Run the license manager with “lmgrd.exe -z -c lic.dat” from “C:\Autodesk\Network License Manager” folder Save lic.dat where you want (we suggest to “C:\Autodesk\Network License Manager” folder)ġ)Start Network License Manager manually : You can change vendor port too and add your previous licenses of course □ System Settings, your HOSTNAME and MAC will be listed) ![]() ![]() Conda create -name keyNet_environment tensorflow-gpu=1.13.1Ĭonda install -c conda-forge scikit-imageĬonda install pytorch=1.2.0 -c pytorch Feature ExtractionĮxtract_multiscale_features.py can be used to extract Key.Net features for a given list of images.To be safe, reboot machine or kill all adesk license instances if you already ran some Autodesk appsĤ) Edit lic.dat and change HOSTNAME and MAC to suit your configuration (if you are not sure start LMTOOLS Utility and go to The list of images must contain the full path to them, if they do not exist, an error will raise. The script generates two numpy files, one '.kpt' for keypoints, and a '.dsc' for descriptors. Results-dir: The output path to save the extracted features.list-images: File containing the image paths for extracting features.keypoints array containing the positions of keypoints x, y, scales s and their scores sc.The output format of the keypoints is as follow: The descriptor used together with Key.Net is HardNet. extract-MS: Set to True if you want to extract multi-scale features.num-points: The number of desired features to extract.checkpoint-desc-dir: The path to the checkpoint file to load the HardNet descriptor weights.checkpoint-det-dir: The path to the checkpoint file to load the detector weights. Pixel-threshold: The distance of pixels for a matching correspondence to be considered correct.top-k-points: The number of top points to use for evaluation.split: The name of the HPatches (HSequences) split.results-dir: The output path to load the extracted features.results-bench-dir: The output path to save the results in a pickle file.Python hsequeces_bench.py -results-dir extracted_features -results-bench-dir HSequences_bench/results -split full Run the following script to generate the keypoint and descriptor numpy files from the image allocated in test_im directory.
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