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patch_generation.py
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patch_generation.py
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import pickle
GRID_SIZE = 32
with open('ground_truth_dataset_{}.pickle'.format(GRID_SIZE),'rb') as f:
data = pickle.load(f)
#print(data,len(data),type(data))
if GRID_SIZE == 64:
PATCH_SIZE = 20
elif GRID_SIZE ==32:
PATCH_SIZE = 10
train = data['train']
test = data['test']
valid = data['validation']
import numpy as np
train = np.array(train)
test = np.array(test)
valid = np.array(valid)
print(train.shape)
print(test.shape)
print(valid.shape)
def get_patch(blur_patch,image,direction,start_pt):
im = np.ones(image.shape)
#print(start_pt)
rc = range(start_pt,start_pt+PATCH_SIZE)
r = range(image.shape[0]-PATCH_SIZE,image.shape[0])
c = range(image.shape[1]-PATCH_SIZE,image.shape[1])
x = range(0,PATCH_SIZE)
if direction == 0:
before = np.sum(im[np.ix_(x,rc)] )
im[np.ix_(x,rc)] = blur_patch
after = np.sum(im[np.ix_(x,rc)] )
elif direction == 1:
before = np.sum(im[np.ix_(r,rc)])
im[np.ix_(r,rc)] = blur_patch
after = np.sum(im[np.ix_(r,rc)] )
elif direction == 2:
before = np.sum(im[np.ix_(rc,x)])
im[np.ix_(rc,x)] = blur_patch
after = np.sum(im[np.ix_(rc,x)])
elif direction == 3:
before = np.sum(im[np.ix_(rc,c)])
im[np.ix_(rc,c)] = blur_patch
after = np.sum(im[np.ix_(rc,c)])
#print(im.shape)
#print("if path exists",np.sum(image[np.ix_(rc,c)]))
#print("before",before,"after",after,"total before",im.shape[0]*im.shape[1],"total after",np.sum(im))
return im
def get_patched_image(image):
dr = np.random.randint(4,size=1)
#print("image is ",image)
#print("number of ones is ", np.sum(image))
start_pt = np.random.randint(image.shape[0]-PATCH_SIZE+1,size=1)
blur_patch = np.zeros(shape=(PATCH_SIZE,PATCH_SIZE))
mask = get_patch(blur_patch,image,dr[0],start_pt[0])
image = np.minimum(image,mask)
#print("direction chosen",dr[0],"starting point is ",start_pt[0])
#print("masked image",image)
#print("sum is ",np.sum(image))
return image,mask
#print(tiles)
import matplotlib.pyplot as plt
# plt.imshow(train_x[0])
# plt.ylabel('y')
# plt.xlabel('x')
# plt.show()
#image = np.float32(get_patched_image(train_x[1]))
fig = plt.figure(figsize=(6,8))
#fig.add_subplot(1,2,1)
# plt.imshow(train_x[1])
# plt.title("True image")
# plt.ylabel('y')
# plt.xlabel('x')
# fig.add_subplot(1,2,2)
# plt.imshow(image)
# title = "Masked for PATCH_SIZE = " + str(PATCH_SIZE)
# plt.title(title)
# plt.ylabel('masked_y')
# plt.xlabel('masked_x')
# plt.show()
# plt.savefig("figure_8.png")
# x = np.zeros(shape=(4,4))
# x[np.ix_([1,2],[1,2])]= np.ones((2,2))
# print(x)
def get_image_and_mask(t,tpe):
d1 = []
d2 = []
for i in range(1,len(t)+1):
#plt.imshow(train_x[i])
#path = "true/figure_true"+str(i)
#plt.savefig(path)
image,mask = np.float32(get_patched_image(t[i-1]))
#plt.imshow(image)
#path = "masked/figure_true"+str(i)
d1.append(image)
d2.append(mask)
#plt.savefig(path)
if (i%500)==0 and i:
print("saved ",i,tpe,"images")
return d1 , d2
#print(np.sum(image),256*256-np.sum(mask))
images = {}
masks = {}
images['train'] , masks['train'] = get_image_and_mask(train,"training")
images['validation'] , masks['validation'] = get_image_and_mask(valid,"validation")
images['test'], masks['test'] = get_image_and_mask(test,"testing")
with open('image_dataset_{}.pickle'.format(GRID_SIZE), 'wb') as handle:
pickle.dump(images, handle)
with open('mask_dataset_{}.pickle'.format(GRID_SIZE), 'wb') as handle:
pickle.dump(masks, handle)
image,mask,gt = images['train'][0], masks['train'][0] , train[0]
fig.add_subplot(1,2,1)
plt.imshow(gt)
plt.title("True image")
plt.ylabel('y')
plt.xlabel('x')
fig.add_subplot(1,2,2)
plt.imshow(mask)
title = "Masked for PATCH_SIZE = " + str(PATCH_SIZE)
plt.title(title)
plt.ylabel('masked_y')
plt.xlabel('masked_x')
plt.show()
plt.savefig("figure_8.png")