Created
April 25, 2016 20:44
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normalize = lambda x: (255 / (x.max() - x.min())) * (x - x.min()) | |
smooth = lambda x: ndimage.convolve(x, np.ones([3,3])/9) | |
def threshold_mask(img, threshold=150): | |
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) | |
mask = np.ones(gray.shape).astype(np.uint8) | |
mask[gray < threshold] = 0 | |
return mask | |
def scanline_mask(imask): | |
mask = np.ndarray(imask.shape) | |
for l in xrange(imask.shape[0]): | |
line = imask[l,:] | |
labels = measure.label(line) | |
land = labels[-1] | |
if land == 1: | |
mask[l,:] = labels | |
else: | |
labels[ labels != land ] = 0 | |
labels[ labels > 0 ] = 1 | |
mask[l,:] = labels | |
return (mask >= 1).astype(np.uint8) | |
def saturation_mask(img, threshold=170): | |
gray = cv2.cvtColor(img, cv2.COLOR_RGB2HSV) | |
normS = ndimage.convolve(~smooth(gray[:,:,1]), np.ones([3,3])/9) | |
mask = normS > threshold | |
return mask | |
region_masks_all = [] | |
region_masks = [] | |
for r in xrange(len(image_regions[0])): | |
_masks = [] | |
for i in xrange(len(image_regions)): | |
img = image_regions[4][r].copy() | |
mask = saturation_mask( img ) | |
_masks.append( mask ) | |
region_masks_all.append(_masks) | |
r_mask = scanline_mask( reduce( lambda s,e: s + e, _masks ) ) | |
render_array(r_mask, image_regions[0][r]) | |
region_masks.append(r_mask) |
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