Removed unused filters and their parameters from config parser.
This commit is contained in:
Rostislav Lán
@ -77,6 +77,9 @@ def parse_conf(preset_name, filters, params, config_file):
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Store filters and their parameters.
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'''
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if not exists(config_file):
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log.error_exit("Config file not found")
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config = json.load(open(config_file))
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# Find preset in config file
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@ -96,7 +99,7 @@ def parse_conf(preset_name, filters, params, config_file):
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log.print_message("Loaded preset:", preset_name,
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"from file:", config_file)
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else:
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log.print_message("Warning: Preset not found in config file")
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log.error_exit("Preset not found in config file")
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def parse_params(params):
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@ -107,12 +110,13 @@ def parse_params(params):
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# TODO: possibly too bloated, sending all possible params to each filter
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# TODO: remove unnecessary params
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possible_params = {"h", "searchWindowSize", "templateWindowSize",
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"ksize", "kernel", "angle",
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"sigmaColor", "sigmaSpace", "diameter", "anchor", "iterations",
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"op", "strength", "amount", "radius", "weight", "channelAxis",
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"theta", "sigma", "lambd", "gamma", "psi", "shape", "percent",
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"threshold", "maxval", "type", "margin", "color", "truncate", "patch_size", "patch_distance"}
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possible_params = {"sigma", "ksize", "kernel",
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"diameter", "sigmaColor", "sigmaSpace",
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"patch_size", "patch_distance", "weight",
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"amount", "radius", "percent",
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"threshold",
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"margin", "color"
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}
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for key in possible_params:
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if params.get(key) is None:
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243
src/filters.py
243
src/filters.py
@ -8,13 +8,11 @@ import cv2 as cv
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from skimage import filters as skiflt
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from skimage import restoration as skirest
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from skimage import morphology as skimorph
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from scipy import ndimage
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from PIL import Image, ImageFilter
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import bm3d
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import matplotlib.pyplot as plt
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class filter:
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class img_filter:
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''' Parent class for all the filters.
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'''
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@ -28,7 +26,7 @@ class filter:
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# --------------------- DENOISING FILTERS ---------------------#
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class gaussian(filter):
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class gaussian(img_filter):
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'''Gaussian blur filter from scikit-image.
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Easier to use than opencv version.
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'''
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@ -45,7 +43,7 @@ class gaussian(filter):
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self.img, sigma=sigma, preserve_range=True)
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class median(filter):
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class median(img_filter):
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''' Median blur filter from scikit-image.
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Using this over opencv version as that one is limited to 5x5 kernel.
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'''
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@ -61,7 +59,7 @@ class median(filter):
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self.img = skiflt.median(self.img, footprint=skimorph.disk(ksize))
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class bilateral(filter):
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class bilateral(img_filter):
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''' Bilateral filter from opencv.
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'''
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@ -87,7 +85,7 @@ class bilateral(filter):
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self.img, diameter, sigmaColor, sigmaSpace)
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class bilateral_scikit(filter):
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class bilateral_scikit(img_filter):
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''' Skimage denoise_bilateral filter.
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Averages pixels based on their distance and color similarity.
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Preserves edges while removing unwanted noise.
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@ -116,7 +114,7 @@ class bilateral_scikit(filter):
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self.img = np.uint8(self.img * 255.0) # converting back to uint8
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class nlmeans(filter):
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class nlmeans(img_filter):
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''' Non-local means filter from scikit-image.
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'''
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@ -143,7 +141,7 @@ class nlmeans(filter):
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self.img = np.uint8(self.img * 255.0) # converting back to uint8
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class total_variation(filter):
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class total_variation(img_filter):
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''' Scikit image denoise_tv_chambolle filter from scikit-image.
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Performs total variation denoising technique based on original Chambolle paper.
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@ -163,7 +161,7 @@ class total_variation(filter):
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self.img = np.uint8(self.img * 255.0) # converting back to uint8
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class block_match(filter):
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class block_match(img_filter):
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'''Block matching filter from bm3d.
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This filter is very slow and should be used only on small images
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@ -179,7 +177,7 @@ class block_match(filter):
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stage_arg=bm3d.BM3DStages.ALL_STAGES)
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class unsharp_mask_scikit(filter):
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class unsharp_mask_scikit(img_filter):
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''' Unsharp mask filter from scikit.
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Apply blurring using gaussian filter, then subtract the blurred image from the original image.
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@ -204,7 +202,7 @@ class unsharp_mask_scikit(filter):
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# ------------------- EDGE DETECTION FILTERS -------------------#
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class farid(filter):
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class farid(img_filter):
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''' Farid filter from filters.
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Not sure what this might be used for yet.
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'''
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@ -212,27 +210,27 @@ class farid(filter):
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def __init__(self, img):
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super().__init__(img)
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def apply(self, params):
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def apply(self, _):
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self.img = skiflt.farid(self.img)
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# ------------------ RIDGE EXTRACTION FILTERS ------------------#
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class meijering(filter):
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class meijering(img_filter):
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''' Meijering filter from scikit-image filters.
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'''
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def __init__(self, img):
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super().__init__(img)
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def apply(self, params):
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def apply(self, _):
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self.img = skiflt.meijering(self.img)
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self.img = np.uint8(self.img * 255.0)
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class sato(filter):
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class sato(img_filter):
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''' Meijering filter from scikit-image filters.
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Exctracts black ridges.
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'''
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@ -240,13 +238,13 @@ class sato(filter):
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def __init__(self, img):
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super().__init__(img)
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def apply(self, params):
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def apply(self, _):
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self.img = skiflt.sato(self.img)
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self.img = np.uint8(self.img * 255.0)
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class hessian(filter):
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class hessian(img_filter):
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''' Hessian filter from scikit-image filters.
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'''
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@ -263,18 +261,18 @@ class hessian(filter):
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# ------------------- MISCELLANEOUS FILTERS -------------------#
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class invert(filter):
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class invert(img_filter):
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''' Invert the image using bitwise_not from opencv.
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'''
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def __init__(self, img):
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super().__init__(img)
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def apply(self, params):
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def apply(self, _):
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self.img = cv.bitwise_not(self.img)
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class scale_values(filter):
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class scale_values(img_filter):
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''' Scale values of the image to use the entire range of data type.
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This should remove the line height issues.
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'''
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@ -294,19 +292,17 @@ class scale_values(filter):
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tmp = tmp * coef
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class binarize(filter):
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class binarize(img_filter):
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def init(self, img):
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super().__init__(img)
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def apply(self, params):
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threshold = int(params["threshold"]) if params["threshold"] else 128
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maxval = int(params["maxval"]) if params["maxval"] else 255
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type = int(params["type"]) if params["type"] else 0
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self.img = cv.threshold(self.img, threshold, maxval, type)[1]
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self.img = cv.threshold(self.img, threshold, 255, cv.THRESH_BINARY)[1]
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class binarize_otsu(filter):
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class binarize_otsu(img_filter):
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''' Otsu binarization filter from opencv.
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'''
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def init(self, img):
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@ -316,7 +312,7 @@ class binarize_otsu(filter):
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self.img = cv.threshold(self.img, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)[1]
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class add_margin(filter):
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class add_margin(img_filter):
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def init(self, img):
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super().__init__(img)
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@ -330,95 +326,52 @@ class add_margin(filter):
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self.img, margin, margin, margin, margin, cv.BORDER_CONSTANT, value=color)
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self.height, self.width = self.img.shape
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# ---------------------- MORPHOLOGICAL OPS --------------------------#
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class erode(img_filter):
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''' General morphological operations from OpenCV.
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Can be used with MORPH_OPEN, MORPH_CLOSE, MORPH_DILATE, MORPH_ERODE and more as 'op'.
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'''
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def __init__(self, img):
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super().__init__(img)
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def apply(self, params):
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# get an ellipse kernel
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kernel = np.matrix(params["kernel"]) if params["kernel"] else cv.getStructuringElement(
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cv.MORPH_ELLIPSE, (3, 3))
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self.img = cv.morphologyEx(
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np.uint8(self.img), op=cv.MORPH_ERODE, kernel=kernel)
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class dilate(img_filter):
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''' General morphological operations from OpenCV.
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Can be used with MORPH_OPEN, MORPH_CLOSE, MORPH_DILATE, MORPH_ERODE and more as 'op'.
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'''
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def __init__(self, img):
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super().__init__(img)
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def apply(self, params):
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# get an ellipse kernel
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kernel = np.matrix(params["kernel"]) if params["kernel"] else cv.getStructuringElement(
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cv.MORPH_ELLIPSE, (3, 3))
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self.img = cv.morphologyEx(
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np.uint8(self.img), op=cv.MORPH_DILATE, kernel=kernel)
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# ---------------------- OLD --------------------------#
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# TODO: REVISE, REMOVE unused filters
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class convolve(filter):
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''' Convolve with custom kernel using opencv.
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If no kernel is given, use default 3x3 kernel for averaging.
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Possibly useful for custom filters.
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'''
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def __init__(self, img):
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super().__init__(img)
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def apply(self, params):
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kernel = np.array(params["kernel"]) if params["kernel"] else np.ones(
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(3, 3), np.float32) / 9
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self.img = cv.filter2D(self.img, -1, kernel)
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class blur(filter):
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''' Blur filter from opencv.
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Performs averaging of the image.
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'''
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def __init__(self, img):
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super().__init__(img)
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def apply(self, params):
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ksize = int(params["ksize"]) if params["ksize"] else 3
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self.img = cv.blur(self.img, ksize=(ksize, ksize))
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class denoise(filter):
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def __init__(self, img):
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super().__init__(img)
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def apply(self, params):
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h = int(params["h"]) if params["h"] else 10
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tWS = int(params["templateWindowSize"]
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) if params["templateWindowSize"] else 7
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sWS = int(params["searchWindowSize"]
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) if params["searchWindowSize"] else 21
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self.img = np.uint8(self.img)
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self.img = cv.fastNlMeansDenoising(
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self.img, h, tWS, sWS)
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class sharpen(filter):
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''' Convolution with a sharpening kernel using opencv.
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'''
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def __init__(self, img):
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super().__init__(img)
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def apply(self, params):
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kernel = np.matrix(params["kernel"]) if params["kernel"] else np.array(
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[[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
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self.img = cv.filter2D(self.img, ddepth=-1, kernel=kernel)
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class unsharp_mask(filter):
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''' Unsharp mask filter from opencv.
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First blur the image a little bit, then calculate Laplacian of the image to get the edges.
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Scale the Laplacian and subtract it from the original image.
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'''
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def __init__(self, img):
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super().__init__(img)
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def apply(self, params):
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strength = float(params["strength"]) if params["strength"] else 1.0
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ksize = int(params["ksize"]) if params["ksize"] else 3
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blurred = cv.medianBlur(self.img, ksize)
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lap = cv.Laplacian(blurred, cv.CV_32F)
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self.img = blurred - strength*lap
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class unsharp_mask_pil(filter):
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class unsharp_mask_pil(img_filter):
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''' Unsharp mask filter from PIL.
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'''
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def __init__(self, img):
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@ -439,73 +392,3 @@ class unsharp_mask_pil(filter):
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tmp = Image.fromarray(self.img)
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tmp = tmp.filter(ImageFilter.UnsharpMask(radius, percent, threshold))
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self.img = np.asarray(tmp)
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class erode(filter):
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''' General morphological operations from OpenCV.
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Can be used with MORPH_OPEN, MORPH_CLOSE, MORPH_DILATE, MORPH_ERODE and more as 'op'.
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'''
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def __init__(self, img):
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super().__init__(img)
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def apply(self, params):
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# get an ellipse kernel
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kernel = np.matrix(params["kernel"]) if params["kernel"] else cv.getStructuringElement(
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cv.MORPH_ELLIPSE, (3, 3))
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self.img = cv.morphologyEx(
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np.uint8(self.img), op=cv.MORPH_ERODE, kernel=kernel)
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class dilate(filter):
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''' General morphological operations from OpenCV.
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Can be used with MORPH_OPEN, MORPH_CLOSE, MORPH_DILATE, MORPH_ERODE and more as 'op'.
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'''
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def __init__(self, img):
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super().__init__(img)
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def apply(self, params):
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# get an ellipse kernel
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kernel = np.matrix(params["kernel"]) if params["kernel"] else cv.getStructuringElement(
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cv.MORPH_ELLIPSE, (3, 3))
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self.img = cv.morphologyEx(
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np.uint8(self.img), op=cv.MORPH_DILATE, kernel=kernel)
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class gabor(filter):
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''' Gabor filter from OpenCV.
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Performs Gabor filtering on the image.
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'''
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def __init__(self, img):
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super().__init__(img)
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def apply(self, params):
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ksize = int(params["ksize"]) if params["ksize"] else 31
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sigma = float(params["sigma"]) if params["sigma"] else 10.0
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theta = params["theta"] if params["theta"] else [
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0, np.pi/16, np.pi-np.pi/16]
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lambd = float(params["lambd"]) if params["lambd"] else 10.0
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gamma = float(params["gamma"]) if params["gamma"] else 0.02
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psi = float(params["psi"]) if params["psi"] else 0.0
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filters = []
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for i in range(len(theta)):
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g_kernel = cv.getGaborKernel(ksize=(
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ksize, ksize), sigma=sigma, theta=theta[i], lambd=lambd, gamma=gamma, psi=psi)
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g_kernel = g_kernel / 1.5 * g_kernel.sum()
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filters.append(g_kernel)
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tmp = np.zeros_like(self.img)
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for i in range(len(filters)):
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tmp = cv.filter2D(self.img, -1, kernel=filters[i])
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self.img += np.maximum(self.img, tmp)
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