xlanro00/BP_DP-xlanro00
1
1
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Added requirements for venv, removed some includes

Browse Source
This commit is contained in:
Rostislav Lán
2023-02-24 14:46:16 +01:00
parent 1d046ed719
commit 33acea6e19
3 changed files with 29 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files

29
src/filters.py
View File

@ -4,11 +4,10 @@
"""
import numpy as np
#import matplotlib.pyplot as plt
import cv2 as cv
from skimage import filters as skiflt
from skimage import restoration as skirest
from scipy import signal as sig
#from scipy import signal as sig
# Parent class for all the filters
@ -142,22 +141,27 @@ class denoise_bilateral(filter):
super().__init__(img)
def apply(self, params):
# Standard deviation for grayvalue/color distance.
# A larger value results in averaging of pixels with larger radiometric differences.
sigmaColor = float(params["sigmaColor"]
) if params["sigmaColor"] else 0.1
# Standard deviation for range distance.
# A larger value results in averaging of pixels with larger spatial differences.
sigmaSpace = float(params["sigmaSpace"]
) if params["sigmaSpace"] else 15.0
channelAxis = int(params["channelAxis"]
) if params["channelAxis"] else None
# Repetition of filter application.
iterations = int(params["iterations"]) if params["iterations"] else 1
#print("with params: sigma_color: " + str(sigmaColor) +
# " sigma_spatial: " + str(sigmaSpace) + " channel_axis: " +
# str(channelAxis) + " iterations: " + str(iterations))
# " sigma_spatial: " + str(sigmaSpace) + " iterations: " + str(iterations))
for i in range(iterations):
self.img = skirest.denoise_bilateral(
self.img, sigma_color=sigmaColor,
sigma_spatial=sigmaSpace, channel_axis=channelAxis)
sigma_spatial=sigmaSpace, channel_axis=None)
class denoise_tv_chambolle(filter):
@ -171,16 +175,18 @@ class denoise_tv_chambolle(filter):
super().__init__(img)
def apply(self, params):
# Denoising weight. The greater weight, the more denoising.
weight = float(params["weight"]) if params["weight"] else 0.1
channelAxis = int(params["channelAxis"]
) if params["channelAxis"] else None
# Maximal number of iterations used for the optimization.
iterations = int(params["iterations"]) if params["iterations"] else 1
#print("with params: weight: " + str(weight) +
# " channel_axis: " + str(channelAxis) + " iterations: " + str(iterations))
# " iterations: " + str(iterations))
for i in range(iterations):
self.img = skirest.denoise_tv_chambolle(
self.img, weight=weight, channel_axis=channelAxis)
self.img, weight=weight, channel_axis=None)
class sharpen(filter):
@ -235,6 +241,7 @@ class unsharp_mask_scikit(filter):
def apply(self, params):
radius = int(params["radius"]) if params["radius"] else 3
amount = float(params["amount"]) if params["amount"] else 1
# TODO: i have no idea what this is or how to use it
channelAxis = int(params["channelAxis"]
) if params["channelAxis"] else None

7
src/main.py
View File

@ -7,15 +7,14 @@
import argparse as ap
import sys
import json
import math
from os.path import exists
# Libraries for image processing
import numpy as np
import matplotlib.pyplot as plt
#from PIL import Image
import cv2 as cv
from stl import mesh
import math
# Import custom image filter library
import filters as flt
@ -158,7 +157,7 @@ class app:
"ksize", "kernel", "sigmaX", "sigmaY",
"sigmaColor", "sigmaSpace", "d", "anchor", "iterations",
"op", "strength", "amount", "radius", "weight", "channelAxis",
"theta", "sigma", "lambda", "gamma", "psi"}
"theta", "sigma", "lambda", "gamma", "psi", "shape"}
for key in possible_params:
if params.get(key) is None:
@ -576,7 +575,7 @@ class app:
def save_stl(self):
'''Save final mesh to stl file.
'''
# TODO: add a hash function to create filename specific to input image and preset
if self.mode == "3d":
self.mesh_finger.save(self.stl_file)
else: