def blur_mask_old(img):
assert isinstance(img, numpy.ndarray), 'img_col must be a numpy array'
assert img.ndim == 3, 'img_col must be a color image ({0} dimensions currently)'.format(img.ndim)
blur_mask = numpy.zeros(img.shape[:2], dtype=numpy.uint8)
for mask, loc in get_masks(img):
logger.debug('Checking Mask: {0}'.format(numpy.unique(mask)))
logger.debug('SuperPixel Mask Percentage: {0}%'.format(int((100.0/255.0)*(numpy.sum(mask)/mask.size))))
img_fft, val, blurry = main.blur_detector(img[loc[0]:loc[2], loc[1]:loc[3]])
logger.debug('Blurry: {0}'.format(blurry))
if blurry:
blur_mask = cv2.add(blur_mask, mask)
result = numpy.sum(blur_mask)/(255.0*blur_mask.size)
logger.info('{0}% of input image is blurry'.format(int(100*result)))
return blur_mask, result
def blur_mask(img):
assert isinstance(img, numpy.ndarray), 'img_col must be a numpy array'
assert img.ndim == 3, 'img_col must be a color image ({0} dimensions currently)'.format(img.ndim)
msk, val, blurry = main.blur_detector(img)
logger.debug('inverting img_fft')
msk = cv2.convertScaleAbs(255-(255*msk/numpy.max(msk)))
msk[msk < 50] = 0
msk[msk > 127] = 255
logger.debug('removing border')
msk = remove_border(msk)
logger.debug('applying erosion and dilation operators')
msk = morphology(msk)
logger.debug('evaluation complete')
result = numpy.sum(msk)/(255.0*msk.size)
logger.info('{0}% of input image is blurry'.format(int(100*result)))
return msk, result, blurry
def blur_mask(img):
assert isinstance(img, numpy.ndarray), 'img_col must be a numpy array'
assert img.ndim == 3, 'img_col must be a color image ({0} dimensions currently)'.format(
img.ndim)
msk, val, blurry = main.blur_detector(img)
logger.debug('inverting img_fft')
msk = cv2.convertScaleAbs(255 - (255 * msk / numpy.max(msk)))
msk[msk < 50] = 0
msk[msk > 127] = 255
logger.debug('removing border')
msk = remove_border(msk)
logger.debug('applying erosion and dilation operators')
msk = morphology(msk)
logger.debug('evaluation complete')
result = numpy.sum(msk) / (255.0 * msk.size)
logger.info('{0}% of input image is blurry'.format(int(100 * result)))
return msk, result, blurry
def blur_mask_old(img):
assert isinstance(img, numpy.ndarray), 'img_col must be a numpy array'
assert img.ndim == 3, 'img_col must be a color image ({0} dimensions currently)'.format(
img.ndim)
blur_mask = numpy.zeros(img.shape[:2], dtype=numpy.uint8)
for mask, loc in get_masks(img):
logger.debug('Checking Mask: {0}'.format(numpy.unique(mask)))
logger.debug('SuperPixel Mask Percentage: {0}%'.format(
int((100.0 / 255.0) * (numpy.sum(mask) / mask.size))))
img_fft, val, blurry = main.blur_detector(img[loc[0]:loc[2],
loc[1]:loc[3]])
logger.debug('Blurry: {0}'.format(blurry))
if blurry:
blur_mask = cv2.add(blur_mask, mask)
result = numpy.sum(blur_mask) / (255.0 * blur_mask.size)
logger.info('{0}% of input image is blurry'.format(int(100 * result)))
return blur_mask, result