codes.py

import os, sys
import numpy as np
from PIL import Image
import itertools
import glob
import random
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.nn.functional import relu as RLU

registration_method = 'Additive_Recurence' #{'Rawblock', 'matching_points', 'Additive_Recurence', 'Multiplicative_Recurence'} #'recurrent_matrix', 
imposed_point = 0
Arch = 'ResNet'
Fix_Torch_Wrap = False
BW_Position = False
dim = 128
dim0 =224
crop_ratio = dim/dim0




class Identity(nn.Module):
    def __init__(self):
        super(Identity, self).__init__()
    def forward(self, x):
        return x

class Build_IRmodel_Resnet(nn.Module):
    def __init__(self, resnet_model, registration_method = 'Additive_Recurence', BW_Position=False):
        super(Build_IRmodel_Resnet, self).__init__()
        self.resnet_model = resnet_model
        self.BW_Position = BW_Position
        self.N_parameters = 6
        self.registration_method = registration_method
        self.fc1 =nn.Linear(6, 64)
        self.fc2 =nn.Linear(64, 128*3)
        self.fc3 =nn.Linear(512, self.N_parameters)
    def forward(self, input_X_batch):
        source = input_X_batch['source']
        target = input_X_batch['target']
        if 'Recurence' in self.registration_method:
            M_i = input_X_batch['M_i'].view(-1, 6)
            M_rep = F.relu(self.fc1(M_i))
            M_rep = F.relu(self.fc2(M_rep)).view(-1,3,1,128)
            concatenated_input = torch.cat((source,target,M_rep), dim=2)
        else:
            concatenated_input = torch.cat((source,target), dim=2)
        resnet_output = self.resnet_model(concatenated_input)
        predicted_line = self.fc3(resnet_output)
        if 'Recurence' in self.registration_method:
            predicted_part_mtrx = predicted_line.view(-1, 2, 3)
            Prd_Affine_mtrx = predicted_part_mtrx + input_X_batch['M_i']
            predction = {'predicted_part_mtrx':predicted_part_mtrx,
                            'Affine_mtrx': Prd_Affine_mtrx}
        else:
            Prd_Affine_mtrx = predicted_line.view(-1, 2, 3)
            predction = {'Affine_mtrx': Prd_Affine_mtrx}
        return predction

def pil_to_numpy(im):
    im.load()
    # Unpack data
    e = Image._getencoder(im.mode, "raw", im.mode)
    e.setimage(im.im)
    # NumPy buffer for the result
    shape, typestr = Image._conv_type_shape(im)
    data = np.empty(shape, dtype=np.dtype(typestr))
    mem = data.data.cast("B", (data.data.nbytes,))
    bufsize, s, offset = 65536, 0, 0
    while not s:
        l, s, d = e.encode(bufsize)
        mem[offset:offset + len(d)] = d
        offset += len(d)
    if s < 0:
        raise RuntimeError("encoder error %d in tobytes" % s)
    return data

def load_image_pil_accelerated(image_path, dim=128):
    image = Image.open(image_path).convert("RGB")
    array = pil_to_numpy(image)
    tensor = torch.from_numpy(np.rollaxis(array,2,0)/255).to(torch.float32)
    tensor = torchvision.transforms.Resize((dim,dim))(tensor)
    return tensor


def preprocess_image(image_path, dim = 128):
    img = load_image_pil_accelerated(image_path, dim)
    return img.unsqueeze(0)

'''
def load_image_from_url(image_path, dim = 128):
    img = Image.open(image_path).convert("RGB")
    img = img.resize((dim, dim))
    return img

def preprocess_image(image_path, dim = 128):
    img = load_img(image_path, target_size=(dim, dim))
    img = img_to_array(img)
    img = np.expand_dims(img, axis=0)
    return img


def create_model(dim = 128):
  # configure unet input shape (concatenation of moving and fixed images)
  volshape = (dim,dim,3)
  unet_input_features = 2*volshape[:-1]
  inshape = (*volshape[:-1],unet_input_features)
  nb_conv_per_level=1
  enc_nf = [dim, dim, dim, dim]
  dec_nf = [dim, dim, dim, dim, dim, int(dim/2)]
  nb_upsample_skips = 0
  nb_dec_convs = len(enc_nf)
  final_convs = dec_nf[nb_dec_convs:]
  dec_nf = dec_nf[:nb_dec_convs]
  nb_levels = int(nb_dec_convs / nb_conv_per_level) + 1
  source = tf.keras.Input(shape=volshape, name='source_input')
  target = tf.keras.Input(shape=volshape, name='target_input')
  inputs = [source, target]
  unet_input = concatenate(inputs, name='input_concat')
  #Define lyers
  ndims = len(unet_input.get_shape()) - 2
  MaxPooling = getattr(tf.keras.layers, 'MaxPooling%dD' % ndims)
  Conv = getattr(tf.keras.layers, 'Conv%dD' % ndims)
  UpSampling = getattr(tf.keras.layers, 'UpSampling%dD' % ndims)
  # Encoder
  enc_layers = []
  lyr = unet_input
  for level in range(nb_levels - 1):
      for conv in range(nb_conv_per_level):
          nfeat = enc_nf[level * nb_conv_per_level + conv]
          lyr = Conv(nfeat, kernel_size=3, padding='same', strides=1,activation = LeakyReLU(0.2), kernel_initializer = 'he_normal')(lyr)
      enc_layers.append(lyr)
      lyr = MaxPooling(2)(lyr)

  # Decoder
  for level in range(nb_levels - 1):
      real_level = nb_levels - level - 2
      for conv in range(nb_conv_per_level):
          nfeat = dec_nf[level * nb_conv_per_level + conv]
          lyr = Conv(nfeat, kernel_size=3, padding='same', strides=1,activation = LeakyReLU(0.2), kernel_initializer = 'he_normal')(lyr)
      # upsample
      if level < (nb_levels - 1 - nb_upsample_skips):
          upsampled = UpSampling(size=(2,) * ndims)(lyr)
          lyr = concatenate([upsampled, enc_layers.pop()])

  # Final convolution
  for num, nfeat in enumerate(final_convs):
      lyr = Conv(nfeat, kernel_size=3, padding='same', strides=1,activation = LeakyReLU(0.2), kernel_initializer = 'he_normal')(lyr)

  unet =  tf.keras.models.Model(inputs=inputs, outputs=lyr)
  # transform the results into a flow field.
  disp_tensor = Conv(ndims, kernel_size=3, padding='same', name='disp')(unet.output)
  # using keras, we can easily form new models via tensor pointers
  def_model = tf.keras.models.Model(inputs, disp_tensor)
  # build transformer layer
  spatial_transformer = SpatialTransformer()
  # warp the moving image with the transformer
  moved_image_tensor = spatial_transformer([source, disp_tensor])
  outputs = [moved_image_tensor, disp_tensor]
  vxm_model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
  return vxm_model
'''