{"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"sourceId":87230,"databundleVersionId":9911932,"sourceType":"competition"}],"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.13"},"papermill":{"default_parameters":{},"duration":30.341309,"end_time":"2024-06-20T12:03:10.276203","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-06-20T12:02:39.934894","version":"2.5.0"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"Open evaluation data\n\nimport os\n# Open the folders and see the first 30 entries for Ground Truth and B-Scans\n\nprint(\"=\" * 40)\nprint('The first 30 entries of the Evaluation Dataset')\nprint(\"=\" * 40)\nos.chdir('/kaggle/input/gpr-max-deep-learning-challenge-gdlc/Evaluation_Dataset')\nos.system('ls -d $(ls | head -30)')","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2024-06-20T12:02:42.689917Z","iopub.status.busy":"2024-06-20T12:02:42.688981Z","iopub.status.idle":"2024-06-20T12:02:42.738289Z","shell.execute_reply":"2024-06-20T12:02:42.737247Z"},"papermill":{"duration":0.055464,"end_time":"2024-06-20T12:02:42.740681","exception":false,"start_time":"2024-06-20T12:02:42.685217","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from matplotlib import pyplot as plt\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nimport numpy as np\nimport cv2\n\nos.chdir('/kaggle/input/gpr-max-deep-learning-challenge-gdlc/Evaluation_Dataset')\n\n# Plot with units and labels\nfor i in range(0,100):\n    bscan = np.load('./Testing_Bscan_{}.npy'.format(i))\n    plt.imshow(bscan, extent=[0,50, 700, 0],aspect = 0.035,cmap='bone')\n    plt.title('Processed Bscan')\n    plt.xlabel('X-axis (meters)')\n    plt.ylabel('Time (ns)')\n    plt.show()\n    \n","metadata":{"execution":{"iopub.execute_input":"2024-06-20T12:02:42.746403Z","iopub.status.busy":"2024-06-20T12:02:42.746080Z","iopub.status.idle":"2024-06-20T12:03:09.749202Z","shell.execute_reply":"2024-06-20T12:03:09.748080Z"},"papermill":{"duration":27.008753,"end_time":"2024-06-20T12:03:09.751529","exception":false,"start_time":"2024-06-20T12:02:42.742776","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]}]}