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  • HoG_implementation
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Created with Raphaël 2.2.021Apr201816151413121115Mar14128Feb242321201817161510874316Jan1411Improved confusion matrix generation, so it uses samples that are roughly uniformly distributed between the 2 classes, across the entire validation set. The matrix agrees with the reported accuracy, suggesting something is wrong with my implementation used with OpenCV in c++ (as the acc. is lower).mainmainRemoved dnn model from git, and added frozen graph folder to gitignore.Fixed DNN detection implementation (now works fully), and refectored some paths to work in development. Reduced epochs to 14, as suggested by training data. Need to fix how app reads files that are in the process of being written.Fixed function declaration so the binaries now correctly build.Added detection logic for TensorFlow dnn model into cpp. Created more ground truth data. Modified dnn model for version 3, during-epoch data shows high accuracy, but have yet to finish training.Added some ground truths, generated new dataset, and updated model due to suggested overfitting.Added base training module for an Inception v3 architecture model; to transfer to IDUN with dataset and train.Modified some relative paths to be compatible with final binaries/executable, that can now be created with pyinstaller. Also added binaries to gitignore.All GUI elements finished, with proper initialization, handling, thread-execution and safety, termination, and serialization. Now works as a standalone software product. (Stopping while trimming a video means that thread waits to finish writing a detection before exiting)Extended main.cpp for future selection of detection algorithm, now allowing adding different methods easily via an enum switch.Parameter input (input folder, file prefix) in GUI operational, with serialization. Some inconsistency of moving data between GUI scenes. Not yet hooked up to core detection and trim functionality.HoG_implementat…HoG_implementationFinished final output reporting, according to spec. Currently uses Windows creation time to label outputs; might swap to file metadata. Working on gui next.testSVM with HoG features implemented, seems to be working well on single tests, but model needs to be serialized and connected to timestamp generator.Hog-ified the datasetStructured files that were merged, and corrected ground truth formats.merging addCuttingVideoAdded functionality to dataset generator; now works with multiple videos, and extracts a given percentage per video.addCuttingVideoaddCuttingVideoReplacing old files in new HoG branch.Restructured for python utilities package, and made standalone utility to generate labelled dataset that can easily be fed into keras later.added ground truth folder and filesFixed .csv reading, now correctly finds ground truth in input folder if it exists. Changed notation to 'groundTruth_[file]'added code and example files for converting from hr.min.sec to only seccsv-timeformat-…csv-timeformat-changeradded .venv to gitignoreRemoved cout'sFixed writing to csv; issue was caused by out of bounds array in dilation logic.Testing OR instead of XOR for change detection.Fully functional Jaccard index/similary score complete. Will now look for ground truth file at end of computation, with name groundTruth[videoFile].csv, and calculate score. Note that the trimming functionality is commented out for faster testing of model.watCleaned up some code and csv reading logic. Should work as standalone.added a groundTruth fileadded .venv to gitignoreAdding CMakeLitst.txt to gititnoreadded CMakeLists.txt to gitignoreCasted output to csv to int.Removed comment, reworded error.Cleaned up main a little.Connected the Python parts that retrieve the generated .csv and trim the video based on the timestamps. Haven't been able to test, but it should work as a complete MVP. Have yet to add the optional success rate calculation functionality if there is a ground truth to compare to.CSV file get's named after video input fileMerge branch 'addCuttingVideo' of https://git.gvk.idi.ntnu.no/LeoDLG/nina-thesis into addCuttingVideo