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979 results for “image dataset”
Dataset for "Deep Learning with remote sensing data for image segmentation: example of rice crop mapping using Sentinel-2 images"
<p>Dataset for "Deep Learning with remote sensing data for image segmentation: example of rice crop mapping using Sentinel-2 images". </p> <p> </p> <p>image_prediction_pt1 and _pt2 have the same content as image_prediction.zip but split in two parts for faster downloading with Google Colab (to avoid time out)</p> <p> </p> <p>Contact</p> <p>Ricardo Dalagnol</p> <p>ricds@hotmail.com</p>
Image classification in Galaxy with fruit 360 dataset
<p>Credit: 'Fruit recognition from images using deep learning' by H. Muresan and M. Oltean (<a href="https://arxiv.org/abs/1712.00580">https://arxiv.org/abs/1712.00580</a>)<br> <br> Fruit 360 is a dataset with 90380 images of 131 fruits and vegetables (<a href="https://www.kaggle.com/moltean/fruits">https://www.kaggle.com/moltean/fruits</a>). Images are 100 pixel by 100 pixel and are RGB (color) images (3 values for each pixel). This dataset is a subset of Fruit 360 dataset, containing only 10 fruits/vegetables (Strawberry, Apple_Red_Delicious, Pepper_Green, Corn, Banana, Tomato_1, Potato_White, Pineapple, Orange, and Peach). We selected a subset of fruits/vegetables, so the dataset size is smaller and the neural network can be trained faster.</p> <p> </p> <p>The utilities used to create the dataset, along with step by step instructions, can be found here: https://github.com/kxk302/fruit_dataset_utilities<br> <br> First, we created feature vectors for each image. Each image is 100 pixel by pixel and are RGB (color) images (3 values for each pixel). Hence, each image can be represented by 30,000 values (100 X 100 X 3). Second, we selected a subset of 10 fruits/vegetables images (training and test dataset sizes go from 7 GB and 2.5 GB for 131 fruits/vegetables to 500 MB and 177 MB for 10 fruits/vegetables, respectively). Third, we created separate files for feature vectors and labels. Finally, we mapped the labels for the 10 selected fruits/vegetables to a range of 0 to 9.</p> <p> </p>
SQUID- Stereo Quantitative Underwater Image Dataset
<p>Underwater Single Image Color Restoration</p> <p>SQUID- Stereo Quantitative Underwater Image Dataset</p> <p><a href="http://www.eng.tau.ac.il/~berman">Dana Berman</a>, Deborah Levy, <a href="http://www.eng.tau.ac.il/~avidan/">Shai Avidan, </a><a href="https://www.viseaon.haifa.ac.il/">Tali Treibitz</a></p> <p> </p> <p>Abstract</p> <p>Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water. Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform. The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult. Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types. By estimating just two additional global parameters: the attenuation ratios of the blue-red and blue-green color channels, the problem is reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since the water type is unknown, we evaluate different parameters out of an existing library of water types. Each type leads to a different restored image and the best result is automatically chosen based on color distribution. We collected a dataset of images taken in different locations with varying water properties, showing color charts in the scenes. Moreover, to obtain ground truth, the 3D structure of the scene was calculated based on stereo imaging. This dataset enables a quantitative evaluation of restoration algorithms on natural images and shows the advantage of our method.</p> <p> </p> <p>Publication</p> <p>The paper is availbale on <a href="https://arxiv.org/abs/1811.01343">arXiv</a>.</p> <p>If you use this dataset please cite it as SQUID [ref].<br> [Ref] Berman, Dana, Deborah Levy, Shai Avidan, and Tali Treibitz. "Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset." IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).<br> <br> Bibtex entry:<br> @article{berman2020underwater,<br> title={Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset},<br> author={Berman, Dana and Levy, Deborah and Avidan, Shai and Treibitz, Tali},<br> journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},<br> year={2020}<br> }</p> <p>Source Code</p> <p>The code is availbale on GitHub: <a href="https://github.com/danaberman/underwater-hl">https://github.com/danaberman/underwater-hl</a>.</p> <p>Dataset</p> <p>The dataset includes RAW images, TIF files, camera clibration files, and distance maps.<br> The database contains 57 stereo pairs from four different sites in Israel, two in the Red Sea (representing tropical water) and two in the Mediterranean Sea (temperate water). In the Red Sea the sites were a coral reef ('Katzaa') which is 10-15 meters deep (15 pairs) and a shipwreck ('Satil'), 20-30 meters deep (8 pairs). In the Mediterranean Sea both sites were rocky reef environments, separated by 30km, Nachsholim at 3-6 meters depth (13 pairs), and Mikhmoret at 10-12 meters depth (21 pairs).<br> <br> For convenience it is divided to the 4 dive sites.<br> <br> <a href="http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/files/README.md">README file</a>.<br> If you use this data, please <a href="http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/underwater_haze_lines_and_dataset.bib">cite the paper</a>.<br> To evaluate your own results, please use this <a href="http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/files/underwater-dataset-evaluation-undisorted-params.zip">evaluation code</a>.</p> <p><br> </p> <p>References</p> <p><strong>[Drews et al. 2013]</strong> P. Drews, E. Nascimento, F. Moraes, S. Botelho, and M. Campos. Transmission estimation in underwater single images. In <em>Proc. IEEE ICCV Underwater Vision Workshop, pages 825–830,</em> 2013.<br> <strong>[Peng et al. 2015]</strong> Y.-T. Peng, X. Zhao, and P. C. Cosman. Single underwater image enhancement using depth estimation based on blurriness. In <em>Proc. IEEE ICIP</em>, 2015.<br> <strong>[Ancuti et al. 2016]</strong> C. Ancuti, C. O. Ancuti, C. De Vleeschouwer, R. Garcia, and A. C. Bovik. Multi-scale underwater descattering. In <em>Proc. ICPR</em>, 2016.<br> <strong>[Ancuti et al. 2017]</strong> C. O. Ancuti, C. Ancuti, C. De Vleeschouwer, L. Neumann, and R. Garcia. Color transfer for underwater dehazing and depth estimation. In <em>Proc. IEEE ICIP</em>, 2017<em>(All color transfers were done with a single image)</em>.<br> <strong>[Emberton et al. 2017]</strong>S. Emberton, L. Chittka, and A. Cavallaro, Underwater image and video dehazing with pure haze region segmentation, <em>Computer Vision and Image Understanding</em>, 2017.<br> <strong>[Ancuti et al. 2018]</strong> . O. Ancuti, C. Ancuti, C. De Vleeschouwer, and P. Bekaert. Color balance and fusion for underwater image enhancement. IEEE Transactions on Image Processing, 27(1):379–393, 2018.</p>
Dataset - Ghost Image Processing
<p>Dataset to accompany the paper titled: Ghost Image Processing. (to be submitted)</p> <p>Files in .txt format with '_ReadMe.txt' providing context. </p>
Figure 1 from: Wilf P, Wing SL, Meyer HW, Rose JA, Saha R, Serre T, Cúneo NR, Donovan MP, Erwin DM, Gandolfo MA, González-Akre E, Herrera F, Hu S, Iglesias A, Johnson KR, Karim TS, Zou X (2021) An image dataset of cleared, x-rayed, and fossil leaves vetted to plant family for human and machine learning. PhytoKeys 187: 93-128. https://doi.org/10.3897/phytokeys.187.72350
Figure 1 Selected image pairs of confamilial extant and fossil (see Appendix 1) leaves from the dataset ABatesia floribunda Spruce ex. Benth. (Fabaceae), NCLC-W 6417, showing typical layout of a cleared-leaf slide with original annotations (other examples are cropped in this figure); source voucher Froes 12074, DS 291771 (at CAS), Amazonas, Brazil BFabaceae sp. CJ1, SGC-ICP-10173; Cerrejón mine, middle-late Paleocene of Guajira Peninsula, Colombia CCrataegus viridis L. (Rosaceae), NCLC-W 11951b; H. Meyer s/n (collected 1974, no other voucher), cultivated, California, USA DCrataegus copeana (Rosaceae), UCMP 3610; Florissant, late Eocene of Colorado, USA; H. Meyer photograph number 0420 ETetracentron sinense Oliv. (Trochodendraceae), S. Wing negative 71-002; E.H. Wilson 659, US 599036, Szechuan, China FZiziphoides flabellum (Trochodendraceae), USNM 560134; Mexican Hat, early Paleocene of Montana, USA GQuercus prinus L. (Fagaceae), NCLC-W 6137; H. Foster 8223, US 1730249, Florida, USA HFagopsis longifolia (Fagaceae), FLFO 003432A; Florissant, late Eocene of Colorado, USA IEucalyptus astringens (Maiden) Maiden (Myrtaceae), NCLC-W 10489; J.H. Maiden (9 November 1909), Western Australia, UC 437518 JEucalyptus frenguelliana (Myrtaceae), MPEF-Pb 2344; Laguna del Hunco, early Eocene of Chubut, Argentina KCercidiphyllum obtritum (Cercidiphyllaceae), DMNH 25061; Republic, early Eocene of Washington, USA LCercidiphyllum japonicum Siebold & Zucc. ex J.J.Hoffm. & J.H.Schult.bis (Cercidiphyllaceae), Axelrod cleared leaf 166; UCMP (no other voucher) MPlatanus racemosa Nutt. (Platanaceae), NCLC-H 6631; Handel s/n (collected 1985, no other voucher), California, USA NErlingdorfia montana (compound-leaved Platanaceae), DMNH 7642; Hell Creek Formation, Late Cretaceous of North Dakota, USA. Scale bars: centimeters as labeled (A, B, L, M); 1 cm when not labeled (C–K, N).
Data from: A dataset of stereoscopic images and ground-truth disparity mimicking human fixations in peripersonal space
[No abstract entered]
The image dataset for mixing ratio prediction
<p>The image dataset for mixing ratio prediction. </p>
Transmission electron microscopy (TEM) image datasets of peptide / protein nanowire morphologies
<p>TEM image dataset containing four nanowire morphologies of bio-derived protein nanowires and synthetic peptide nanowires.</p> <p>The peptide / protein nanowires used in this study were synthesized and imaged by Brian Montz in Prof. Todd Emrick's research group at the Department of Polymer Science and Engineering Department, University of Massachusetts Amherst. </p> <p>We acknowledge financial support from the U.S. National Science Foundation, Grant NSF DMREF #1921839 and DMREF #1921871.</p> <p>Nanowires were classified into either of the four morphologies: bundle, singular, dispersed or network. Each morphology contains 100 images (jpg files).</p> <p>For the dispersed and network morphologies, because these two morphologies are harder to visually distinguish, we have created manual segmentation labels of the nanowires (included in these two morphology folders as png files). Percolation analysis was done on these manually segmented nanowires to provide quantitative metric on whether the nanowires form a network in the image. </p> <p>seg_mask_5_resolutions.zip contains ground truth 2D binary encoding of segmented nanowires at 5 resolutions.</p> <p>encoders_trained_with_optimized_hyperparameter.zip contains 4 sets of encoders trained with either SimCLR or Barlow-Twins self-supervised methods on either generic TEM images, or generic everyday photographic images (each with 5 replicates with different random seed) with optimized hyperparameters.</p> <p>Open-access datasets that have been used during self-supervised training.</p> <ul> <li>2021-CEM500K.zip contains 10,000 images that was used as "generic TEM images" to train the encoders with self-supervised methods, these are a random selection from the CEM500k open-access dataset. DOI: <a href="https://doi.org/10.7554/eLife.65894">10.7554/eLife.65894</a></li> <li>2022-1000-ImageNet.zip contains 1,000 images from the ImageNet1k dataset, each come from a different category. DOI: <a href="http://doi.org/10.1007/s11263-015-0816-y">10.1007/s11263-015-0816-y</a></li> </ul> <p>Open-access datasets that our machine learning workflow have been applied to:</p> <ul> <li>2022-AutoDetect-mNP-morphology.zip contains a selected TEM images of nanoparticles categorized in 3 morphologies from the AutoDetect-mNP datasets: DOI: <a href="http://doi.org/10.6078/D1WT44">10.6078/D1WT44</a> and DOI: <a href="http://doi.org/10.6078/D1S12H">10.6078/D1S12H</a></li> <li>2021-TEM virus.zip contains TEM images of 9 types of viruses from the TEM virus dataset. Matuszewski, Damian; Sintorn, Ida-Maria (2021), “TEM virus dataset”, Mendeley Data, V3, DOI: <a href="http://doi.org/10.17632/x4dwwfwtw3.3">10.17632/x4dwwfwtw3.3</a></li> </ul> <p>The official github page of the implementation of the machine learning models is <a href="https://github.com/arthijayaraman-lab/semi-supervised_learning_microscopy_images">semi-supervised_learning_microscopy_images</a>.</p> <p>If you use the dataset or the codes in the repository linked above, please cite the following <a href="https://doi.org/10.1039/D2DD00066K">manuscript</a>:</p> <p>S. Lu, B. Montz, T. Emrick and A. Jayaraman, <em>Digital Discovery</em>, 2022, <strong>1</strong>, 816-833 , <strong>DOI: </strong>10.1039/D2DD00066K</p>
Bivalve and Brachiopod Fossil Image Dataset (BBFID)
<p>This is the dataset of paper "Automatic identification and morphological comparison of bivalve and brachiopod fossils based on deep learning".</p>
Figure 1 from: Rane S, Jolly E, Park A, Jang H, Craddock C (2017) Developing predictive imaging biomarkers using whole-brain classifiers: Application to the ABIDE I dataset. Research Ideas and Outcomes 3: e12733. https://doi.org/10.3897/rio.3.e12733
Figure 1 - Weights (β-coefficients) for voxel-wise ReHo features from a support vector machine (SVM) classifier mapped on the glass brain to separate individuals with and without Autism Spectrum Disorder
Datasets for "Exposing Image Splicing Traces in Scientific Publications via Uncertainty-guided Refinement"
<p>Official datasets for the manuscript "Exposing Image Splicing Traces in Scientific Publications via Uncertainty-guided Refinement".</p> <p>SciSp-C and SciSp-H are presented in the dataset files.</p> <p>Due to our inability to get copyright permissions from various publishers for each image in the SciSp-C dataset, we provide the detailed identifiers for each image to prevent potential copyright disputes. Please kindly track the identifiers to get the original images. If you have any questions about this dataset, please contact linxun@buaa.edu.cn.</p>
Boston & Corniolo Datasets - road segmentation - described in "An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images""
<p>In Corniolo.rar the masks (values {0,1}) are saved in the png files</p>
CelebA-HQ Dataset Generated Images with StyleSwin Method
Open the record for dataset details and reuse information.
M3: A Multi-Image Multi-Modal Entity Alignment Dataset
<p><span>M3, an MMEA benchmark equipped with multiple images retrieved from respective search engines, which better mirrors real-life challenges. We utilize the widely used DBP15K dataset as the foundational dataset, which includes three cross-ingual datasets: Chinese-English (ZH-EN), Japanese-English (JA-EN), and French-English (FR-EN).</span></p>
Demo Datasets for Non-light-sheet Imaging Modalities for PetaKit5D
<p>Demo dataset for PetaKit5D (<a href="https://github.com/abcucberkeley/LLSM5DTools">https://github.com/abcucberkeley/PetaKit5D</a>). </p> <p>There are four datasets for non-light-sheet modalities: 2-photon, confocal, oblique illumination "phase", and widefield. Some demos in PetaKit5D use this dataset to demonstrate the usage. Please refer to the readme.txt for the file structures, parameters, and other information.</p> <p>Please cite our paper (<a href="https://doi.org/10.1101/2023.12.31.573734">https://doi.org/10.1101/2023.12.31.573734</a>) if you use this dataset in your research:</p> <p><code>Xiongtao Ruan, Matthew Mueller, Gaoxiang Liu, Frederik Görlitz, Tian-Ming Fu, Daniel E. Milkie, Joshua L. Lillvis, Alexander Kuhn, Chu Yi Aaron Herr, Wilmene Hercule, Marc Nienhaus, Alison N. Killilea, Eric Betzig, Srigokul Upadhyayula. Image processing tools for petabyte-scale light sheet microscopy data. bioRxiv 2023.12.31.573734; doi: <a href="https://doi.org/10.1101/2023.12.31.573734">https://doi.org/10.1101/2023.12.31.573734</a></code></p>
MINT dataset-image data
<p>This dataset is the image set of the MINT dataset , corresponding to the manuscript "<strong><a href="https://arxiv.org/abs/2406.10591">MINT: a Multi-modal Image and Narrative Text Dubbing Dataset for Foley Audio Content Planning and Generation</a></strong>".</p> <h1>Abstract</h1> <p>Foley audio, critical for enhancing the immersive experience in multimedia content, faces significant challenges in the AI-generated content (AIGC) landscape. Despite advancements in AIGC technologies for text and image generation, the foley audio dubbing remains rudimentary due to difficulties in cross-modal scene matching and content correlation. Current text-to-audio technology, which relies on detailed and acoustically relevant textual descriptions, falls short in practical video dubbing applications. Existing datasets like AudioSet, AudioCaps, Clotho, Sound-of-Story, and WavCaps do not fully meet the requirements for real-world foley audio dubbing task. To address this, we introduce the Multi-modal Image and Narrative Text Dubbing Dataset (MINT), designed to enhance mainstream dubbing tasks such as literary story audiobooks dubbing, image/silent video dubbing. Besides, to address the limitations of existing TTA technology in understanding and planning complex prompts, a Foley Audio Content Planning, Generation, and Alignment (CPGA) framework is proposed, which includes a content planning module leveraging large language models for complex multi-modal prompts comprehension. Additionally, the training process is optimized using Proximal Policy Optimization based reinforcement learning, significantly improving the alignment and auditory realism of generated foley audio. Experimental results demonstrate that our approach significantly advances the field of foley audio dubbing, providing robust solutions for the challenges of multi-modal dubbing. Even when utilizing the relatively lightweight GPT-2 model, our framework outperforms open-source multimodal large models such as LLaVA, DeepSeek-VL, and Moondream2. The dataset is available at <a title="https://github.com/borisfrb/MINT" href="https://github.com/borisfrb/MINT">https://github.com/borisfrb/MINT</a><br><br></p> <h1>Ethics Statement</h1> <p><br><strong>The dataset is licensed under CC BY-NC-SA-4.0 license.</strong><br><br>The access to this MINT dataset is limited to academic institutions and is for research purposes only. We include frames extracted from each YouTube video as images in the dataset and ensure that these images do not adversely affect the copyright owner's ability to generate revenue from their original content, thereby complying with YouTube's fair use policy. If any copyright owner believes their rights have been infringed, we commit to promptly removing the disputed materials from our dataset.</p>
ROV-Based Multi-Sensor Dataset: Synchronized Camera and Sonar images taken in the Tropical Waters of the Red Sea, Eilat
<p><strong>Description:</strong></p> <p>This dataset consists of approximately 46,928 synchronized image pairs collected by the Blue-ROV2. The images were captured using a machine-vision camera (IDS UI-3260CP-C-HQ) and a BluePrint Oculus M1200d Forward-Looking Sonar (FLS). Both sensors were installed with the FLS tilted 15 degrees downward to achieve optimal coverage of the terrain and optimal FOV overlap.</p> <p>The data was collected to train and evaluate a comprehensive perception and obstacle avoidance framework.</p> <p> </p> <p><strong>Context:</strong></p> <p>This dataset is the second installment in our collection of synchronized multi-sensor underwater datasets, aimed at enabling advanced research in multi-modal sensor fusion, obstacle detection, and navigation for autonomous underwater vehicles (AUVs). The data was collected using the Blue-ROV2 Remotely Operated Vehicle (ROV) in the tropical waters of the Red Sea, off the coast of Eilat, Israel. This data captures diverse underwater environments and is part of a research project focused on developing fusion models for improved obstacle detection and navigation in AUVs.</p> <p> </p> <p><strong>Content:</strong></p> <p>The data encompasses several sites within the tropical waters of the Red Sea, Eilat, including corals, rocks, shipwrecks, man-made structures, piers, and caves. The ROV platform was operated by divers, ensuring accurate positioning and coverage. Data was acquired at depths ranging from 3 to 12 meters at different times from dawn to dusk.</p> <p><strong> </strong></p> <p><strong>Dataset Composition:</strong><strong><br></strong></p> <div> <table> <tbody> <tr> <td> <p>Site</p> </td> <td> <p>Recording Session</p> </td> <td> <p>Image Pairs</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p>Tropical Site 1</p> </td> <td> <p>20221211_092506</p> <p>20221211_133252</p> </td> <td> <p>10,915</p> <p>7,978</p> </td> <td> <p>Pier, rocks, corals</p> </td> </tr> <tr> <td> <p>Tropical Site 2</p> </td> <td> <p>20221212_095821</p> <p>20221212_141308</p> </td> <td> <p>9,900</p> <p>8,475</p> </td> <td> <p>Man-made structure, </p> <p>rocks, corals</p> </td> </tr> <tr> <td> <p>Tropical Site 3</p> </td> <td> <p>20221213_102542</p> </td> <td> <p>9,390</p> </td> <td> <p>Rocks, corals</p> </td> </tr> <tr> <td> <p>Total</p> </td> <td> </td> <td> <p>46,928 </p> </td> <td> </td> </tr> </tbody> </table> </div> <p><strong> </strong></p> <p>The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, data is further categorized into modalities: camera (FLC images), sonar (FLS images), and depth. Each modality directory contains the corresponding data files in PNG format for images and CSV format for depth data.</p> <p><strong> </strong></p> <p>Each modality directory includes:</p> <ul> <li> <p>A `camera.csv` file for the camera modality that maps each image file to its respective timestamp.</p> </li> <li> <p>A `sonar.csv` file for the sonar modality that maps each image file to its respective timestamp.</p> </li> <li> <p>The depth data in `depth.csv` formatted with `timestamp` and `value`.</p> </li> </ul> <p>Additionally, a `samples.json` file documents the relationship between uni-modal and multi-modal samples, enabling easy association of data from different modalities.</p> <p><strong> </strong></p> <p><strong>Technical Details:</strong></p> <ul> <li> <p>Camera: IDS UI-3260CP-C-HQ</p> </li> <ul> <li> <p>Image dimensions: 1936x1216 pixels (downscaled to 968 × 608 for this dataset)</p> </li> <li> <p>Sensor type: Sony IMX249 1/1.2" CMOS</p> </li> <li> <p>Lens: Tamron M112FM06</p> </li> <li> <p>Captured bit depth: 8-bit</p> </li> <li> <p>Frame rate: 5 Hz</p> </li> </ul> <li> <p>Sonar: BluePrint Oculus M1200d</p> </li> <ul> <li> <p>Operating frequency: 1.2 MHz (low frequency mode)</p> </li> <li> <p>Maximum range: 40 m (set to 15 m for this dataset)</p> </li> <li> <p>Horizontal aperture: 130°</p> </li> <li> <p>Vertical aperture: 20°</p> </li> <li> <p>Number of beams: 512</p> </li> <li> <p>Angular resolution: 0.6°</p> </li> <li> <p>Beam separation: 0.25°</p> </li> <li> <p>Image resolution: 544x300 pixels</p> </li> <li> <p>Coordinate system: Polar</p> </li> <li> <p>Frame rate: 5 Hz</p> </li> </ul> <li> <p>Depth: Blue-Robotics Ping2 Sonar Altimeter and Echosounder</p> </li> <ul> <li> <p>Frequency: 115 kHz</p> </li> <li> <p>Source Level: 198 dB re 1µPa @ 1m</p> </li> <li> <p>Beamwidth: 25 degrees</p> </li> <li> <p>Typical Minimum Range: 0.3 m (1 ft)</p> </li> <li> <p>Typical Usable Range: 100 m (328 ft)</p> </li> <li> <p>Range Resolution: 0.5% of range</p> </li> <li> <p>Depth Rating: 300 m (984 ft)</p> </li> <li> <p>Data format: CSV</p> </li> <li> <p>Columns:</p> </li> <ul> <li> <p>timestamp: Unix timestamp (seconds)</p> </li> <li> <p>value: Depth value (meters)</p> </li> </ul> <li> <p>Sample rate: 5 Hz</p> </li> </ul> </ul> <p><strong> </strong></p> <p><strong>Example File Tree Layout:</strong></p> <p>```<br>${session}/<br>${dataset}/<br>camera/<br>camera.csv<br>00000001.png<br>00000002.png<br>…<br>sonar/<br>sonar.csv<br>00000001.png<br>00000002.png<br>…<br>depth/<br>depth.csv<br>samples.json<br>```<strong> <br><br>Example File Content:</strong></p> <p><strong> </strong>camera.csv<br>```<br>timestamp,filename<br>1644234340.181234,00000001.png<br>1644234343.375667,00000002.png<br>```</p> <p><strong> </strong>sonar.csv<br>```<br>timestamp,filename<br>1644234340.181234,00000001.png<br>1644234343.375667,00000002.png<br>```</p> <p><strong> </strong>depth.csv<br>```<br>timestamp,value<br>1644234340.181234,5.4<br>1644234343.375667,6.1<br>```</p> <p><strong> </strong>samples.json</p> <p>```<br>{<br> "samples": [<br> {<br> "camera": [<br> 0<br> ],<br> "depth": [<br> 0<br> ],<br> "sonar": [<br> 0<br> ]<br> },<br> {<br> "camera": [<br> 1<br> ],<br> "depth": [<br> 1<br> ],<br> "sonar": [<br> 1<br> ]<br> }<br>]</p> <p>```</p> <p>By providing synchronized and aligned camera, sonar imagery, and depth data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles operating in the tropical waters of the Red Sea.</p> <p><strong>Acknowledgements</strong></p> <p>The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.</p>
DAPI and Phase Contrast Images Dataset
<p>Data was acquired on an Olympus IX83 microscope using a 20x/0.4 Ph2 Objective.</p> <p>Kohler illimination was established before acquistion started.</p> <p>Sample consists of cultured HeLa Cells on #1.5 (170 um) coverslips, 12mm diameter.</p> <p> </p> <p>A single coverlsip was imaged in a 10x10 grid with no overlap (total: 100 images)</p> <p>Each image was acquired sequentially in</p> <ul> <li><strong>Phase Contrast</strong> (50ms exposure time, using a 535nm LED)</li> <li><strong>DAPI</strong> 100 ms exposure time</li> </ul>
68 image lysozyme dataset recorded on the Jungfrau 16M detector at SwissFEL and formatted as a NeXus file, revised for clean cnxvalidate error report
<p>This kit includes an additional revised master file, lyso009a_0087.JF07T32V01_master_rev.h5 that provides compliance with the October 2019 NXmx specification as proposed in https://github.com/HDRMX/definitions.git</p> <p>To create a new NeXus master file, assuming DIALS is installed in the folder $DIALS, use this command:</p> <p>libtbx.python $DIALS/modules/cctbx_project/xfel/swissfel/jf16m_cxigeom2nexus.py unassembled_file=lyso009a_0087.JF07T32V01.h5 geom_file=16M_bernina_backview_optimized_adu_quads.geom wavelength=1.368479 detector_distance=97.830 mask_file=lyso009a_0087.JF07T32V01.mask.h5</p> <p>Geometry file is in CrystFEL format but has been realigned to group the modules hierarchically into quadrants.</p> <p>View the data using DIALS: dials.image_viewer lyso009a_0087.JF07T32V01_master.h5</p> <p>Process the data using DIALS, treating the images as stills, assuming 64 cores available on the system:<br> dials.stills_process mp.nproc=64 lyso009a_0087.JF07T32V01_master.h5 dispersion.gain=10 known_symmetry.space_group=P43212 known_symmetry.unit_cell=77,77,37,90,90,90 refinement_protocol.d_min_start=2.5</p> <p>Download DIALS at dials.github.io.</p> <p>After the DIALS run, for full NXmx compliance you will need the jungfrau portions of the script that was used to generate lyso009a_0087.JF07T32V01_master_rev.h5</p> <p> </p> <pre>#!/bin/bash cp Therm_6_2.nxs Therm_6_2_rev.nxs cp Therm_6_2_master.h5 Therm_6_2_master_rev.h5 cp jungfrau/lyso009a_0087.JF07T32V01_master.h5 jungfrau/lyso009a_0087.JF07T32V01_master_rev.h5 export curdat=`date +%FT%T.%3` export LD_LIBRARY_PATH=$HOME/lib export HDF5_PLUGIN_PATH=$HOME/lib export PATH=$HOME/bin:$PATH h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_master_rev.h5 -s /entry/instrument/name -d /entry/instrument/name -f ref h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_master_rev.h5 -s /entry/instrument/source -d /entry/source -f ref h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_rev.nxs -s /entry/instrument/source -d /entry/source -f ref h5copy -i jungfrau/lyso009a_0087.JF07T32V01_master.h5 -o jungfrau/lyso009a_0087.JF07T32V01_master_rev.h5 -s /entry/sample/beam -d /entry/instrument/beam -f ref export end_time=`h5dump -d "/entry/end_time" Therm_6_2_master.h5 | grep ":" | sed 's/^.........//'|sed 's/.\$//'` echo "end_time: $end_time" python << 'EOL' import h5py as h5 import numpy as np import os end_time=os.environ['end_time'] curdat=os.environ['curdat'] fvds = h5.File('Therm_6_2_rev.nxs','r+') fmaster = h5.File('Therm_6_2_master_rev.h5','r+') jungfrau= h5.File('jungfrau/lyso009a_0087.JF07T32V01_master_rev.h5','r+') fvds_keys=fvds.keys() fmaster_keys=fmaster.keys() jungfrau_keys=jungfrau.keys() fvds_entry=fvds['entry'] fmaster_entry=fmaster['entry'] jungfrau_entry=jungfrau['entry'] fvds_entry_keys=fvds_entry.keys() fmaster_entry_keys=fmaster_entry.keys() jungfrau_entry_keys=jungfrau_entry.keys() fvds_entry_instrument=fvds['entry']['instrument'] fmaster_entry_instrument=fmaster['entry']['instrument'] jungfrau_entry_instrument=jungfrau['entry']['instrument'] fvds_entry_instrument_keys=fvds_entry_instrument.keys() fmaster_entry_instrument_keys=fmaster_entry_instrument.keys() jungfrau_entry_instrument_keys=jungfrau_entry_instrument.keys() fvds_entry_instrument_name=(fvds['entry']['instrument']['name']) fmaster_entry_instrument_name=(fmaster['entry']['instrument']['name']) jungfrau['entry']['instrument'].create_dataset("name", data=np.string_("Paul Scherrer Institute SwissFEL Aramis 1 (Alvra)")) jungfrau_entry_instrument_name=(jungfrau['entry']['instrument']['name']) fvds_entry_instrument_short_name=fvds_entry_instrument.attrs['short_name'] fmaster_entry_instrument_short_name=fmaster_entry_instrument.attrs['short_name'] jungfrau_entry_instrument_name.attrs.modify('short_name',np.string_("Alvra")) jungfrau_entry_instrument_short_name=jungfrau_entry_instrument_name.attrs['short_name'] zero_offset=fmaster_entry_instrument['detector']['module']['fast_pixel_direction'].attrs['offset'] fmaster_det_z=fmaster_entry_instrument['transformations']['det_z'] fvds_det_z=fvds_entry_instrument['transformations']['det_z'] print('fvds_keys: ',fvds_keys) print('fmaster_keys: ',fmaster_keys) print('jungfrau_keys: ',jungfrau_keys) print('fvds_entry_keys: ',fvds_entry_keys) print('fmaster_entry_keys: ',fmaster_entry_keys) print('jungfrau_entry_keys: ',jungfrau_entry_keys) print('fvds_entry_instrument_keys: ',fvds_entry_instrument_keys) print('fmaster_entry_instrument_keys: ',fmaster_entry_instrument_keys) print('jungfrau_entry_instrument_keys: ',jungfrau_entry_instrument_keys) print('fvds_entry_instrument_name: ',fvds_entry_instrument_name) print('fmaster_entry_instrument_name: ',fmaster_entry_instrument_name) print('jungfrau_entry_instrument_name: ',jungfrau_entry_instrument_name) print('fvds_entry_instrument_short_name: ',fvds_entry_instrument_short_name) print('fmaster_entry_instrument_short_name: ',fmaster_entry_instrument_short_name) print('jungfrau_entry_instrument_short_name: ',jungfrau_entry_instrument_short_name) print('fmaster_entry_instrument_detector_module_fast_pixel_direction_offset: ',zero_offset) print('fmaster_entry_instrument_detector_detector_z_det_z: ',fmaster_det_z) print('fmaster_entry_end_time: ',end_time) fmaster.attrs.modify('file_time',np.string_(end_time)) fmaster.attrs.modify('file_name',np.string_('Therm_6_2_master_rev.h5')) fmaster.attrs.modify('HDF5_Version',np.string_('hdf5-1.8.18')) fvds.attrs.modify('file_time',np.string_(end_time)) fvds.attrs.modify('file_name',np.string_('Therm_6_2_master_rev.h5')) fvds.attrs.modify('HDF5_Version',np.string_('hdf5-1.10.5')) jungfrau.attrs.modify('file_time',np.string_(curdat)) jungfrau.attrs.modify('file_name',np.string_('lyso009a_0087.JF07T32V01_master.h5')) jungfrau.attrs.modify('HDF5_Version',np.string_('hdf5-1.10.5')) fvds_entry_instrument_name.attrs.modify('short_name',np.string_(fvds_entry_instrument.attrs['short_name'])) fmaster_entry_instrument_name.attrs.modify('short_name',np.string_(fmaster_entry_instrument.attrs['short_name'])) fmaster_entry_instrument['attenuator']['attenuator_transmission'].attrs.modify('units',np.string_("")) fmaster_entry_instrument['detector']['count_time'].attrs.modify('units',np.string_("s")) fvds_entry_instrument_name.attrs.modify('short_name',np.string_(fvds_entry_instrument.attrs['short_name'])) fvds_entry_instrument['attenuator']['attenuator_transmission'].attrs.modify('units',np.string_("")) fvds_entry_instrument['detector']['count_time'].attrs.modify('units',np.string_("s")) fmaster_det_z.attrs.modify('offset',zero_offset) fvds_det_z.attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['phi'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['chi'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_x'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_y'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_z'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['omega'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['phi'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['chi'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_x'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_y'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_z'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['omega'].attrs.modify('offset',zero_offset) print(fmaster['entry']['instrument']['name'].attrs['short_name']) print(fmaster['entry']['instrument']['name'].attrs['short_name'].shape) print(fmaster['entry']['instrument']['name'].attrs['short_name'].dtype) print("/entry/instrument/ELE_D0/pixel_mask_applied :",jungfrau_entry_instrument['ELE_D0']['pixel_mask_applied']) del jungfrau_entry_instrument['ELE_D0']['pixel_mask_applied'] jungfrau_entry_instrument['ELE_D0'].create_dataset("pixel_mask_applied",dtype='int8', data=1) print("/entry/instrument/ELE_D0/pixel_mask_applied :",jungfrau_entry_instrument['ELE_D0']['pixel_mask_applied']) jungfrau_entry_source=jungfrau_entry.create_group('source') jungfrau_entry_source=jungfrau_entry['source'] jungfrau_entry_source.attrs.modify('NX_class',np.string_("NXsource")) jungfrau_entry_source.create_dataset("name",data=np.string_("Paul Scherrer Institute SwissFEL")) jungfrau_entry_source['name'].attrs.modify('short_name',np.string_("SwissFEL")) #jungfrau_entry_instrument.create_group['beam'] #jungfrau_entry_instrument['beam']=jungfrau_entry['sample']['beam'] jungfrau_entry_instrument['beam'].create_dataset('total_flux',dtype='float64',data=1000000000000.) jungfrau_entry_instrument['beam']['total_flux'].attrs.modify('units',np.string_('/pulse')) del jungfrau_entry['sample']['beam'] del fvds_entry_instrument.attrs['short_name'] del fmaster_entry_instrument.attrs['short_name'] del fmaster_entry_instrument['source'] fvds.close() fmaster.close() jungfrau.close() quit() EOL $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions Therm_6_2_master_rev.h5 $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions Therm_6_2_rev.nxs $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions jungfrau/lyso009a_0087.JF07T32V01_master_rev.h5 </pre> <p> </p>
DsCGF: A large-scale open image dataset for deep learning enabled intelligent sorting and analyzing of raw coal (part 2)
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