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process.py
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process.py
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import cv2
import numpy as np
import math
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torch.nn.functional as F
from PIL import Image , ImageFilter
import matplotlib.pyplot as plt
import time
import sys
import os
from LDR import *
from tone import *
from genStroke_origin import *
from drawpatch import rotate
from tools import *
from ETF.edge_tangent_flow import *
from Edge.pencil import *
from deblue import deblue
from extract import extract_pro
# args
input_path = './input/jm.png'
output_path = './output'
np.random.seed(1)
n = 7 # Quantization order
period = 4 # line period
direction = 10 # num of dir
Freq = 100 # save every（freq) lines drawn
deepen = 1 # for edge
transTone = False # for Tone
kernel_radius = 3 # for ETF
iter_time = 15 # for ETF
background_dir = None # for ETF
CLAHE = True
edge_CLAHE = True
draw_new = True
if __name__ == '__main__':
####### ETF #######
time_start=time.time()
ETF_filter = ETF(input_path=input_path, output_path=output_path+'/mask',\
dir_num=direction, kernel_radius=kernel_radius, iter_time=iter_time, background_dir=background_dir)
ETF_filter.forward()
print('ETF done')
input_img = cv2.imread(input_path, cv2.IMREAD_GRAYSCALE)
(h0,w0) = input_img.shape
cv2.imwrite(output_path + "/input_gray.png", input_img)
# if h0>w0:
# input_img = cv2.resize(input_img,(int(256*w0/h0),256))
# else:
# input_img = cv2.resize(input_img,(256,int(256*h0/w0)))
# (h0,w0) = input_img.shape
if transTone == True:
input_img = transferTone(input_img)
now_ = np.uint8(np.ones((h0,w0)))*255
step = 0
if draw_new==True:
time_start=time.time()
for dirs in range(direction):
angle = -90+dirs*180/direction
img,_ = rotate(input_img, -angle)
############ Adjust Histogram ############
if CLAHE==True:
img = HistogramEqualization(img)
# cv2.imshow('HistogramEqualization', res)
# cv2.waitKey(0)
# cv2.imwrite(output_path + "/HistogramEqualization.png", res)
print('HistogramEqualization done')
############ Quantization ############
ldr = LDR(img, n)
# cv2.imshow('Quantization', ldr)
# cv2.waitKey(0)
cv2.imwrite(output_path + "/Quantization.png", ldr)
# LDR_single(ldr,n,output_path) # debug
############ Cumulate ############
LDR_single_add(ldr,n,output_path)
print('Quantization done')
# get tone
(h,w) = ldr.shape
canvas = Gassian((h+4*period,w+4*period), mean=250, var = 3)
for j in range(n):
print('tone:',j)
distribution = ChooseDistribution(period=period,Grayscale=j*256/n)
mask = cv2.imread(output_path + '/mask/mask{}.png'.format(j),cv2.IMREAD_GRAYSCALE)/255
dir_mask = cv2.imread(output_path + '/mask/dir_mask{}.png'.format(dirs),cv2.IMREAD_GRAYSCALE)
# if angle==0:
# dir_mask[::] = 255
dir_mask,_ = rotate(dir_mask, -angle, pad_color=0)
dir_mask[dir_mask<128]=0
dir_mask[dir_mask>127]=1
distensce = Gassian((1,int(h/period)+4), mean = period, var = 1)
distensce = np.uint8(np.round(np.clip(distensce, period*0.8, period*1.25)))
raw = -int(period/2)
for i in np.squeeze(distensce).tolist():
if raw < h:
y = raw + 2*period
raw += i
for interval in get_start_end(mask[y-2*period]*dir_mask[y-2*period]):
begin = interval[0]
end = interval[1]
length = end - begin
begin -= 2*period
end += 2*period
length = end - begin
newline = Getline(distribution=distribution, length=length)
if length<1000 or begin == -2*period or end == w-1+2*period:
temp = canvas[y-int(period/2):y-int(period/2)+2*period,2*period+begin:2*period+end]
m = np.minimum(temp, newline[:,:temp.shape[1]])
canvas[y-int(period/2):y-int(period/2)+2*period,2*period+begin:2*period+end] = m
else:
temp = canvas[y-int(period/2):y-int(period/2)+2*period,2*period+begin-2*period:2*period+end+2*period]
m = np.minimum(temp, newline)
canvas[y-int(period/2):y-int(period/2)+2*period,2*period+begin-2*period:2*period+end+2*period] = m
if step % Freq == 0:
if step > Freq: # not first time
before = cv2.imread(output_path + "/process/{0:04d}.png".format(int(step/Freq)-1), cv2.IMREAD_GRAYSCALE)
now,_ = rotate(canvas[2*period:2*period+h,2*period:2*period+w], angle)
(H,W) = now.shape
now = now[int((H-h0)/2):int((H-h0)/2)+h0, int((W-w0)/2):int((W-w0)/2)+w0]
now = np.minimum(before,now)
else: # first time to save
now,_ = rotate(canvas[2*period:2*period+h,2*period:2*period+w], angle)
(H,W) = now.shape
now = now[int((H-h0)/2):int((H-h0)/2)+h0, int((W-w0)/2):int((W-w0)/2)+w0]
cv2.imwrite(output_path + "/process/{0:04d}.png".format(int(step/Freq)), now)
# cv2.imshow('step', canvas)
# cv2.waitKey(0)
now,_ = rotate(canvas[2*period:2*period+h,2*period:2*period+w], angle)
(H,W) = now.shape
now = now[int((H-h0)/2):int((H-h0)/2)+h0, int((W-w0)/2):int((W-w0)/2)+w0]
now = np.minimum(now,now_)
step += 1
cv2.imshow('step', now_)
cv2.waitKey(1)
now_ = now
now,_ = rotate(canvas[2*period:2*period+h,2*period:2*period+w], angle)
(H,W) = now.shape
now = now[int((H-h0)/2):int((H-h0)/2)+h0, int((W-w0)/2):int((W-w0)/2)+w0]
cv2.imwrite(output_path + "/pro/{}_{}.png".format(dirs,j), now)
now,_ = rotate(canvas[2*period:2*period+h,2*period:2*period+w], angle)
(H,W) = now.shape
now = now[int((H-h0)/2):int((H-h0)/2)+h0, int((W-w0)/2):int((W-w0)/2)+w0]
cv2.imwrite(output_path + "/{:.1f}.png".format(angle), now)
cv2.destroyAllWindows()
time_end=time.time()
print('total time',time_end-time_start)
print('stoke number',step)
cv2.imwrite(output_path + "/draw.png", now_)
cv2.imshow('draw', now_)
cv2.waitKey(0)
############ gen edge ###########
# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# pc = PencilDraw(device=device, gammaS=1)
# pc(input_path)
# edge = cv2.imread('output/Edge.png', cv2.IMREAD_GRAYSCALE)
edge = genStroke(input_img,18)
edge = np.power(edge, deepen)
edge = np.uint8(edge*255)
if edge_CLAHE==True:
edge = HistogramEqualization(edge)
cv2.imwrite(output_path + '/edge.png', edge)
cv2.imshow("edge",edge)
cv2.waitKey(0)
############# merge #############
edge = np.float32(edge)
now_ = cv2.imread(output_path + "/draw.png", cv2.IMREAD_GRAYSCALE)
result = res_cross= np.float32(now_)
result[1:,1:] = np.uint8(edge[:-1,:-1] * res_cross[1:,1:]/255)
result[0] = np.uint8(edge[0] * res_cross[0]/255)
result[:,0] = np.uint8(edge[:,0] * res_cross[:,0]/255)
result = edge*res_cross/255
result=np.uint8(result)
cv2.imwrite(output_path + '/result.png', result)
cv2.imshow("result",result)
cv2.waitKey(0)
# deblue
deblue(result, output_path)
# RGB
img_rgb_original = cv2.imread(input_path, cv2.IMREAD_COLOR)
cv2.imwrite(output_path + "/input.png", img_rgb_original)
img_yuv = cv2.cvtColor(img_rgb_original, cv2.COLOR_BGR2YUV)
img_yuv[:,:,0] = result
img_rgb = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)
cv2.imshow("RGB",img_rgb)
cv2.waitKey(0)
cv2.imwrite(output_path + "/result_RGB.png",img_rgb)
# extract drawing process
extract_pro(input_path=input_path, output_path=output_path+'/step', Quantization=n, direction=direction)