kopia lustrzana https://github.com/LingDong-/linedraw
code rewrite
Rahul
rodzic
3aedc2f61d
commit
af7694797c
291
linedraw.py
291
linedraw.py
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@ -1,263 +1,54 @@
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from random import *
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import math
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import argparse
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from PIL import Image, ImageDraw, ImageOps
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from linedraw import sketch
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from linedraw.default import argument
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from filters import *
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from strokesort import *
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import perlin
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from util import *
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no_cv = False
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export_path = "output/out.svg"
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draw_contours = True
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draw_hatch = True
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show_bitmap = False
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resolution = 1024
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hatch_size = 16
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contour_simplify = 2
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try:
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import numpy as np
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import cv2
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except:
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print("Cannot import numpy/openCV. Switching to NO_CV mode.")
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no_cv = True
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def find_edges(IM):
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print("finding edges...")
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if no_cv:
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#appmask(IM,[F_Blur])
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appmask(IM,[F_SobelX,F_SobelY])
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else:
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im = np.array(IM)
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im = cv2.GaussianBlur(im,(3,3),0)
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im = cv2.Canny(im,100,200)
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IM = Image.fromarray(im)
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return IM.point(lambda p: p > 128 and 255)
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def getdots(IM):
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print("getting contour points...")
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PX = IM.load()
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dots = []
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w,h = IM.size
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for y in range(h-1):
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row = []
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for x in range(1,w):
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if PX[x,y] == 255:
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if len(row) > 0:
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if x-row[-1][0] == row[-1][-1]+1:
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row[-1] = (row[-1][0],row[-1][-1]+1)
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else:
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row.append((x,0))
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else:
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row.append((x,0))
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dots.append(row)
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return dots
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def connectdots(dots):
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print("connecting contour points...")
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contours = []
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for y in range(len(dots)):
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for x,v in dots[y]:
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if v > -1:
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if y == 0:
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contours.append([(x,y)])
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else:
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closest = -1
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cdist = 100
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for x0,v0 in dots[y-1]:
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if abs(x0-x) < cdist:
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cdist = abs(x0-x)
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closest = x0
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if cdist > 3:
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contours.append([(x,y)])
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else:
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found = 0
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for i in range(len(contours)):
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if contours[i][-1] == (closest,y-1):
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contours[i].append((x,y,))
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found = 1
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break
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if found == 0:
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contours.append([(x,y)])
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for c in contours:
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if c[-1][1] < y-1 and len(c)<4:
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contours.remove(c)
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return contours
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def getcontours(IM,sc=2):
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print("generating contours...")
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IM = find_edges(IM)
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IM1 = IM.copy()
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IM2 = IM.rotate(-90,expand=True).transpose(Image.FLIP_LEFT_RIGHT)
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dots1 = getdots(IM1)
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contours1 = connectdots(dots1)
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dots2 = getdots(IM2)
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contours2 = connectdots(dots2)
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for i in range(len(contours2)):
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contours2[i] = [(c[1],c[0]) for c in contours2[i]]
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contours = contours1+contours2
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for i in range(len(contours)):
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for j in range(len(contours)):
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if len(contours[i]) > 0 and len(contours[j])>0:
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if distsum(contours[j][0],contours[i][-1]) < 8:
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contours[i] = contours[i]+contours[j]
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contours[j] = []
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for i in range(len(contours)):
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contours[i] = [contours[i][j] for j in range(0,len(contours[i]),8)]
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contours = [c for c in contours if len(c) > 1]
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for i in range(0,len(contours)):
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contours[i] = [(v[0]*sc,v[1]*sc) for v in contours[i]]
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for i in range(0,len(contours)):
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for j in range(0,len(contours[i])):
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contours[i][j] = int(contours[i][j][0]+10*perlin.noise(i*0.5,j*0.1,1)),int(contours[i][j][1]+10*perlin.noise(i*0.5,j*0.1,2))
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return contours
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def hatch(IM,sc=16):
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print("hatching...")
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PX = IM.load()
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w,h = IM.size
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lg1 = []
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lg2 = []
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for x0 in range(w):
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for y0 in range(h):
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x = x0*sc
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y = y0*sc
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if PX[x0,y0] > 144:
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pass
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elif PX[x0,y0] > 64:
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lg1.append([(x,y+sc/4),(x+sc,y+sc/4)])
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elif PX[x0,y0] > 16:
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lg1.append([(x,y+sc/4),(x+sc,y+sc/4)])
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lg2.append([(x+sc,y),(x,y+sc)])
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else:
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lg1.append([(x,y+sc/4),(x+sc,y+sc/4)])
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lg1.append([(x,y+sc/2+sc/4),(x+sc,y+sc/2+sc/4)])
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lg2.append([(x+sc,y),(x,y+sc)])
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lines = [lg1,lg2]
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for k in range(0,len(lines)):
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for i in range(0,len(lines[k])):
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for j in range(0,len(lines[k])):
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if lines[k][i] != [] and lines[k][j] != []:
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if lines[k][i][-1] == lines[k][j][0]:
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lines[k][i] = lines[k][i]+lines[k][j][1:]
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lines[k][j] = []
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lines[k] = [l for l in lines[k] if len(l) > 0]
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lines = lines[0]+lines[1]
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for i in range(0,len(lines)):
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for j in range(0,len(lines[i])):
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lines[i][j] = int(lines[i][j][0]+sc*perlin.noise(i*0.5,j*0.1,1)),int(lines[i][j][1]+sc*perlin.noise(i*0.5,j*0.1,2))-j
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return lines
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def sketch(path):
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IM = None
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possible = [path,"images/"+path,"images/"+path+".jpg","images/"+path+".png","images/"+path+".tif"]
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for p in possible:
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try:
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IM = Image.open(p)
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break
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except FileNotFoundError:
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print("The Input File wasn't found. Check Path")
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exit(0)
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pass
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w,h = IM.size
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IM = IM.convert("L")
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IM=ImageOps.autocontrast(IM,10)
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lines = []
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if draw_contours:
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lines += getcontours(IM.resize((resolution//contour_simplify,resolution//contour_simplify*h//w)),contour_simplify)
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if draw_hatch:
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lines += hatch(IM.resize((resolution//hatch_size,resolution//hatch_size*h//w)),hatch_size)
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lines = sortlines(lines)
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if show_bitmap:
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disp = Image.new("RGB",(resolution,resolution*h//w),(255,255,255))
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draw = ImageDraw.Draw(disp)
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for l in lines:
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draw.line(l,(0,0,0),5)
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disp.show()
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f = open(export_path,'w')
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f.write(makesvg(lines))
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f.close()
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print(len(lines),"strokes.")
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print("done.")
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return lines
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def makesvg(lines):
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print("generating svg file...")
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out = '<svg xmlns="http://www.w3.org/2000/svg" version="1.1">'
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for l in lines:
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l = ",".join([str(p[0]*0.5)+","+str(p[1]*0.5) for p in l])
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out += '<polyline points="'+l+'" stroke="black" stroke-width="2" fill="none" />\n'
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out += '</svg>'
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return out
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if __name__ == "__main__":
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Convert image to vectorized line drawing for plotters.')
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parser.add_argument('-i','--input',dest='input_path',
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default='lenna',action='store',nargs='?',type=str,
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help='Input path')
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parser.add_argument('-i', '--input', dest='input_path',
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default='lenna', action='store', nargs='?', type=str,
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help='Input image path')
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parser.add_argument('-o','--output',dest='output_path',
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default=export_path,action='store',nargs='?',type=str,
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help='Output path.')
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parser.add_argument('-o', '--output', dest='output_path',
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default=argument.export_path, action='store', nargs='?', type=str,
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help='Output image path')
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parser.add_argument('-b','--show_bitmap',dest='show_bitmap',
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const = not show_bitmap,default= show_bitmap,action='store_const',
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help="Display bitmap preview.")
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parser.add_argument('-r', '--resolution', dest='resolution',
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default=argument.show_bitmap, action='store_const',
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help='Resolution of the output image')
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parser.add_argument('-nc','--no_contour',dest='no_contour',
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const = draw_contours,default= not draw_contours,action='store_const',
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help="Don't draw contours.")
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parser.add_argument('-nh','--no_hatch',dest='no_hatch',
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const = draw_hatch,default= not draw_hatch,action='store_const',
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help='Disable hatching.')
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parser.add_argument('-b', '--show_bitmap', dest='show_bitmap',
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const=not argument.show_bitmap, default=argument.show_bitmap, action='store_const',
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help='Display bitmap preview.')
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parser.add_argument('--no_cv',dest='no_cv',
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const = not no_cv,default= no_cv,action='store_const',
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help="Don't use openCV.")
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parser.add_argument('-nc', '--no_contour', dest='no_contour',
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const=argument.draw_contours, default=not argument.draw_contours, action='store_const',
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help="Don't draw contours.")
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parser.add_argument('-nh', '--no_hatch', dest='no_hatch',
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const=argument.draw_hatch, default=not argument.draw_hatch, action='store_const',
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help='Disable hatching.')
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parser.add_argument('--hatch_size',dest='hatch_size',
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default=hatch_size,action='store',nargs='?',type=int,
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help='Patch size of hatches. eg. 8, 16, 32')
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parser.add_argument('--contour_simplify',dest='contour_simplify',
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default=contour_simplify,action='store',nargs='?',type=int,
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help='Level of contour simplification. eg. 1, 2, 3')
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parser.add_argument('--no_cv', dest='no_cv',
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const=not argument.no_cv, default=argument.no_cv, action='store_const',
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help="Don't use openCV.")
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parser.add_argument('--hatch_size', dest='hatch_size',
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default=argument.hatch_size, action='store', nargs='?', type=int,
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help='Patch size of hatches. eg. 8, 16, 32')
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parser.add_argument('--contour_simplify', dest='contour_simplify',
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default=argument.contour_simplify, action='store', nargs='?', type=int,
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help='Level of contour simplification. eg. 1, 2, 3')
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args = parser.parse_args()
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input_path = args.input_path
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export_path = args.output_path
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draw_hatch = not args.no_hatch
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draw_contours = not args.no_contour
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hatch_size = args.hatch_size
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contour_simplify = args.contour_simplify
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show_bitmap = args.show_bitmap
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no_cv = args.no_cv
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sketch(args.input_path)
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argument.draw_hatch = not args.no_hatch
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argument.contour_simplify = not args.no_contour
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argument.hatch_size = args.hatch_size
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argument.contour_simplify = args.contour_simplify
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argument.show_bitmap = args.show_bitmap
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argument.no_cv = args.no_cv
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argument.resolution = args.resolution
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sketch(input_path, export_path)
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@ -0,0 +1 @@
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from linedraw.helper import sketch
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@ -0,0 +1,11 @@
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class Default:
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export_path = "output/out.svg"
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show_bitmap = False
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draw_contours = True
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draw_hatch = True
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no_cv = False
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hatch_size = 16
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contour_simplify = 2
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resolution = 1024
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argument = Default()
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@ -0,0 +1,199 @@
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from PIL import Image, ImageOps, ImageDraw
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import linedraw.perlin as perlin
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from linedraw.filters import appmask, F_SobelX, F_SobelY
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from linedraw.default import argument
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from linedraw.util import distsum
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from linedraw.strokesort import sortlines
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def sketch(input_path, output_path):
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IMAGE = None
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try:
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IMAGE = Image.open(input_path)
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except FileNotFoundError:
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return print("The Input File wasn't found. Check Path")
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width, height = IMAGE.size
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IMAGE = IMAGE.convert("L")
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IMAGE = ImageOps.autocontrast(IMAGE, 10)
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lines = []
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if argument.draw_contours:
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lines += get_contours(IMAGE.resize((argument.resolution // argument.contour_simplify,
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argument.resolution // argument.contour_simplify * height // width)))
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if argument.draw_hatch:
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lines += hatch(IMAGE.resize(
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(argument.resolution // argument.hatch_size, argument.resolution // argument.hatch_size * height // width)))
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lines = sortlines(lines)
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if argument.show_bitmap:
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disp = Image.new("RGB", (argument.resolution, argument.resolution * height // width), (255, 255, 255))
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draw = ImageDraw.Draw(disp)
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for l in lines:
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draw.line(l, (0, 0, 0), 5)
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disp.show()
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file = open(output_path, 'w')
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file.write(make_svg(lines))
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file.close()
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print(len(lines), "strokes.")
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print("done.")
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return lines
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def get_contours(image):
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print("Generating Contours....")
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image = find_edges(image)
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image_copy1 = image.copy()
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image_copy2 = image.rotate(-90, expand=True).transpose(Image.FLIP_LEFT_RIGHT)
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image_copy1_dots = get_dots(image_copy1)
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image_copy1_contours = connect_dots(image_copy1_dots)
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image_copy2_dots = get_dots(image_copy2)
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image_copy2_contours = connect_dots(image_copy2_dots)
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for i in range(len(image_copy2_contours)):
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image_copy2_contours[1] = [(c[1], c[0]) for c in image_copy2_contours[i]]
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contours = image_copy1_contours + image_copy2_contours
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for i in range(len(contours)):
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for j in range(len(contours)):
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if len(contours[i]) > 0 and len(contours[j]) > 0:
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if distsum(contours[j][0], contours[i][-1]) < 8:
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contours[i] = contours[i] + contours[j]
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contours[j] = []
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for i in range(len(contours)):
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contours[i] = [contours[i][j] for j in range(0, len(contours[i]), 8)]
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contours = [c for c in contours if len(c) > 1]
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for i in range(0, len(contours)):
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contours[i] = [(v[0] * argument.contour_simplify, v[1] * argument.contour_simplify) for v in contours[i]]
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for i in range(0, len(contours)):
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for j in range(0, len(contours[i])):
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contours[i][j] = int(contours[i][j][0] + 10 * perlin.noise(i * 0.5, j * 0.1, 1)), int(
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contours[i][j][1] + 10 * perlin.noise(i * 0.5, j * 0.1, 2))
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return contours
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def find_edges(image):
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print("Fining Edges....")
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if argument.no_cv:
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appmask(image, [F_SobelX, F_SobelY])
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else:
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import numpy as np
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import cv2
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image = np.array(image)
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image = cv2.GaussianBlur(image, (3, 3), 0)
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image = cv2.Canny(image, 100, 200)
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image = Image.fromarray(image)
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return image.point(lambda p: p > 128 and 255)
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def get_dots(image):
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print("Getting contour points...")
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PX = image.load()
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dots = []
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width, height = image.size
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for y in range(height - 1):
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row = []
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for x in range(1, width):
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if PX[x, y] == 255:
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if len(row) > 0:
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if x - row[-1][0] == row[-1][-1] + 1:
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row[-1] = (row[-1][0], row[-1][-1] + 1)
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else:
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row.append((x, 0))
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else:
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row.append((x, 0))
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dots.append(row)
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return dots
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def connect_dots(dots):
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print("Connecting contour points....")
|
||||
contours = []
|
||||
for y in range(len(dots)):
|
||||
for x, v in dots[y]:
|
||||
if v > -1:
|
||||
if y == 0:
|
||||
contours.append([(x, y)])
|
||||
else:
|
||||
closest = -1
|
||||
cdist = 100
|
||||
for x0, v0 in dots[y - 1]:
|
||||
if abs(x0 - x) < cdist:
|
||||
cdist = abs(x0 - x)
|
||||
closest = x0
|
||||
if cdist > 3:
|
||||
contours.append([(x, y)])
|
||||
else:
|
||||
found = 0
|
||||
for i in range(len(contours)):
|
||||
if contours[i][-1] == (closest, y - 1):
|
||||
contours[i].append((x, y,))
|
||||
found = 1
|
||||
break
|
||||
if found == 0:
|
||||
contours.append([(x, y)])
|
||||
for c in contours:
|
||||
if c[-1][1] < y - 1 and len(c) < 4:
|
||||
contours.remove(c)
|
||||
return contours
|
||||
|
||||
|
||||
def hatch(image):
|
||||
print("Hatching....")
|
||||
PX = image.load()
|
||||
width, height = image.size
|
||||
lg1 = []
|
||||
lg2 = []
|
||||
for x0 in range(width):
|
||||
for y0 in range(height):
|
||||
x = x0 * argument.hatch_size
|
||||
y = y0 * argument.hatch_size
|
||||
if PX[x0, y0] > 144:
|
||||
pass
|
||||
elif PX[x0, y0] > 64:
|
||||
lg1.append([(x, y + argument.hatch_size / 4), (x + argument.hatch_size, y + argument.hatch_size / 4)])
|
||||
elif PX[x0, y0] > 16:
|
||||
lg1.append([(x, y + argument.hatch_size / 4), (x + argument.hatch_size, y + argument.hatch_size / 4)])
|
||||
lg2.append([(x + argument.hatch_size, y), (x, y + argument.hatch_size)])
|
||||
else:
|
||||
lg1.append([(x, y + argument.hatch_size / 4), (x + argument.hatch_size, y + argument.hatch_size / 4)])
|
||||
lg1.append([(x, y + argument.hatch_size / 2 + argument.hatch_size / 4),
|
||||
(x + argument.hatch_size, y + argument.hatch_size / 2 + argument.hatch_size / 4)])
|
||||
lg2.append([(x + argument.hatch_size, y), (x, y + argument.hatch_size)])
|
||||
lines = [lg1, lg2]
|
||||
for k in range(0, len(lines)):
|
||||
for i in range(0, len(lines[k])):
|
||||
for j in range(0, len(lines[k])):
|
||||
if lines[k][i] != [] and lines[k][j] != []:
|
||||
if lines[k][i][-1] == lines[k][j][0]:
|
||||
lines[k][i] = lines[k][i] + lines[k][j][1:]
|
||||
lines[k][j] = []
|
||||
lines[k] = [l for l in lines[k] if len(l) > 0]
|
||||
lines = lines[0] + lines[1]
|
||||
for i in range(0, len(lines)):
|
||||
for j in range(0, len(lines[i])):
|
||||
lines[i][j] = int(lines[i][j][0] + argument.hatch_size * perlin.noise(i * 0.5, j * 0.1, 1)), int(
|
||||
lines[i][j][1] + argument.hatch_size * perlin.noise(i * 0.5, j * 0.1, 2)) - j
|
||||
return lines
|
||||
|
||||
|
||||
def make_svg(lines):
|
||||
print("Generating SVG file....")
|
||||
out = '<svg xmlns="http://www.w3.org/2000/svg" version="1.1">'
|
||||
for l in lines:
|
||||
l = ",".join([str(p[0] * 0.5) + "," + str(p[1] * 0.5) for p in l])
|
||||
out += '<polyline points="' + l + '" stroke="black" stroke-width="2" fill="none" />\n'
|
||||
out += '</svg>'
|
||||
return out
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
from random import *
|
||||
from PIL import Image, ImageDraw, ImageOps
|
||||
from util import *
|
||||
from linedraw.util import *
|
||||
|
||||
|
||||
def sortlines(lines):
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
Pillow
|
||||
Numpy
|
||||
opencv-python
|
||||
Ładowanie…
Reference in New Issue