it probably is
-[ ] test(run it in a loop for the result)
-
-
-## In case results are bad
-
-[ ] modify score system to use squared error rather than normal error
-[ ] noramlize the mask before use
-[ ] if still bad, maybe contact professor for improvement idea?

classify.py

@@ -4,10 +4,6 @@ import h5py as h5
 
 db = None
 
-def init_train():
-    ''' Default init based on the train set `train.h5` '''
-    init('train.h5')
-
 def init(path):
     ''' initializes the database, must be called before any use '''
     global db
@@ -55,41 +51,18 @@ def get_avg_color(img, mask):
     gets avg color from an image that is underneath a mask,
     img and mask needs to be of same size(in x,y) but img can
     have any third dimension size it want(usually 3 for rgb or 1 for grayscale)
-
-    mask needs to be of shape(img.width, img.height, 1)
     '''
     sx, sy, sw = img.shape
     mx, my = mask.shape
     if sx != mx or sy != my:
-        print('Image and mask size doesnt match!')
+        print('Image and mask shape doesnt match!')
         return None
-    avg = np.zeros(sw, dtype=np.float32)
+    avg = np.array(sw, dtype=np.float32)
     count = 0.0
     for x in range(sx):
         for y in range(sy):
-            m = mask[x, y]
+            m = mas[x, y]
             avg += img[x, y] * m
             count += m
     avg /= count
     return avg
-
-def calc_score(img, mask, avg_color):
-    '''
-    Calculates the score for each mask with each color
-    '''
-    sx, sy, sw = img.shape
-    mx, my = mask.shape
-    if sx != mx or sy != my:
-        print('Image and mask size doesnt match!')
-        return 0.0
-    if sw != avg_color.shape[0]:
-        print('Image width doesnt match color width!')
-        return 0.0
-    score = 0.0
-    for x in range(sx):
-        for y in range(sy):
-            m = 0.5 - mask[x, y]
-            diff = img[x, y] - avg_color
-            mag = np.sqrt(diff.dot(diff)) # calculate magnitude
-            score += mag * m
-    return score