Bin to png

Image Processing (screen_1.png) ________0_ Ansi based on Image Processing (screen_1.png) ________0_?l__l______q____?__ Ansi based on Image Processing (screen_0.png) __acrt_iob_func Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __C_specific_handler Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __chk_fail Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __CxxFrameHandler3 Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __i,,?_a_,i',0 Ansi based on Image Processing (screen_0.png) __p___argc Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __p___wargv Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __p__commode Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __setusermatherr Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __stack_chk_fail Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __std_exception_copy Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __std_exception_destroy Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __std_terminate Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __stdio_common_vfprintf Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __stdio_common_vsprintf Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __stdio_common_vsprintf_s Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __stdio_common_vsscanf Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) __vcrt_InitializeCriticalSectionEx Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _BAD_KEYH Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _callnewh Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _configthreadlocale Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _configure_wide_argv Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _crt_atexit Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _CxxThrowException Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _DS_BUSYH Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _fseeki64 Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _get_initial_wide_environment Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _get_stream_buffer_pointers Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _initialize_onexit_table Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _initialize_wide_environment Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _initterm Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _initterm_e Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _invalid_parameter_noinfo_noreturn Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _localtime64_s Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _lock_file Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _pointerH Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _register_onexit_function Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _register_thread_local_exe_atexit_callback Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _SEGMENTH Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _seh_filter_exe Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _set_app_type Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _set_fmode Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _set_new_mode Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _STORAGEH Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _UNKNOWNH Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) _unlock_file Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) `[_^A\A]A^A_ Ansi based on Memory/File Scan (25ee9e85b38fbf4a0da471ad7cf876af62c566f445cccc98fad87fba05a68d25.bin) AL_INSTRH Ansi based on

Must npm install guetzli --save, this library does not work properly on some OS and platforms.For jpegRecompress - ['--quality', 'high', '--min', '60'] in details jpegRecompress;For jpegoptim - ['--all-progressive', '-d']To use jpegoptim you must npm install jpegoptim-bin --save, this library does not work properly on some OS and platforms.from be a problems with installation and use on Win 7 x32 and maybe other OS:compress-images - issues/21Caution! if do not specify '-d' all images will be compressed in the source folder and will be replaced.For Windows x32 and x63 also, you can use Copy jpegoptim-32.exe and replace and rename in "node_modules\jpegoptim-bin\vendor\jpegoptim.exe"For tinify - ['copyright', 'creation', 'location'] In details tinify;key (type:string): Key used for engine tinify. In details; tinify; Example: 1. {jpg: {engine: 'mozjpeg', command: ['-quality', '60']}; 2. {jpg: {engine: 'tinify', key: "sefdfdcv335fxgfe3qw", command: ['copyright', 'creation', 'location']}}; 3. {jpg: {engine: 'tinify', key: "sefdfdcv335fxgfe3qw", command: false}};enginepng (type:plainObject): Engine for compressing png and options for compression. Key to be png;engine (type:string): Engine for compressing png. Possible values:pngquant,optipng, pngout, webp, pngcrush, tinify;command (type:boolean|array): Options for compression. Can be false or commands array.For pngquant - ['--quality=20-50', '-o'] If you want to compress in the same folder, as example: ['--quality=20-50', '--ext=.png', '--force']. To use this library you need to install it manually. It does not work properly on some OS (Win 7 x32 and maybe other). npm install pngquant-bin --saveQuality should be in format min-max where min and max are numbers in range 0-100. Can be problems with cyrillic filename issues/317In details:pngquant andpngquant-bin - wrapperFor optipng - To use this library you need to install it manually.It does not work properly on some OS (Win 7 x32 and maybe other). npm install --save optipng-bin in details optipng-bin - wrapperand optipng;For pngout - in details pngout;For webp - ['-q', '60'] in details webp;For pngcrush (It does not work properly on some OS) - ['-reduce', '-brute'] in details pngcrush;For tinify - ['copyright', 'creation', 'location'] in details tinify;key (type:string): Key used for engine tinify. In details; tinify; Example: 1. {png: {engine: 'webp', command: ['-q', '100']}; 2. {png: {engine: 'tinify', key: "sefdfdcv335fxgfe3qw", command: ['copyright', 'creation', 'location']}}; 3. {png: {engine: 'optipng', command: false}};enginesvg (type:plainObject): Engine for compressing svg and options for compression. Key to be svg;engine (type:string): Engine for compressing svg. Possible values:svgo;command (type:string): Options for compression. Can be false or commands type string.For svgo - '--multipass' in details svgo; Example: 1. {svg: {engine: 'svgo', command: '--multipass'}; 2. {svg: {engine: 'svgo', command: false}};enginegif (type:plainObject): Engine for compressing gif and options for compression. Key to be gif;engine (type:string): Engine for compressing gif. Possible values:gifsicle, giflossy, gif2webp;command (type:boolean|array): Options for compression. Can be false or commands type array.For gifsicle - To use this library you need to install it manually.It does not work properly on

Bin/evaluate_predicts.py \$(pwd)/configs/eval2_gpu.yaml \$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \$(pwd)/inference/random_thick_512 \$(pwd)/inference/random_thick_512_metrics.csv"># Download data from Places365-Standard: Train(105GB)/Test(19GB)/Val(2.1GB) from High-resolution images sectionwget Unpack train/test/val data and create .yaml config for itbash fetch_data/places_standard_train_prepare.shbash fetch_data/places_standard_test_val_prepare.sh# Sample images for test and viz at the end of epochbash fetch_data/places_standard_test_val_sample.shbash fetch_data/places_standard_test_val_gen_masks.sh# Run trainingpython3 bin/train.py -cn lama-fourier location=places_standard# To evaluate trained model and report metrics as in our paper# we need to sample previously unseen 30k images and generate masks for thembash fetch_data/places_standard_evaluation_prepare_data.sh# Infer model on thick/thin/medium masks in 256 and 512 and run evaluation # like this:python3 bin/predict.py \model.path=$(pwd)/experiments/__lama-fourier_/ \indir=$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \outdir=$(pwd)/inference/random_thick_512 model.checkpoint=last.ckptpython3 bin/evaluate_predicts.py \$(pwd)/configs/eval2_gpu.yaml \$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \$(pwd)/inference/random_thick_512 \$(pwd)/inference/random_thick_512_metrics.csvDocker: TODOCelebAOn the host machine:__lama-fourier-celeba_/ \indir=$(pwd)/celeba-hq-dataset/visual_test_256/random_thick_256/ \outdir=$(pwd)/inference/celeba_random_thick_256 model.checkpoint=last.ckpt"># Make shure you are in lama foldercd lamaexport TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)# Download CelebA-HQ dataset# Download data256x256.zip from unzip & split into train/test/visualization & create config for itbash fetch_data/celebahq_dataset_prepare.sh# generate masks for test and visual_test at the end of epochbash fetch_data/celebahq_gen_masks.sh# Run trainingpython3 bin/train.py -cn lama-fourier-celeba data.batch_size=10# Infer model on thick/thin/medium masks in 256 and run evaluation # like this:python3 bin/predict.py \model.path=$(pwd)/experiments/__lama-fourier-celeba_/ \indir=$(pwd)/celeba-hq-dataset/visual_test_256/random_thick_256/ \outdir=$(pwd)/inference/celeba_random_thick_256 model.checkpoint=last.ckptDocker: TODOPlaces ChallengeOn the host machine:# This script downloads multiple .tar files in parallel and unpacks them# Places365-Challenge: Train(476GB) from High-resolution images (to train Big-Lama) bash places_challenge_train_download.shTODO: prepareTODO: train TODO: evalDocker: TODOCreate your dataPlease check bash scripts for data preparation and mask generation from CelebaHQ section,if you stuck at one of the following steps.On the host machine:_512.yaml \ # thick, thin, mediummy_dataset/val_source/ \my_dataset/val/random__512.yaml \# thick, thin, medium--ext jpg# So the mask generator will: # 1. resize and crop val images and save them as .png# 2. generate masksls my_dataset/val/random_medium_512/image1_crop000_mask000.pngimage1_crop000.pngimage2_crop000_mask000.pngimage2_crop000.png...# Generate thick, thin, medium masks for visual_test folder:python3 bin/gen_mask_dataset.py \$(pwd)/configs/data_gen/random__512.yaml \ #thick, thin, mediummy_dataset/visual_test_source/ \my_dataset/visual_test/random__512/ \ #thick, thin, medium--ext jpgls my_dataset/visual_test/random_thick_512/image1_crop000_mask000.pngimage1_crop000.pngimage2_crop000_mask000.pngimage2_crop000.png...# Same process for eval_source image folder:python3 bin/gen_mask_dataset.py \$(pwd)/configs/data_gen/random__512.yaml \ #thick, thin, mediummy_dataset/eval_source/ \my_dataset/eval/random__512/ \ #thick, thin, medium--ext jpg# Generate location config file which locate these folders:touch my_dataset.yamlecho "data_root_dir: $(pwd)/my_dataset/" >> my_dataset.yamlecho "out_root_dir: $(pwd)/experiments/" >> my_dataset.yamlecho "tb_dir: $(pwd)/tb_logs/" >> my_dataset.yamlmv my_dataset.yaml ${PWD}/configs/training/location/# Check data config for consistency with my_dataset folder structure:$ cat ${PWD}/configs/training/data/abl-04-256-mh-dist...train: indir: ${location.data_root_dir}/train ...val: indir: ${location.data_root_dir}/val img_suffix: .pngvisual_test: indir: ${location.data_root_dir}/visual_test img_suffix: .png# Run trainingpython3 bin/train.py -cn lama-fourier location=my_dataset data.batch_size=10# Evaluation: LaMa training procedure picks best few models according to # scores on my_dataset/val/ # To evaluate one of your best models (i.e. at epoch=32) # on previously unseen my_dataset/eval do the following # for thin, thick and medium:# infer:python3 bin/predict.py \model.path=$(pwd)/experiments/__lama-fourier_/ \indir=$(pwd)/my_dataset/eval/random__512/ \outdir=$(pwd)/inference/my_dataset/random__512 \model.checkpoint=epoch32.ckpt# metrics calculation:python3 bin/evaluate_predicts.py \$(pwd)/configs/eval2_gpu.yaml \$(pwd)/my_dataset/eval/random__512/ \$(pwd)/inference/my_dataset/random__512 \$(pwd)/inference/my_dataset/random__512_metrics.csv"># Make shure you are in lama foldercd lamaexport TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)# You need to prepare following image folders:$ ls my_datasettrainval_source # 2000 or more imagesvisual_test_source # 100 or more imageseval_source # 2000 or more images# LaMa generates random masks for the train data on the flight,# but needs fixed masks for test and visual_test for consistency of evaluation.# Suppose, we want to evaluate and pick best models # on 512x512 val dataset with thick/thin/medium masks # And your images have .jpg extention:python3 bin/gen_mask_dataset.py \$(pwd)/configs/data_gen/random__512.yaml \ # thick, thin, mediummy_dataset/val_source/ \my_dataset/val/random__512.yaml \# thick, thin, medium--ext jpg# So the mask generator will: # 1.