Added requirements for venv, removed some includes

requirements.txt

@@ -0,0 +1,8 @@
+matplotlib==3.5.3
+numpy==1.23.3
+numpy-stl==3.0.0
+opencv-python==4.7.0.72
+scikit-image==0.19.3
+scipy==1.9.3
+stl==0.0.3
+

src/filters.py

@@ -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
 

src/main.py

@@ -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: