def __init__(self,
canvas=None,
trajectory=None,
fraction=None,
path_to_save=None,
max_len=60):
if canvas is None:
self.canvas = (canvas, canvas)
else:
self.canvas = (canvas, canvas)
if trajectory is None:
self.trajectory = Trajectory(canvas=canvas,
expl=0.005,
max_len=max_len).fit(show=False,
save=False)
else:
self.trajectory = trajectory.x
if fraction is None:
self.fraction = [1 / 2, 1]
else:
self.fraction = fraction
self.path_to_save = path_to_save
self.PSFnumber = len(self.fraction)
self.iters = len(self.trajectory)
self.PSFs = []
def blur(args):
# Unpack args
img_path, folder_in, folder_out, canvas_size = args #Crank the canvas_size value for more blur!!!
print(img_path)
# Blur images
params = [0.01, 0.009, 0.008, 0.007, 0.005, 0.003]
trajectory = Trajectory(
canvas=canvas_size, expl=np.random.choice(params)
).fit(
show=True
) #UPDATED: I removed the max_len parameter. It is now a function of canvas size. I wanted it to scale without having people think about it. -David 2
psf = PSF(canvas=canvas_size, trajectory=trajectory).fit()
BlurImage(
os.path.join(folder_in, img_path),
PSFs=psf,
path__to_save=folder_out,
part=np.random.choice([1, 2, 3])).\
blur_image(save=True)
def __init__(self,
canvas=None,
trajectory=None,
fraction=None,
path_to_save=None):
if canvas is None:
self.canvas = (canvas, canvas)
else:
self.canvas = (canvas, canvas)
if trajectory is None:
self.trajectory = Trajectory(canvas=canvas,
expl=0.005).fit(show=False,
save=False)
else:
self.trajectory = trajectory.x
if fraction is None:
self.fraction = [1] #[1/100, 1/20, 1/10, 1/5, 1/2, 1/1.5, 1]
else:
self.fraction = fraction
self.path_to_save = path_to_save
self.PSFnumber = len(self.fraction) #the original code
#print(self.PSFnumber)
self.iters = len(self.trajectory)
self.PSFs = []
for i in range(len(self.result)):
axes[i].imshow(self.result[i])
else:
plt.axis('off')
plt.imshow(self.result[0])
if show and save:
if self.path_to_save is None:
raise Exception('Please create Trajectory instance with path_to_save')
cv2.imwrite(os.path.join(self.path_to_save, self.image_path.split('/')[-1]), self.result[0] * 255)
plt.show()
elif save:
if self.path_to_save is None:
raise Exception('Please create Trajectory instance with path_to_save')
cv2.imwrite(os.path.join(self.path_to_save, self.image_path.split('/')[-1]), self.result[0] * 255)
elif show:
plt.show()
if __name__ == '__main__':
folder = '/home/ubuntu/Downloads/DeblurGAN/datasets/trainB'
folder_to_save = '/home/ubuntu/Downloads/DeblurGAN/datasets/blured'
params = [0.01, 0.009, 0.008, 0.007, 0.005, 0.003]
for path in os.listdir(folder):
print(path)
trajectory = Trajectory(canvas=64, max_len=60, expl=np.random.choice(params)).fit()
psf = PSF(canvas=64, trajectory=trajectory).fit()
BlurImage(os.path.join(folder, path), PSFs=psf,
path__to_save=folder_to_save, part=np.random.choice([1, 2, 3])).\
blur_image(save=True)
for i in range(record_num):
tfrecord_name = path_to_save + "N%05d.tfrecord" % (i + 1 +
record_start_cnt)
writer = tf.python_io.TFRecordWriter(tfrecord_name)
for j in range(min(number - example_num * i, example_num)):
params = [0.03, 0.009, 0.008, 0.007, 0.005, 0.004]
max_len = np.random.choice(np.arange(1, 31),
p=[
0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.02, 0.02, 0.02, 0.02,
0.02, 0.02, 0.06, 0.06, 0.06, 0.06,
0.06, 0.06, 0.06, 0.06, 0.06, 0.06
])
trajectory = Trajectory(
canvas=32, max_len=max_len, expl=np.random.choice(
params)).fit() #60#np.random.randint(3,15)
psf = PSF(canvas=32,
trajectory=trajectory,
path_to_save=path_to_save).fit(show=False, save=False)
#print(np.sum(psf[0]))
psfchoice = 0 #np.random.choice(np.arange(7))
cv2.normalize(
psf[psfchoice], psf[psfchoice], 1.0, 0.0, cv2.NORM_L1
) #, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)#NORM_MINMAX
#print(psf[2].shape)
#print(psf[0])
#print(np.sum(psf[psfchoice]))
print("process %d in %d" % (example_num * i + j, number))
psf_bytes = (psf[psfchoice]).tobytes()
example = gen_example_v0([psf_bytes], shape=(32, 32), psf_num=1)