diff --git a/VorTes docs.pdf b/VorTes docs.pdf
index 53ef27b..b70eff6 100644
Binary files a/VorTes docs.pdf and b/VorTes docs.pdf differ
diff --git a/VoronoiMain.py b/VoronoiMain.py
index e62c523..4417d1a 100644
--- a/VoronoiMain.py
+++ b/VoronoiMain.py
@@ -2,118 +2,126 @@
import numpy as np
from tessellate_fast import tessel_fast
from tessellate_lowmem import tessel_low_mem
-from InverseTransformSampling import transformp,gaussian
+from InverseTransformSampling import transformp, gaussian
import argparse
# Parse command line arguments
parser = argparse.ArgumentParser()
-parser.add_argument('input',help='Input Image file')
-parser.add_argument('output',help='Output Image file')
-parser.add_argument('cn',default=0,help='Number of clusters (default = 0.1*size)',type=int)
-parser.add_argument('--rescale',default = 1,help='Rescaling factor for large images',type=float)
-parser.add_argument('--border',default=0,help='Make border [1/0]?',type=int)
-parser.add_argument('--method',default='low_mem',help='fast vs low_mem methods. Default is low_mem.')
-parser.add_argument('--threshold',default=200,help='Only for borders. Threshold distance.',type=float)
-parser.add_argument('--clusmap',default=0,help='Load a specific cluster map as tab-separated text file')
-parser.add_argument('--probmap',default=0,help='Load a 2D probability map for cluster generation')
-parser.add_argument('--channel',default=0,help='Whether to tessellate along only R,G,B or combinations?',choices=['r','g','b','rand','rb','rg','bg','randdual'])
-parser.add_argument('--verbose',default=1,help='Print progress?[1/0]',type=int)
-parser.add_argument('--seed',default='None',help='Seed for PRNG')
-parser.add_argument('--gaussianvars',nargs='*',help='Only for gaussian probmap (mx,my,sigmax,sigmay,corr(opt),spacing(opt))')
+parser.add_argument('input', help='Input Image file')
+parser.add_argument('output', help='Output Image file')
+parser.add_argument('cn', default=0, help='Number of clusters (default = 0.1*smaller_dimension)', type=int)
+parser.add_argument('--rescale', default=1, help='Rescaling factor for large images', type=float)
+parser.add_argument('--border', default=0, help='Make border [1/0]?', type=int)
+parser.add_argument('--method', default='low_mem', help='fast vs low_mem methods. Default is low_mem.')
+parser.add_argument('--threshold', default=200, help='Only for borders. Threshold distance.', type=float)
+parser.add_argument('--clusmap', default=0, help='Load a specific cluster map as tab-separated text file')
+parser.add_argument('--probmap', default=0, help='Load a 2D probability map for cluster generation')
+parser.add_argument('--channel', default=0, help='Whether to tessellate along only R,G,B or combinations?',
+ choices=['r', 'g', 'b', 'rand', 'rb', 'rg', 'bg', 'randdual'])
+parser.add_argument('--verbose', default=1, help='Print progress?[1/0]', type=int)
+parser.add_argument('--seed', default='None', help='Seed for PRNG')
+parser.add_argument('--gaussianvars', nargs='*',
+ help='Only for gaussian probmap (mx,my,sigmax,sigmay,corr(opt),spacing(opt))')
args = parser.parse_args()
# seed
-if not args.seed=='None':
+if not args.seed == 'None':
np.random.seed(int(args.seed))
# load and rescale input image
img = Image.open(args.input)
-img = img.resize((int(img.size[0]/args.rescale),int(img.size[1]/args.rescale)))
+img = img.resize((int(img.size[0] / args.rescale), int(img.size[1] / args.rescale)))
img = np.array(img)
# verbose mode?
-verb = [False,True][args.verbose]
-
+verb = [False, True][args.verbose]
if verb:
- print('Making Clusters.')
+ print('Making cluster centers.')
+
+# default cn
+if args.cn == 0:
+ args.cn = int(0.1 * np.min(img.shape))
+
# Cluster generation
-if not (args.clusmap == 0): # Pre-formed cluster-map
+if not (args.clusmap == 0): # Pre-formed cluster-map is desired
clusters = []
with open(args.clusmap) as f:
for row in f:
- row = row.split('\n')[0]
- clusters.append(list(map(float,row.split('\t'))))
+ row = row.split('\n')[0] # Just for extra protection against bad lines?
+ clusters.append(list(map(float, row.split('\t'))))
clusters = np.array(clusters)
args.cn = clusters.shape[0]
-elif not (args.probmap == 0): #Probability distribution for Inverse Transform Sampling
+elif not (args.probmap == 0): # Probability distribution for Inverse Transform Sampling given
if args.probmap == 'gaussian':
- if len(args.gaussianvars)<6:
- defs = [0.5,0.5,100,100,0,None]
- args.gaussianvars = list(map(float,args.gaussianvars)) + defs[len(args.gaussianvars):len(defs)]
- arguments = args.gaussianvars+[img.shape]
+ if len(args.gaussianvars) < 6:
+ defs = [0.5, 0.5, 100, 100, 0, None] # defaults
+ args.gaussianvars = list(map(float, args.gaussianvars)) + defs[len(args.gaussianvars):len(defs)]
+ arguments = args.gaussianvars + [img.shape]
g = gaussian(*arguments)
- clusters = transformp(args.cn,g[0],g[1],g[2],img.shape)
+ clusters = transformp(args.cn, g[0], g[1], g[2], img.shape)
clusters = np.array(tuple(clusters))
-else:
- clusters = np.array(tuple(zip(np.random.rand(args.cn)*img.shape[0],np.random.rand(args.cn)*img.shape[1])))
+else: # random cluster map
+ clusters = np.array(tuple(zip(np.random.rand(args.cn) * img.shape[0], np.random.rand(args.cn) * img.shape[1])))
if verb:
- print('Done.')
+ print('Cluster centers are ready.')
print('Making Voronoi Tessellations....')
# Tessellating
-if args.method=='fast':
- dist = tessel_fast(clusters,img.shape,[False,True][args.verbose],[False,True][args.border],args.threshold)
-elif args.method=='low_mem':
- dist = tessel_low_mem(clusters,img.shape,[False,True][args.verbose],[False,True][args.border],args.threshold)
+if args.method == 'fast': # dist is the cluster membership array
+ dist = tessel_fast(clusters, img.shape, [False, True][args.verbose], [False, True][args.border], args.threshold)
+elif args.method == 'low_mem':
+ dist = tessel_low_mem(clusters, img.shape, [False, True][args.verbose], [False, True][args.border], args.threshold)
else:
print("ERROR: Invalid Method")
- quit(1)
+ quit(5)
if verb:
print('\t\t\t\t\t\nDone.')
-# Averaging over Voronoi clusters
+# Averaging pixels over the clusters
s = set(dist.flatten())
sl = len(s)
-x=0 # counter for Verbose mode
+x = 0 # counter for verbose mode
if verb:
print('\nAveraging over Voronoi clusters.')
-for i in (set(list(range(args.cn))) & s):
+for i in (set(list(range(args.cn))) & s): # To exclude centers without any membership
if verb:
- print(str(int(x/sl*100))+'% done \t\t\r',end='')
+ print(str(int(x / sl * 100)) + '% done \t\t\r', end='')
x += 1
- if args.channel in ['r','g','b']:
- chn = {'r':0,'g':1,'b':2}[args.channel]
+ if args.channel in ['r', 'g', 'b']: # If averaging only along 1 channel
+ chn = {'r': 0, 'g': 1, 'b': 2}[args.channel]
img[dist == i, chn] = int(np.mean(img[dist == i, chn].flatten()))
continue
- elif args.channel=='rand':
- chn = np.random.randint(0,3)
+ elif args.channel == 'rand': # Randomly select a channel and average
+ chn = np.random.randint(0, 3)
img[dist == i, chn] = int(np.mean(img[dist == i, chn].flatten()))
continue
- elif args.channel=='randdual':
- chn1 = np.random.randint(0,3)
- chn2 = np.random.randint(0,3)
- img[dist == i, chn1],img[dist == i, chn2] = (np.array(img[dist == i, chn2],copy=True),np.array(img[dist == i, chn1],copy=True))
+ elif args.channel == 'randdual': # Randomly exchange the channels
+ chn1 = np.random.randint(0, 3)
+ chn2 = np.random.randint(0, 3)
+ img[dist == i, chn1], img[dist == i, chn2] = (
+ np.array(img[dist == i, chn2], copy=True), np.array(img[dist == i, chn1], copy=True))
continue
- elif args.channel in ['rb','rg','gb']:
- perms = ['rb','rg','gb']
- chn1,chn2 = [(0,2),(0,1),(1,2)][perms.index(args.channel)]
- chn1,chn2 = [(chn1,chn2),(0,0)][np.random.randint(0,2)]
- if chn1==chn2:
+ elif args.channel in ['rb', 'rg', 'gb']: # Exchange two channels (specific)
+ perms = ['rb', 'rg', 'gb']
+ chn1, chn2 = [(0, 2), (0, 1), (1, 2)][perms.index(args.channel)]
+ chn1, chn2 = [(chn1, chn2), (0, 0)][np.random.randint(0, 2)]
+ if chn1 == chn2:
continue
- img[dist == i, chn1],img[dist == i, chn2] = (np.array(img[dist == i, chn2],copy=True),np.array(img[dist == i, chn1],copy=True))
+ img[dist == i, chn1], img[dist == i, chn2] = (
+ np.array(img[dist == i, chn2], copy=True), np.array(img[dist == i, chn1], copy=True))
continue
- img[dist==i,0]=int(np.mean(img[dist==i,0].flatten()))
- img[dist==i,1]=int(np.mean(img[dist==i,1].flatten()))
- img[dist==i,2]=int(np.mean(img[dist==i,2].flatten()))
-if [False,True][args.border]:
- img[dist==args.cn+1,0]=0
- img[dist==args.cn+1,1]=0
- img[dist==args.cn+1,2]=0
+ img[dist == i, 0] = int(np.mean(img[dist == i, 0])) # The vanilla averaging
+ img[dist == i, 1] = int(np.mean(img[dist == i, 1]))
+ img[dist == i, 2] = int(np.mean(img[dist == i, 2]))
+if [False, True][args.border]:
+ img[dist == args.cn + 1, 0] = 0 # If drawing borders is set to True
+ img[dist == args.cn + 1, 1] = 0
+ img[dist == args.cn + 1, 2] = 0
if verb:
print('\t\t\t\t\t\nDone.')
- print('Saving Output file.')
+ print('Saving output file.')
img = Image.fromarray(img)
img.save(args.output)
diff --git a/gui_clusmap.py b/gui_clusmap.py
index be7caf1..6e77392 100644
--- a/gui_clusmap.py
+++ b/gui_clusmap.py
@@ -4,26 +4,28 @@
import numpy as np
import sys
+
def labelupdate(which):
- if which==0:
+ if which == 0:
stat.set('Dot Mode')
- if which==1:
+ if which == 1:
stat.set('Gaussian Spray')
+
def saver(event=None):
global finalarr
- finalarr = finalarr*fac
- finalarr[:,0],finalarr[:,1]=(np.array(finalarr[:,1],copy=False),np.array(finalarr[:,0],copy=True))
- with open(outfilename,'w',newline='') as f:
- f.write('\n'.join(list(map(lambda x: '\t'.join([str(x[0]),str(x[1])]),finalarr))))
+ finalarr = finalarr * fac
+ finalarr[:, 0], finalarr[:, 1] = (np.array(finalarr[:, 1], copy=True), np.array(finalarr[:, 0], copy=True))
+ with open(outfilename, 'w', newline='') as f:
+ f.write('\n'.join(list(map(lambda x: '\t'.join([str(x[0]), str(x[1])]), finalarr))))
root.destroy()
def uniform(event=None):
global finalarr
n = int(scale2.get())
- px = np.random.rand(n)*img.size[0]
- py = np.random.rand(n)*img.size[1]
+ px = np.random.rand(n) * img.size[0]
+ py = np.random.rand(n) * img.size[1]
if len(finalarr) == 0:
finalarr = np.array(tuple(zip(px.tolist(), py.tolist())))
else:
@@ -32,43 +34,45 @@ def uniform(event=None):
def move_sprayer(event):
- if stat.get()=='Gaussian Spray':
+ if stat.get() == 'Gaussian Spray':
mainc.delete('pointer')
sd = int(scale.get())
- mainc.create_oval((event.x-sd,event.y-sd,event.x+sd,event.y+sd),fill='',tags=('pointer',))
+ mainc.create_oval((event.x - sd, event.y - sd, event.x + sd, event.y + sd), fill='', tags=('pointer',))
+
def draw_points():
mainc.delete('points')
- if len(finalarr.shape)>1:
+ if len(finalarr.shape) > 1:
for i in finalarr:
- valx,valy = i
- cx,cy = (int(valx),int(valy))
- mainc.create_oval((cx-1,cy-1,cx+1,cy+1),fill='red',outline='red',tag='points')
+ valx, valy = i
+ cx, cy = (int(valx), int(valy))
+ mainc.create_oval((cx - 1, cy - 1, cx + 1, cy + 1), fill='red', outline='red', tag='points')
else:
- valx,valy = finalarr
- cx, cy = (int(valx),int(valy))
+ valx, valy = finalarr
+ cx, cy = (int(valx), int(valy))
mainc.create_oval((cx - 1, cy - 1, cx + 1, cy + 1), fill='red', outline='red', tag='points')
+
def spray(event):
global finalarr
- mode = {'Gaussian Spray':1,'Dot Mode':0}[stat.get()]
- if mode==0:
+ mode = {'Gaussian Spray': 1, 'Dot Mode': 0}[stat.get()]
+ if mode == 0:
px = mainc.canvasx(event.x)
py = mainc.canvasy(event.y)
- if len(finalarr)==0:
- finalarr = np.array([float(px),float(py)])
+ if len(finalarr) == 0:
+ finalarr = np.array([float(px), float(py)])
else:
- finalarr = np.vstack((finalarr,np.array([float(px),float(py)])))
+ finalarr = np.vstack((finalarr, np.array([float(px), float(py)])))
draw_points()
- elif mode==1:
+ elif mode == 1:
px = mainc.canvasx(event.x)
py = mainc.canvasy(event.y)
- px = np.random.normal(float(px),float(scale.get()),int(nums.get()))
- py = np.random.normal(float(py),float(scale.get()),int(nums.get()))
- if len(finalarr)==0:
- finalarr = np.array(tuple(zip(px.tolist(),py.tolist())))
+ px = np.random.normal(float(px), float(scale.get()), int(nums.get()))
+ py = np.random.normal(float(py), float(scale.get()), int(nums.get()))
+ if len(finalarr) == 0:
+ finalarr = np.array(tuple(zip(px.tolist(), py.tolist())))
else:
- finalarr = np.vstack((finalarr,np.array(tuple(zip(px.tolist(),py.tolist())))))
+ finalarr = np.vstack((finalarr, np.array(tuple(zip(px.tolist(), py.tolist())))))
draw_points()
@@ -78,47 +82,48 @@ def reset(event=None):
mainc.delete('points')
-finalarr = np.array([])
+finalarr = np.array([]) # To be converted into output
filename = sys.argv[1]
outfilename = sys.argv[2]
-root = tk.Tk()
+root = tk.Tk() # Root window
img = Image.open(filename)
-fac = 1
-if img.size[0]>1200 or img.size[1]>900:
- fac = max((img.size[0]/1000),(img.size[1]/700))
-img = img.resize((int(img.size[0]/fac),int(img.size[1]/fac)))
-mainf = tk.Frame(root,width=img.size[0]+200,height=img.size[1])
+fac = 1 # To adjust display size
+if img.size[0] > 1200 or img.size[1] > 900:
+ fac = max((img.size[0] / 1000), (img.size[1] / 700))
+img = img.resize((int(img.size[0] / fac), int(img.size[1] / fac)))
+mainf = tk.Frame(root, width=img.size[0] + 200, height=img.size[1]) # Main frame
mainf.pack()
-mainc = tk.Canvas(mainf,width=img.size[0],height=img.size[1],bg='white')
-mainc.grid(row=0,column=0,sticky='ns')
-panel = tk.Frame(mainf,width=200,height=img.size[1],bg='white')
-panel.grid(row=0,column=1,sticky='ns')
-sharp = ImageEnhance.Sharpness(img)
+mainc = tk.Canvas(mainf, width=img.size[0], height=img.size[1], bg='white') # Canvas for displaying image
+mainc.grid(row=0, column=0, sticky='ns')
+panel = tk.Frame(mainf, width=200, height=img.size[1], bg='white')
+panel.grid(row=0, column=1, sticky='ns')
+sharp = ImageEnhance.Sharpness(img) # To allow for better visualisation despite increasing alpha
img = sharp.enhance(0)
img.putalpha(210)
im = PhotoImage(img)
-imc = mainc.create_image(0,0,image=im,anchor=tk.NW)
-mainc.image=im
-mainc.bind('
',move_sprayer)
-mainc.bind('',spray)
+imc = mainc.create_image(0, 0, image=im, anchor=tk.NW)
+mainc.image = im # Reference to not let the image be garbage collected
+mainc.bind('', move_sprayer)
+mainc.bind('', spray)
stat = tk.StringVar(root)
stat.set('Select a mode')
-l0 = tk.Label(panel,textvariable=stat,bg='black',fg='white',font=('lucida console',20))
-l1 = tk.Button(panel,text='Dot Mode',command = lambda : labelupdate(0))
-l2 = tk.Button(panel,text='Gaussian Spray mode',command = lambda : labelupdate(1))
-nums = tk.Scale(panel,label='No. per click',from_=1,to=500,orient=tk.HORIZONTAL,resolution=1)
-l0.grid(row=0,column=0,sticky='ew')
-l1.grid(row=1,column=0,sticky='ew')
-l2.grid(row=2,column=0,sticky='ew',pady=(20,0))
-scale = tk.Scale(panel,from_=int(0.01*min(img.size)),to=max(img.size),label='SD for Spray',orient=tk.HORIZONTAL,resolution=0.1)
-nums.grid(row=3,column=0,sticky='ew')
-scale.grid(row=4,column=0,sticky='ew')
-l3 = tk.Button(panel,text='add Uniform Random',command=uniform)
-l3.grid(row=5,column=0,sticky='ew',pady=(20,0))
-scale2 = tk.Scale(panel,from_=1,to=5000,label='No. of points to add',orient=tk.HORIZONTAL)
-scale2.grid(row=6,column=0,sticky='ew')
-l4 = tk.Button(panel,text='Reset',command=reset)
-l4.grid(row=7,column=0,sticky='ew',pady=10)
-l5 = tk.Button(panel,text='Save',command=saver)
-l5.grid(row=8,column=0,sticky='ew')
+l0 = tk.Label(panel, textvariable=stat, bg='black', fg='white', font=('lucida console', 20))
+l1 = tk.Button(panel, text='Dot Mode', command=lambda: labelupdate(0))
+l2 = tk.Button(panel, text='Gaussian Spray mode', command=lambda: labelupdate(1))
+nums = tk.Scale(panel, label='No. per click', from_=1, to=500, orient=tk.HORIZONTAL, resolution=1)
+l0.grid(row=0, column=0, sticky='ew')
+l1.grid(row=1, column=0, sticky='ew')
+l2.grid(row=2, column=0, sticky='ew', pady=(20, 0))
+scale = tk.Scale(panel, from_=int(0.01 * min(img.size)), to=max(img.size), label='SD for Spray', orient=tk.HORIZONTAL,
+ resolution=0.1)
+nums.grid(row=3, column=0, sticky='ew')
+scale.grid(row=4, column=0, sticky='ew')
+l3 = tk.Button(panel, text='add Uniform Random', command=uniform)
+l3.grid(row=5, column=0, sticky='ew', pady=(20, 0))
+scale2 = tk.Scale(panel, from_=1, to=5000, label='No. of points to add', orient=tk.HORIZONTAL)
+scale2.grid(row=6, column=0, sticky='ew')
+l4 = tk.Button(panel, text='Reset', command=reset)
+l4.grid(row=7, column=0, sticky='ew', pady=10)
+l5 = tk.Button(panel, text='Save', command=saver)
+l5.grid(row=8, column=0, sticky='ew')
root.mainloop()
diff --git a/tessellate_fast.py b/tessellate_fast.py
index 3606461..4770d33 100644
--- a/tessellate_fast.py
+++ b/tessellate_fast.py
@@ -1,23 +1,24 @@
import numpy as np
-def bordercalc(x,threshold,cn):
- if True in (-np.diff(np.sort(x,0)))<=threshold:
- return cn+1
- else:
- return np.argmin(x)
+def bordercalc(x, threshold, cn):
+ if True in (-np.diff(np.sort(x, 0))) <= threshold:
+ return cn + 1
+ else:
+ return np.argmin(x)
-def tessel_fast(clusters,shape,verbose=True,border=False,threshold=200):
+
+def tessel_fast(clusters, shape, verbose=True, border=False, threshold=200):
if verbose:
- print("Loading Array of Indices")
- indices = np.indices((shape[0],shape[1]),dtype=np.int16).transpose([1,2,0])
+ print("Loading array of indices")
+ indices = np.indices((shape[0], shape[1]), dtype=np.uint16).transpose([1, 2, 0])
if verbose:
- print('Computing Distances.')
- indices = np.sum((clusters.transpose()[None,None,:,:]-indices[:,:,:,None])**2,axis=2)
- if not border:
+ print('Computing distances.')
+ indices = np.sum((clusters.transpose()[None, None, :, :] - indices[:, :, :, None]) ** 2, axis=2)
+ if not border: # I seem to have done some indexing dark magic above. It is all Greek to me now.
if verbose:
print('Assigning Clusters.')
- return np.apply_along_axis(np.argmin,2,indices)
+ return np.apply_along_axis(np.argmin, 2, indices)
if verbose:
print('Assigning Clusters and Border tags.')
- return np.apply_along_axis(lambda x: bordercalc(x,threshold,len(clusters)),2,indices)
\ No newline at end of file
+ return np.apply_along_axis(lambda x: bordercalc(x, threshold, len(clusters)), 2, indices)
diff --git a/tessellate_lowmem.py b/tessellate_lowmem.py
index 1e7a4bd..af75e49 100644
--- a/tessellate_lowmem.py
+++ b/tessellate_lowmem.py
@@ -1,28 +1,31 @@
import numpy as np
-def bordercalc(a,t,amin):
- m1 = a[amin]
+
+def bordercalc(a, t, amin): # If drawing borders is True
+ m1 = a[amin] # Distance from the closest cluster center
a = a.tolist()
- m2 = np.min(a[0:amin]+a[amin+1:len(a)])
+ m2 = np.min(a[0:amin] + a[amin + 1:len(a)]) # Distance from the second closest cluster center.
if m2 - m1 <= t:
return True
else:
return False
-def tessel_low_mem(clusters,shape,verbose=True,border=False,threshold=200):
- dist = np.zeros(shape[0:2], dtype=np.int16)
- for i in range(1,shape[0]+1):
+def tessel_low_mem(clusters, shape, verbose=True, border=False, threshold=200):
+ dist = np.zeros(shape[0:2], dtype=np.uint16) # The 2D cluster membership array (up to 65536 clusters)
+ for i in range(1, shape[0] + 1):
if verbose:
print(str(int(i / shape[0] * 100)) + '% done \t\t\r', end='')
- for j in range(1,shape[1] + 1):
+ for j in range(1, shape[1] + 1):
subdists = (clusters[:, 0] - i) ** 2 + (clusters[:, 1] - j) ** 2
clus = np.argmin(np.array(subdists))
- if border:
+ if border: # I am assuming the np.array() call above is just for my mental peace and serves no purpose
if bordercalc(subdists, threshold, clus):
- dist[i - 1, j - 1] = len(clusters[:,0]) + 1
+ dist[i - 1, j - 1] = len(clusters[:, 0]) + 1 # Special index to identify borders
else:
dist[i - 1, j - 1] = clus
else:
- dist[i - 1, j - 1] = clus
- return dist
\ No newline at end of file
+ dist[
+ i - 1, j - 1] = clus # I can't see why I start i,j from 1, and then subtract 1 at the end.
+ # But I am keeping it like this in hope that my past self had some rationale.
+ return dist
