model_learning_rate = 0.1**5
model_512 = define_epinet(round(img_scale * image_h),
round(img_scale * image_w),
Setting02_AngualrViews, model_conv_depth,
model_filt_num, model_learning_rate)
''' Model Initialization '''
model_512.load_weights(path_weight)
dum_sz = model_512.input_shape[0]
dum = np.zeros((1, dum_sz[1], dum_sz[2], dum_sz[3]), dtype=np.float32)
dummy = model_512.predict([dum, dum, dum, dum], batch_size=1)
""" Depth Estimation """
for image_path in dir_LFimages:
(val_90d, val_0d, val_45d,
val_M45d) = make_multiinput(image_path, image_h, image_w,
Setting02_AngualrViews)
# print(val_90d.shape)
# exit()
start = time.time()
# predict
val_output_tmp = model_512.predict([
val_90d[:, ::img_scale_inv, ::img_scale_inv],
val_0d[:, ::img_scale_inv, ::img_scale_inv],
val_45d[:, ::img_scale_inv, ::img_scale_inv],
val_M45d[:, ::img_scale_inv, ::img_scale_inv]
],
batch_size=1)
runtime = time.time() - start
center_offset = (0, 0)
# center_os_x = center_x - (an - 1)//2
# center_os_y = center_y - (an - 1)//2
# center_offset = (center_os_x, center_os_y)
#if subsample_type == "center":
print("center offset: ")
print(center_offset)
for image_path in dir_LFimages:
(val_90d, val_0d, val_45d, val_M45d,
impaths) = make_multiinput(image_path,
image_h,
image_w,
Setting02_AngualrViews,
ds=ds,
center_offsets=center_offset,
net_type="original",
corner_code=None)
start = time.clock()
all_outputs = model.predict([
val_90d[:, ::img_scale_inv, ::img_scale_inv],
val_0d[:, ::img_scale_inv, ::img_scale_inv],
val_45d[:, ::img_scale_inv, ::img_scale_inv],
val_M45d[:, ::img_scale_inv, ::img_scale_inv]
],
batch_size=1)
# predict
val_output_tmp = all_outputs[-1]
# write_graph=True, write_images=True)
#
#tbCallback.set_model(model_512)
""" Depth Estimation """
for center_coord in center_coords:
center_offset = infer_cos_from_cc(center_coord,ds)
#for center_offset in center_offsets:
subsample = ("center",center_offset) #"center" #"upperleft"
print ( "center offset: " )
print ( center_offset )
# for image_path in dir_LFimages:
for sample in sample_names:
(val_90d , val_0d, val_45d, val_M45d,impaths)=make_multiinput(sample,
Setting02_AngualrViews,
ds=ds, center_offsets = center_offset, net_type='original',
crop_size=crop_size, crop_or_resize=crop_or_resize)
start=time.clock()
# predict
val_output_tmp=model_512.predict([ val_90d[:,::img_scale_inv,::img_scale_inv],
val_0d[:,::img_scale_inv,::img_scale_inv],
val_45d[:,::img_scale_inv,::img_scale_inv],
val_M45d[:,::img_scale_inv,::img_scale_inv]],
batch_size=1);
runtime=time.clock() - start
plt.imshow(val_output_tmp[0,:,:,0])
iar_center, iac_center = center_coord
center_offset = infer_cos_from_corner(corner_coords,
corner_code,
an=ds.iar_max,
model_cw=model_cw)
print("center offset: ")
print(center_offset)
print("corner code:")
print(corner_code)
for sample in samples:
(val_90d, val_0d, val_M45d,
impaths) = make_multiinput(sample,
Setting02_AngualrViews,
ds=ds,
center_offsets=center_offset,
net_type="corner",
corner_code=corner_code)
start = time.clock()
# predict
val_output_tmp = model_512.predict([
val_90d[:, ::img_scale_inv, ::img_scale_inv],
val_0d[:, ::img_scale_inv, ::img_scale_inv],
val_M45d[:, ::img_scale_inv, ::img_scale_inv]
],
batch_size=1)
if corner_code == "NE":
val_output_tmp = np.rot90(val_output_tmp, axes=(2, 1))
model_learning_rate = 0.1**5
model_512 = define_epinet(int(img_scale_inv * image_h),
int(img_scale_inv * image_w), angular_views,
model_conv_depth, model_filt_num,
model_learning_rate)
''' Model Initalization '''
model_512.load_weights(path_weight)
dum_sz = model_512.input_shape[0]
dum = np.zeros((1, dum_sz[1], dum_sz[2], dum_sz[3]), dtype=np.float32)
dummy = model_512.predict([dum, dum, dum, dum], batch_size=1)
""" Depth Estimation """
for image_path in dir_LFimages:
(val_90d, val_0d, val_45d,
val_M45d) = make_multiinput(image_path, image_h, image_w,
angular_views)
start = time.clock()
# predict
val_output_tmp = model_512.predict([
val_90d[:, ::img_scale_inv, ::img_scale_inv],
val_0d[:, ::img_scale_inv, ::img_scale_inv],
val_45d[:, ::img_scale_inv, ::img_scale_inv],
val_M45d[:, ::img_scale_inv, ::img_scale_inv]
],
batch_size=1)
runtime = time.clock() - start
plt.imshow(val_output_tmp[0, :, :, 0])
print("%.5f(s)" % runtime)