def blur_detector(img_col, thresh=10, mask=False):
assert isinstance(img_col, numpy.ndarray), 'img_col must be a numpy array'
assert img_col.ndim == 3, 'img_col must be a color image ({0} dimensions currently)'.format(img_col.ndim)
args = scripts.gen_args()
args.thresh = thresh
if mask:
return FocusMask.blur_mask(img)
else:
return evaluate(img_col=img_col, args=args)
def blur_detector(img_col, thresh=10, mask=False):
assert isinstance(img_col, numpy.ndarray), 'img_col must be a numpy array'
assert img_col.ndim == 3, 'img_col must be a color image ({0} dimensions currently)'.format(img_col.ndim)
args = scripts.gen_args()
args.thresh = thresh
if mask:
return FocusMask.blur_mask(img)
else:
return evaluate(img_col=img_col, args=args)
scripts.display('dialog (blurry: Y?)', img)
blurry = False
if cv2.waitKey(0) in map(lambda i: ord(i), ['Y', 'y']):
blurry = True
x_axis = [1, 3, 5, 7, 9]
for x in x_axis:
img_mod = cv2.GaussianBlur(img, (x, x), 0)
y = evaluate(img_mod, args=args)[0]
if blurry:
x_blur.append(x)
y_blur.append(y)
else:
x_okay.append(x)
y_okay.append(y)
elif args.mask:
msk, res, blurry = FocusMask.blur_mask(img)
img_msk = cv2.bitwise_and(img, img, mask=msk)
if args.display:
scripts.display('res', img_msk)
scripts.display('msk', msk)
scripts.display('img', img)
cv2.waitKey(0)
else:
img_fft, result, val = evaluate(img, args=args)
logger.info('fft average of {0}'.format(result))
if args.display:
scripts.display('input', img)
scripts.display('img_fft', img_fft)
cv2.waitKey(0)
if args.display and args.testing:
import matplotlib.pyplot as plt
scripts.display('dialog (blurry: Y?)', img)
blurry = False
if cv2.waitKey(0) in map(lambda i: ord(i), ['Y', 'y']):
blurry = True
x_axis = [1, 3, 5, 7, 9]
for x in x_axis:
img_mod = cv2.GaussianBlur(img, (x, x), 0)
y = evaluate(img_mod, args=args)[0]
if blurry:
x_blur.append(x)
y_blur.append(y)
else:
x_okay.append(x)
y_okay.append(y)
elif args.mask:
msk, res, blurry = FocusMask.blur_mask(img)
img_msk = cv2.bitwise_and(img, img, mask=msk)
if args.display:
scripts.display('res', img_msk)
scripts.display('msk', msk)
scripts.display('img', img)
cv2.waitKey(0)
else:
img_fft, result, val = evaluate(img, args=args)
logger.info('fft average of {0}'.format(result))
if args.display:
scripts.display('input', img)
scripts.display('img_fft', img_fft)
cv2.waitKey(0)
if args.display and args.testing:
import matplotlib.pyplot as plt
