GIMP-ML/gimp-plugins/PD-Denoising-pytorch/denoiser.py

import cv2
import os
import argparse
import glob
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from models import *
from utils import *
from PIL import Image
import scipy.io as sio

#the limitation range of each type of noise level: [0]Gaussian [1]Impulse
limit_set = [[0,75], [0, 80]]

def img_normalize(data):
    return data/255.

def denoiser(Img, c, pss, model, model_est, opt):

    w, h, _ = Img.shape   
    Img = pixelshuffle(Img, pss)
    Img = img_normalize(np.float32(Img))
 
    noise_level_list = np.zeros((2 * c,1))  #two noise types with three channels
    if opt.cond == 0:  #if we use the ground truth of noise for denoising, and only one single noise type
        noise_level_list = np.array(opt.test_noise_level)
    elif opt.cond == 2:  #if we use an external fixed input condition for denoising
        noise_level_list = np.array(opt.ext_test_noise_level)
    
    #Clean Image Tensor for evaluation
    ISource = np2ts(Img) 
    # noisy image and true residual
    if opt.real_n == 0 and opt.spat_n == 0:  #no spatial noise setting, and synthetic noise
        noisy_img = generate_comp_noisy(Img, np.array(opt.test_noise_level) / 255.)
        if opt.color == 0:
            noisy_img = np.expand_dims(noisy_img[:,:,0], 2) 
    elif opt.real_n == 1 or opt.real_n == 2:  #testing real noisy images
        noisy_img = Img
    elif opt.spat_n == 1:
        noisy_img = generate_noisy(Img, 2, 0, 20, 40)
    INoisy = np2ts(noisy_img, opt.color)
    INoisy = torch.clamp(INoisy, 0., 1.)
    True_Res = INoisy - ISource
    if torch.cuda.is_available():
        ISource, INoisy, True_Res = Variable(ISource.cuda(),volatile=True), Variable(INoisy.cuda(),volatile=True), Variable(True_Res.cuda(),volatile=True)
    else:
        ISource, INoisy, True_Res = Variable(ISource,volatile=True), Variable(INoisy,volatile=True), Variable(True_Res,volatile=True)


    
    if opt.mode == "MC":
        # obtain the corrresponding input_map
        if opt.cond == 0 or opt.cond == 2:  #if we use ground choose level or the given fixed level
            #normalize noise leve map to [0,1]
            noise_level_list_n = np.zeros((2*c, 1))
            print(c)
            for noise_type in range(2):
                for chn in range(c):
                    noise_level_list_n[noise_type * c + chn] = normalize(noise_level_list[noise_type * 3 + chn], 1, limit_set[noise_type][0], limit_set[noise_type][1])  #normalize the level value
            #generate noise maps
            noise_map = np.zeros((1, 2 * c, Img.shape[0], Img.shape[1]))  #initialize the noise map
            noise_map[0, :, :, :] = np.reshape(np.tile(noise_level_list_n, Img.shape[0] * Img.shape[1]), (2*c, Img.shape[0], Img.shape[1]))
            NM_tensor = torch.from_numpy(noise_map).type(torch.FloatTensor)
            NM_tensor = Variable(NM_tensor.cuda(),volatile=True)
        #use the estimated noise-level map for blind denoising
        elif opt.cond == 1:  #if we use the estimated map directly
            NM_tensor = torch.clamp(model_est(INoisy), 0., 1.)
            if opt.refine == 1:  #if we need to refine the map before putting it to the denoiser
                NM_tensor_bundle = level_refine(NM_tensor, opt.refine_opt, 2*c)  #refine_opt can be max, freq and their average
                NM_tensor = NM_tensor_bundle[0]
                noise_estimation_table = np.reshape(NM_tensor_bundle[1], (2 * c,))
            if opt.zeroout == 1:
                NM_tensor = zeroing_out_maps(NM_tensor, opt.keep_ind)
        Res = model(INoisy, NM_tensor)

    elif opt.mode == "B":
        Res = model(INoisy)

    Out = torch.clamp(INoisy-Res, 0., 1.)  #Output image after denoising
    
    #get the maximum denoising result
    max_NM_tensor = level_refine(NM_tensor, 1, 2*c)[0]
    max_Res = model(INoisy, max_NM_tensor)
    max_Out = torch.clamp(INoisy - max_Res, 0., 1.)
    max_out_numpy = visual_va2np(max_Out, opt.color, opt.ps, pss, 1, opt.rescale, w, h, c)
    del max_Out
    del max_Res
    del max_NM_tensor
    
    if (opt.ps == 1 or opt.ps == 2) and pss!=1:  #pixelshuffle multi-scale
        #create batch of images with one subsitution
        mosaic_den = visual_va2np(Out, opt.color, 1, pss, 1, opt.rescale, w, h, c)
        out_numpy = np.zeros((pss ** 2, c, w, h))
        #compute all the images in the ps scale set
        for row in range(pss):
            for column in range(pss):
                re_test = visual_va2np(Out, opt.color, 1, pss, 1, opt.rescale, w, h, c, 1, visual_va2np(INoisy, opt.color), [row, column])/255.
                #cv2.imwrite(os.path.join(opt.out_dir,file_name + '_%d_%d.png' % (row, column)), re_test[:,:,::-1]*255.)
                re_test = np.expand_dims(re_test, 0)
                if opt.color == 0:  #if gray image
                    re_test = np.expand_dims(re_test[:, :, :, 0], 3)
                re_test_tensor = torch.from_numpy(np.transpose(re_test, (0,3,1,2))).type(torch.FloatTensor)
                if torch.cuda.is_available():
                    re_test_tensor = Variable(re_test_tensor.cuda(),volatile=True)
                else:
                    re_test_tensor = Variable(re_test_tensor, volatile=True)

                re_NM_tensor = torch.clamp(model_est(re_test_tensor), 0., 1.)
                if opt.refine == 1:  #if we need to refine the map before putting it to the denoiser
                        re_NM_tensor_bundle = level_refine(re_NM_tensor, opt.refine_opt, 2*c)  #refine_opt can be max, freq and their average
                        re_NM_tensor = re_NM_tensor_bundle[0]
                re_Res = model(re_test_tensor, re_NM_tensor)
                Out2 = torch.clamp(re_test_tensor - re_Res, 0., 1.)
                out_numpy[row*pss+column,:,:,:] = Out2.data.cpu().numpy()
                del Out2
                del re_Res
                del re_test_tensor
                del re_NM_tensor
                del re_test
            
        out_numpy = np.mean(out_numpy, 0)
        out_numpy = np.transpose(out_numpy, (1,2,0)) * 255.0
    elif opt.ps == 0 or pss==1:  #other cases
        out_numpy = visual_va2np(Out, opt.color, 0, 1, 1, opt.rescale, w, h, c)
    
    out_numpy = out_numpy.astype(np.float32)  #details
    max_out_numpy = max_out_numpy.astype(np.float32)  #background

    #merging the details and background to balance the effect
    k = opt.k
    merge_out_numpy = (1-k)*out_numpy + k*max_out_numpy
    merge_out_numpy = merge_out_numpy.astype(np.float32)
    
    return merge_out_numpy