Python load_SfSNet_data Examples, dataLoading.load_SfSNet_data Python Examples

Example #1

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def var(x):
    if IS_CUDA:
        x = x.cuda()
    return Variable(x)


# End of Helper routines

# Load synthetic dataset
syn_image1, syn_image2, syn_label = dataLoading.load_synthetic_ldan_data(
    synthetic_image_dataset_path, batch_size=global_batch_size)
#real_image, sirfs_normal, sirfs_SH, sirfs_shading, real_image_val, sirfs_sh_val, sirfs_normal_val, sirfs_shading_val = dataLoading.load_real_images_celebA(real_image_dataset_path, validation = True)
#real_image_mask = dataLoading.getMask(real_image_mask, global_batch_size)
#real_image, sirfs_normal, sirfs_SH, sirfs_shading, tNormal, real_image_mask, tSH, real_image_val, sirfs_sh_val, sirfs_normal_val, sirfs_shading_val, true_normal_val, mask_val, true_lighting_val = dataLoading.load_SfSNet_data(sfs_net_path, validation = True, twoLevel = True)

real_image, real_normal, real_sh, real_shading, mask, sirfs_shading, sirfs_normal, sirfs_sh, _, _, _, _, _, _, _, _ = dataLoading.load_SfSNet_data(
    sfs_net_path, twoLevel=True, batch_size=global_batch_size)

real_image_val, real_normal_val, real_lighting_val, real_albedo_val, mask_val, sirfs_shading_val, sirfs_normal_val, sirfs_sh_val, _, _, _, _, _, _, _, _ = dataLoading.load_SfSNet_Albedo_data(
    sfs_net_val_path, twoLevel=True, batch_size=global_batch_size)

# Transforms being used
#if SHOW_IMAGES:

real_image_mask_test = next(iter(mask_val))
#utils.save_image(torchvision.utils.make_grid(real_image_mask_test*255, padding=1), output_path+'val/MASK_TEST.png')

tmp = var(next(iter(syn_image1)))
tmp = denorm(tmp).data
utils.save_image(torchvision.utils.make_grid(tmp, padding=1),
                 output_path + 'val/test_synthetic_img.png')

Example #2

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File: basicNetSfsNet.py Project: andrewhuman/Light-Estimation

def var(x):
    if IS_CUDA:
        x = x.cuda()
    return Variable(x)


# End of Helper routines

# Load synthetic dataset
syn_image1, syn_image2, syn_label = dataLoading.load_synthetic_ldan_data(
    synthetic_image_dataset_path)
#real_image, sirfs_normal, sirfs_SH, sirfs_shading, real_image_val, sirfs_sh_val, sirfs_normal_val, sirfs_shading_val = dataLoading.load_real_images_celebA(real_image_dataset_path, validation = True)
#real_image_mask = dataLoading.getMask(real_image_mask, global_batch_size)
#real_image, sirfs_normal, sirfs_SH, sirfs_shading, tNormal, real_image_mask, tSH, real_image_val, sirfs_sh_val, sirfs_normal_val, sirfs_shading_val, true_normal_val, mask_val, true_lighting_val = dataLoading.load_SfSNet_data(sfs_net_path, validation = True, twoLevel = True)

real_image, real_normal, real_sh, real_shading, mask, sirfs_shading, sirfs_normal, sirfs_sh, real_image_val, real_normal_val, real_lighting_val, real_shading_val, mask_val, sirfs_shading_val, sirfs_normal_val, sirfs_sh_val = dataLoading.load_SfSNet_data(
    sfs_net_path, validation=True, twoLevel=True)

# Transforms being used
#if SHOW_IMAGES:

real_image_mask_test = next(iter(mask_val))
utils.save_image(
    torchvision.utils.make_grid(real_image_mask_test * 255, padding=1),
    output_path + 'images/MASK_TEST.png')

tmp = next(iter(syn_image1))
utils.save_image(torchvision.utils.make_grid(tmp, padding=1),
                 output_path + 'images/test_synthetic_img.png')

tmp = var(next(iter(real_image_val)))
tmp = denorm(tmp)

Example #3

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File: basicNetSfsNet.py Project: andrewhuman/Light-Estimation

    save_image(outShadingB[0], path + name+'_0.png')


def var(x):
    if IS_CUDA:
        x = x.cuda()
    return Variable(x)
# End of Helper routines

# Load synthetic dataset
#syn_image1, syn_image2, syn_label = dataLoading.load_synthetic_ldan_data(synthetic_image_dataset_path)
#real_image, sirfs_normal, sirfs_SH, sirfs_shading, real_image_val, sirfs_sh_val, sirfs_normal_val, sirfs_shading_val = dataLoading.load_real_images_celebA(real_image_dataset_path, validation = True)
#real_image_mask = dataLoading.getMask(real_image_mask, global_batch_size)
#real_image, sirfs_normal, sirfs_SH, sirfs_shading, tNormal, real_image_mask, tSH, real_image_val, sirfs_sh_val, sirfs_normal_val, sirfs_shading_val, true_normal_val, mask_val, true_lighting_val = dataLoading.load_SfSNet_data(sfs_net_path, validation = True, twoLevel = True)

syn_image, syn_normal, syn_sh, syn_shading, syn_mask, syn_sirfs_shading, syn_sirfs_normal, syn_sirfs_sh, syn_image_val, syn_normal_val, syn_lighting_val, syn_shading_val, syn_mask_val, syn_sirfs_shading_val, syn_sirfs_normal_val, syn_sirfs_sh_val  = dataLoading.load_SfSNet_data(synthetic_data_path, twoLevel = True)

real_image, real_normal, real_sh, real_shading, real_mask, real_sirfs_shading, real_sirfs_normal, real_sirfs_sh, real_image_val, real_normal_val, real_lighting_val, real_shading_val, real_mask_val, real_sirfs_shading_val, real_sirfs_normal_val, real_sirfs_sh_val  = dataLoading.load_SfSNet_data(real_data_path, validation = True, twoLevel = True)

# Transforms being used
# if SHOW_IMAGES:
'''
syn_image_mask_test = next(iter(mask_val))
utils.save_image(torchvision.utils.make_grid(syn_image_mask_test*255, padding=1), output_path+'images/MASK_TEST.png')

tmp = next(iter(syn_image1))
utils.save_image(torchvision.utils.make_grid(tmp, padding=1), output_path+'images/test_synthetic_img.png')

tmp = var(next(iter(syn_image_val)))
tmp = denorm(tmp)
print(tmp.data.shape)

Example #4

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File: resNet_celebA.py Project: andrewhuman/Light-Estimation

def var(x):
    if IS_CUDA:
        x = x.cuda()
    return Variable(x)


# End of Helper routines

# Load synthetic dataset
#syn_image1, syn_image2, syn_label = dataLoading.load_synthetic_ldan_data(synthetic_image_dataset_path)
#real_image, sirfs_normal, sirfs_SH, sirfs_shading, real_image_val, sirfs_sh_val, sirfs_normal_val, sirfs_shading_val = dataLoading.load_real_images_celebA(real_image_dataset_path, validation = True)
#real_image_mask = dataLoading.getMask(real_image_mask, global_batch_size)
#real_image, sirfs_normal, sirfs_SH, sirfs_shading, tNormal, real_image_mask, tSH, real_image_val, sirfs_sh_val, sirfs_normal_val, sirfs_shading_val, true_normal_val, mask_val, true_lighting_val = dataLoading.load_SfSNet_data(sfs_net_path, validation = True, twoLevel = True)

syn_image, syn_normal, syn_sh, syn_shading, syn_mask, syn_sirfs_shading, syn_sirfs_normal, syn_sirfs_sh, syn_image_val, syn_normal_val, syn_lighting_val, syn_shading_val, syn_mask_val, syn_sirfs_shading_val, syn_sirfs_normal_val, syn_sirfs_sh_val = dataLoading.load_SfSNet_data(
    synthetic_data_path, twoLevel=True)

#real_image, real_normal, real_sh, real_shading, real_mask, real_sirfs_shading, real_sirfs_normal, real_sirfs_sh, real_image_val, real_normal_val, real_lighting_val, real_shading_val, real_mask_val, real_sirfs_shading_val, real_sirfs_normal_val, real_sirfs_sh_val  = dataLoading.load_SfSNet_data(real_data_path, validation = True, twoLevel = True)

real_image, real_sirfs_normal, real_sirfs_sh, real_sirfs_shading, real_mask, real_image_val, real_sirfs_sh_val, real_sirfs_normal_val, real_sirfs_shading_val, real_mask_val = dataLoading.load_real_images_celebA(
    real_data_path)

real_image_val, real_sirfs_normal_val, real_sirfs_sh_val, real_sirfs_shading_val, real_mask_val, _, _, _, _, _ = dataLoading.load_real_images_celebA(
    real_val_data_path, load_mask=True)
# Transforms being used
# if SHOW_IMAGES:

real_image_mask_test = next(iter(real_mask_val))
utils.save_image(
    torchvision.utils.make_grid(real_image_mask_test * 255, padding=1),
    output_path + 'images/MASK_TEST.png')