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depth_estimation_test.py
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depth_estimation_test.py
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import tensorflow as tf
import scipy.io as sio
import numpy as np
import matplotlib.image
import os
import Network
import imageio
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
results_dir = "./trained_framework/"
DATA_PATH = './Data/'
TFRECORD_TEST_PATH = [DATA_PATH + 'test.tfrecord']
########################################## Functions #############################################
def parse_element(example):
# from tfrecord file to data
N = 278
N_Phi = 21
features = tf.parse_single_example(example,
features={
'RGB': tf.FixedLenFeature([], tf.string),
'DPPhi': tf.FixedLenFeature([], tf.string),
'DP': tf.FixedLenFeature([], tf.string),
})
RGB_flat = tf.decode_raw(features['RGB'], tf.uint8)
RGB = tf.reshape(RGB_flat, [N, N, 3])
DPPhi_flat = tf.decode_raw(features['DPPhi'], tf.uint8)
DPPhi = tf.reshape(DPPhi_flat, [N, N, N_Phi])
DP_flat = tf.decode_raw(features['DP'], tf.uint8)
DP = tf.reshape(DP_flat, [N, N])
return RGB, DPPhi, DP
def read2batch(TFRECORD_PATH, batchsize):
# load tfrecord and make them to be usable data
dataset = tf.data.TFRecordDataset(TFRECORD_PATH)
dataset = dataset.map(parse_element).repeat()
dataset = dataset.batch(batchsize, drop_remainder=True)
iterator = dataset.make_one_shot_iterator()
RGB_batch, DPPhi_batch, DP_batch = iterator.get_next()
RGB_batch_float = tf.image.convert_image_dtype(RGB_batch, tf.float32)
DPPhi_float = tf.cast(DPPhi_batch, tf.float32)
Phi_batch_scaled = (tf.cast(DP_batch, tf.float32) - 10) / 210
return RGB_batch_float, DPPhi_float, Phi_batch_scaled
def add_gaussian_noise(images, std):
noise = tf.random_normal(shape=tf.shape(images), mean=0.0, stddev=std, dtype=tf.float32)
return tf.nn.relu(images + noise)
def add_SDGN(images, std):
noise0 = tf.random_normal(shape = tf.shape(images), mean = 0.0, stddev = std, dtype = tf.float32)
noise1 = tf.multiply(tf.sqrt(images),noise0) # Noise = N(0,std)*sqrt(I)
return tf.nn.relu(images+noise1)
def fft2dshift(input):
dim = int(input.shape[1].value) # dimension of the data
if dim % 2 == 0:
print('Please make the size of kernel odd')
channel1 = int(input.shape[0].value) # channels for the first dimension
# shift up and down
u = tf.slice(input, [0, 0, 0], [channel1, int((dim + 1) / 2), dim])
d = tf.slice(input, [0, int((dim + 1) / 2), 0], [channel1, int((dim - 1) / 2), dim])
du = tf.concat([d, u], axis=1)
# shift left and right
l = tf.slice(du, [0, 0, 0], [channel1, dim, int((dim + 1) / 2)])
r = tf.slice(du, [0, 0, int((dim + 1) / 2)], [channel1, dim, int((dim - 1) / 2)])
output = tf.concat([r, l], axis=2)
return output
def gen_OOFphase(Phi_list, N_B, wvls):
# return (Phi_list,pixel,pixel,color)
N = N_B
x0 = np.linspace(-1.1, 1.1, N)
xx, yy = np.meshgrid(x0, x0)
OOFphase = np.empty([len(Phi_list), N, N, len(wvls)], dtype=np.float32)
for j in range(len(Phi_list)):
Phi = Phi_list[j]
for k in range(len(wvls)):
OOFphase[j, :, :, k] = Phi * (xx ** 2 + yy ** 2) * wvls[1] / wvls[k];
return OOFphase
def gen_PSFs(h, OOFphase, wvls, idx, N_R, N_G, N_B):
n = 1.5 # diffractive index
with tf.variable_scope("Red"):
OOFphase_R = OOFphase[:, :, :, 0]
phase_R = tf.add(2 * np.pi / wvls[0] * (n - 1) * h, OOFphase_R)
Pupil_R = tf.pad(tf.multiply(tf.complex(idx, 0.0), tf.exp(tf.complex(0.0, phase_R))),
[[0, 0], [(N_R - N_B) // 2, (N_R - N_B) // 2], [(N_R - N_B) // 2, (N_R - N_B) // 2]],
name='Pupil_R')
Norm_R = tf.cast(N_R * N_R * np.sum(idx ** 2), tf.float32)
PSF_R = tf.divide(tf.square(tf.abs(fft2dshift(tf.fft2d(Pupil_R)))), Norm_R, name='PSF_R')
with tf.variable_scope("Green"):
OOFphase_G = OOFphase[:, :, :, 1]
phase_G = tf.add(2 * np.pi / wvls[1] * (n - 1) * h, OOFphase_G)
Pupil_G = tf.pad(tf.multiply(tf.complex(idx, 0.0), tf.exp(tf.complex(0.0, phase_G))),
[[0, 0], [(N_G - N_B) // 2, (N_G - N_B) // 2], [(N_G - N_B) // 2, (N_G - N_B) // 2]],
name='Pupil_G')
Norm_G = tf.cast(N_G * N_G * np.sum(idx ** 2), tf.float32)
PSF_G = tf.divide(tf.square(tf.abs(fft2dshift(tf.fft2d(Pupil_G)))), Norm_G, name='PSF_G')
with tf.variable_scope("Blue"):
OOFphase_B = OOFphase[:, :, :, 2]
phase_B = tf.add(2 * np.pi / wvls[2] * (n - 1) * h, OOFphase_B)
Pupil_B = tf.multiply(tf.complex(idx, 0.0), tf.exp(tf.complex(0.0, phase_B)), name='Pupil_B')
Norm_B = tf.cast(N_B * N_B * np.sum(idx ** 2), tf.float32)
PSF_B = tf.divide(tf.square(tf.abs(fft2dshift(tf.fft2d(Pupil_B)))), Norm_B, name='PSF_B')
N_crop_R = int((N_R - N_B) / 2) # Num of pixel need to cropped at each side for R
N_crop_G = int((N_G - N_B) / 2) # Num of pixel need to cropped at each side for G
PSFs = tf.stack(
[PSF_R[:, N_crop_R:-N_crop_R, N_crop_R:-N_crop_R], PSF_G[:, N_crop_G:-N_crop_G, N_crop_G:-N_crop_G], PSF_B],
axis=3)
return PSFs
def blurImage(RGBPhi, DPPhi, PSFs):
N_B = PSFs.shape[1].value
N_crop = np.int32((N_B - 1) / 2)
N_Phi = PSFs.shape[0].value
with tf.variable_scope("Red"):
sharp_R = RGBPhi[:, :, :, 0:1]
PSFs_R = tf.reshape(tf.transpose(PSFs[:, :, :, 0], perm=[1, 2, 0]), [N_B, N_B, 1, N_Phi])
blurAll_R = tf.nn.conv2d(sharp_R, PSFs_R, strides=[1, 1, 1, 1], padding='VALID')
blur_R = tf.reduce_sum(tf.multiply(blurAll_R, DPPhi[:, N_crop:-N_crop, N_crop:-N_crop, :]), axis=-1)
with tf.variable_scope("Green"):
sharp_G = RGBPhi[:, :, :, 1:2]
PSFs_G = tf.reshape(tf.transpose(PSFs[:, :, :, 1], perm=[1, 2, 0]), [N_B, N_B, 1, N_Phi])
blurAll_G = tf.nn.conv2d(sharp_G, PSFs_G, strides=[1, 1, 1, 1], padding='VALID')
blur_G = tf.reduce_sum(tf.multiply(blurAll_G, DPPhi[:, N_crop:-N_crop, N_crop:-N_crop, :]), axis=-1)
with tf.variable_scope("Green"):
sharp_B = RGBPhi[:, :, :, 2:3]
PSFs_B = tf.reshape(tf.transpose(PSFs[:, :, :, 2], perm=[1, 2, 0]), [N_B, N_B, 1, N_Phi])
blurAll_B = tf.nn.conv2d(sharp_B, PSFs_B, strides=[1, 1, 1, 1], padding='VALID')
blur_B = tf.reduce_sum(tf.multiply(blurAll_B, DPPhi[:, N_crop:-N_crop, N_crop:-N_crop, :]), axis=-1)
blur = tf.stack([blur_R, blur_G, blur_B], axis=3)
return blur
def system(PSFs, RGB_batch_float, DPPhi_float, Phi_batch_scaled, phase_BN=True):
with tf.variable_scope("system", reuse=tf.AUTO_REUSE):
blur = blurImage(RGB_batch_float, DPPhi_float, PSFs)
# noise
sigma = 0.01
blur_noisy = add_gaussian_noise(blur, sigma)
Phi_hat = Network.UNet_2(blur_noisy, phase_BN)
N_B = PSFs.shape[1].value
Phi_GT = tf.expand_dims(
Phi_batch_scaled[:, int((N_B - 1) / 2):-int((N_B - 1) / 2), int((N_B - 1) / 2):-int((N_B - 1) / 2)], -1)
cost = 20 * tf.sqrt(tf.reduce_mean(tf.square(Phi_GT - Phi_hat))) #RMS, scale from 0-1 to (-10,10)
return cost, blur_noisy, Phi_hat, Phi_GT
######################################### Set parameters ###############################################
# def main():
zernike = sio.loadmat('zernike_basis.mat')
u2 = zernike['u2'] # basis of zernike poly
idx = zernike['idx']
idx = idx.astype(np.float32)
N_R = 31
N_G = 27
N_B = 23 # size of the blur kernel
wvls = np.array([610, 530, 470]) * 1e-9
N_modes = u2.shape[1] # load zernike modes
# generate the defocus phase
Phi_list = np.linspace(-10, 10, 21, np.float32)
OOFphase = gen_OOFphase(Phi_list, N_B, wvls) # return (N_Phi,N_B,N_B,N_color)
#################################### Build the architecture #####################################################
hh = np.loadtxt(results_dir + 'HeightMap.txt')
with tf.variable_scope("PSFs"):
h = tf.constant(hh,tf.float32, name='heightMap') # height map of the phase mask, should be all positive
PSFs = gen_PSFs(h, OOFphase, wvls, idx, N_R, N_G, N_B) # return (N_Phi, N_B, N_B, N_color)
batch_size = 40
RGB_batch_float_test, DPPhi_float_test, Phi_batch_scaled_test = read2batch(TFRECORD_TEST_PATH, batch_size)
[test_cost, blur, Phi_hat, Phi_GT] = system(PSFs, RGB_batch_float_test, DPPhi_float_test, Phi_batch_scaled_test,
phase_BN=False)
saver = tf.train.Saver()
########################################## Test #############################################
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
model_path = tf.train.latest_checkpoint(results_dir)
load_path = saver.restore(sess, model_path)
print('Testing model from: ', results_dir)
out_dir = 'test_all/'
if not os.path.exists(results_dir + out_dir):
os.makedirs(results_dir + out_dir)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
test_loss_all = []
for i in range(10):
[test_loss, Phi_hatt, Phi_GTt, blurt, sharpt] = sess.run([test_cost, Phi_hat, Phi_GT, blur, RGB_batch_float_test[:, int((N_B - 1) / 2):-int((N_B - 1) / 2), int((N_B - 1) / 2):-int((N_B - 1) / 2),:]])
print("Batch " + str(i) + ", Test Loss = " + "{:.6f}".format(test_loss))
test_loss_all.append(test_loss)
for j in range(batch_size):
matplotlib.image.imsave(results_dir+out_dir+'%d_%d_phiHat.png' %(i,j), Phi_hatt[j,:,:,0],vmin = 0.0, vmax = 1.0, cmap='jet')
matplotlib.image.imsave(results_dir+out_dir+'%d_%d_phiGT.png' %(i,j), Phi_GTt[j,:,:,0],vmin = 0.0, vmax = 1.0, cmap='jet')
imageio.imwrite(results_dir+out_dir+'%d_%d_blur.png' %(i,j),np.uint8(blurt[j,:,:,:]*255))
imageio.imwrite(results_dir+out_dir+'%d_%d_sharp.png' %(i,j),np.uint8(sharpt[j,:,:,:]*255))
test_loss_avg = np.mean(test_loss_all)
print("Average Loss = " + "{:.6f}".format(test_loss_avg))
np.savetxt(results_dir + out_dir + 'test_loss_' + "{:.6f}".format(test_loss_avg) + '.txt', test_loss_all)
coord.request_stop()
coord.join(threads)