{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This kernel imports code from codebase.py, a custom script I made.\n# It is available at: https://www.kaggle.com/samuelepino/codebase\n\nimport os\nimport sys\nimport json\nsys.path.append(\"../usr/lib/codebase/\")\nfrom codebase import ICPR, Segmenter, Explainer, Pipeline, VideoLoader","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Single frame","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"# Configuration\nN_VIDEOS = 5\nFRAMES_PER_VIDEO = 1\nSHAP_SAMPLES = [250, 500, 1000, 2000, 4000, 8000, 16000, 32000, 64000]\nN_SEGMENTS = 200\n\n# Initialization\nclassif = ICPR(frames_per_video=FRAMES_PER_VIDEO)\nseg = Segmenter(mode=\"color\", segmentsNumber=N_SEGMENTS)\nexpl = Explainer(classifier=classif, trackTime=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dfdc_vidlist = VideoLoader.loadFilenamesDFDC(videoCount=N_VIDEOS, \n                                   fakeClassValue=classif.FAKE_CLASS_VAL, realClassValue=classif.REAL_CLASS_VAL)\n\nfor shapSamples in SHAP_SAMPLES:\n    \n    print(f\"\\n=== SHAP SAMPLES : {shapSamples} ===\\n\")\n    p = Pipeline(classif, seg, expl, segmentationDim=\"3D\", explanationMode=\"frame\", nSegments=N_SEGMENTS, shapSamples=shapSamples,\n                displayIFigures=False)\n    \n    for (vidName, vidClass) in dfdc_vidlist:\n\n        print(\"Analyzing sequence\", vidName)\n\n        vidPath = os.path.join(VideoLoader.DFDC_trainVideoDir, vidName)\n        # Get image sequence from folder\n        imageSequence = classif.getFaceCroppedVideo(vidPath)\n\n        p.start(imageSequence, vidClass, vidName)\n\n    with open(f\"shap_values_sf_{shapSamples}.json\", 'w') as f:\n        f.write(json.dumps(p.getShapValuesCollection(), indent=2))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Multi-frame","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"# Configuration\nN_VIDEOS = 1\nFRAMES_PER_VIDEO = 10\nSHAP_SAMPLES = [250, 500, 1000, 2000, 4000, 8000, 16000]\nN_SEGMENTS = 200\n\n# Initialization\nclassif = ICPR(frames_per_video=FRAMES_PER_VIDEO)\nseg = Segmenter(mode=\"color\", segmentsNumber=N_SEGMENTS)\nexpl = Explainer(classifier=classif, trackTime=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dfdc_vidlist = VideoLoader.loadFilenamesDFDC(videoCount=N_VIDEOS, \n                                   fakeClassValue=classif.FAKE_CLASS_VAL, realClassValue=classif.REAL_CLASS_VAL)\n\nfor shapSamples in SHAP_SAMPLES:\n    \n    print(f\"\\n=== SHAP SAMPLES : {shapSamples} ===\\n\")\n    p = Pipeline(classif, seg, expl, segmentationDim=\"3D\", explanationMode=\"frame\", nSegments=N_SEGMENTS, shapSamples=shapSamples,\n                displayIFigures=False)\n    \n    for (vidName, vidClass) in dfdc_vidlist:\n\n        print(\"Analyzing sequence\", vidName)\n\n        vidPath = os.path.join(VideoLoader.DFDC_trainVideoDir, vidName)\n        # Get image sequence from folder\n        imageSequence = classif.getFaceCroppedVideo(vidPath)\n\n        p.start(imageSequence, vidClass, vidName)\n        \n    with open(f\"shap_values_mf_{shapSamples}.json\", 'w') as f:\n        f.write(json.dumps(p.getShapValuesCollection(), indent=2))","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}