`iDSSKJr SSKrSSKrSSKrSrSrSrSSjr g))ImageNc$[[S.nX$)z Set up the data preprocess function name and function dict. parameter preprocess_func_name: name of the preprocess function return: function pointer preprocess_method1preprocess_method2r)preprocess_func_namefuncdicts m/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/onnxruntime_tools/quantization/data_preprocess.pyset_preprocessr s '9PbcH  ))c\[R"SX!45nUR[R"U5R X!455 [ R "U5S- S- nU[ R"U5-n[ R"USS9nURSSSS5nU$) aC Resizes image to NCHW format. Image is scaled to range [-1, 1]. This method is suitable for the mobilenet model from mlperf inference git repo. parameter image_filepath: path to image files parameter height: image height in pixels parameter width: image width in pixels return: matrix characterizing image RGBg_@g?raxis) rnewpasteopenresizenpfloat32mean expand_dims transposeimage_filepathheightwidth pillow_img input_data nhwc_data nchw_datas r rrs55/2JUZZ/66GHJ'%/#5J"''*%%Jz2I##Aq!Q/I r cj[R"SX!45nUR[R"U5R X!455 [ R "U5[ R"/SQ[ R S9- n[ R"USS9nURSSSS5nU$) a1 Resizes and normalizes image to NCHW format. This method is suitable for the resnet50 model from mlperf inference git repo. parameter image_filepath: path to image files parameter height: image height in pixels parameter width: image width in pixels return: matrix characterizing image r)gQ^@gR1]@g\(Y@)dtyperrrrr) rrrrrrrarrayrrrs r rr*s55/2JUZZ/66GHJ' )<=Jz2I##Aq!Q/I r cf[R"U5nUS:a-[U5U:a[U5Vs/sHoeUPM nnOUn/n[ U5n UH%n US-U -n U "XU5n UR U 5 M' [ R"[ R"USS9SS9n U $s snf)ax Loads a batch of images parameter images_folder: path to folder storing images parameter height: image height in pixels parameter width: image width in pixels parameter size_limit: number of images to load. Default is 0 which means all images are picked. parameter preprocess_func_name: name of the preprocess function return: list of matrices characterizing multiple images r/r) oslistdirlenranger appendr concatenater) images_folderrr r size_limit image_namesibatch_filenamesunconcatenated_batch_datapreprocess_func image_namerr$ batch_datas r load_batchr9<s**]+KA~#k*j8383DE3Daq>3DE% "$%9:O% &,z9#NEB !((3&/Hq QXYZJ FsB.)r) PILrr*sysnumpyrr rrr9r r r>s' *$$r