Filters now all convert image to uint_8 after application.

master
Rostislav Lán 2 years ago
parent 54435a02e4
commit 5e8805f493

@ -31,7 +31,7 @@ class gaussian(img_filter):
Easier to use than opencv version.
'''
def __init__(self, img):
def __init__(self, img):
super().__init__(img)
def apply(self, params):
@ -41,6 +41,7 @@ class gaussian(img_filter):
self.img = skiflt.gaussian(
self.img, sigma=sigma, preserve_range=True)
self.img = np.uint8(self.img)
class median(img_filter):
@ -56,7 +57,9 @@ class median(img_filter):
# Used kernel is disk of size ksize
ksize = int(params["ksize"]) if params["ksize"] else 3
self.img = skiflt.median(self.img, footprint=skimorph.disk(ksize))
self.img = np.uint8(self.img)
class bilateral(img_filter):
@ -175,6 +178,8 @@ class block_match(img_filter):
self.img = bm3d.bm3d(self.img, sigma_psd=sigma,
stage_arg=bm3d.BM3DStages.ALL_STAGES)
self.img = np.uint8(self.img)
class unsharp_mask_scikit(img_filter):
@ -199,6 +204,30 @@ class unsharp_mask_scikit(img_filter):
amount=amount, channel_axis=None)
self.img = np.uint8(self.img * 255.0) # converting back to uint
class unsharp_mask_pil(img_filter):
''' Unsharp mask filter from PIL.
'''
def __init__(self, img):
super().__init__(img)
def apply(self, params):
# Blur radius
radius = int(params["radius"]) if params["radius"] else 2
# Unsharp strength in percent
percent = int(params["percent"]) if params["percent"] else 150
# Threshold controls the minimum brightness change that will be sharpened
threshold = int(params["threshold"]) if params["threshold"] else 3
self.img = np.uint8(self.img)
tmp = Image.fromarray(self.img)
tmp = tmp.filter(ImageFilter.UnsharpMask(radius, percent, threshold))
self.img = np.uint8(np.asarray(tmp))
# ------------------- EDGE DETECTION FILTERS -------------------#
@ -213,6 +242,8 @@ class farid(img_filter):
def apply(self, _):
self.img = skiflt.farid(self.img)
self.img = np.uint8(self.img)
# ------------------ RIDGE EXTRACTION FILTERS ------------------#
@ -270,6 +301,8 @@ class invert(img_filter):
def apply(self, _):
self.img = cv.bitwise_not(self.img)
self.img = np.uint8(self.img)
class scale_values(img_filter):
@ -290,6 +323,7 @@ class scale_values(img_filter):
self.img = cv.bitwise_not(tmp.astype(np.uint8))
coef = 255 / np.max(tmp)
tmp = tmp * coef
self.img = np.uint8(tmp)
class binarize(img_filter):
@ -300,6 +334,8 @@ class binarize(img_filter):
threshold = int(params["threshold"]) if params["threshold"] else 128
self.img = cv.threshold(self.img, threshold, 255, cv.THRESH_BINARY)[1]
self.img = np.uint8(self.img)
class binarize_otsu(img_filter):
@ -310,6 +346,8 @@ class binarize_otsu(img_filter):
def apply(self, _):
self.img = cv.threshold(self.img, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)[1]
self.img = np.uint8(self.img)
class add_margin(img_filter):
@ -325,6 +363,8 @@ class add_margin(img_filter):
self.img = cv.copyMakeBorder(
self.img, margin, margin, margin, margin, cv.BORDER_CONSTANT, value=color)
self.height, self.width = self.img.shape
self.img = np.uint8(self.img)
# ---------------------- MORPHOLOGICAL OPS --------------------------#
@ -345,6 +385,8 @@ class erode(img_filter):
self.img = cv.morphologyEx(
np.uint8(self.img), op=cv.MORPH_ERODE, kernel=kernel)
self.img = np.uint8(self.img)
class dilate(img_filter):
''' General morphological operations from OpenCV.
@ -363,32 +405,4 @@ class dilate(img_filter):
self.img = cv.morphologyEx(
np.uint8(self.img), op=cv.MORPH_DILATE, kernel=kernel)
# ---------------------- OLD --------------------------#
# TODO: REVISE, REMOVE unused filters
class unsharp_mask_pil(img_filter):
''' Unsharp mask filter from PIL.
'''
def __init__(self, img):
super().__init__(img)
def apply(self, params):
# Blur radius
radius = int(params["radius"]) if params["radius"] else 2
# Unsharp strength in percent
percent = int(params["percent"]) if params["percent"] else 150
# Threshold controls the minimum brightness change that will be sharpened
threshold = int(params["threshold"]) if params["threshold"] else 3
self.img = np.uint8(self.img)
tmp = Image.fromarray(self.img)
tmp = tmp.filter(ImageFilter.UnsharpMask(radius, percent, threshold))
self.img = np.asarray(tmp)

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