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simulate.py
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simulate.py
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import cv2
import numpy as np
import math
constant_length = 1000
def Gassian(size, mean = 0, var = 0):
norm = np.random.randn(*size)
denorm = norm * np.sqrt(var) + mean
return np.uint8(np.round(np.clip(denorm,0,255)))
def Getline(distribution, length):
period = distribution.shape[0]
if length < constant_length: # if length is too short, lines are Aligned
patch = Gassian((2*period, length), mean=250, var = 3)
begin = 0
end = 1
for i in range(period):
patch[i]=Gassian((1,length), mean=distribution[i,0], var=distribution[i,1])
else: # if length is't too short, lines is't Aligned
patch = Gassian((2*period, length+4*period), mean=250, var = 3)
begin = Gassian((1,1), mean=2.0*period, var=2*period)
# egin = Gassian((1,1), mean=2.0*period, var=0)
begin = np.uint8(np.round(np.clip(begin,0,4*period)))
begin = int(begin[0,0])
end = Gassian((1,1), mean=2.0*period, var=2*period)
# end = Gassian((1,1), mean=2.0*period, var=0)
end = np.uint8(np.round(np.clip(end,1,4*period+1)))
end = int(end[0,0])
real_length = length+4*period-end-begin
for i in range(period):
patch[i,begin:-end]=Gassian((1,real_length), mean=distribution[i,0], var=distribution[i,1])
patch = Attenuation(patch, period=period, distribution=distribution,begin=begin, end=end)
patch = Distortion(patch, begin=begin, end=end)
return np.uint8(np.round(np.clip(patch,0,255)))
def Attenuation(patch, period, distribution, begin, end):
order = int((patch.shape[1]-begin-end)/2)+1
radius = (period-1)/2
canvas = Gassian((patch.shape[0], patch.shape[1]), mean=250, var=3)
patch = np.float32(patch)
canvas = np.float32(canvas)
for i in range(begin, patch.shape[1]-end+1):
for j in range(period):
a = np.abs((1.0-(i-begin)/order)**2)/3
b = np.abs((1.0-j/radius)**2)*1
patch[j,i] += (canvas[j,i]-patch[j,i])*np.sqrt(a+b)/1.5
# patch[j,i] += 0.75*(canvas[j,i]-patch[j,i]) * (np.abs((1.0-(i-begin)/order)**2))**0.5
return np.uint8(np.round(np.clip(patch,0,255)))
def Distortion(patch,begin,end):
height = int(patch.shape[0]/2)
length = patch.shape[1]
patch = np.float32(patch)
patch_copy = patch.copy()
# central = ((length-begin-end)/2+begin) + np.random.randn()*length/30
central = ((length-begin-end)/2+begin)
if length>100:
radius = length**2/(4*height)
else:
radius = length**2/(2*height)
for i in range(length):
offset = ((central-i)**2)/(2*radius)
int_offset = int(offset)
decimal_offset = offset-int_offset
for j in range(height):
if j>int_offset:
patch[j,i]=int(decimal_offset*patch_copy[j-1-int_offset,i]+(1-decimal_offset)*patch_copy[j-int_offset,i])
else:
patch[j,i]= np.random.randn() * np.sqrt(3) + 250
patch_copy = patch.copy()
if length>100:
for i in range(length):
offset = ((central-i)**2)/(2*radius)
int_offset = int(offset)
decimal_offset = offset-int_offset
for j in range(patch.shape[0]):
if j>int_offset:
patch[j,i]=int(decimal_offset*patch_copy[j-1-int_offset,i]+(1-decimal_offset)*patch_copy[j-int_offset,i])
else:
patch[j,i]= np.random.randn() * np.sqrt(3) + 250
# else:
# radius = length**2/(4*height)
# for i in range(length):
# offset = ((central-i)**2)/(2*radius)
# int_offset = int(offset)
# decimal_offset = offset-int_offset
# for j in range(patch.shape[0]):
# if j>int_offset:
# patch[j,i]=int(decimal_offset*patch_copy[j-1-int_offset,i]+(1-decimal_offset)*patch_copy[j-int_offset,i])
# else:
# patch[j,i]= np.random.randn() * np.sqrt(3) + 250
return np.uint8(np.round(np.clip(patch,0,255)))
def GetParallel(distribution, height, length, period):
if length<constant_length: # constant length
canvas = Gassian((height+2*period,length), mean=250, var = 3)
else: # variable length
canvas = Gassian((height+2*period,length+4*period), mean=250, var = 3)
distensce = Gassian((1,int(height/period)+2), mean = period, var = period/5)
# distensce = Gassian((1,int(height/period)+1), mean = period, var = 0)
distensce = np.uint8(np.round(np.clip(distensce, period*0.8,period*1.25)))
begin = 0
for i in np.squeeze(distensce).tolist():
newline = Getline(distribution=distribution, length=length)
h,w = newline.shape
# cv2.imshow('line', newline)
# cv2.waitKey(0)
# cv2.imwrite("D:/ECCV2020/simu_patch/Line3.jpg",newline)
if begin < height:
m = np.minimum(canvas[begin:(begin + h),:], newline)
canvas[begin:(begin + h),:] = m
begin += i
else:
break
return canvas[:height,:]
def ChooseDistribution(period, Grayscale):
distribution = np.zeros((period,2))
c = period/2.0
difference = 250-Grayscale
for i in range(distribution.shape[0]):
distribution[i][0] = Grayscale + difference*abs(i-c)/c
distribution[i][1] = np.cos((i-c)/c*(0.5*3.1415929))*difference
# distribution[i][0] -= np.cos((i-4)/4.0*(0.5*3.1415929))*difference
# distribution[i][1] += np.cos((i-4)/4.0*(0.5*3.1415929))*difference
return np.abs(distribution)
if __name__ == '__main__':
np.random.seed(100)
canvas = Gassian((500,500), mean=250, var = 3)
# distribution = np.array([[245,31],[238,27],[218,48],[205,33],[214,38],[234,24],[240,42]])
###################################################
###################################################
###################################################
period = 7
Grayscale = 128
H,L = (100,200)
###################################################
###################################################
###################################################
distribution = ChooseDistribution(period=period, Grayscale=Grayscale)
print(distribution)
patch = GetParallel(distribution=distribution, height=H, length=L, period=period)
(h,w) = patch.shape
# patch = GetOffsetParallel(offset=4, distribution=distribution, patch_size=(40,200), period_mean=distribution.shape[0], period_var=1)
# (h,w) = patch.shape
canvas[250-int(h/2):250-int(h/2)+h,250-int(w/2):250-int(w/2)+w] = patch
# cv2.imshow('Parallel', patch[:, 2*distribution.shape[0]:w-2*distribution.shape[0]])
cv2.imshow('Parallel', canvas)
cv2.waitKey(0)
cv2.imwrite("D:/ECCV2020/simu_patch/Parallel4.jpg",patch)
print("done")