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,max_len=canvas*0.8, expl=0.003).fit(show=False, save=False).x
else:
self.trajectory = trajectory.x
if fraction is None:
self.fraction = [1/100, 1/10, 1/2, 1]
#self.fraction = [1 , 1 , 1 , 1]
else:
self.fraction = fraction
self.path_to_save = path_to_save
self.PSFnumber = len(self.fraction)
self.iters = len(self.trajectory)
self.PSFs = []
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 = '/Users/mykolam/PycharmProjects/University/DeblurGAN2/results_sharp'
folder_to_save = '/Users/mykolam/PycharmProjects/University/DeblurGAN2/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)
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 = './sharp'
folder_to_save = './blured'
# params = [0.5, 0.9, 0.8, 0.7, 0.5, 3]
params = 3
for path in os.listdir(folder):
print(path)
# trajectory = Trajectory(canvas=64, max_len=200, expl=np.random.choice(params)).fit()
trajectory = Trajectory(canvas=32, max_len=200, expl=3).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)
ax = fig.gca(projection='3d')
# ax.plot_wireframe(X,Y,kernel,color='c')
# plt.show()
# 曲面，x,y,z坐标，横向步长，纵向步长，颜色，线宽，是否渐变
# cm.coolwarm
surf = ax.plot_surface(X, Y, kernel, rstride=1, cstride=1, cmap='plasma', edgecolor='none')
plt.show()
if __name__ == '__main__':
can = 15
motion_blur_list = []
params = [0.01, 0.009, 0.008, 0.007,0.006, 0.005,0.004, 0.003,0.002]
for p in params:
for i in range(10):
trajectory = Trajectory(canvas=can, max_len=can*0.8, expl=p).fit()
psf = PSF(canvas=can, trajectory=trajectory).fit()
motion_blur_list.append(np.reshape(psf[3],(1,can,can)))
print("w")
motion_blur_list = np.concatenate(motion_blur_list,axis=0)
print(motion_blur_list.shape)
save_path = "motion_blur_kernel.npy"
np.save(save_path,motion_blur_list)
'''
img = Image.open('0001.png')
#img.show()
img = Image.fromarray(convolve(img, np.reshape(psf.PSFs[3],(can,can,1)), mode='nearest'))
img.show()
self.result[0] * 255)
elif show:
plt.show()
if __name__ == '__main__':
#folder = '/Users/mykolam/PycharmProjects/University/DeblurGAN2/results_sharp'
#folder_to_save = '/Users/mykolam/PycharmProjects/University/DeblurGAN2/blured'
folder = '/Users/apple/Desktop/research_summer/MSKCC_deblur/image/sharp'
folder_to_save = '/Users/apple/Desktop/research_summer/MSKCC_deblur/image/blur'
if not os.path.exists(folder_to_save):
os.makedirs(folder_to_save)
#params = [0.01, 0.009, 0.008, 0.007, 0.005, 0.003]
#params=[0.2,0.15,0.1,0.07,0.05,0.03,0.01,0.007,0.005]
params = [0.2, 0.17, 0.15, 0.13, 0.1, 0.07, 0.05, 0.03, 0.01,
0.007] #Usual tasks (first 8437 pairs)
#params=[0.3,0.25,0.2,0.17,0.15,0.13,0.1,0.07,0.05,0.01] #for the brain images, easier.
#for path in os.listdir(folder):
for path in os.listdir(folder)[:]:
print(path)
expl = np.random.choice(params)
trajectory = Trajectory(canvas=64, max_len=60, expl=expl).fit()
print(expl)
psf = PSF(canvas=64, trajectory=trajectory).fit()
#part=np.random.choice([1,2,3])
part = 3
print(part)
BlurImage(os.path.join(folder, path), PSFs=psf,
path__to_save=folder_to_save, part=part).\
blur_image(save=True)