![]() ![]() Image_name = "./data/test_image.jpg" # call function to run pipeline and generate_topography_maps # expects image to be in. base_dir # base directory skip_angles =, ] # get details for current test image base_dir = args. ![]() working_distance, sensor_dims,į_len, args. split()) # focal length of the camera # create the corneal_top_gen class object corneal_top_obj = corneal_top_gen(Īrgs. poly1d() # 5 mm gap2, mire_21, id_20 sensor_dims = (įloat( args. Example Code Snippet:įrom main import * # assuming args has camera-parameters, base_dir, start/end angle, jump, model-file, working_distance, number of mires, upsample flag # getting parameters for corneal_top_obj f_inv_20_5 = np. The final corneal topography is estimated by running the Arc-Step method followed by zernike polynomial based smoothening. The next step is to get the mire-placido mapping, this is followed by estimation of placido ring location and working distance. The analysis pipeline first performs pre-processing ( image cropping, mire segmentation, and center detection) on the input image. ![]() The image analysis pipeline takes as input the image collected via our setup (3D attachment and smartphone), and outputs the corneal topography heatmaps along with quantitative values. Please use the module at location: /SmartKC/data/zernike (I yet am to make a pull request to the original repository). Note: The zernike polynomial smoothening function has been modified from the version above. It is recommended to use a python virtual environment to setup the dependencies and the code. Any reasonably recent version of these packages should work. These are all easily installable via, e.g., pip install numpy. * Pytorch (torch, torchvision and other dependencies for Pytorch) ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |