Added abd replaced some filters, added docstrings.
This commit is contained in:
Rostislav Lán
152
src/filters.py
152
src/filters.py
@ -1,17 +1,18 @@
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"""! @file filters.py
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@brief Filter library for the application
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@author xlanro00
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@brief Filter library for the application
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@author xlanro00
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"""
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import numpy as np
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#import matplotlib.pyplot as plt
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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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# Parent class for all the filters
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class filter:
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'''
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Parent class for all the filters
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''' Parent class for all the filters.
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'''
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def __init__(self, img):
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@ -19,8 +20,9 @@ class filter:
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class convolve(filter):
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''' Convolve using custom kernel,
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if no kernel is given, use default 3x3 kernel for averaging
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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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@ -31,15 +33,20 @@ class convolve(filter):
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kernel = np.array(params["kernel"]) if params["kernel"] else np.ones(
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(ksize, ksize), np.float32) / np.sqrt(ksize)
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#print("with params: " + " ksize: " + str(ksize) + " kernel: \n" + str(kernel))
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print("with params: ksize: " +
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str(ksize) + " kernel: \n" + str(kernel))
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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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# TODO remove try-except
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if(params["anchor"]):
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try:
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anchor = tuple(map(int, params["anchor"].split(',')))
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@ -49,11 +56,14 @@ class blur(filter):
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anchor = (-1, -1)
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ksize = int(params["ksize"]) if params["ksize"] else 3
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#print("with params: " + " ksize: " + str(ksize) + " anchor: " + str(anchor))
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print("with params: ksize: " +
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str(ksize) + " anchor: " + str(anchor))
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self.img = cv.blur(self.img, ksize=(ksize, ksize), anchor=anchor)
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class gaussian(filter):
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''' Gaussian blur filter 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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@ -62,22 +72,27 @@ class gaussian(filter):
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sigmaX = float(params["sigmaX"]) if params["sigmaX"] else 0
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sigmaY = float(params["sigmaY"]) if params["sigmaY"] else 0
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#print("with params: " + " ksize: " + str(ksize) + " sigmaX: " + str(sigmaX) + " sigmaY: " + str(sigmaY))
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print("with params: ksize: " + str(ksize) +
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" sigmaX: " + str(sigmaX) + " sigmaY: " + str(sigmaY))
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self.img = cv.GaussianBlur(self.img, (ksize, ksize), sigmaX, sigmaY)
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class median(filter):
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''' Median blur filter 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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ksize = int(params["ksize"]) if params["ksize"] else 3
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#print("with params: " + " ksize: " + str(ksize))
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self.img = cv.medianBlur(np.uint8(self.img), ksize)
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print("with params: ksize: " + str(ksize))
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self.img = cv.medianBlur(np.float32(self.img), ksize)
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class bilateral(filter):
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''' Bilateral filter 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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@ -87,27 +102,87 @@ class bilateral(filter):
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sigmaColor = int(params["sigmaColor"]) if params["sigmaColor"] else 75
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sigmaSpace = int(params["sigmaSpace"]) if params["sigmaSpace"] else 75
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#print("with params: " + " d: " + str(d) + " sigmaColor: " + str(sigmaColor) + " sigmaSpace: " + str(sigmaSpace))
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print("with params: d: " + str(d) + " sigmaColor: " +
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str(sigmaColor) + " sigmaSpace: " + str(sigmaSpace))
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self.img = cv.bilateralFilter(self.img, d, sigmaColor, sigmaSpace)
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class denoise(filter):
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# TODO possibly not necessary
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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 20
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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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#print("with params: " + " h: " + str(h) + " tWS: " + str(tWS) + " sWS: " + str(sWS))
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print("with params: h: " + str(h) +
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" tWS: " + str(tWS) + " sWS: " + str(sWS))
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self.img = np.uint8(self.img)
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self.img = cv.fastNlMeansDenoising(self.img, h, tWS, sWS)
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self.img = cv.fastNlMeansDenoising(
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self.img, h, tWS, sWS)
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class denoise_bilateral(filter):
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''' Scikit image denoise_bilateral filter.
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Performs bilateral denoising technique on the image.
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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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'''
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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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sigmaColor = float(params["sigmaColor"]
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) if params["sigmaColor"] else 0.1
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sigmaSpace = float(params["sigmaSpace"]
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) if params["sigmaSpace"] else 15.0
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channelAxis = int(params["channelAxis"]
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) if params["channelAxis"] else None
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iterations = int(params["iterations"]) if params["iterations"] else 1
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print("with params: sigma_color: " + str(sigmaColor) +
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" sigma_spatial: " + str(sigmaSpace) + " channel_axis: " +
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str(channelAxis) + " iterations: " + str(iterations))
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for i in range(iterations):
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self.img = skirest.denoise_bilateral(
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self.img, sigma_color=sigmaColor,
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sigma_spatial=sigmaSpace, channel_axis=channelAxis)
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class denoise_tv_chambolle(filter):
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''' Scikit image denoise_tv_chambolle filter from scikit-image.
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Performs total variation denoising technique on the image.
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This filter removes fine detail, but preserves edges.
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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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weight = float(params["weight"]) if params["weight"] else 0.1
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channelAxis = int(params["channelAxis"]
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) if params["channelAxis"] else None
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iterations = int(params["iterations"]) if params["iterations"] else 1
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print("with params: weight: " + str(weight) +
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" channel_axis: " + str(channelAxis) + " iterations: " + str(iterations))
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for i in range(iterations):
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self.img = skirest.denoise_tv_chambolle(
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self.img, weight=weight, channel_axis=channelAxis)
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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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# TODO possibly unnecessary, because unsharp masking is working better
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def __init__(self, img):
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super().__init__(img)
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@ -115,13 +190,13 @@ class sharpen(filter):
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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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#print("with params: " + " kernel: \n" + str(kernel))
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print("with params: kernel: \n" + str(kernel))
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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.
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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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@ -132,13 +207,43 @@ class unsharp_mask(filter):
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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(np.uint8(self.img), ksize)
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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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print("with params: strength: " +
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str(strength) + " ksize: " + str(ksize))
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self.img = blurred - strength*lap
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class unsharp_mask_scikit(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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Radius parameter is the sigma parameter of the gaussian filter.
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Amount parameter regulates the strength of the unsharp mask.
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Better results than using opencv module.
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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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radius = int(params["radius"]) if params["radius"] else 3
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amount = float(params["amount"]) if params["amount"] else 1
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channelAxis = int(params["channelAxis"]
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) if params["channelAxis"] else None
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#self.img = cv.cvtColor(self.img, cv.COLOR_GRAY2RGB)
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print("with params: radius: " +
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str(radius) + " amount: " + str(amount))
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self.img = skiflt.unsharp_mask(
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self.img, radius=radius, amount=amount, channel_axis=channelAxis)
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#self.img = cv.cvtColor(self.img, cv.COLOR_RGB2GRAY)
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class morph(filter):
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''' General morphological operations.
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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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@ -152,6 +257,7 @@ class morph(filter):
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iterations = int(params["iterations"]) if params["iterations"] else 1
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op = getattr(cv, params["op"]) if params["op"] else cv.MORPH_OPEN
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if(params["anchor"]):
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# TODO remove try-except
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try:
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anchor = tuple(map(int, params["anchor"].split(',')))
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except AttributeError:
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@ -159,6 +265,8 @@ class morph(filter):
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else:
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anchor = (-1, -1)
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#print("with params: " + " kernel: \n" + str(kernel) + " anchor: " + str(anchor) + " iterations: " + str(iterations) + " op: " + str(op))
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print("with params: kernel: \n" + str(kernel) + " anchor: " +
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str(anchor) + " iterations: " + str(iterations) + " op: " + str(op))
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self.img = cv.morphologyEx(
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self.img, op=op, kernel=kernel, anchor=anchor, iterations=iterations)
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np.uint8(self.img), op=op, kernel=kernel,
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anchor=anchor, iterations=iterations)
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81
src/main.py
81
src/main.py
@ -1,13 +1,12 @@
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"""! @file main.py
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@brief Main file for the application
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@author xlanro00
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@brief Main file for the application
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@author xlanro00
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"""
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# Import basic libraries
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import argparse as ap
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import sys
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import json
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#from datetime import datetime
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# Libraries for image processing
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import numpy as np
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@ -33,7 +32,6 @@ class apply_filters:
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self.config_file = self.args.config[0]
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self.preset_name = self.args.config[1]
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self.config = json.load(open(self.config_file))
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print("Config loaded")
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self.parse_conf()
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# If no config file given, expect filters in command line
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@ -63,11 +61,12 @@ class apply_filters:
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def run(self):
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# read as numpy.array
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self.img = cv.imread(self.input_file, cv.IMREAD_GRAYSCALE)
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self.img = cv.imread(
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self.input_file, cv.IMREAD_GRAYSCALE).astype(np.uint8)
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self.width = self.img.shape[1]
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self.height = self.img.shape[0]
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print(self.width, self.height)
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self.print_size(self.img.shape)
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fig = plt.figure(figsize=(self.width, self.height),
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frameon=False, dpi=self.dpi / 100) # dpi is in cm
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@ -86,21 +85,27 @@ class apply_filters:
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if self.args.stl:
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self.make_lithophane()
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def parse_params(self, params):
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''' Parse parameters of filters.
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Set to None if not given.
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They are later set in the filter method.
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'''
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possible_params = {"h", "searchWindowSize", "templateWindowSize",
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"ksize", "kernel", "sigmaX", "sigmaY",
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"sigmaColor", "sigmaSpace", "d", "anchor", "iterations",
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"op", "strength"}
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"sigmaColor", "sigmaSpace", "d", "anchor", "iterations",
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"op", "strength", "amount", "radius", "weight", "channelAxis"}
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for key in possible_params:
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try:
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params[key] = params[key]
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except KeyError:
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if params.get(key) is None:
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params[key] = None
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else:
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params[key] = params[key]
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def parse_conf(self):
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# Parse configuration file if given.
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try:
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''' Parse configuration file if one was given and store filters with their parameters
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'''
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if self.preset_name in self.config:
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filter_array = self.config[self.preset_name]
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for i, filter in enumerate(range(len(filter_array)), start=1):
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self.filters.append(filter_array[filter]["name"])
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@ -109,13 +114,15 @@ class apply_filters:
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if attribute != "name":
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self.params[i][attribute] = value
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self.parse_params(self.params[i])
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except(KeyError):
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print("Loaded preset: " + self.preset_name +
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" from file: " + self.config_file)
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else:
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print("Preset not found", file=sys.stderr)
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def parse_arguments(self):
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''' Parse arguments from command line
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'''
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# Parse arguments
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parser = ap.ArgumentParser(prog='main.py',
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description='Program for processing a 2D image into 3D fingerprint.',
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usage='%(prog)s [-h] [-m | --mirror | --no-mirror] input_file output_file dpi ([-c config_file preset | --config config_file preset] | [filters ...])')
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@ -127,10 +134,11 @@ class apply_filters:
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help="output file location")
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parser.add_argument("dpi", type=int, help="scanner dpi")
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# boolean switch
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# boolean switch argument
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parser.add_argument('-m', "--mirror", help="mirror input image",
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type=bool, action=ap.BooleanOptionalAction)
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# another boolean switch argument
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parser.add_argument('-s', '--stl', help="make stl model from processed image",
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type=bool, action=ap.BooleanOptionalAction)
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@ -149,7 +157,7 @@ class apply_filters:
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''' Selects filter method of filters library.
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'''
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print("Applying " + filter_name + " filter", file=sys.stderr)
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print("Applying " + filter_name + " filter ", end='')
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return getattr(flt, filter_name)
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def resize_image(self):
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@ -163,16 +171,16 @@ class apply_filters:
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should be used only if we want a positive model
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'''
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#TODO make this automatic for positive STL
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# TODO make this automatic for positive STL
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print("Mirroring image", file=sys.stderr)
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self.img = cv.flip(self.img, 1) # 1 for vertical mirror
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def apply_filter(self):
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''' Apply filters to image.
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Applies the filters one by one, if no filters were given, just save original image output.
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'''
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if len(self.filters) == 0:
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# No filter given, just save the image
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pass
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@ -180,11 +188,13 @@ class apply_filters:
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# Apply all filters
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for i, filter_name in enumerate(self.filters):
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filter = self.filter_factory(filter_name)
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# print(self.img.dtype)
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filter.apply(self, self.params[i+1])
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# print(self.img.dtype)
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def print_size(self, size):
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print("Width: " + str(size[0]), file=sys.stderr)
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print("Height: " + str(size[1]), file=sys.stderr)
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print("Height: " + str(size[0]), file=sys.stderr)
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print("Width: " + str(size[1]), file=sys.stderr)
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def save_image(self, fig, ax):
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''' Save processed image.
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@ -196,7 +206,6 @@ class apply_filters:
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fig.savefig(fname=self.output_file)
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def make_lithophane(self):
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pass
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'''After processing image, make a lithophane from it.
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'''
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@ -208,15 +217,21 @@ class apply_filters:
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self.save_model()
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def make_meshgrid(self):
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''' Create numpy meshgrid.
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Modify image values to get more usable depth values.
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Add zero padding to image to make sides of the plate.
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'''
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# Modify image to make it more suitable depth
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# values1 = (1 + (1 - self.img/255)/6) * 255/10 # this works
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# values2 = (1 - (1 - self.img/255)/6) * 255/10 # TODO: i dont know how to make white surrounding be extruded
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# values2 = (1 - (1 - self.img/255)/6) * 255/10 #
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# TODO: i dont know how to make white surrounding be extruded
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|
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values1better = 28.05 - 0.01*self.img
|
||||
#values2better = 22.95 - 0.01*self.img
|
||||
# (np.around(values2[::300],3))
|
||||
|
||||
# Add zero padding to image to make sides of the plate
|
||||
# Add zero padding to image
|
||||
# TODO this better be done in the next function to keep dimensions intact
|
||||
self.height = self.img.shape[0] + 2
|
||||
self.width = self.img.shape[1] + 2
|
||||
self.img = np.zeros([self.height, self.width])
|
||||
@ -228,6 +243,11 @@ class apply_filters:
|
||||
self.meshgrid = np.meshgrid(verticesX, verticesY)
|
||||
|
||||
def make_mesh(self):
|
||||
''' Create mesh from image.
|
||||
Create vertices from meshgrid, add depth values from image.
|
||||
Create faces from vertices.
|
||||
'''
|
||||
|
||||
# Convert meshgrid and image matrix to array of 3D points
|
||||
vertice_arr = np.vstack(list(map(np.ravel, self.meshgrid))).T
|
||||
z = (self.img / 10).reshape(-1, 1)
|
||||
@ -278,7 +298,10 @@ class apply_filters:
|
||||
self.model.vectors[i][j] = vertices[face[j], :]
|
||||
|
||||
def save_model(self):
|
||||
print("Saving stl model", file=sys.stderr)
|
||||
''' Save final model to stl file.
|
||||
'''
|
||||
|
||||
print("Saving lithophane to stl file", file=sys.stderr)
|
||||
self.model.save('res/test.stl')
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user