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| # Author: Tobias Plötz, TU Darmstadt ([email protected]) | |
| # This file is part of the implementation as described in the CVPR 2017 paper: | |
| # Tobias Plötz and Stefan Roth, Benchmarking Denoising Algorithms with Real Photographs. | |
| # Please see the file LICENSE.txt for the license governing this code. | |
| import numpy as np | |
| import scipy.io as sio | |
| import os | |
| import h5py | |
| def bundle_submissions_raw(submission_folder,session): | |
| ''' | |
| Bundles submission data for raw denoising | |
| submission_folder Folder where denoised images reside | |
| Output is written to <submission_folder>/bundled/. Please submit | |
| the content of this folder. | |
| ''' | |
| out_folder = os.path.join(submission_folder, session) | |
| # out_folder = os.path.join(submission_folder, "bundled/") | |
| try: | |
| os.mkdir(out_folder) | |
| except:pass | |
| israw = True | |
| eval_version="1.0" | |
| for i in range(50): | |
| Idenoised = np.zeros((20,), dtype=np.object) | |
| for bb in range(20): | |
| filename = '%04d_%02d.mat'%(i+1,bb+1) | |
| s = sio.loadmat(os.path.join(submission_folder,filename)) | |
| Idenoised_crop = s["Idenoised_crop"] | |
| Idenoised[bb] = Idenoised_crop | |
| filename = '%04d.mat'%(i+1) | |
| sio.savemat(os.path.join(out_folder, filename), | |
| {"Idenoised": Idenoised, | |
| "israw": israw, | |
| "eval_version": eval_version}, | |
| ) | |
| def bundle_submissions_srgb(submission_folder,session): | |
| ''' | |
| Bundles submission data for sRGB denoising | |
| submission_folder Folder where denoised images reside | |
| Output is written to <submission_folder>/bundled/. Please submit | |
| the content of this folder. | |
| ''' | |
| out_folder = os.path.join(submission_folder, session) | |
| # out_folder = os.path.join(submission_folder, "bundled/") | |
| try: | |
| os.mkdir(out_folder) | |
| except:pass | |
| israw = False | |
| eval_version="1.0" | |
| for i in range(50): | |
| Idenoised = np.zeros((20,), dtype=np.object) | |
| for bb in range(20): | |
| filename = '%04d_%02d.mat'%(i+1,bb+1) | |
| s = sio.loadmat(os.path.join(submission_folder,filename)) | |
| Idenoised_crop = s["Idenoised_crop"] | |
| Idenoised[bb] = Idenoised_crop | |
| filename = '%04d.mat'%(i+1) | |
| sio.savemat(os.path.join(out_folder, filename), | |
| {"Idenoised": Idenoised, | |
| "israw": israw, | |
| "eval_version": eval_version}, | |
| ) | |
| def bundle_submissions_srgb_v1(submission_folder,session): | |
| ''' | |
| Bundles submission data for sRGB denoising | |
| submission_folder Folder where denoised images reside | |
| Output is written to <submission_folder>/bundled/. Please submit | |
| the content of this folder. | |
| ''' | |
| out_folder = os.path.join(submission_folder, session) | |
| # out_folder = os.path.join(submission_folder, "bundled/") | |
| try: | |
| os.mkdir(out_folder) | |
| except:pass | |
| israw = False | |
| eval_version="1.0" | |
| for i in range(50): | |
| Idenoised = np.zeros((20,), dtype=np.object) | |
| for bb in range(20): | |
| filename = '%04d_%d.mat'%(i+1,bb+1) | |
| s = sio.loadmat(os.path.join(submission_folder,filename)) | |
| Idenoised_crop = s["Idenoised_crop"] | |
| Idenoised[bb] = Idenoised_crop | |
| filename = '%04d.mat'%(i+1) | |
| sio.savemat(os.path.join(out_folder, filename), | |
| {"Idenoised": Idenoised, | |
| "israw": israw, | |
| "eval_version": eval_version}, | |
| ) |