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Optimize mask computation in bar decensoring mode.

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Use numpy vector operations for massive speedup, ~30x on 1000x1500 image.
This commit is contained in:
cat 2018-11-04 20:24:16 +05:00
parent 78f1473183
commit 97d53fd2f3
1 changed files with 4 additions and 8 deletions
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12
decensor.py
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@ -21,14 +21,10 @@ class Decensor:
self.load_model()
def get_mask(self, colored, width, height):
def get_mask(self, colored):
mask = np.ones(colored.shape, np.uint8)
#count = 0
#TODO: change to iterate over all images in batch when implementing batches
for row in range(height):
for col in range(width):
if np.array_equal(colored[0][row][col], self.mask_color):
mask[0, row, col] = 0
i, j = np.where(np.all(colored[0] == self.mask_color, axis=-1))
mask[0, i, j] = 0
return mask
def load_model(self):
@ -93,7 +89,7 @@ class Decensor:
#if mosaic decensor, mask is empty
mask = np.ones(ori_array.shape, np.uint8)
else:
mask = self.get_mask(ori_array, width, height)
mask = self.get_mask(ori_array)
#colored image is only used for finding the regions
regions = find_regions(colored.convert('RGB'))