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)
if __name__ == '__main__':
args = scripts.get_args()
logger = scripts.get_logger(quite=args.quite, debug=args.debug)
x_okay, y_okay = [], []
x_blur, y_blur = [], []
for path in args.image_paths:
for img_path in scripts.find_images(path):
logger.debug('evaluating {0}'.format(img_path))
img = cv2.imread(img_path)
if isinstance(img, numpy.ndarray):
if args.testing:
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)
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)
if __name__ == '__main__':
args = scripts.get_args()
logger = scripts.get_logger(quite=args.quite, debug=args.debug)
x_okay, y_okay = [], []
x_blur, y_blur = [], []
for path in args.image_paths:
for img_path in scripts.find_images(path):
logger.debug('evaluating {0}'.format(img_path))
img = cv2.imread(img_path)
if isinstance(img, numpy.ndarray):
if args.testing:
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:
assert isinstance(args, argparse.Namespace), 'args must be an argparse.Namespace'
args.save = save
args.display = display
detector = SkinDetector(args)
if segment:
slic = SpeedySuperPixels.SuperContour()
skin = numpy.zeros(image.shape, dtype=image.dtype)
for roi, contour in slic.process(image):
pxl = cv2.bitwise_and(image, image, mask=contour)
msk = detector.process(pxl)
ret = msk.sum()/contour.sum()
if ret > 0.8:
skin = numpy.min(255, cv2.add(skin, contour))
return skin
else:
return detector.process(image)
if __name__ == '__main__':
args = scripts.get_args()
logger = scripts.get_logger(quite=args.quite, debug=args.debug)
args.image_paths = scripts.find_images(args.image_paths[0])
for image_path in args.image_paths:
img_col = cv2.imread(image_path, 1)
img_msk = process(img_col, args=args)
if not args.display:
scripts.display('img_col', img_col)
scripts.display('img_msk', img_msk)
scripts.display('img_skn', cv2.bitwise_and(img_col, img_col, mask=img_msk))
cv2.waitKey(1)