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2 changed files with 3 additions and 45 deletions

15
TODO.md
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@ -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?

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@ -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