├── .gitignore ├── .gitmodules ├── LICENSE ├── Makefile ├── Makefile.caffe ├── README.md ├── cut_movie.cpp ├── cut_movie.hpp ├── images ├── accuracy.png ├── prediction_weights.jpg └── train_loss.png ├── miles-deep.cpp ├── model ├── deploy.prototxt ├── labels.txt ├── mean.binaryproto └── weights.caffemodel ├── util.cpp └── util.hpp /.gitignore: -------------------------------------------------------------------------------- 1 | *.o 2 | miles-deep 3 | .DS_Store 4 | .DS_Store? 5 | ._* 6 | .Spotlight-V100 7 | .Trashes 8 | ethumbs.db 9 | Thumbs.db 10 | -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "caffe"] 2 | path = caffe 3 | url = https://github.com/BVLC/caffe 4 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | 2 | #Created by Ryan Jay 30.10.16 3 | #Covered by the GPL. v3 (see included LICENSE) 4 | 5 | 6 | CUDA := -I/usr/local/cuda/include -L/usr/local/cuda/lib64 -lcublas -lcudart -lcurand 7 | #CUDNN := -lcudnn -DUSE_CUDNN 8 | #CPU_ONLY := -DCPU_ONLY 9 | 10 | appname := miles-deep 11 | libcaffe := caffe/distribute/lib/libcaffe.a 12 | 13 | CXX := g++ 14 | CXXFLAGS := -std=c++11 -Wno-sign-compare -Wall -pthread -fPIC -DNDEBUG -O2 -DUSE_OPENCV 15 | INCLUDES := -I/usr/local/include -I. -I./caffe/distribute/include 16 | LDLIBS := -L/usr/lib -L/usr/local/lib -L/usr/lib/x86_64-linux-gnu 17 | LDFLAGS := -lm -lglog -lopencv_core -lopencv_highgui -lopencv_imgproc \ 18 | -lstdc++ -lhdf5 -lhdf5_hl -lopenblas 19 | CAFFE := -Wl,--whole-archive $(libcaffe) -Wl,--no-whole-archive 20 | 21 | S := /usr/lib/x86_64-linux-gnu 22 | 23 | #if you have trouble making, try removing any problematic static lib 24 | #and replacing it with -lsomelib in the LDFLAGS instead 25 | STATIC_LIBS := $S/libgflags.a $S/libboost_thread.a $S/libboost_system.a $S/libprotobuf.a 26 | 27 | srcfiles := *.cpp 28 | cores := $(shell grep -c ^processor /proc/cpuinfo) 29 | 30 | all: $(appname) 31 | 32 | $(appname): $(libcaffe) $(srcfiles) 33 | $(CXX) $(CXXFLAGS) -o $(appname) $(srcfiles) $(CAFFE) $(LDFLAGS) $(INCLUDES) \ 34 | $(CUDA) $(CUDNN) $(CPU_ONLY) $(LDLIBS) $(STATIC_LIBS) 35 | 36 | clean: 37 | rm -rf $(appname) 38 | 39 | superclean: 40 | rm -rf $(appname) 41 | make -C caffe clean 42 | 43 | $(libcaffe): 44 | cp Makefile.caffe caffe/Makefile.config 45 | make -C caffe clean 46 | make -C caffe all -j $(cores) 47 | make -C caffe distribute 48 | -------------------------------------------------------------------------------- /Makefile.caffe: -------------------------------------------------------------------------------- 1 | ## Refer to http://caffe.berkeleyvision.org/installation.html 2 | # Contributions simplifying and improving our build system are welcome! 3 | 4 | # cuDNN acceleration switch (uncomment to build with cuDNN). 5 | # USE_CUDNN := 1 6 | 7 | # CPU-only switch (uncomment to build without GPU support). 8 | # CPU_ONLY := 1 9 | 10 | # uncomment to disable IO dependencies and corresponding data layers 11 | # USE_OPENCV := 0 12 | USE_LEVELDB := 0 13 | USE_LMDB := 0 14 | 15 | # uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary) 16 | # You should not set this flag if you will be reading LMDBs with any 17 | # possibility of simultaneous read and write 18 | # ALLOW_LMDB_NOLOCK := 1 19 | 20 | # Uncomment if you're using OpenCV 3 21 | # OPENCV_VERSION := 3 22 | 23 | # To customize your choice of compiler, uncomment and set the following. 24 | # N.B. the default for Linux is g++ and the default for OSX is clang++ 25 | # CUSTOM_CXX := g++ 26 | 27 | # CUDA directory contains bin/ and lib/ directories that we need. 28 | CUDA_DIR := /usr/local/cuda 29 | # On Ubuntu 14.04, if cuda tools are installed via 30 | # "sudo apt-get install nvidia-cuda-toolkit" then use this instead: 31 | # CUDA_DIR := /usr 32 | 33 | # CUDA architecture setting: going with all of them. 34 | # For CUDA < 6.0, comment the *_50 lines for compatibility. 35 | CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \ 36 | -gencode arch=compute_35,code=sm_35 \ 37 | -gencode arch=compute_35,code=compute_35 \ 38 | -gencode arch=compute_50,code=sm_50 \ 39 | -gencode arch=compute_50,code=compute_50 \ 40 | -gencode arch=compute_52,code=sm_52 \ 41 | -gencode arch=compute_52,code=compute_52 \ 42 | -gencode arch=compute_62,code=sm_62 \ 43 | -gencode arch=compute_62,code=compute_62 44 | 45 | # BLAS choice: 46 | # atlas for ATLAS (default) 47 | # mkl for MKL 48 | # open for OpenBlas 49 | BLAS := open 50 | # Custom (MKL/ATLAS/OpenBLAS) include and lib directories. 51 | # Leave commented to accept the defaults for your choice of BLAS 52 | # (which should work)! 53 | # BLAS_INCLUDE := /path/to/your/blas 54 | # BLAS_LIB := /path/to/your/blas 55 | 56 | # Homebrew puts openblas in a directory that is not on the standard search path 57 | # BLAS_INCLUDE := $(shell brew --prefix openblas)/include 58 | # BLAS_LIB := $(shell brew --prefix openblas)/lib 59 | 60 | # This is required only if you will compile the matlab interface. 61 | # MATLAB directory should contain the mex binary in /bin. 62 | # MATLAB_DIR := /usr/local 63 | # MATLAB_DIR := /Applications/MATLAB_R2012b.app 64 | 65 | # NOTE: this is required only if you will compile the python interface. 66 | # We need to be able to find Python.h and numpy/arrayobject.h. 67 | PYTHON_INCLUDE := /usr/include/python2.7 \ 68 | /usr/lib/python2.7/dist-packages/numpy/core/include 69 | # Anaconda Python distribution is quite popular. Include path: 70 | # Verify anaconda location, sometimes it's in root. 71 | # ANACONDA_HOME := $(HOME)/anaconda 72 | # PYTHON_INCLUDE := $(ANACONDA_HOME)/include \ 73 | # $(ANACONDA_HOME)/include/python2.7 \ 74 | # $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \ 75 | 76 | # Uncomment to use Python 3 (default is Python 2) 77 | # PYTHON_LIBRARIES := boost_python3 python3.5m 78 | # PYTHON_INCLUDE := /usr/include/python3.5m \ 79 | # /usr/lib/python3.5/dist-packages/numpy/core/include 80 | 81 | # We need to be able to find libpythonX.X.so or .dylib. 82 | PYTHON_LIB := /usr/lib/x86_64-linux-gnu 83 | # PYTHON_LIB := $(ANACONDA_HOME)/lib 84 | 85 | # Homebrew installs numpy in a non standard path (keg only) 86 | # PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include 87 | # PYTHON_LIB += $(shell brew --prefix numpy)/lib 88 | 89 | # Uncomment to support layers written in Python (will link against Python libs) 90 | # WITH_PYTHON_LAYER := 1 91 | 92 | # Whatever else you find you need goes here 93 | INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial 94 | LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib 95 | 96 | # If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies 97 | # INCLUDE_DIRS += $(shell brew --prefix)/include 98 | # LIBRARY_DIRS += $(shell brew --prefix)/lib 99 | 100 | # Uncomment to use `pkg-config` to specify OpenCV library paths. 101 | # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.) 102 | # USE_PKG_CONFIG := 1 103 | 104 | # N.B. both build and distribute dirs are cleared on `make clean` 105 | BUILD_DIR := build 106 | DISTRIBUTE_DIR := distribute 107 | 108 | # Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 109 | # DEBUG := 1 110 | 111 | # The ID of the GPU that 'make runtest' will use to run unit tests. 112 | TEST_GPUID := 0 113 | 114 | # enable pretty build (comment to see full commands) 115 | Q ?= @ 116 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | #Miles Deep - AI Porn Video Editor 3 | 4 | Using a deep convolutional neural network with residual connections, Miles Deep quickly classifies each second of a pornographic video into 6 categories based on sexual act with 95% accuracy. Then it uses that classification to automatically edit the video. It can remove all the scenes not containing sexual contact, or edit out just a specific act. 5 | 6 | Unlike Yahoo's recently released [NSFW model](https://github.com/yahoo/open_nsfw), which uses a similar architecture, Miles Deep can tell the difference between nudity and various explicit sexual acts. As far as I know this is the first and only public pornography classification or editing tool. 7 | 8 | This program can also be viewed as a general framework for classifying video with a [Caffe](http://caffe.berkeleyvision.org/) model, using batching and threading in C++. By replacing the weights, model definition, and mean file it can immediately be used to edit videos with other classes without recompiling. See below for an [example](https://github.com/ryanjay0/miles-deep#using-miles-deep-with-your-own-caffe-model). 9 | 10 | ##Installation 11 | 12 | ###Ubuntu Installation (16.04) 13 | 14 | ####Dependencies 15 | 16 | `sudo apt install ffmpeg libopenblas-base libhdf5-serial-dev libgoogle-glog-dev libopencv-dev` 17 | 18 | #####Additional 14.04 Dependencies 19 | 20 | `sudo apt install libgflags-dev` 21 | 22 | #####CUDA (Recommended) 23 | For GPU usage you need an Nvidia GPU and CUDA 8.0 drivers. Highly recommended; increases speed 10x. This can be installed from a package or by downloading from [NVIDIA directly](https://developer.nvidia.com/cuda-downloads). 24 | 25 | 26 | #####CuDNN (Optional) 27 | 28 | Additional drivers from NVIDIA that make the CUDA GPU support even faster. 29 | [Download here](https://developer.nvidia.com/cudnn). (Requires registration) 30 | 31 | 32 | 33 | ####Download Miles Deep 34 | 35 | * [miles-deep (GPU + CuDNN)](https://github.com/ryanjay0/miles-deep/releases/download/v0.4/miles-deep-xxx.v0.4.tgz) 36 | * [miles-deep (GPU)](https://github.com/ryanjay0/miles-deep/releases/download/v0.4/miles-deep-gpu.v0.4.tgz) 37 | * [miles-deep (CPU)](https://github.com/ryanjay0/miles-deep/releases/download/v0.4/miles-deep-cpu.v0.4.tgz) 38 | 39 | Download the [model](https://github.com/ryanjay0/miles-deep/files/587616/model.v0.1.tar.gz) too. Put miles-deep in the same location as the model folder (not in it). 40 | 41 | 42 | Version | Runtime 43 | :---:|---: 44 | GPU + CuDNN | 15s 45 | GPU | 19s 46 | CPU | 1m 59s 47 | *on a 24.5 minute video with a GTX 960 4GB 48 | ###Windows and OSX 49 | I'm working on a version for Windows. Sorry, I don't have a Mac but it should run on OSX with few changes. [Compilations instructions](https://github.com/ryanjay0/miles-deep#compiling) below. I'll accept pull requests related to OSX or other linux compatibility. Windows will likely require anothe repository to link with Caffe for windows. 50 | 51 | ##Usage 52 | 53 | Example: 54 | ```bash 55 | miles-deep -t sex_back,sex_front movie.mp4 56 | ``` 57 | 58 | This finds the scenes sex from the back or front and outputs the result 59 | in `movie.cut.avi` 60 | 61 | Example: 62 | ```bash 63 | miles-deep -x movie.avi 64 | ``` 65 | 66 | This edits out all the non-sexual scenes from `movie.avi` 67 | and outputs the result in `movie.cut.avi`. 68 | 69 | Example: 70 | ```bash 71 | miles-deep -b 16 -t cunnilingus -o /cut_movies movie.mkv 72 | ``` 73 | 74 | This reduces the batch size to 16 (default 32). 75 | Finds only scenes with cunnilingus, 76 | outputs result in `/cut_movies/movie.cut.mkv`. 77 | 78 | **NOTE: Reduce the batch size if you run out of memory** 79 | 80 | 81 | ####GPU VRAM used and runtime for various batch sizes: 82 | 83 | VRAM(GB) | batch\_size | run time 84 | ---: | ---: | ---: 85 | 3.5 | 32 | 14.9s 86 | 1.9 | 16 | 15.7s 87 | 1.2 | 8 | 16.9s 88 | 0.8 | 4 | 19.5s 89 | 0.6 | 2 | 24.3s 90 | 0.1 | 1 | 36.2s 91 | 92 | Tested on an Nvidia GTX 960 with 4GB VRAM and a 24.5 minute video file. At batch\_size 32 it took approximately 0.6 seconds to process 1 minute of input video or about 36 seconds per hour. 93 | 94 | In addition to batching, Miles Deep also uses threading, which allows the screenshots to be captured and processed while they are classified. 95 | 96 | ###Auto-Tagging Without Cutting 97 | 98 | Example: 99 | ```bash 100 | miles-deep movie.mp4 -a 101 | ``` 102 | 103 | By popular demand I added this option, which outputs `movie.tag`: 104 | 105 | ``` 106 | movie_name, label_1, ..., label_n 107 | total_time, label_1_time, ..., label_n_time 108 | label, start, end, score, coverage 109 | . 110 | . 111 | . 112 | 113 | ``` 114 | 115 | The file contains the cuts for each target, ordered as they occur in the movie. The first lines gives the movie name, the labels, the total movie time, and the total seconds for each label. Then for each cut it list the start time, end time, average score, and coverage. Because of the threshold and the gaps, these cuts may overlap and aren't guaranteed to cover every second. 116 | 117 | ###Prediction Weights 118 | Here is an example of the predictions for each second of a video: 119 | 120 | ![predictions for each second of a video](images/prediction_weights.jpg?raw=true) 121 | 122 | 123 | ###Using Miles Deep with your own Caffe model 124 | ####Cat finding 125 | 126 | Here's an example of how to use the program with your own model (or a pre-trained one): 127 | 128 | 129 | ```bash 130 | MODEL_PATH=/models/bvlc_reference_caffenet/ 131 | 132 | miles-deep -t n02123045 \ 133 | -p caffe/${MODEL_PATH}/deploy.prototxt \ 134 | -m caffe/data/ilsvrc12/imagenet_mean.binaryproto \ 135 | -w caffe/${MODEL_PATH/bvlc_reference_caffenet.caffemodel \ 136 | -l caffe/data/ilsvrc12/synsets.txt \ 137 | movie.mp4 138 | ``` 139 | This finds the scenes in `movie.mp4` with a tabby cat and returns `movie.cut.mp4` with only those parts. n02123045 is the category for tabby cats. You can find the category names in `caffe/data/ilsvrc12/synset_words.txt`. You can use a pre-trained model from the [model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo) instead. 140 | 141 | *Note: This example is just to show the syntax. It performs somewhat poorly in my experience, likely due to the 1000 classes. This program is ideally suited to models with a smaller number of categories with an 'other' category too.* 142 | 143 | 144 | ##Model 145 | 146 | The model is a CNN with [residual connections](https://arxiv.org/abs/1512.03385) created by [pynetbuilder](https://github.com/jay-mahadeokar/pynetbuilder/tree/master/models/imagenet). These models are pre-trained on ImageNet. Then the final layer is changed to fit the new number of classes and [fine-tuned](http://caffe.berkeleyvision.org/gathered/examples/finetune_flickr_style.html). 147 | 148 | As [Karpathy et al](http://cs.stanford.edu/people/karpathy/deepvideo/) suggest, I train the weights for the top-3 layers not just the top-layer, which improves the accuracy slightly: 149 | 150 | RetunedLayers | Accuracy 151 | ---|--- 152 | Top3 | 94.6 153 | Top1 | 93.9 154 | 155 | 156 | Below are the results for fine-tuning the top3 layers with different models, 157 | tested on 2500 images, taken from different videos than the training set. 158 | 159 | Model | Accuracy(%) | Flops(millions) | Params(millions) 160 | --- | ---: | ---: | ---: 161 | resnet50 | 80.0 | 3858 | 25.5 162 | resnet50\_1by2 | 94.6 | 1070 | 6.9 163 | resnet77\_1by2 | 95.7 | 1561 | 9.4 164 | 165 | The training loss and test accuracy: 166 | 167 | ![fine-tuning training loss](images/train_loss.png?raw=true) 168 | ![test accuracy vs step](images/accuracy.png?raw=true) 169 | 170 | Of all the models tested, the resnet50\_1by2 provides the best balance between 171 | runtime, memory, and accuracy. I believe the full resnet50's low accuracy is due 172 | to overfitting because it has more parameters, or perhaps the training could 173 | be done differently. 174 | 175 | The results above were obtained with mirroring but not cropping. Using cropping slightly improves the results on the resnet50\_1by2 to **95.2%**, therefore it is used as the final model. 176 | 177 | [Fine-tuning](https://www.tensorflow.org/versions/r0.9/how_tos/image_retraining/index.html) the Inception V3 model with Tensorflow also only achieved 80% accuracy. However, that is with a 299x299 image size instead of 224x224 with no mirroring or cropping, so the result is not directly comparable. Overfitting may be a problem with this model too. 178 | 179 | ###Editing the Movie 180 | 181 | Given the predictions for a frame each second, it takes the argmax of those predictions and creates cut blocks of the movie where argmax equals the target and the score is greater than some threshold. The gap size, the minimum fraction of frames matching the target in each block, and the score threshold are all adjustable. 182 | 183 | FFmpeg supports a lot of codecs including: mp4, avi, flv, mkv, wmv, and many more. 184 | 185 | ###Single Frame vs Multiple Frames 186 | 187 | This model doesn't make use of any temporal information since it treats each image separately. *Karpathy et al* showed that other models which use multiple frames don't perform much better. They have difficulty dealing with camera movement. It would still be interesting to compare their slow fusion model with the results here. 188 | 189 | ##Data 190 | 191 | The training database consists of 36,000 (and 2500 test images) images divided into 6 categories: 192 | 193 | 0. blowjob\_handjob 194 | 1. cunnilingus 195 | 2. other 196 | 3. sex\_back 197 | 4. sex\_front 198 | 6. titfuck 199 | 200 | The images are resized to 256x256 with horizontal mirroring and random cropping 201 | to 224x224 for data augmentation. A lot of the experiments were done without cropping but it slightly improves the results for the resnet50\_1by2. 202 | 203 | For now the dataset is limited to two heterosexual performers. But given the success of this method, I plan to expand the number of categories. Due to the nature of the material, I will not be releasing the database itself; only the trained model. 204 | 205 | ####Sex back and front 206 | 207 | Sex front and back are defined by the position of the camera, instead of the orientation of the performers. If the female's body is facing the camera so the front of the vagina is shown, it's sex front. If the female's rear is shown instead, it's sex back. This creates two visually distinct classes. No distinction is made between vaginal and anal intercourse; sex back or sex front could include either. 208 | 209 | ##Compiling 210 | 211 | * Clone the git repository which includes Caffe as an external dependency. 212 | 213 | * Follow the step-by-step [instructions](http://caffe.berkeleyvision.org/installation.html) to install the Caffe dependencies for your plaform. [Ubuntu instructions](http://caffe.berkeleyvision.org/install_apt.html). The default is OpenBlas. Don't worry about editing the Makefile.config or making Caffe. On Ubuntu 16.04 try this in addition to the dependencies at the top: 214 | 215 | ```bash 216 | sudo apt install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler 217 | sudo apt install --no-install-recommends libboost-all-dev 218 | sudo apt install libopenblas-dev python-numpy 219 | 220 | #Add symbolic links for hdf5 library 221 | #(not necessary on LinuxMint 18) 222 | 223 | cd /usr/lib/x86_64-linux-gnu 224 | sudo ln -s libhdf5_serial.so libhdf5.so 225 | sudo ln -s libhdf5_serial_hl.so libhdf5_hl.so 226 | 227 | ``` 228 | 229 | * The default is GPU without CuDNN. If you want something else edit `Makefile` and `Makefile.caffe`. Comment out or uncomment the proper lines in both files. 230 | 231 | * `make` 232 | 233 | #####License 234 | Code licensed under GPLv3, including the trained model. Caffe is licensed under BSD 2. 235 | 236 | ####Contact 237 | 238 | If you have problems, suggestions, or thoughts open an issue or send me email nipplerdeeplearning at gmail. 239 | 240 | -------------------------------------------------------------------------------- /cut_movie.cpp: -------------------------------------------------------------------------------- 1 | /* 2 | * Created by Ryan Jay 30.10.16 3 | * Covered by the GPL. v3 (see included LICENSE) 4 | */ 5 | 6 | #include 7 | #include 8 | #include 9 | #include 10 | #include 11 | #include 12 | #include 13 | #include 14 | 15 | #include "cut_movie.hpp" 16 | #include "util.hpp" 17 | 18 | using namespace std; 19 | 20 | //find the cuts using the winners and their values (returns the cut_list) 21 | int findTheCuts(int score_list_size, const vector& winners,const vector& vals, 22 | const vector& target_on, string target, int min_cut, int max_gap, float threshold, float min_coverage, 23 | CutList* cut_list) 24 | { 25 | 26 | int cut_start = -1; 27 | int gap = 0; 28 | int win_sum = 0; 29 | float val_sum = 0.0; 30 | int total_size = 0; 31 | 32 | for( int i=0; i= 0 ) 35 | { 36 | if( target_on[winners[i]] ) 37 | { 38 | win_sum++; 39 | val_sum += vals[i]; 40 | } 41 | 42 | if( !target_on[winners[i]] || vals[i] < threshold || i == score_list_size-1 ) 43 | { 44 | if(i < score_list_size-1) gap++; 45 | 46 | if( gap > max_gap || i == score_list_size-1 ) 47 | { 48 | if( cut_start < i - gap - min_cut ) 49 | { 50 | //output cut 51 | int win_size = (i - gap) - cut_start + 1; 52 | float score_avg = val_sum / (float)win_sum; 53 | float coverage = (float)win_sum / (float)win_size; 54 | 55 | if(coverage >= min_coverage) 56 | { 57 | cout << PrettyTime(cut_start) << " - " << PrettyTime(i - gap) 58 | << ": size= " << PrettyTime(win_size) << " coverage= " << coverage 59 | << " score= " << score_avg << '\n'; 60 | total_size += win_size; 61 | Cut cut; 62 | cut.s = cut_start; 63 | cut.e = i-gap; 64 | cut.score = score_avg; 65 | cut.coverage = coverage; 66 | cut.label = target; 67 | cut_list->push_back(cut); 68 | } 69 | } 70 | cut_start = -1; 71 | gap = 0; 72 | val_sum = 0.0; 73 | win_sum = 0; 74 | } 75 | } 76 | else 77 | { 78 | gap = 0; 79 | } 80 | 81 | } 82 | else if( target_on[winners[i]] && vals[i] >= threshold ) 83 | { 84 | cut_start = i; 85 | gap = 0; 86 | val_sum = 0.0; 87 | win_sum = 0.0; 88 | } 89 | } 90 | 91 | return(total_size); 92 | 93 | } 94 | 95 | 96 | void TagTargets( ScoreList score_list, string movie_file, string output_dir, 97 | vector labels, int total_targets, int min_cut, int max_gap, 98 | float threshold, float min_coverage) 99 | { 100 | //path stuff with movie file 101 | char sep = '/'; 102 | #ifdef _WIN32 103 | char sep = '\\'; 104 | #endif 105 | string movie_base = getBaseName(getFileName(movie_file)); 106 | string movie_directory = getDirectory(movie_file); 107 | string tag_movie = movie_base + ".tag"; 108 | if(output_dir == "") 109 | output_dir = movie_directory; 110 | string tag_path = output_dir + sep + tag_movie; 111 | 112 | //find winners and their scores 113 | vector winners(score_list.size()); 114 | vector vals(score_list.size()); 115 | for( int i=0; i < score_list.size(); i++ ) 116 | { 117 | winners[i] = scoreArgMax(score_list[i]); 118 | vals[i] = scoreMax(score_list[i]); 119 | } 120 | 121 | //open tag output file 122 | ofstream f(tag_path); 123 | if(!f) 124 | { 125 | cerr << "Cannot open file: " << tag_path << endl; 126 | exit(EXIT_FAILURE); 127 | } 128 | 129 | //write header 130 | f << getFileName(movie_file) << ","; 131 | for(int i=0; i < labels.size(); i++) 132 | f << labels[i] << ","; 133 | f << endl; 134 | 135 | //find the predicted cuts for each target 136 | vector target_time(total_targets,0); 137 | CutList cut_list; 138 | for(int i=0; i < total_targets; i++) 139 | { 140 | 141 | vector target_on(total_targets,0); 142 | target_on[i] = 1; 143 | 144 | cout << "Target [" << labels[i] << "]" << endl; 145 | 146 | target_time[i] = findTheCuts(score_list.size(), winners, vals, target_on, labels[i], min_cut, 147 | max_gap, threshold, min_coverage, &cut_list); 148 | 149 | 150 | cout << "Total cut length: " << PrettyTime(target_time[i]) << endl; 151 | cout << endl; 152 | 153 | } 154 | 155 | //write target total information 156 | f << score_list.size() << ","; 157 | for(int i=0; i< total_targets; i++) 158 | f << target_time[i] << ","; 159 | f << endl; 160 | 161 | //sort the list based on cut start time 162 | sort(cut_list.begin(),cut_list.end(), [](const Cut &x, const Cut &y){ return (x.s < y.s);}); 163 | 164 | 165 | //write cutlist to tag file 166 | f << "label,start,end,score,coverage" << endl; 167 | for( int j=0; j target_list, 181 | string output_dir, string temp_dir, int total_targets, int min_cut, int max_gap, 182 | float threshold, float min_coverage, bool do_concat, bool remove_original) 183 | { 184 | 185 | //path stuff with movie file 186 | char sep = '/'; 187 | #ifdef _WIN32 188 | char sep = '\\'; 189 | #endif 190 | string movie_base = getBaseName(getFileName(movie_file)); 191 | string movie_type = getFileExtension(movie_file); 192 | string cut_movie = movie_base + ".cut"; 193 | string temp_base = temp_dir + sep + "cuts"; 194 | string temp_path = temp_base + sep + cut_movie; 195 | string movie_directory = getDirectory(movie_file); 196 | 197 | //will come from input 198 | vector target_on(total_targets,0); 199 | for(int i=0; i winners(score_list.size()); 218 | vector vals(score_list.size()); 219 | 220 | for( int i=0; i < score_list.size(); i++ ) 221 | { 222 | winners[i] = scoreArgMax(score_list[i]); 223 | vals[i] = scoreMax(score_list[i]); 224 | } 225 | 226 | int total = findTheCuts(score_list.size(), winners, vals, target_on, 227 | "", min_cut, max_gap, threshold, min_coverage, &cut_list); 228 | cout << "Total cut length: " << PrettyTime(total) << endl; 229 | //make the cuts 230 | if( cut_list.size() > 0 ) 231 | { 232 | cout << "Making the cuts" << endl; 233 | string part_file_path = temp_dir + sep + "cuts.txt"; 234 | ofstream part_file; 235 | part_file.open(part_file_path.c_str()); 236 | if(!part_file.is_open()) 237 | { 238 | cout << "Cannot open file for writing: " << part_file_path << endl; 239 | exit(EXIT_FAILURE); 240 | } 241 | 242 | 243 | //use output_seek for wmv. fixed bug where cuts would freeze 244 | bool output_seek = false; 245 | if(movie_type == ".wmv" || movie_type == ".WMV" || movie_type == ".Wmv") 246 | { 247 | output_seek = true; 248 | movie_type = ".mkv"; 249 | } 250 | 251 | 252 | //output a file for each cut in the list 253 | for( int i=0; i 10 | #include 11 | #include 12 | #include 13 | 14 | using namespace std; 15 | 16 | typedef struct { 17 | int s; 18 | int e; 19 | float score; 20 | float coverage; 21 | string label; 22 | } Cut; 23 | 24 | typedef vector > ScoreList; 25 | typedef vector CutList; 26 | 27 | 28 | void CutMovie( ScoreList score_list, string movie_file, vector target_list, 29 | string output_dir="", string temp_dir="/tmp", int total_targets = 6, int min_cut=5, 30 | int max_gap=2, float threshold=0.5, float min_coverage=0.4, bool do_concat=true, 31 | bool remove_original = true); 32 | 33 | void TagTargets( ScoreList score_list, string movie_file, string output_dir, vector labels, 34 | int total_targets, int min_cut, int max_gap, float threshold, float min_coverage); 35 | 36 | string PrettyTime(int seconds); 37 | 38 | #endif 39 | -------------------------------------------------------------------------------- /images/accuracy.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ryanjay0/miles-deep/c3918cb3dcd65eefd6012ffdb08315abf196501a/images/accuracy.png -------------------------------------------------------------------------------- /images/prediction_weights.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ryanjay0/miles-deep/c3918cb3dcd65eefd6012ffdb08315abf196501a/images/prediction_weights.jpg -------------------------------------------------------------------------------- /images/train_loss.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ryanjay0/miles-deep/c3918cb3dcd65eefd6012ffdb08315abf196501a/images/train_loss.png -------------------------------------------------------------------------------- /miles-deep.cpp: -------------------------------------------------------------------------------- 1 | /* 2 | * Created by Ryan Jay 30.10.16 3 | * Covered by the GPL. v3 (see included LICENSE) 4 | */ 5 | 6 | #include 7 | #include 8 | #include 9 | #include 10 | #include 11 | #include 12 | #include 13 | #include 14 | #include 15 | #include 16 | #include 17 | #include 18 | #include 19 | #include 20 | #include 21 | #include 22 | #include "cut_movie.hpp" 23 | #include "util.hpp" 24 | 25 | 26 | using namespace caffe; // NOLINT(build/namespaces) 27 | using namespace std; 28 | using std::string; 29 | 30 | int global_ffmpeg_done = -1; 31 | 32 | 33 | class Classifier 34 | { 35 | public: 36 | Classifier(const string& model_file, 37 | const string& trained_file, 38 | const string& mean_file, 39 | const string& label_file); 40 | 41 | ScoreList Classify(const vector& imgs); 42 | 43 | std::vector labels_; 44 | 45 | private: 46 | void SetMean(const string& mean_file); 47 | 48 | std::vector > Predict(const vector& imgs); 49 | 50 | void WrapInputLayer(std::vector* input_channels, int n); 51 | 52 | void Preprocess(const cv::Mat& img, 53 | std::vector* input_channels); 54 | 55 | private: 56 | boost::shared_ptr > net_; 57 | cv::Size input_geometry_; 58 | int num_channels_; 59 | cv::Mat mean_; 60 | }; 61 | 62 | Classifier::Classifier(const string& model_file, 63 | const string& trained_file, 64 | const string& mean_file, 65 | const string& label_file) 66 | { 67 | #ifdef CPU_ONLY 68 | Caffe::set_mode(Caffe::CPU); 69 | #else 70 | Caffe::set_mode(Caffe::GPU); 71 | #endif 72 | 73 | 74 | /* Load the network. */ 75 | net_.reset(new Net(model_file, TEST)); 76 | net_->CopyTrainedLayersFrom(trained_file); 77 | 78 | CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input."; 79 | CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output."; 80 | 81 | Blob* input_layer = net_->input_blobs()[0]; 82 | num_channels_ = input_layer->channels(); 83 | CHECK(num_channels_ == 3 || num_channels_ == 1) 84 | << "Input layer should have 1 or 3 channels."; 85 | input_geometry_ = cv::Size(input_layer->width(), input_layer->height()); 86 | 87 | /* Load the binaryproto mean file. */ 88 | SetMean(mean_file); 89 | 90 | /* Load labels. */ 91 | std::ifstream labels(label_file.c_str()); 92 | CHECK(labels) << "Unable to open labels file " << label_file; 93 | string line; 94 | while (std::getline(labels, line)) 95 | labels_.push_back(string(line)); 96 | 97 | Blob* output_layer = net_->output_blobs()[0]; 98 | CHECK_EQ(labels_.size(), output_layer->channels()) 99 | << "Number of labels is different from the output layer dimension."; 100 | } 101 | 102 | 103 | //Utility Functions 104 | 105 | int IndexOf(string label, vector labels) 106 | { 107 | for( int k=0; k Split(const string &s, char delim) 204 | { 205 | stringstream ss(s); 206 | string item; 207 | vector tokens; 208 | while (getline(ss, item, delim)) 209 | { 210 | tokens.push_back(item); 211 | } 212 | return tokens; 213 | } 214 | 215 | vector allExceptOther(vector labels) 216 | { 217 | vector output; 218 | for(int i=0; i& imgs) 232 | { 233 | ScoreList outputs = Predict(imgs); 234 | return outputs; 235 | } 236 | 237 | /* Load the mean file in binaryproto format. */ 238 | void Classifier::SetMean(const string& mean_file) 239 | { 240 | BlobProto blob_proto; 241 | ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto); 242 | 243 | /* Convert from BlobProto to Blob */ 244 | Blob mean_blob; 245 | mean_blob.FromProto(blob_proto); 246 | CHECK_EQ(mean_blob.channels(), num_channels_) 247 | << "Number of channels of mean file doesn't match input layer."; 248 | 249 | /* The format of the mean file is planar 32-bit float BGR or grayscale. */ 250 | std::vector channels; 251 | float* data = mean_blob.mutable_cpu_data(); 252 | for (int i = 0; i < num_channels_; ++i) 253 | { 254 | /* Extract an individual channel. */ 255 | cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data); 256 | channels.push_back(channel); 257 | data += mean_blob.height() * mean_blob.width(); 258 | } 259 | 260 | /* Merge the separate channels into a single image. */ 261 | cv::Mat mean; 262 | cv::merge(channels, mean); 263 | 264 | /* Compute the global mean pixel value and create a mean image 265 | * filled with this value. */ 266 | cv::Scalar channel_mean = cv::mean(mean); 267 | mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean); 268 | } 269 | 270 | std::vector > Classifier::Predict(const vector& imgs) 271 | { 272 | Blob* input_layer = net_->input_blobs()[0]; 273 | input_layer->Reshape(imgs.size(), num_channels_, 274 | input_geometry_.height, input_geometry_.width); 275 | /* Forward dimension change to all layers. */ 276 | net_->Reshape(); 277 | 278 | for( int i=0; i < imgs.size(); ++i) 279 | { 280 | vector input_channels; 281 | WrapInputLayer(&input_channels,i); 282 | Preprocess(imgs[i], &input_channels); 283 | } 284 | 285 | net_->Forward(); 286 | 287 | vector > outputs; 288 | 289 | Blob* output_layer = net_->output_blobs()[0]; 290 | for( int i=0; i < output_layer->num(); ++i) 291 | { 292 | const float* begin = output_layer->cpu_data() + i * output_layer->channels(); 293 | const float* end = begin + output_layer->channels(); 294 | /* Copy the output layer to a std::vector */ 295 | outputs.push_back(vector(begin, end)); 296 | } 297 | return outputs; 298 | } 299 | 300 | /* Wrap the input layer of the network in separate cv::Mat objects 301 | * (one per channel). This way we save one memcpy operation and we 302 | * don't need to rely on cudaMemcpy2D. The last preprocessing 303 | * operation will write the separate channels directly to the input 304 | * layer. */ 305 | void Classifier::WrapInputLayer(std::vector* input_channels, int n) 306 | { 307 | Blob* input_layer = net_->input_blobs()[0]; 308 | 309 | int width = input_layer->width(); 310 | int height = input_layer->height(); 311 | int channels = input_layer->channels(); 312 | float* input_data = input_layer->mutable_cpu_data() + n * width * height * channels; 313 | for (int i = 0; i < channels; ++i) 314 | { 315 | cv::Mat channel(height, width, CV_32FC1, input_data); 316 | input_channels->push_back(channel); 317 | input_data += width * height; 318 | } 319 | } 320 | 321 | void Classifier::Preprocess(const cv::Mat& img, 322 | std::vector* input_channels) 323 | { 324 | /* Convert the input image to the input image format of the network. */ 325 | cv::Mat sample; 326 | if (img.channels() == 3 && num_channels_ == 1) 327 | cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY); 328 | else if (img.channels() == 4 && num_channels_ == 1) 329 | cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY); 330 | else if (img.channels() == 4 && num_channels_ == 3) 331 | cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR); 332 | else if (img.channels() == 1 && num_channels_ == 3) 333 | cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR); 334 | else 335 | sample = img; 336 | 337 | cv::Mat sample_resized; 338 | if (sample.size() != input_geometry_) 339 | cv::resize(sample, sample_resized, input_geometry_); 340 | else 341 | sample_resized = sample; 342 | 343 | cv::Mat sample_float; 344 | if (num_channels_ == 3) 345 | sample_resized.convertTo(sample_float, CV_32FC3); 346 | else 347 | sample_resized.convertTo(sample_float, CV_32FC1); 348 | 349 | cv::Mat sample_normalized; 350 | cv::subtract(sample_float, mean_, sample_normalized); 351 | 352 | /* This operation will write the separate BGR planes directly to the 353 | * input layer of the network because it is wrapped by the cv::Mat 354 | * objects in input_channels. */ 355 | cv::split(sample_normalized, *input_channels); 356 | 357 | } 358 | 359 | 360 | int main(int argc, char** argv) 361 | { 362 | 363 | int batch_size = 32; 364 | int MAX_IMG_IDX = 99999; 365 | int report_interval = 100; 366 | int sleep_time = 1; 367 | int min_cut = 4; 368 | int max_gap = 2; 369 | double min_score = 0.5; 370 | double min_coverage = 0.4; 371 | vector target_list; 372 | target_list.push_back("blowjob_handjob"); //the default target 373 | string movie_file; 374 | string screenshot_directory = "/tmp/screenshots/"; 375 | 376 | string model_dir = "model/"; 377 | string model_weights = model_dir + "weights.caffemodel"; 378 | string model_def = model_dir + "deploy.prototxt"; 379 | string mean_file = model_dir + "mean.binaryproto"; 380 | string label_file = model_dir + "labels.txt"; 381 | string output_directory = ""; 382 | string temp_directory = "/tmp"; 383 | bool auto_tag = false; 384 | bool do_concat = true; 385 | bool remove_original = true; 386 | 387 | 388 | 389 | 390 | 391 | //parse command line flags 392 | int opt; 393 | bool set_all_but_other = false; 394 | while ((opt = getopt(argc, argv, "act:b:d:o:m:ng:s:hxp:w:u:l:v:")) != -1) 395 | { 396 | switch (opt) { 397 | case 'a': 398 | auto_tag = true; 399 | break; 400 | case 'c': 401 | do_concat = false; 402 | break; 403 | case 't': 404 | target_list = Split(optarg,','); 405 | break; 406 | case 'b': 407 | batch_size = atoi(optarg); 408 | break; 409 | case 'd': 410 | temp_directory = optarg; 411 | break; 412 | case 'o': 413 | output_directory = optarg; 414 | break; 415 | case 'u': 416 | min_cut = atoi(optarg); 417 | break; 418 | case 'g': 419 | max_gap = atoi(optarg); 420 | break; 421 | case 's': 422 | min_score = atof(optarg); 423 | break; 424 | case 'v': 425 | min_coverage = atof(optarg); 426 | break; 427 | case 'm': 428 | mean_file = optarg; 429 | break; 430 | case 'p': 431 | model_def = optarg; 432 | break; 433 | case 'w': 434 | model_weights = optarg; 435 | break; 436 | case 'l': 437 | label_file = optarg; 438 | break; 439 | case 'n': 440 | remove_original = false; 441 | break; 442 | case 'h': 443 | PrintHelp(); 444 | exit(0); 445 | case 'x': 446 | set_all_but_other = true; 447 | break; 448 | default: /* '?' */ 449 | PrintUsage(argv[0]); 450 | exit(EXIT_FAILURE); 451 | } 452 | } 453 | 454 | if(optind >= argc) 455 | { 456 | cerr << "No input movie file." << endl; 457 | PrintUsage(argv[0]); 458 | exit(EXIT_FAILURE); 459 | } 460 | movie_file = argv[optind]; 461 | 462 | 463 | //keep Caffe quiet 464 | FLAGS_minloglevel = 3; 465 | ::google::InitGoogleLogging(argv[0]); 466 | 467 | //create the classifier 468 | Classifier classifier(model_def, model_weights, mean_file, label_file); 469 | 470 | if(set_all_but_other) 471 | target_list = allExceptOther(classifier.labels_); 472 | 473 | //print targets 474 | if(auto_tag) 475 | cout << "Auto-tag mode" << endl; 476 | else 477 | { 478 | cout << "Targets: ["; 479 | for(int i=0; i imgs; 502 | //fill a batch with screenshots to classify 503 | for( int i=0; i < batch_size; i++ ) 504 | { 505 | 506 | int idx = epoch * batch_size + i + 1; 507 | 508 | //print some progress updates 509 | if(idx % report_interval == 0) 510 | { 511 | if(global_ffmpeg_done < MAX_IMG_IDX) 512 | cout << PrettyTime(idx) << "/" << PrettyTime(global_ffmpeg_done) << endl; 513 | else 514 | cout << PrettyTime(idx) << endl; 515 | } 516 | 517 | string the_image = "img_" + FormatFileNumber(idx) + ".jpg"; 518 | string the_image_path = screenshot_directory + the_image; 519 | 520 | //wait for screenshots from ffmpeg thread 521 | while( !FileExists( the_image_path ) ) 522 | { 523 | //if ffmpeg is done getting screenshots quit waiting 524 | if(idx >= global_ffmpeg_done) 525 | { 526 | no_more = true; 527 | break; 528 | } 529 | cout << " Waiting for: " + the_image_path << endl; 530 | sleep(sleep_time); 531 | } 532 | 533 | if(!no_more) 534 | { 535 | cv::Mat img = cv::imread(the_image_path,-1); 536 | CHECK(!img.empty()) << "Unable to decode image " << the_image_path; 537 | imgs.push_back(img); 538 | } 539 | else 540 | break; 541 | } 542 | 543 | //don't try to classify an empty batch 544 | if(imgs.size() == 0) 545 | break; 546 | 547 | //perform classification 548 | ScoreList ordered_preds = classifier.Classify(imgs); 549 | for( size_t i=0; i < ordered_preds.size(); ++i) 550 | score_list.push_back(ordered_preds[i]); 551 | 552 | if(no_more) 553 | break; 554 | 555 | epoch += 1; 556 | } 557 | 558 | //Either create a file out the cuts for all targets 559 | //or make the cuts from the input list 560 | if(auto_tag) 561 | { 562 | TagTargets( score_list, movie_file, output_directory, classifier.labels_, 563 | classifier.labels_.size(), min_cut, max_gap, min_score ,min_coverage); 564 | } 565 | else 566 | { 567 | //make the cuts based on the predictions 568 | vector target_ints; 569 | for(int i=0; i 7 | #include 8 | #include 9 | #include 10 | #include 11 | #include 12 | #include 13 | #include 14 | 15 | #include "util.hpp" 16 | 17 | using namespace std; 18 | 19 | float scoreMax(vector x) 20 | { 21 | return *max_element(x.begin(), x.end()); 22 | } 23 | 24 | int scoreArgMax(vector x) 25 | { 26 | return distance(x.begin(), max_element(x.begin(), x.end())); 27 | } 28 | 29 | string getFileName(const string& s) 30 | { 31 | char sep = '/'; 32 | 33 | #ifdef _WIN32 34 | sep = '\\'; 35 | #endif 36 | 37 | size_t i = s.rfind(sep, s.length()); 38 | if( i != string::npos) 39 | return(s.substr(i+1, s.length() -i)); 40 | return(s); 41 | } 42 | 43 | string getFileExtension(const string& s) 44 | { 45 | char sep = '.'; 46 | 47 | size_t i = s.rfind(sep, s.length()); 48 | if( i != string::npos) 49 | return(s.substr(i, s.length() -i)); 50 | return(s); 51 | } 52 | 53 | string getBaseName(const string& s) 54 | { 55 | char sep = '.'; 56 | 57 | size_t i = s.rfind(sep, s.length()); 58 | if( i != string::npos) 59 | return(s.substr(0, i)); 60 | return(s); 61 | } 62 | 63 | bool queryYesNo() 64 | { 65 | cout << "Replace movie with cut? This will delete the movie. [y/N]?"; 66 | string input; 67 | getline(cin, input); 68 | if( input == "YES" || input == "Yes" || input == "yes" 69 | || input == "Y" || input == "y") 70 | return true; 71 | else 72 | return false; 73 | } 74 | 75 | string PrettyTime(int seconds) 76 | { 77 | int s, h, m; 78 | string pTime = ""; 79 | 80 | m = (seconds / 60); 81 | h = int(m / 60)%60; 82 | m = int(m % 60); 83 | s = int(seconds%60); 84 | 85 | if(h > 0) 86 | pTime += to_string(h) + "h"; 87 | if(seconds >= 60) 88 | pTime += to_string(m) + "m"; 89 | pTime += to_string(s) + "s"; 90 | return(pTime); 91 | 92 | } 93 | 94 | std::string getDirectory (const std::string& path) 95 | { 96 | int found = path.find_last_of("/\\"); 97 | if(found < 0) 98 | return("."); 99 | else 100 | return(path.substr(0, found)); 101 | } 102 | 103 | -------------------------------------------------------------------------------- /util.hpp: -------------------------------------------------------------------------------- 1 | /* 2 | * Created by Ryan Jay 15.11.16 3 | * Covered by the GPL. v3 (see included LICENSE) 4 | */ 5 | 6 | #ifndef UTIL_HPP 7 | #define UTIL_HPP 8 | 9 | #include 10 | #include 11 | #include 12 | 13 | using namespace std; 14 | 15 | 16 | float scoreMax(vector x); 17 | int scoreArgMax(vector x); 18 | string getFileName(const string& s); 19 | string getFileExtension(const string& s); 20 | string getBaseName(const string& s); 21 | bool queryYesNo(); 22 | string PrettyTime(int seconds); 23 | string getDirectory(const string& path); 24 | 25 | #endif 26 | --------------------------------------------------------------------------------