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image_creation_murakami_variations.py
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import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import cv2
import random
#from microsaccades_functions import *
import sys
import os
#---------------------------------------------------------------------------------------IMAGE-CREATION
#radius = 4. #dot size of 4arcmin -> 8px
#rect_size = 110. #size of inner radius
#vel = 0.06 #velocity is 30 arcmin/s -> 60px/1000ms
#direction = float(angle)*np.pi/8. #dot moving direction in arc (get right velocities !)
def gauss(x,x0,sigma):
return (np.exp(-(x-x0)*(x-x0)/(2*sigma*sigma)))
image_height = 600
image_width = 600
suff = 'normal_100_bg'
#suff = sys.argv[1]
#cyc = float(sys.argv[2])
#film_length = int(sys.argv[3])
circle_radius=140
#for normal distributed microsaccades
mu = 0.
sigma = 0.447
#file_location = "/home/schrader/Documents/microsaccades/video/img_input/oof/"+str(suff)
#save the different conditions
#get normal 2d distribution
#mean = [0, 0]
#cov = [[1, 0], [0, 1]]
#tl_x, tl_y = sigma*np.random.multivariate_normal(mean, cov, film_length).T
disp = np.random.normal(mu,sigma,400)
#disp = open('data/oof/displacement.data','w+')
#use displacement here?
pos = (image_height/2.+0.5,image_width/2+0.5)
center = (image_height/2.-0.5,image_width/2.-0.5)
sigma2 = 3.
rpw = 3
q = np.random.randint(2, size=(int(image_height/rpw)+1,int(image_width/rpw)+1))
qcenter = []
for k in range(400):
qcenter += [(np.random.triangular(0, 1, 1, 1)[0],np.random.uniform(0,1,1)[0])] #np.random.rand(100,2)
qall = np.random.rand(2000,2)
#print qcenter
canvas = np.zeros((image_height, image_width))
canvas2 = np.zeros((image_height, image_width))
current_col = 0
'''
#NORMAL ILLUSION
for i in range(image_height):
for j in range(image_width):
dist = np.sqrt((float(i)-center[0])*(float(i)-center[0])+(float(j)-center[1])*(float(j)-center[1]))
if dist >= circle_radius+30:
canvas[i,j]=float(q[int(i/rpw)][int(j/rpw)])
canvas2[i,j]=float(q[int(i/rpw)][int(j/rpw)])
if dist > circle_radius-30:
if dist < circle_radius+30:
canvas[i,j]=(circle_radius-30.-dist)/60.*canvas[i,j]-(circle_radius-30.-dist)/60.*float(q[int(i/rpw)][int(j/rpw)])
#canvas2[i,j]=(circle_radius-30.-dist)/60.*canvas[i,j]-(circle_radius-30.-dist)/60.*float(q[int(i/rpw)][int(j/rpw)])
canvas2[i,j]=-(circle_radius-30.-dist)/60.*float(q[int(i/rpw)][int(j/rpw)])
for qc in qcenter:
#print qc
for i in range(int(image_height/2)-circle_radius-15,int(image_height/2)+circle_radius+15):
for j in range(int(image_width/2)-circle_radius-15,int(image_width/2)+circle_radius+15):
#print i
pos=[image_height/2.+circle_radius*qc[0]*np.cos(2.*np.pi*qc[1]),image_width/2.+circle_radius*qc[0]*np.sin(2.*np.pi*qc[1])]
dist = np.sqrt((pos[0]-float(j))*(pos[0]-float(j))+(pos[1]-float(i))*(pos[1]-float(i)))
val = canvas[i,j]+gauss(dist,0,sigma2)
if val > 1.:
val = 1.
canvas[i,j] = val
#val = canvas2[i,j]+gauss(dist,0,sigma2)
#if val > 1.:
# val = 1.
#canvas2[i,j] = val
'''
#NORMAL ILLUSION WITH LESS BACKGROUND
for i in range(image_height):
for j in range(image_width):
dist = np.sqrt((float(i)-center[0])*(float(i)-center[0])+(float(j)-center[1])*(float(j)-center[1]))
if dist >= circle_radius+30:
canvas[i,j]=(0.4+0.2*float(q[int(i/rpw)][int(j/rpw)]))
canvas2[i,j]=0.5
#canvas2[i,j]=(0.1+0.8*float(q[int(i/rpw)][int(j/rpw)]))
if dist > circle_radius-30:
if dist < circle_radius+30:
canvas[i,j]=(circle_radius-30.-dist)/60.*canvas[i,j]-(circle_radius-30.-dist)/60.*(0.4+0.2*float(q[int(i/rpw)][int(j/rpw)]))
#canvas2[i,j]=(circle_radius-30.-dist)/60.*canvas[i,j]-(circle_radius-30.-dist)/60.*float(q[int(i/rpw)][int(j/rpw)])
canvas2[i,j]=-(circle_radius-30.-dist)/60.*0.5
for qc in qcenter:
#print qc
pos=[image_height/2.+circle_radius*qc[0]*np.cos(2.*np.pi*qc[1]),image_width/2.+circle_radius*qc[0]*np.sin(2.*np.pi*qc[1])]
dist_c = np.sqrt((pos[0]-center[0])*(pos[0]-center[0])+(pos[1]-center[1])*(pos[1]-center[1]))
for i in range(int(pos[1])-20,int(pos[1])+20):
for j in range(int(pos[0])-20,int(pos[0])+20):
#print i
dist = np.sqrt((pos[0]-float(j))*(pos[0]-float(j))+(pos[1]-float(i))*(pos[1]-float(i)))
val = canvas[i,j]+gauss(dist,0,sigma2)
if val > 1.:
val = 1.
canvas[i,j] = val
val = canvas2[i,j]+gauss(dist,0,sigma2)
if val > 1.:
val = 1.
canvas2[i,j] = val
'''
#ILLUSION USING JUST DOTS
for qc in qall:
#print qc
pos=[image_height*qc[0],image_width*qc[1]]
dist_c = np.sqrt((pos[0]-center[0])*(pos[0]-center[0])+(pos[1]-center[1])*(pos[1]-center[1]))
for i in range(int(pos[1])-20,int(pos[1])+20):
for j in range(int(pos[0])-20,int(pos[0])+20):
if (i >= 0 and i < image_height) and (j >= 0 and j < image_width):
#print i
dist = np.sqrt((pos[0]-float(j))*(pos[0]-float(j))+(pos[1]-float(i))*(pos[1]-float(i)))
val = canvas[i,j]+gauss(dist,0,sigma2)
if val > 1.:
val = 1.
canvas[i,j] = val
val = canvas2[i,j]+gauss(dist,0,sigma2)
if dist_c <= circle_radius:
if val > 1.:
val = 1.
canvas2[i,j] = val
'''
'''
#ILLUSION WITH LOWER CONTRAST
for i in range(image_height):
for j in range(image_width):
dist = np.sqrt((float(i)-center[0])*(float(i)-center[0])+(float(j)-center[1])*(float(j)-center[1]))
if dist >= circle_radius+30:
canvas[i,j]=0.8*float(q[int(i/rpw)][int(j/rpw)])
if dist > circle_radius-30:
if dist < circle_radius+30:
canvas[i,j]=(circle_radius-30.-dist)/60.*canvas[i,j]-(circle_radius-30.-dist)/60.*0.8*float(q[int(i/rpw)][int(j/rpw)])
for qc in qcenter:
#print qc
for i in range(int(image_height/2)-circle_radius-15,int(image_height/2)+circle_radius+15):
for j in range(int(image_width/2)-circle_radius-15,int(image_width/2)+circle_radius+15):
#print i
pos=[image_height/2.+circle_radius*qc[0]*np.cos(2.*np.pi*qc[1]),image_width/2.+circle_radius*qc[0]*np.sin(2.*np.pi*qc[1])]
dist = np.sqrt((pos[0]-float(j))*(pos[0]-float(j))+(pos[1]-float(i))*(pos[1]-float(i)))
val = canvas[i,j]+gauss(dist,0,sigma2)
if val > 1.:
val = 1.
canvas[i,j] = val
'''
'''
#ILLUSION WITH SAME PATTERN BUT DIFFERENT COLOUR
for i in range(image_height):
for j in range(image_width):
dist = np.sqrt((float(i)-center[0])*(float(i)-center[0])+(float(j)-center[1])*(float(j)-center[1]))
canvas[i,j] = 0.5*float(q[int(i/rpw)][int(j/rpw)])
canvas2[i,j] = 0.5*float(q[int(i/rpw)][int(j/rpw)])
#inverse
#canvas2[i,j]=0.
if dist >= circle_radius+30:
canvas2[i,j]=0.
canvas[i,j]=float(q[int(i/rpw)][int(j/rpw)])
#inverse
#canvas2[i,j]=float(q[int(i/rpw)][int(j/rpw)])
if dist > circle_radius-30:
if dist < circle_radius+30:
canvas[i,j]=((-circle_radius+30.+dist)/60.+0.5)*float(q[int(i/rpw)][int(j/rpw)])
canvas2[i,j]=(circle_radius+30.-dist)/60.*canvas2[i,j]
#inverse
#canvas2[i,j]=((-circle_radius+30.+dist)/60.)*float(q[int(i/rpw)][int(j/rpw)])
#if dist < circle_radius-30:
# canvas[i,j]=float(q[int(i/rpw)][int(j/rpw)])
# canvas2[i,j]=float(q[int(i/rpw)][int(j/rpw)])
#if dist < circle_radius:
# canvas[i,j]=canvas[i,j]*2.
# canvas2[i,j]=canvas2[i,j]*2.
#else:
# canvas2[i,j]=0.
'''
'''
#ILLUSION WITH SAME PATTERN BUT SHARP AREAS
for i in range(image_height):
for j in range(image_width):
dist = np.sqrt((float(i)-center[0])*(float(i)-center[0])+(float(j)-center[1])*(float(j)-center[1]))
canvas[i,j] = 0.5*float(q[int(i/rpw)][int(j/rpw)])
canvas2[i,j] = 0.5*float(q[int(i/rpw)][int(j/rpw)])
#inverse
#canvas2[i,j]=0.
if dist >= circle_radius:
canvas2[i,j]=0.
canvas[i,j]=float(q[int(i/rpw)][int(j/rpw)])
#inverse
#canvas2[i,j]=float(q[int(i/rpw)][int(j/rpw)])
#if dist > circle_radius-30:
# if dist < circle_radius+30:
# canvas[i,j]=((-circle_radius+30.+dist)/60.+0.5)*float(q[int(i/rpw)][int(j/rpw)])
# canvas2[i,j]=(circle_radius+30.-dist)/60.*canvas2[i,j]
'''
'''
#ILLUSION WITH SAME PATTERN AND NO CONTRAST BG
for i in range(image_height):
for j in range(image_width):
dist = np.sqrt((float(i)-center[0])*(float(i)-center[0])+(float(j)-center[1])*(float(j)-center[1]))
canvas[i,j] = 0.3+0.4*float(q[int(i/rpw)][int(j/rpw)])
canvas2[i,j] = 0.5
#inverse
#canvas2[i,j]=0.
if dist >= circle_radius+30:
canvas[i,j]=0.5
#inverse
#canvas2[i,j]=float(q[int(i/rpw)][int(j/rpw)])
if dist > circle_radius-30:
if dist < circle_radius+30:
canvas[i,j]=((circle_radius+30.-dist)/60.)*(0.3+0.4*float(q[int(i/rpw)][int(j/rpw)]))+0.5*((-circle_radius+30.+dist)/60.)
#inverse
#canvas2[i,j]=((-circle_radius+30.+dist)/60.)*float(q[int(i/rpw)][int(j/rpw)])
#if dist < circle_radius-30:
# canvas[i,j]=float(q[int(i/rpw)][int(j/rpw)])
# canvas2[i,j]=float(q[int(i/rpw)][int(j/rpw)])
#if dist < circle_radius:
# canvas[i,j]=canvas[i,j]*2.
# canvas2[i,j]=canvas2[i,j]*2.
#else:
# canvas2[i,j]=0.
'''
'''
#cm = plt.get_cmap('RdBu', 3)
cm = mpl.colors.ListedColormap(['black', 'white', 'chocolate'])
fig = plt.figure()
fig.set_size_inches(3,3)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(canvas, cmap = cm )
plt.savefig("/home/schrader/Documents/microsaccades/img/murakami_illusion/"+str(suff)+".png", dpi = 200)
plt.close()
fig = plt.figure()
fig.set_size_inches(3,3)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(canvas2, cmap = cm )
plt.savefig("/home/schrader/Documents/microsaccades/img/murakami_illusion/"+str(suff)+"_off.png", dpi = 200)
'''
#for non-coloured
fig = plt.figure()
fig.set_size_inches(3,3)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(canvas, cmap='gray',norm=mpl.colors.Normalize(vmin=0.,vmax=1.))
plt.savefig("/home/schrader/Documents/microsaccades/img/murakami_illusion/"+str(suff)+".png", dpi = 200)
plt.close()
fig = plt.figure()
fig.set_size_inches(3,3)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(canvas2, cmap='gray',norm=mpl.colors.Normalize(vmin=0.,vmax=1.))
plt.savefig("/home/schrader/Documents/microsaccades/img/murakami_illusion/"+str(suff)+"_off.png", dpi = 200)
#plt.show()
#plt.close()
'''
plt.savefig(file_location + "/first.png", dpi = 150)
img = cv2.imread(file_location + "/first.png",0)
rows,cols = img.shape
d_file = open('data/phase_displacement.data','r+')
disp = np.load(d_file)
d_file.close()
for f in range(film_length):
#------------------------------------------------------------------------------NORMAL-DISPLACEMENT
fig = plt.figure()
fig.set_size_inches(2,0.25)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
#ax.imshow(canvas, cmap='gray')
transl = np.float32([[1,0,int(disp[f])],[0,1,0]])
tlFig = cv2.warpAffine(img,transl,(cols,rows))
#save to file, comment for additional rotation
#fig = plt.figure()
tlFig = tlFig[:,30:270]
tlFig = cv2.GaussianBlur(tlFig,(3,3),0.5)
#fig.set_size_inches(2,0.25)
ax.imshow(tlFig,cmap='gray')
plt.savefig(file_location + "/second"+str(f+1).zfill(3)+".png", dpi = 120)
plt.close()
'''
'''
#-----------------------------------------------------------------------------------------ROTATION
rot = cv2.getRotationMatrix2D((cols/2.,rows/2.),degrees,1)
rotFig = cv2.warpAffine(tlFig,rot,(cols,rows))
#save to file
rotFig = rotFig[160:400,160:400]
fig.set_size_inches(1, 1)
plt.imshow(rotFig,cmap='gray')
plt.savefig(file_location + "/second"+str(f+1).zfill(3)+".png", dpi = 240)
plt.close()
'''