r"""
=========================================
06. Denoised Completion Network (DCNet)
=========================================
.. _tuto_dcnet_split_measurements:
This tutorial shows how to perform image reconstruction using the denoised
completion network (DCNet) with a trainable image denoiser. In the next
tutorial, we will plug a denoiser into a DCNet, which requires no training.

.. figure:: ../fig/tuto3.png
   :width: 600
   :align: center
   :alt: Reconstruction and neural network denoising architecture sketch using split measurements
"""

######################################################################
# .. note::
#   As in the previous tutorials, we consider a split Hadamard operator and
#   measurements corrupted by Poisson noise (see :ref:`Tutorial 5 <tuto_acquisition_split_measurements>`).

# %%
# Load a batch of images
# =========================================

######################################################################
# Update search path

# sphinx_gallery_thumbnail_path = 'fig/tuto6.png'
import os

spyritPath = os.getcwd()
imgs_path = os.path.join(spyritPath, "images/")

######################################################################
# Images :math:`x` for training neural networks expect values in [-1,1]. The images are normalized and resized using the :func:`transform_gray_norm` function.
from spyrit.misc.statistics import transform_gray_norm

h = 64  # image is resized to h x h
transform = transform_gray_norm(img_size=h)

######################################################################
# Create a data loader from some dataset (images must be in the folder `images/test/`)
import torch
import torchvision

dataset = torchvision.datasets.ImageFolder(root=imgs_path, transform=transform)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=7)

x, _ = next(iter(dataloader))
print(f"Shape of input images: {x.shape}")

######################################################################
# Select the `i`-th image in the batch
i = 1  # Image index (modify to change the image)
x = x[i : i + 1, :, :, :]
x = x.detach().clone()
b, c, h, w = x.shape

######################################################################
# Plot the selected image
from spyrit.misc.disp import imagesc

x_plot = x.view(-1, h, h).cpu().numpy()
imagesc(x_plot[0, :, :], r"$x$ in [-1, 1]")

# %%
# Forward operators for split measurements
# =========================================

######################################################################
# We consider noisy measurements obtained from a split Hadamard operator, and a subsampling strategy that retaines the coefficients with the largest variance (for more details, refer to :ref:`Tutorial 5 <tuto_acquisition_split_measurements>`).

######################################################################
# First, we download the covariance matrix from our warehouse.

import girder_client
from spyrit.misc.load_data import download_girder
import numpy as np

# Get covariance matrix
url = "https://pilot-warehouse.creatis.insa-lyon.fr/api/v1"
data_folder = "./stat/"
dataId = "63935b624d15dd536f0484a5"
cov_name = "Cov_64x64.npy"
# download
file_abs_path = download_girder(url, dataId, data_folder, cov_name)

try:
    Cov = np.load(file_abs_path)
    print(f"Cov matrix {cov_name} loaded")
except:
    Cov = np.eye(h * h)
    print(f"Cov matrix {cov_name} not found! Set to the identity")

######################################################################
# We define the measurement, noise and preprocessing operators and then simulate a measurement vector corrupted by Poisson noise. As in the previous tutorials, we simulate an accelerated acquisition by subsampling the measurement matrix by retaining only the first rows of a Hadamard matrix that is permuted looking at the diagonal of the covariance matrix.

from spyrit.core.meas import HadamSplit
from spyrit.core.noise import Poisson
from spyrit.misc.sampling import meas2img
from spyrit.misc.statistics import Cov2Var
from spyrit.core.prep import SplitPoisson

# Measurement parameters
M = 64 * 64 // 4  # Number of measurements (here, 1/4 of the pixels)
alpha = 100.0  # number of photons

# Measurement and noise operators
Ord = Cov2Var(Cov)
meas_op = HadamSplit(M, h, torch.from_numpy(Ord))
noise_op = Poisson(meas_op, alpha)
prep_op = SplitPoisson(alpha, meas_op)

# Vectorize image
x = x.view(b * c, h * w)
print(f"Shape of vectorized image: {x.shape}")
# Measurements
y = noise_op(x)  # a noisy measurement vector
m = prep_op(y)  # preprocessed measurement vector

m_plot = m.detach().numpy()
m_plot = meas2img(m_plot, Ord)
imagesc(m_plot[0, :, :], r"Measurements $m$")

# %%
# Pseudo inverse solution
# =========================================

######################################################################
# We compute the pseudo inverse solution using :class:`spyrit.core.recon.PinvNet` class as in the previous tutorial.

# Instantiate a PinvNet (with no denoising by default)
from spyrit.core.recon import PinvNet

pinvnet = PinvNet(noise_op, prep_op)

# Use GPU, if available
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
pinvnet = pinvnet.to(device)
y = y.to(device)

# Reconstruction
with torch.no_grad():
    z_invnet = pinvnet.reconstruct(y)

# %%
# Denoised completion network (DCNet)
# =========================================

######################################################################
# .. image:: ../fig/dcnet.png
#    :width: 400
#    :align: center
#    :alt: Sketch of the DCNet architecture

######################################################################
# The DCNet is based on four sequential steps:
#
# i) Denoising in the measurement domain.
#
# ii) Estimation of the missing measurements from the denoised ones.
#
# iii) Image-domain mapping.
#
# iv) (Learned) Denoising in the image domain.
#
# Typically, only the last step involves learnable parameters.


# %%
# Denoised completion
# =========================================

######################################################################
# The first three steps implement denoised completion, which corresponds to Tikhonov regularization. Considering linear measurements :math:`y = Hx`, where :math:`H` is the  measurement matrix and :math:`x` is the unknown image, it estimates :math:`x` from :math:`y` by minimizing
#
# .. math::
#    \| y - Hx \|^2_{\Sigma^{-1}_\alpha} + \|x\|^2_{\Sigma^{-1}},
#
# where :math:`\Sigma` is a covariance prior and :math:`\Sigma_\alpha` is the noise covariance. Denoised completation can be performed using  the :class:`~spyrit.core.recon.TikhonovMeasurementPriorDiag` class (see documentation for more details).

######################################################################
# In practice, it is more convenient to use the :class:`spyrit.core.recon.DCNet` class, which relies on a forward operator, a preprocessing operator, and a covariance prior.

from spyrit.core.recon import DCNet

dcnet = DCNet(noise_op, prep_op, torch.from_numpy(Cov))

# Use GPU, if available
dcnet = dcnet.to(device)
y = y.to(device)

with torch.no_grad():
    z_dcnet = dcnet.reconstruct(y)

######################################################################
# .. note::
#   In this tutorial, the covariance matrix used to define subsampling is also used as prior knowledge during reconstruction.


# %%
# (Learned) Denoising in the image domain
# =========================================

######################################################################
# To implement denoising in the image domain, we provide a :class:`spyrit.core.nnet.Unet` denoiser to a :class:`spyrit.core.recon.DCNet`.

from spyrit.core.nnet import Unet

denoi = Unet()
dcnet_unet = DCNet(noise_op, prep_op, torch.from_numpy(Cov), denoi)
dcnet_unet = dcnet_unet.to(device)  # Use GPU, if available

########################################################################
# We load pretrained weights for the UNet

from spyrit.core.train import load_net

local_folder = "./model/"
# Create model folder
if os.path.exists(local_folder):
    print(f"{local_folder} found")
else:
    os.mkdir(local_folder)
    print(f"Created {local_folder}")

# Load pretrained model
url = "https://tomoradio-warehouse.creatis.insa-lyon.fr/api/v1"
dataID = "667ebfcbbaa5a90007058961"  # unique ID of the file
data_name = "tuto6_dc-net_unet_stl10_N0_100_N_64_M_1024_epo_30_lr_0.001_sss_10_sdr_0.5_bs_512_reg_1e-07.pth"
model_unet_path = os.path.join(local_folder, data_name)

if os.path.exists(model_unet_path):
    print(f"Model found : {data_name}")

else:
    print(f"Model not found : {data_name}")
    print(f"Downloading model... ", end="")
    try:
        gc = girder_client.GirderClient(apiUrl=url)
        gc.downloadFile(dataID, model_unet_path)
        print("Done")
    except Exception as e:
        print("Failed with error: ", e)

# Load pretrained model
load_net(model_unet_path, dcnet_unet, device, False)

######################################################################
# We reconstruct the image
with torch.no_grad():
    z_dcnet_unet = dcnet_unet.reconstruct(y)

# %%
# Results
# =========================================

import matplotlib.pyplot as plt
from spyrit.misc.disp import add_colorbar, noaxis

x_plot = x.view(-1, h, h).cpu().numpy()
x_plot2 = z_invnet.view(-1, h, h).cpu().numpy()
x_plot3 = z_dcnet.view(-1, h, h).cpu().numpy()
x_plot4 = z_dcnet_unet.view(-1, h, h).cpu().numpy()
f, axs = plt.subplots(2, 2, figsize=(10, 10))

# Plot the ground-truth image
im1 = axs[0, 0].imshow(x_plot[0, :, :], cmap="gray")
axs[0, 0].set_title("Ground-truth image", fontsize=16)
noaxis(axs[0, 0])
add_colorbar(im1, "bottom")

# Plot the pseudo inverse solution
im2 = axs[0, 1].imshow(x_plot2[0, :, :], cmap="gray")
axs[0, 1].set_title("Pseudo inverse", fontsize=16)
noaxis(axs[0, 1])
add_colorbar(im2, "bottom")

# Plot the solution obtained from denoised completion
im3 = axs[1, 0].imshow(x_plot3[0, :, :], cmap="gray")
axs[1, 0].set_title(f"Denoised completion", fontsize=16)
noaxis(axs[1, 0])
add_colorbar(im3, "bottom")

# Plot the solution obtained from denoised completion with UNet denoising
im4 = axs[1, 1].imshow(x_plot4[0, :, :], cmap="gray")
axs[1, 1].set_title(f"Denoised completion with UNet denoising", fontsize=16)
noaxis(axs[1, 1])
add_colorbar(im4, "bottom")

plt.show()

######################################################################
# .. note::
#   While the pseudo inverse reconstrcution is pixelized, the solution obtained by denoised completion is smoother. DCNet with UNet denoising in the image domain provides the best reconstruction.

######################################################################
# .. note::
#   We refer to `spyrit-examples tutorials <http://github.com/openspyrit/spyrit-examples/tree/master/tutorial>`_ for a comparison of different solutions (pinvNet, DCNet and DRUNet) that can be run in colab.