diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..14634a9 --- /dev/null +++ b/.gitignore @@ -0,0 +1,13 @@ +.DS_Store +.opencode +openspec +*.tiff +*.ARW +*.CR2 +*.NEF +*.DNG +*.RW2 +*.ORF +*.SR2 +*.PEF +models/nind_denoise/2021* diff --git a/src/nind_denoise/.python-version b/src/nind_denoise/.python-version new file mode 100644 index 0000000..bd28b9c --- /dev/null +++ b/src/nind_denoise/.python-version @@ -0,0 +1 @@ +3.9 diff --git a/src/nind_denoise/denoise.sh b/src/nind_denoise/denoise.sh new file mode 100755 index 0000000..b380561 --- /dev/null +++ b/src/nind_denoise/denoise.sh @@ -0,0 +1,10 @@ +#!/bin/bash +# Denoise the input image using the NIND method +# Usage: ./denoise.sh input_image + +uv run denoise_image.py \ + --input "$1" \ + --output "denoised_$1.tiff" \ + --model_path ../../models/nind_denoise/2021-05-23T10_16_nn_train.py_--config_configs-train_conf_unet.yaml_--debug_options_output_val_images_keep_all_output_images_--test_interval_0_--epochs_1000_--reduce_lr_factor_0.95_--patience_3/generator_734.pt \ + --network UNet + diff --git a/src/nind_denoise/denoise_image.py b/src/nind_denoise/denoise_image.py index 1e4c923..59081cf 100644 --- a/src/nind_denoise/denoise_image.py +++ b/src/nind_denoise/denoise_image.py @@ -24,6 +24,7 @@ from torch.utils.data import DataLoader from torch.utils.data import Dataset import time +import tempfile import configargparse from nn_common import Model try: @@ -32,6 +33,7 @@ pass import subprocess import numpy as np +import cv2 import sys sys.path.append('..') from common.libs import pt_helpers @@ -73,6 +75,72 @@ def autodetect_network_cs_ucs(args) -> None: args.cs, args.ucs = CS_UNK, UCS_UNK print(f'cs={args.cs}, ucs={args.ucs}') + +def convert_arw_if_needed(input_path: str) -> str: + if not input_path.upper().endswith('.ARW'): + return input_path + try: + import rawpy + except ImportError: + print('Error: rawpy is required to process ARW files.') + print('Install it with: uv add rawpy or pip install rawpy') + sys.exit(1) + try: + raw = rawpy.imread(input_path) + except Exception as e: + print(f'Error reading ARW file {input_path}: {e}') + sys.exit(1) + h, w = raw.raw_image_visible.shape + print(f'Detected ARW raw file: {input_path}') + make, model = 'N/A', 'N/A' + try: + result = subprocess.run( + ['exiftool', '-s', '-S', '-Make', '-Model', input_path], + capture_output=True, text=True, timeout=10) + if result.returncode == 0: + parts = result.stdout.strip().split('\n') + if len(parts) >= 2: + make, model = parts[0].strip() or 'N/A', parts[1].strip() or 'N/A' + elif len(parts) == 1: + make = parts[0].strip() or 'N/A' + except Exception: + pass + print(f' Camera: {make} {model}') + print(f' Resolution: {w} x {h}') + o = raw.other + iso = getattr(o, 'iso_speed', None) + if iso is not None: + print(f' ISO: {iso}') + else: + print(' ISO: N/A') + shutter = getattr(o, 'shutter_speed', None) + if shutter is not None: + if shutter < 1.0: + denom = int(round(1.0 / shutter)) + print(f' Shutter: 1/{denom} s') + else: + print(f' Shutter: {shutter:.1f} s') + else: + print(' Shutter: N/A') + aperture = getattr(o, 'aperture', None) + if aperture is not None: + print(f' Aperture: f/{aperture:.1f}') + else: + print(' Aperture: N/A') + focal = getattr(o, 'focal_length', None) + if focal is not None: + print(f' Focal length: {focal:.0f} mm') + else: + print(' Focal length: N/A') + rgb = raw.postprocess(use_camera_wb=True, output_bps=16) + raw.close() + fd, tmp_path = tempfile.mkstemp(dir=os.path.dirname(os.path.abspath(input_path)), suffix='.tiff') + os.close(fd) + rgb_bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR) + cv2.imwrite(tmp_path, rgb_bgr) + print(f' Converted to temporary TIFF: {tmp_path}') + return tmp_path + class OneImageDS(Dataset): ''' PyTorch compatible single-image dataset which crops an image into equal pieces with overlap and @@ -204,84 +272,97 @@ def argdevice(s): args, _ = parser.parse_known_args() assert args.model_path is not None autodetect_network_cs_ucs(args) - - def make_seamless_edges(tcrop, x0, y0): - if x0 != 0:#left - tcrop[:,:,0:args.overlap] = tcrop[:,:,0:args.overlap].div(2) - if y0 != 0:#top - tcrop[:,0:args.overlap,:] = tcrop[:,0:args.overlap,:].div(2) - if x0 + args.ucs < fswidth and args.overlap:#right - tcrop[:,:,-args.overlap:] = tcrop[:,:,-args.overlap:].div(2) - if y0 + args.ucs < fsheight and args.overlap:#bottom - tcrop[:,-args.overlap:,:] = tcrop[:,-args.overlap:,:].div(2) - return tcrop - if isinstance(args.cuda_device, int) and args.cuda_device >= 0: - if torch.cuda.is_available(): - torch.cuda.set_device(args.cuda_device) - torch.backends.cudnn.benchmark = True - torch.cuda.manual_seed(123) - else: - print("warning: PyTorch is not installed with CUDA. Defaulting to CPU.") - torch.manual_seed(123) - device = pt_helpers.get_device(args.cuda_device) - if args.output is None: - args.output = make_output_fpath(args.input, args.model_path) + # Convert ARW to TIFF if needed + arw_input = convert_arw_if_needed(args.input) + original_input = args.input if arw_input != args.input else None + if original_input is not None: + args.input = arw_input - # ugly hardcoded hack for now - if args.model_parameters is None and 'activation' in args.model_path: - args.model_parameters = f"activation={args.model_path.split('activation')[-1].split('_')[1].split('_')[0]}" - print(f'set model_parameters to {args.model_parameters} based on model_path') - model = Model.instantiate_model(network=args.g_network, model_path=args.model_path, - strparameters=args.model_parameters, keyword='generator', - device=device, models_dpath=args.models_dpath) - model.eval() # evaluation mode - model = model.to(device) - ds = OneImageDS(args.input, args.cs, args.ucs, args.overlap, whole_image=args.whole_image, pad=args.pad) - DLoader = DataLoader(dataset=ds, - num_workers=0 if args.batch_size == 1 else max(min(args.batch_size, os.cpu_count()//4), 1), - drop_last=False, batch_size=args.batch_size, shuffle=False) - topil = torchvision.transforms.ToPILImage() - fswidth, fsheight = Image.open(args.input).size - newimg = torch.zeros(3, fsheight, fswidth, dtype=torch.float32) + try: + if args.output is None: + args.output = make_output_fpath(original_input if original_input else args.input, args.model_path) - start_time = time.time() - for n_count, ydat in enumerate(DLoader): - print(str(n_count)+'/'+str(int(len(ds)/args.batch_size))) - ybatch, usefuldims, usefulstarts = ydat - if args.max_subpixels is not None and math.prod(ybatch.shape) > args.max_subpixels: - sys.exit(f'denoise_image.py: {ybatch.shape=}, {math.prod(ybatch.shape)=} > {args.max_subpixels=} for {args.input=}; aborting') - ybatch = ybatch.to(device) - xbatch = model(ybatch) - if torch.cuda.is_available(): - torch.cuda.synchronize() - for i in range(ybatch.size(0)): - ud = usefuldims[i] - # pytorch represents images as [channels, height, width] - # TODO test leaving on GPU longer - # TODO reconstruct image with batch_size > 1 - tensimg = xbatch[i][:,ud[1]:ud[3], ud[0]:ud[2]].cpu().detach() - if args.whole_image: - newimg = tensimg - else: - absx0, absy0 = tuple(usefulstarts[i].tolist()) - tensimg = make_seamless_edges(tensimg, absx0, absy0) - if args.debug: - os.makedirs('dbg', exist_ok=True) - torchvision.utils.save_image(xbatch[i], 'dbg/crop'+str(n_count)+'_'+str(i)+'_denoised.jpg') - torchvision.utils.save_image(tensimg, 'dbg/crop'+str(n_count)+'_'+str(i)+'_tensimg.jpg') - torchvision.utils.save_image(ybatch[i], 'dbg/crop'+str(n_count)+'_'+str(i)+'_noisy.jpg') - print(tensimg.shape) - print((absx0,absy0,ud)) - newimg[:,absy0:absy0+tensimg.shape[1],absx0:absx0+tensimg.shape[2]] = newimg[:,absy0:absy0+tensimg.shape[1],absx0:absx0+tensimg.shape[2]].add(tensimg) - if args.debug: - torchvision.utils.save_image(xbatch[i].cpu().detach(), args.output+'dbg_inclborders.tif') # dbg: get img with borders - pt_helpers.tensor_to_imgfile(newimg, args.output) - print(f'Denoised image written to {args.output}') - if args.output[:-4] == '.jpg' and args.exif_method == 'piexif': - piexif.transplant(args.input, args.output) - elif args.exif_method != 'noexif': - cmd = ['exiftool', '-TagsFromFile', args.input, args.output, '-all', '-icc_profile', '-overwrite_original'] - subprocess.run(cmd) - print(f'Wrote denoised image to {args.output}') + def make_seamless_edges(tcrop, x0, y0): + if x0 != 0:#left + tcrop[:,:,0:args.overlap] = tcrop[:,:,0:args.overlap].div(2) + if y0 != 0:#top + tcrop[:,0:args.overlap,:] = tcrop[:,0:args.overlap,:].div(2) + if x0 + args.ucs < fswidth and args.overlap:#right + tcrop[:,:,-args.overlap:] = tcrop[:,:,-args.overlap:].div(2) + if y0 + args.ucs < fsheight and args.overlap:#bottom + tcrop[:,-args.overlap:,:] = tcrop[:,-args.overlap:,:].div(2) + return tcrop + + if isinstance(args.cuda_device, int) and args.cuda_device >= 0: + if torch.cuda.is_available(): + torch.cuda.set_device(args.cuda_device) + torch.backends.cudnn.benchmark = True + torch.cuda.manual_seed(123) + else: + print("warning: PyTorch is not installed with CUDA. Defaulting to CPU.") + torch.manual_seed(123) + device = pt_helpers.get_device(args.cuda_device) + + # ugly hardcoded hack for now + if args.model_parameters is None and 'activation' in args.model_path: + args.model_parameters = f"activation={args.model_path.split('activation')[-1].split('_')[1].split('_')[0]}" + print(f'set model_parameters to {args.model_parameters} based on model_path') + model = Model.instantiate_model(network=args.g_network, model_path=args.model_path, + strparameters=args.model_parameters, keyword='generator', + device=device, models_dpath=args.models_dpath) + model.eval() # evaluation mode + model = model.to(device) + ds = OneImageDS(args.input, args.cs, args.ucs, args.overlap, whole_image=args.whole_image, pad=args.pad) + DLoader = DataLoader(dataset=ds, + num_workers=0 if args.batch_size == 1 else max(min(args.batch_size, os.cpu_count()//4), 1), + drop_last=False, batch_size=args.batch_size, shuffle=False) + topil = torchvision.transforms.ToPILImage() + fswidth, fsheight = Image.open(args.input).size + newimg = torch.zeros(3, fsheight, fswidth, dtype=torch.float32) + + start_time = time.time() + for n_count, ydat in enumerate(DLoader): + print(str(n_count)+'/'+str(int(len(ds)/args.batch_size))) + ybatch, usefuldims, usefulstarts = ydat + if args.max_subpixels is not None and math.prod(ybatch.shape) > args.max_subpixels: + sys.exit(f'denoise_image.py: {ybatch.shape=}, {math.prod(ybatch.shape)=} > {args.max_subpixels=} for {args.input=}; aborting') + ybatch = ybatch.to(device) + xbatch = model(ybatch) + if torch.cuda.is_available(): + torch.cuda.synchronize() + for i in range(ybatch.size(0)): + ud = usefuldims[i] + # pytorch represents images as [channels, height, width] + # TODO test leaving on GPU longer + # TODO reconstruct image with batch_size > 1 + tensimg = xbatch[i][:,ud[1]:ud[3], ud[0]:ud[2]].cpu().detach() + if args.whole_image: + newimg = tensimg + else: + absx0, absy0 = tuple(usefulstarts[i].tolist()) + tensimg = make_seamless_edges(tensimg, absx0, absy0) + if args.debug: + os.makedirs('dbg', exist_ok=True) + torchvision.utils.save_image(xbatch[i], 'dbg/crop'+str(n_count)+'_'+str(i)+'_denoised.jpg') + torchvision.utils.save_image(tensimg, 'dbg/crop'+str(n_count)+'_'+str(i)+'_tensimg.jpg') + torchvision.utils.save_image(ybatch[i], 'dbg/crop'+str(n_count)+'_'+str(i)+'_noisy.jpg') + print(tensimg.shape) + print((absx0,absy0,ud)) + newimg[:,absy0:absy0+tensimg.shape[1],absx0:absx0+tensimg.shape[2]] = newimg[:,absy0:absy0+tensimg.shape[1],absx0:absx0+tensimg.shape[2]].add(tensimg) + if args.debug: + torchvision.utils.save_image(xbatch[i].cpu().detach(), args.output+'dbg_inclborders.tif') # dbg: get img with borders + pt_helpers.tensor_to_imgfile(newimg, args.output) + print(f'Denoised image written to {args.output}') + exif_input = original_input if original_input else args.input + if args.output[:-4] == '.jpg' and args.exif_method == 'piexif': + piexif.transplant(exif_input, args.output) + elif args.exif_method != 'noexif': + cmd = ['exiftool', '-TagsFromFile', exif_input, args.output, '-all', '-icc_profile', '-overwrite_original'] + subprocess.run(cmd) + print(f'Wrote denoised image to {args.output}') + finally: + if original_input is not None and os.path.exists(args.input): + os.unlink(args.input) + print(f' Cleaned up temporary TIFF: {args.input}') print('Elapsed time: '+str(time.time()-start_time)+' seconds') diff --git a/src/nind_denoise/pyproject.toml b/src/nind_denoise/pyproject.toml new file mode 100644 index 0000000..675ea25 --- /dev/null +++ b/src/nind_denoise/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "nind-denoise" +version = "0.1.0" +description = "Master thesis on natural image noise removal using Convolutional Neural Networks. 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