├── .gitignore ├── Cargo.lock ├── Cargo.toml ├── LICENSE ├── README.md ├── build.sh ├── containerbuild.sh ├── convert.sh ├── nativecontainerbuild.sh ├── release.sh ├── src ├── instrumentation.rs ├── main.rs ├── ml.rs ├── vio.rs ├── zoneminder.rs └── zoneminder │ ├── db.rs │ └── shm.rs ├── yolov4-tiny.cfg ├── yolov4-tiny.weights └── zm-aidect@.service /.gitignore: -------------------------------------------------------------------------------- 1 | /target 2 | .idea 3 | artifact 4 | -------------------------------------------------------------------------------- /Cargo.lock: -------------------------------------------------------------------------------- 1 | # This file is automatically @generated by Cargo. 2 | # It is not intended for manual editing. 3 | version = 3 4 | 5 | [[package]] 6 | name = "adler" 7 | version = "1.0.2" 8 | source = "registry+https://github.com/rust-lang/crates.io-index" 9 | checksum = "f26201604c87b1e01bd3d98f8d5d9a8fcbb815e8cedb41ffccbeb4bf593a35fe" 10 | 11 | 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"simple_moving_average", 1485 | "stderrlog", 1486 | "tiny_http", 1487 | ] 1488 | -------------------------------------------------------------------------------- /Cargo.toml: -------------------------------------------------------------------------------- 1 | [package] 2 | name = "zoneminder-aidect" 3 | version = "0.1.0" 4 | edition = "2021" 5 | 6 | # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html 7 | 8 | [[bin]] 9 | name = "zm-aidect" 10 | path = "src/main.rs" 11 | 12 | [dependencies] 13 | libc = "0.2" 14 | simple_moving_average = "0.1.2" 15 | mysql = { version = "22.2.0", default-features = false } 16 | flate2 = { version = "*", default-features = false, features = ["zlib"] } 17 | tiny_http = "0.11.0" 18 | prometheus = { version = "0.13.1", features = ["process"], default-features = false } 19 | lazy_static = "1.4.0" 20 | log = "0.4.17" 21 | stderrlog = "0.5.3" 22 | clap = { version = "3.2.12", features = ["derive"] } 23 | anyhow = "1.0.58" 24 | regex = "1.6.0" 25 | serde = { version = "1.0", features = ["derive"] } 26 | serde_json = "1" 27 | 28 | [dependencies.opencv] 29 | version = "0.66.0" 30 | default-features = false 31 | features = [ 32 | "dnn", 33 | "imgcodecs", 34 | "imgproc", 35 | "features2d", # required for some reason I don't understand 36 | ] 37 | 38 | [profile.tagged] 39 | inherits = "release" 40 | # -rwxr-xr-x 1 root root 10177232 Jun 13 18:33 zm-aidect 41 | 42 | lto = true 43 | # -rwxr-xr-x 1 root root 6398288 Jun 13 18:25 zm-aidect 44 | 45 | opt-level = "z" 46 | # -rwxr-xr-x 1 root root 6622352 Jun 13 18:27 zm-aidect 47 | 48 | #strip = true 49 | # -rwxr-xr-x 1 root root 3512792 Jun 13 18:28 zm-aidect 50 | 51 | panic = "abort" 52 | # -rwxr-xr-x 1 root root 3164632 Jun 13 18:29 zm-aidect 53 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU AFFERO GENERAL PUBLIC LICENSE 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There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # zm-aidect 2 | 3 | -- A turnkey object (human, animal, vehicle) detection system for ZoneMinder -- 4 | 5 | How it works: zm-aidect runs alongside ZoneMinder and feeds images from ZoneMinder into a highly sophisticated 6 | artifical intelligence (actually it's tinyYOLO-V4 and ML and not AI but marketing said otherwise). If the AI says something is there, 7 | zm-aidect tells ZoneMinder to record it. Configuration happens through ZoneMinder itself, just like motion detection. Events are recorded 8 | by ZoneMinder as per the usual. 9 | 10 | zm-aidect uses images decoded by ZoneMinder, so there is no double decoding of streams, and also no increase in camera traffic. 11 | zm-aidect works with any camera/video source configurable in ZoneMinder, not just IP cameras. 12 | 13 | ## Installation 14 | 15 | ### When using a Debian Bullseye or newer system 16 | 17 | You're in luck - I make binaries for these. 18 | 19 | ### From source 20 | 21 | * install opencv in whichever flavor you need (e.g. opencv-cuda package if you want to use CUDA) 22 | * install Rust 23 | * "cargo build --release" 24 | * ??? 25 | * copy some files around I guess 26 | 27 | ## Configuration 28 | 29 | Assuming the systemd service has been installed, you only need to enable (and start) zm-aidect for the monitors it 30 | should run on (using `3` as the monitor ID; repeat these commands for every monitor you wish to use): 31 | 32 | # systemctl enable zm-aidect@3 33 | # systemctl start zm-aidect@3 34 | 35 | # systemctl status zm-aidect@3 36 | ● zm-aidect@3.service 37 | Loaded: loaded (/etc/systemd/system/zm-aidect@.service; enabled; vendor preset: enabled) 38 | Drop-In: /run/systemd/system/service.d 39 | └─zzz-lxc-service.conf 40 | Active: active (running) since Fri 2022-06-10 22:51:56 CEST; 1h 6min ago 41 | Main PID: 293563 (zm-aidect) 42 | Tasks: 4 (limit: 19145) 43 | Memory: 60.7M 44 | CPU: 3.141s 45 | CGroup: /system.slice/system-zm\x2daidect.slice/zm-aidect@3.service 46 | └─293563 /zm-aidect/zm-aidect run 3 47 | 48 | zm-aidect is pretty turnkey beyond this. You configure it ZoneMinder's web interface by adding a zone 49 | named "aidect". Objects will be detected if within the zone. You can additionally tweak various settings by 50 | adding them to the zone's name: 51 | 52 | * Threshold=XX adjusts the confidence threshold for detection (0-100 %). The default is 50%. 53 | * Size=XX adjusts the input size handed to the model. The default is 416 (pixels), 256 pixels works generally fine. 54 | 128 and 192 requires the objects to be fairly large in the frame. Inference is sped up by the *square* of this number, e.g. 55 | 256 is around 2.5x faster than 416, 128 can be up to 10x faster (depending on CPU thread count and/or if an accelerator is used). 56 | * Classes=1,2,3,... sets which classes trigger detection. By default only humans and cars will be detected. 57 | See class names. Because the length of the zone name is limited, we can't use human-readable names here. 58 | The default is: 1,3,15,16,17 (persons, cars, birds, cats and dogs). 59 | * MinArea=51600 filters detections by their area. In triggered events the area is indicated "aidect: Human (51.1%) 90x177 (=**15930**) at 440x385". 60 | Some things can persistently trigger medium-to-high confidence detections and filtering by area is a simple way to get rid of these. 61 | Alternatively, consider having the aidect zone not cover those patterns if they are static. 62 | * FPS=XX sets the maximum analysis fps for zm-aidect and zm-aidect alone. The default is the analysis FPS set in the monitor, 63 | and if that isn't set zm-aidect will, just like ZoneMinder's own analysis, run as fast as possible to try and catch them all. 64 | * Trigger=XX sets an alternative monitor ID for triggering. This is useful when evaluating zm-aidect, because 65 | you can attach zm-aidect to your normal substream monitor, but trigger events on a secondary nodect monitor so that 66 | you can compare whatever method you normally use and zm-aidect, without having to have two monitors decode 67 | the same stream. That's the only thing this option is good for. 68 | 69 | For example: 70 | 71 | aidect Size=128 Threshold=40 FPS=5 72 | 73 | Multiple "aidect" zones should not be added to a single monitor and aren't supported. 74 | 75 | Changing settings in ZoneMinder will be reflected within a few seconds in zm-aidect; you don't need to systemctl-restart 76 | it manually. 77 | 78 | Making the zone smaller does not speed things up (except if it's really small), but can improve detection accuracy. 79 | Note that the zone is embedded in a rectangular region, meaning that the ML model will see a rectangle fitting around 80 | the zone; having long protrusions thus reduces effectiveness. Overall, the ML model used here is good enough even 81 | for full-screen detection on very wide angle cameras. Don't sweat it. Zone placement and size matters A LOT less with 82 | zm-aidect than it does with traditional motion detection. 83 | 84 | ### Testing changes 85 | 86 | You can also run `zm-aidect test `, which will go through the startup, perform a single inference 87 | to ensure the process works, trigger an event, and exit. Some diagnostics will be printed as well, like if and which 88 | hardware accelerator is used by zm-aidect. This can be used to confirm that the settings are applied as wanted. 89 | 90 | Run `zm-aidect event [--monitor=ID] ` to have zm-aidect analyze the given event as-if it were watching live, using the current settings 91 | of the monitor the event belongs to. Detections will be printed, no triggering takes place. 92 | This can be used to verify that aidect does (not) detect something you (don't) want to detect without getting up. 93 | If you let zm-aidect analyze one monitor and trigger another, then you can use the `--monitor` option here 94 | to have zm-aidect use the correct monitor for reading the zone configuration. 95 | 96 | ## Performance 97 | 98 | Machine learning is very resource intensive. It *can* be done on CPUs, but it is vastly more CPU-intensive than 99 | ZoneMinder's motion detection (~100x more CPU used). This means that 1.) if you use zm-aidect without a GPU or other 100 | accelerator, you almost certainly will have to reduce the analysis FPS **a lot**. "Going from 10 fps to 1-2 fps"-lot. 101 | 2.) You will see **way** higher CPU load (and temperatures, power draw as well as possibly noise). 102 | 103 | That being said, the fact that ML is *extremely* insensitive towards lighting changes, as well as rapid non-object motion 104 | and very slow object motion (e.g. objects approaching head-on, slowly)¸ makes it worth it in my opinion. As far as I can tell 105 | analysis running at just one or two fps is not a significant issue for detection. ML is also very good at rejecting noise, 106 | probably in not-so-small part because the image is downscaled a ton. 107 | 108 | Finally, this uses a general-purpose object detection model, which has been pre-trained on a generic dataset. Hardly optimal. 109 | The model is designed to detect *eighty different classes* of objects, but even if you want to detect and capture cats and 110 | stray dogs - you most likely use less than five classes. It seems a certainty to me that performance could be greatly improved 111 | with a model tailored to and trained for this application. 112 | 113 | ### Performance at full-size (416x416) 114 | 115 | Input image size: 1280x720 116 | 117 | Network input size: 416x416 (default for tinyYOLO-v4) 118 | 119 | Note that this testing has been performed like the application works in practice, i.e. the batch size is one, 120 | with one inferrence being serially pre-processed, inferred and post-processed on the same thread. OpenCV can 121 | parallelize only the inferrence itself (theoretically the RGB->NCHW conversion and the NMS suppression as well, 122 | but that seems unlikely, the former is basically memcpy). 123 | 124 | * System idle 125 | * CPU: 26-30 W 126 | * GPU: 13 W (P8) 127 | * Ryzen 5600X using all cores, running at ~4.5 GHz with this load: 128 | * 17-18 ms per inference 129 | * 900 % CPU (~75 % utilization) 130 | * ~300 MB CPU memory 131 | * 100% PPT (83 W), core power ~60 W 132 | * Ryzen 5600X using one thread (at ~4.7 GHz): 133 | * 70-72 ms per inference 134 | * ~300 MB CPU memory 135 | * 60% PPT (~50 W), core power 20-23 W 136 | * 1080 Ti using CUDA/cuDNN and CV-managed threads: 137 | * 2.2-2.4 ms per inference 138 | * 75% core load 139 | * roughly 180/250 W 140 | * 140 % CPU (apparently CPU-limited) 141 | * CPU consumes around 50 W 142 | * Peak GPU memory use ~500 MB 143 | * Peak main memory use ~1.3 GB 144 | * 1080 Ti with just one CPU thread: 145 | * 2.3-2.4 ms per inference 146 | * 71% core load 147 | * ~170 W, 500 MB GPU mem, ~1.3 GB CPU mem 148 | 149 | GPU usage, power and memory numbers were taken from `nvidia-smi`, while the Ryzen power figures are taken from the SMU. 150 | 151 | Conclusions: Unsurprisingly, the GPU is quite a lot quicker with far superior perf/W. Single-threaded inference 152 | is very wasteful on the Ryzen due to the large I/O die and fabric power overhead (generally around ~20 W, which 153 | for lightly threaded workloads on Ryzen means that less than 50 % of the power goes into the cores). 154 | 155 | ``` 156 | Ryzen R5 5600X 1T 14 Hz 50 W = 0.3 Hz/W 157 | Ryzen R5 5600X All-core 58 Hz 83 W = 0.7 Hz/W 158 | nVidia 1080 Ti 455 Hz 180+50 W = 2 Hz/W 159 | - 3x better power efficiency, even including CPU power on the inefficient Zen 3 Ryzen 160 | - 8x higher performance 161 | ``` 162 | 163 | Another way to look at power efficiency is to consider the rise above idle. Because the 1080 Ti is much better at 164 | having low-power modes for idle and low-intensity workloads (like P8, with a claimed, but also independently 165 | verified ~13 W total board power), its power spread is larger than the Ryzen, which is generally quite poor about this 166 | (the multi-die SKUs that is, the monolithically made Ryzen APUs are quite good, because they're a totally different 167 | SoC architecture). 168 | 169 | Regardless, the four year older GPU maintains a more than comfortable ~2.4x power efficiency lead, as you would expect. 170 | 171 | ``` 172 | Ryzen R5 5600X 1T 24 W = 0.6 Hz/W 173 | Ryzen R5 5600X All-core 57 W = 1 Hz/W 174 | nVidia 1080 Ti 167+24 W = 2.4 Hz/W 175 | ``` 176 | 177 | Moral of the story: Don't do ML inference on CPUs. At least not desktop parts. Maybe AVX-512 on an Intel 7 Xeon changes 178 | the picture (a bit). 179 | 180 | ### Performance at reduced size (256x256) 181 | 182 | tinyYOLO-v4 retains pretty good detection performance for large-ish objects at this resolution. Depending on the 183 | exact application, 192x192 may still work satisfactorily as well. 184 | 185 | ``` 186 | 5600X 1T 37 Hz (2.6x) 50 W = 0.7 Hz/W 187 | 5600X All-core 139 Hz (2.4x) 83 W = 1.7 Hz/W 188 | 1080 Ti 740 Hz (1.6x) 140+45 W = 4 Hz/W 189 | ``` 190 | 191 | Efficiency gap shrinks (unsurprisingly: GPUs don't like small, one-off jobs). Performance scales almost perfectly in 192 | accordance with the size reduction (416/256**2 = 2.65), at least when using one thread. Multi-threading diminishes 193 | the impact a little bit; this gap would likely increase if you were to use more cores (per Amdahl). The GPU benefits 194 | least from reducing the size, which I'd interpret as the overhead of submitting the small inference jobs. Throughput 195 | should increase in a much more faithful manner if batching or multiple parallel processes are used. 196 | -------------------------------------------------------------------------------- /build.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh -xue 2 | /bin/time podman run --rm \ 3 | -v "$PWD":/code -w /code -e CARGO_HOME=/code/target/podmancargocache \ 4 | rust:1-bullseye \ 5 | ./nativecontainerbuild.sh 6 | 7 | -------------------------------------------------------------------------------- /containerbuild.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash -xue 2 | apt update 3 | apt install -y clang libclang-dev llvm-dev cmake 4 | 5 | cd / 6 | git clone --branch 4.6.0 --depth 1 https://github.com/opencv/opencv 7 | mkdir opencv/build 8 | cd opencv/build 9 | cmake .. -DCPU_BASELINE=SSE4_2 -DCPU_DISPATCH= -DOPENCV_GENERATE_PKGCONFIG=YES -DBUILD_opencv_python2=OFF -DBUILD_opencv_python3=OFF \ 10 | -DWITH_OPENCL=OFF -DWITH_FFMPEG=OFF -DWITH_V4L=OFF -DWITH_GSTREAMER=OFF -DWITH_1394=OFF \ 11 | -DWITH_OPENEXR=OFF -DWITH_OPENJPEG=OFF -DWITH_JASPER=OFF -DWITH_WEBP=OFF -DWITH_TIFF=OFF -DWITH_JPEG=OFF \ 12 | -DWITH_IMGCODEC_HDR=OFF -DWITH_IMGCODEC_SUNRASTER=OFF -DWITH_IMGCODEC_PXM=OFF -DWITH_IMGCODEC_PFM=OFF \ 13 | -DBUILD_LIST=dnn -DBUILD_WITH_DYNAMIC_IPP=OFF -DWITH_GTK=OFF -DWITH_QT=OFF -DWITH_OPENGL=OFF 14 | make -j12 15 | make install 16 | 17 | cd /code 18 | 19 | #export OPENCV_INCLUDE_PATHS=/opencv/install/usr/local/include/opencv4/ 20 | #export OPENCV_LINK_PATHS=/opencv/usr/local/lib 21 | #export OPENCV_LINK_LIBS=opencv_core,opencv_dnn,opencv_imgproc 22 | #,ittnotify,libprotobuf,ippip,ippicv,z,va,va-drm,OpenGL,GLX,GLU,dl,m,pthread,rt 23 | # ittnotify -llibprotobuf -lippiw -lippicv -L/lib64 -lz -lva -lva-drm -L/usr/lib -lOpenGL -lGLX -lGLU -ldl -lm -lpthread -lrt 24 | RUSTFLAGS='-C link-args=-Wl,-rpath,$ORIGIN' cargo build --release 25 | 26 | mkdir -p artifact 27 | cp target/release/zm-aidect artifact 28 | cp yolov4-tiny.cfg yolov4-tiny.weights artifact 29 | cp /usr/local/lib/libopencv_*.so.406 artifact 30 | -------------------------------------------------------------------------------- /convert.sh: -------------------------------------------------------------------------------- 1 | scp $1/tmp/imago . 2 | convert -size 1280x720 -depth 8 rgba:imago imago.png 3 | -------------------------------------------------------------------------------- /nativecontainerbuild.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash -xue 2 | 3 | # Builds a binary linked against system OpenCV 4 | 5 | apt-get update 6 | apt-get install -y clang libclang-dev llvm-dev libopencv-dev libopencv-dnn-dev 7 | 8 | cargo build --release 9 | mkdir -p artifact 10 | cp target/release/zm-aidect artifact 11 | cp yolov4-tiny.cfg yolov4-tiny.weights artifact 12 | 13 | cp zm-aidect@.service artifact/ 14 | sed -i "s#ExecStart=#ExecStart=/opt#" artifact/zm-aidect@.service 15 | 16 | tee artifact/INSTALL < String { 17 | let mut buffer = Vec::new(); 18 | let encoder = TextEncoder::new(); 19 | let metric_families = prometheus::gather(); 20 | encoder.encode(&metric_families, &mut buffer).unwrap(); 21 | String::from_utf8(buffer.clone()).unwrap() 22 | } 23 | 24 | pub fn spawn_prometheus_client(address: String, port: u16) { 25 | std::thread::spawn(move || { 26 | let server = tiny_http::Server::http((address, port)).unwrap(); 27 | for request in server.incoming_requests() { 28 | let response = tiny_http::Response::from_string(collect()); 29 | let _ = request.respond(response); 30 | } 31 | }); 32 | } 33 | -------------------------------------------------------------------------------- /src/main.rs: -------------------------------------------------------------------------------- 1 | use std::collections::HashMap; 2 | use std::env; 3 | use std::sync::mpsc; 4 | use std::time::{Duration, Instant}; 5 | 6 | use anyhow::{anyhow, Context, Result}; 7 | use clap::{Parser, Subcommand}; 8 | use lazy_static::lazy_static; 9 | use log::{debug, error, info, warn}; 10 | use opencv::core::{Mat, MatTraitConst, Rect}; 11 | use simple_moving_average::SMA; 12 | 13 | use crate::ml::Detection; 14 | use crate::zoneminder::db::Bounding; 15 | use crate::zoneminder::{MonitorTrait}; 16 | 17 | mod instrumentation; 18 | mod ml; 19 | mod vio; 20 | mod zoneminder; 21 | 22 | // TODO: Heed analysis images setting in ZM and generate those from within zm-aidect (sparsely, only for frames actually analyzed, not sure if the DB schema allows for that) 23 | 24 | #[derive(Parser, Debug)] 25 | #[clap(disable_help_subcommand = true)] 26 | struct Args { 27 | #[clap( 28 | long, 29 | short = 'v', 30 | parse(from_occurrences), 31 | global = true, 32 | help = "Increase log verbosity (stacks up to -vvv)" 33 | )] 34 | verbose: usize, 35 | 36 | #[clap(subcommand)] 37 | mode: Mode, 38 | } 39 | 40 | #[derive(Subcommand, Debug)] 41 | enum Mode { 42 | Run { 43 | /// Zoneminder monitor ID 44 | #[clap(value_parser)] 45 | monitor_id: u32, 46 | #[clap(long)] 47 | instrumentation_address: Option, 48 | #[clap(long, default_value_t = 9000)] 49 | instrumentation_port: u16, 50 | }, 51 | Test { 52 | /// Zoneminder monitor ID 53 | #[clap(value_parser)] 54 | monitor_id: u32, 55 | }, 56 | Event { 57 | /// Zoneminder event ID to check for detections 58 | #[clap(value_parser)] 59 | event_id: u64, 60 | 61 | /// Zoneminder monitor ID for the zone configuration 62 | #[clap(long, short = 'm')] 63 | monitor_id: Option, 64 | }, 65 | } 66 | 67 | fn main() -> Result<()> { 68 | env::set_current_dir(env::current_exe()?.parent().unwrap())?; 69 | 70 | let args: Args = Args::parse(); 71 | stderrlog::new() 72 | .module(module_path!()) 73 | .verbosity(args.verbose + 1) 74 | .timestamp(stderrlog::Timestamp::Off) 75 | .init() 76 | .unwrap(); 77 | 78 | match args.mode { 79 | Mode::Run { monitor_id, instrumentation_address, instrumentation_port } => run(monitor_id, instrumentation_address, instrumentation_port), 80 | Mode::Test { monitor_id } => test(monitor_id), 81 | Mode::Event { 82 | event_id, 83 | monitor_id, 84 | } => event(event_id, monitor_id), 85 | } 86 | } 87 | 88 | fn event(event_id: u64, monitor_id: Option) -> Result<()> { 89 | let zm_conf = zoneminder::ZoneMinderConf::parse_default()?; 90 | let event = zoneminder::db::Event::query(&zm_conf, event_id)?; 91 | let monitor_id = monitor_id.unwrap_or(event.monitor_id); 92 | let mut ctx = connect_zm(monitor_id, &zm_conf)?; // TODO: If this errors on "Error: No aidect zone found for monitor 6", suggest --monitor-id 93 | 94 | let video_path = event.video_path()?; 95 | println!("Analyzing video file {}", video_path.display()); 96 | let props = vio::properties(&video_path)?; 97 | 98 | if props.width != ctx.monitor_settings.width || props.height != ctx.monitor_settings.height { 99 | println!("Note: Recording is from a different (higher?) resolution, so performance is not indicative due to rescaling"); 100 | } 101 | 102 | println!("Note: Timestamps [mm:ss:ts] are at best a rough approximation."); 103 | println!("Note: Because analysis start frames aren't aligned between what zm-aidect might have originally done,"); 104 | println!(" and this run, results can and will differ."); // TODO: This can be a good thing of course, but maybe add a way to analyse the logged alarm frames only or something like that 105 | 106 | let mut inference_durations = vec![]; 107 | let mut videotime = Duration::default(); // EXTREMELY approximate 108 | let timestep = Duration::from_secs_f32(1f32 / ctx.max_fps); // video people are crying at this 109 | for image in vio::stream_file( 110 | &video_path, 111 | ctx.monitor_settings.width, 112 | ctx.monitor_settings.height, 113 | ctx.max_fps, 114 | )? { 115 | let result = infer(image, ctx.bounding_box, &ctx.zone_config, &mut ctx.yolo)?; 116 | if result.detections.len() > 0 { 117 | // TODO: How could we get the actual frame number or timestamp here? 118 | 119 | let ts = videotime.as_secs_f32(); 120 | let frac = (ts.fract() * 1000f32) as u32; 121 | let seconds = ts.trunc() as u32; 122 | let secs = seconds % 60; 123 | let mins = seconds / 60; 124 | 125 | let description: Vec = result 126 | .detections 127 | .iter() 128 | .map(|d| describe(&CLASSES, &d)) 129 | .collect(); 130 | println!( 131 | "[{:02}:{:02}:{:03}] Inference took {:?}: {}", 132 | mins, 133 | secs, 134 | frac, 135 | result.duration, 136 | description.join(", ") 137 | ); 138 | } 139 | inference_durations.push(result.duration); 140 | videotime += timestep; 141 | } 142 | 143 | let total_duration = inference_durations.iter().sum::(); 144 | println!( 145 | "Processed {} frames, total ML time {:?}, average time {:?}", 146 | inference_durations.len(), 147 | total_duration, 148 | total_duration / inference_durations.len() as u32 149 | ); 150 | 151 | Ok(()) 152 | } 153 | 154 | struct MonitorContext<'zm_conf> { 155 | zm_conf: &'zm_conf zoneminder::ZoneMinderConf, 156 | monitor: zoneminder::Monitor<'zm_conf>, 157 | trigger_monitor: zoneminder::Monitor<'zm_conf>, 158 | zone_config: zoneminder::db::ZoneConfig, 159 | monitor_settings: zoneminder::db::MonitorSettings, 160 | bounding_box: Rect, 161 | yolo: ml::YoloV4Tiny, 162 | max_fps: f32, 163 | } 164 | 165 | fn connect_zm(monitor_id: u32, zm_conf: &zoneminder::ZoneMinderConf) -> Result { 166 | let monitor = zoneminder::Monitor::connect(zm_conf, monitor_id)?; 167 | let zone_config = zoneminder::db::ZoneConfig::get_zone_config(zm_conf, monitor_id)?; 168 | let monitor_settings = zoneminder::db::MonitorSettings::query(zm_conf, monitor_id)?; 169 | 170 | info!( 171 | "{}: Picked up zone configuration: {:?}", 172 | monitor_id, zone_config 173 | ); 174 | 175 | let bounding_box = zone_config.shape.bounding_box(); 176 | info!("{}: Picked up zone bounds {:?}", monitor_id, bounding_box); 177 | 178 | let max_fps = monitor_settings.analysis_fps_limit; 179 | let max_fps = zone_config.fps.or(max_fps); 180 | let max_fps = max_fps.ok_or(anyhow!("No analysis FPS limit set - set either \"Analysis FPS\" in the Zoneminder web console, or set the FPS key in the aidect zone."))?; 181 | info!("{}: Setting maximum fps to {}", monitor_id, max_fps); 182 | 183 | let trigger_id = zone_config.trigger.unwrap_or(monitor_id); 184 | info!("{}: Connecting to trigger monitor {}", monitor_id, trigger_id); 185 | let trigger_monitor = zoneminder::Monitor::connect(zm_conf, trigger_id)?; 186 | 187 | let size = zone_config.size.unwrap_or(256); 188 | let threshold = zone_config.threshold.unwrap_or(0.5); 189 | let yolo = ml::YoloV4Tiny::new( 190 | threshold, 191 | size, 192 | false, 193 | )?; 194 | 195 | instrumentation::SIZE.set(size as f64); 196 | 197 | Ok(MonitorContext { 198 | zm_conf, 199 | monitor, 200 | trigger_monitor, 201 | zone_config, 202 | monitor_settings, 203 | bounding_box, 204 | yolo, 205 | max_fps, 206 | }) 207 | } 208 | 209 | struct Inferred { 210 | duration: Duration, 211 | detections: Vec, 212 | } 213 | 214 | fn infer( 215 | image: Mat, 216 | bounding_box: Rect, 217 | zone_config: &zoneminder::db::ZoneConfig, 218 | yolo: &mut ml::YoloV4Tiny, 219 | ) -> Result { 220 | assert_eq!(image.typ(), opencv::core::CV_8UC3); 221 | // TODO: blank remaining area outside zone polygon 222 | let image = Mat::roi(&image, bounding_box)?; 223 | 224 | let start = Instant::now(); 225 | let detections = yolo.infer(&image)?; 226 | let duration = start.elapsed(); 227 | 228 | let detections: Vec = detections 229 | .iter() 230 | .filter(|d| CLASSES.contains_key(&d.class_id)) 231 | .filter(|d| { 232 | (d.bounding_box.width * d.bounding_box.height) as u32 233 | > zone_config.min_area.unwrap_or(0) 234 | }) 235 | .map(|d| Detection { 236 | // Adjust bounding box to zone bounding box (RoI) 237 | bounding_box: Rect { 238 | x: d.bounding_box.x + bounding_box.x, 239 | y: d.bounding_box.y + bounding_box.y, 240 | ..d.bounding_box 241 | }, 242 | ..*d 243 | }) 244 | .collect(); 245 | 246 | Ok(Inferred { 247 | duration, 248 | detections, 249 | }) 250 | } 251 | 252 | fn trigger(ctx: &MonitorContext, description: &str, score: u32) -> Result { 253 | ctx.trigger_monitor 254 | .trigger("aidect", description, score) 255 | .with_context(|| format!("Failed to trigger monitor ID {}", ctx.trigger_monitor.id())) 256 | } 257 | 258 | fn test(monitor_id: u32) -> Result<()> { 259 | let zm_conf = zoneminder::ZoneMinderConf::parse_default()?; 260 | let mut ctx = connect_zm(monitor_id, &zm_conf)?; 261 | 262 | println!( 263 | "Connected to monitor ID {}: {}", 264 | monitor_id, ctx.monitor_settings.name 265 | ); 266 | 267 | let num_images = 3; 268 | println!("Grabbing {} images and running detection", num_images); 269 | for image in ctx.monitor.stream_images()?.take(num_images) { 270 | let image = image?.convert_to_rgb24()?; 271 | let result = infer(image, ctx.bounding_box, &ctx.zone_config, &mut ctx.yolo)?; 272 | let description: Vec = result 273 | .detections 274 | .iter() 275 | .map(|d| describe(&CLASSES, &d)) 276 | .collect(); 277 | println!( 278 | "Inference took {:?}: {}", 279 | result.duration, 280 | description.join(", ") 281 | ); 282 | } 283 | 284 | println!("Triggering an event on monitor {}", ctx.trigger_monitor.id()); 285 | let event_id = trigger(&ctx, "zm-aidect test", 1)?; 286 | println!("Success, event ID is {}", event_id); 287 | 288 | Ok(()) 289 | } 290 | 291 | lazy_static! { 292 | static ref CLASSES: HashMap = [ // TODO this should be loaded at runtime from the model definition 293 | (1, "Human"), 294 | (3, "Car"), 295 | (15, "Bird"), 296 | (16, "Cat"), 297 | (17, "Dog"), 298 | ].into(); 299 | } 300 | 301 | fn run(monitor_id: u32, instrumentation_address: Option, instrumentation_port: u16) -> Result<()> { 302 | let zm_conf = zoneminder::ZoneMinderConf::parse_default()?; 303 | let mut ctx = connect_zm(monitor_id, &zm_conf)?; 304 | 305 | if let Some(address) = instrumentation_address { 306 | instrumentation::spawn_prometheus_client(address, instrumentation_port + monitor_id as u16); 307 | } 308 | 309 | let mut pacemaker = RealtimePacemaker::new(ctx.max_fps); 310 | let mut event_tracker = coalescing::EventTracker::new(); 311 | 312 | // watchdog is set to 20x max_fps frame interval 313 | let watchdog = ThreadedWatchdog::new(Duration::from_secs_f32(20.0 / ctx.max_fps)); 314 | 315 | fn process_update_event(ctx: &MonitorContext, update: Option) { 316 | if let Some(update) = update { 317 | let description = describe(&CLASSES, &update.detection); 318 | if let Err(e) = 319 | zoneminder::db::update_event_notes(&ctx.zm_conf, update.event_id, &description) 320 | { 321 | error!( 322 | "{}: Failed to update event {} notes: {}", 323 | ctx.trigger_monitor.id(), update.event_id, e 324 | ); 325 | } 326 | } 327 | } 328 | 329 | // For yolov4-tiny and moderate input sizes, multithreading does speed things up, but at the expense 330 | // of higher overall CPU usage. As you would usually have multiple zm-aidect processes running, as 331 | // well as zmc, there is no particular need for a single zm-aidect process to scale to multiple cores, 332 | // especially when that comes with an efficiency hit. Large inputs and/or high framerates aren't 333 | // sensible on a CPU anyway. 334 | opencv::core::set_num_threads(1)?; 335 | 336 | for image in ctx.monitor.stream_images()? { 337 | let image = image?.convert_to_rgb24()?; 338 | let Inferred { 339 | duration: inference_duration, 340 | detections, 341 | } = infer(image, ctx.bounding_box, &ctx.zone_config, &mut ctx.yolo)?; 342 | 343 | if detections.len() > 0 { 344 | debug!( 345 | "{}: Inference result (took {:?}): {:?}", 346 | monitor_id, inference_duration, detections 347 | ); 348 | 349 | let d = detections 350 | .iter() 351 | .max_by_key(|d| (d.confidence * 1000.0) as u32) 352 | .unwrap(); // generally there will only be one anyway 353 | let score = (d.confidence * 100.0) as u32; 354 | let description = describe(&CLASSES, &d); 355 | 356 | let event_id = trigger(&ctx, &description, score)?; 357 | let update = event_tracker.push_detection(d.clone(), event_id); 358 | process_update_event(&ctx, update); 359 | } 360 | 361 | if ctx.trigger_monitor.is_idle()? { 362 | // Not recording any more, flush current event description if any 363 | let update = event_tracker.clear(); 364 | if update.is_some() { 365 | debug!("Flushing event because idle"); 366 | } 367 | process_update_event(&ctx, update); 368 | } 369 | 370 | if inference_duration.as_secs_f32() > pacemaker.target_interval { 371 | warn!( 372 | "{}: Cannot keep up with max-analysis-fps (inference taking {:?})!", 373 | monitor_id, inference_duration, 374 | ); 375 | } 376 | 377 | instrumentation::INFERENCE_DURATION.observe(inference_duration.as_secs_f64()); 378 | instrumentation::INFERENCES.inc(); 379 | 380 | pacemaker.tick(); 381 | watchdog.reset(); 382 | let current_fps = pacemaker.current_frequency() as f64; 383 | instrumentation::FPS.set(current_fps); 384 | instrumentation::FPS_DEVIATION.set(current_fps - ctx.max_fps as f64); 385 | } 386 | Ok(()) 387 | } 388 | 389 | fn describe(classes: &HashMap, d: &Detection) -> String { 390 | format!( 391 | "{} ({:.1}%) {}x{} (={}) at {}x{}", 392 | classes[&d.class_id], 393 | d.confidence * 100.0, 394 | d.bounding_box.width, 395 | d.bounding_box.height, 396 | d.bounding_box.width * d.bounding_box.height, 397 | d.bounding_box.x, 398 | d.bounding_box.y, 399 | ) 400 | } 401 | 402 | mod coalescing { 403 | use log::trace; 404 | 405 | use crate::ml::Detection; 406 | 407 | struct TrackedEvent { 408 | event_id: u64, 409 | detections: Vec, 410 | } 411 | 412 | pub struct UpdateEvent { 413 | pub event_id: u64, 414 | pub detection: Detection, 415 | } 416 | 417 | pub struct EventTracker { 418 | current_event: Option, 419 | } 420 | 421 | impl EventTracker { 422 | pub fn new() -> EventTracker { 423 | EventTracker { 424 | current_event: None, 425 | } 426 | } 427 | 428 | pub fn push_detection(&mut self, d: Detection, event_id: u64) -> Option { 429 | let mut update = None; 430 | if let Some(current_event) = self.current_event.as_mut() { 431 | if current_event.event_id != event_id { 432 | trace!("Flushing event {} -> {}", current_event.event_id, event_id); 433 | update = self.clear(); 434 | } else { 435 | current_event.detections.push(d); 436 | return None; 437 | } 438 | } 439 | self.current_event = Some(TrackedEvent { 440 | event_id, 441 | detections: vec![d], 442 | }); 443 | update 444 | } 445 | 446 | pub fn clear(&mut self) -> Option { 447 | let current_event = self.current_event.take()?; 448 | let detection = current_event 449 | .detections 450 | .iter() 451 | .max_by_key(|d| (d.confidence * 1000.0) as u32) 452 | .unwrap(); 453 | // TODO: aggregate by classes, annotate counts. 454 | trace!( 455 | "Coalesce {} with {:?} to {:?}", 456 | current_event.event_id, 457 | current_event.detections, 458 | detection 459 | ); 460 | Some(UpdateEvent { 461 | event_id: current_event.event_id, 462 | detection: detection.clone(), 463 | }) 464 | } 465 | } 466 | } 467 | 468 | trait Pacemaker { 469 | fn tick(&mut self); 470 | fn current_frequency(&self) -> f32; 471 | } 472 | 473 | struct RealtimePacemaker { 474 | target_interval: f32, 475 | last_tick: Option, 476 | avg: simple_moving_average::NoSumSMA, 477 | current_frequency: f32, 478 | } 479 | 480 | impl RealtimePacemaker { 481 | fn new(frequency: f32) -> RealtimePacemaker { 482 | RealtimePacemaker { 483 | target_interval: 1.0f32 / frequency, 484 | last_tick: None, 485 | avg: simple_moving_average::NoSumSMA::new(), 486 | current_frequency: 0.0, 487 | } 488 | } 489 | } 490 | 491 | impl Pacemaker for RealtimePacemaker { 492 | fn tick(&mut self) { 493 | if let Some(last_iteration) = self.last_tick { 494 | let now = Instant::now(); 495 | let frame_duration = (now - last_iteration).as_secs_f32(); // how long the paced workload ran 496 | // smoothing using moving average 497 | self.avg.add_sample(frame_duration); 498 | let average_duration = self.avg.get_average(); 499 | 500 | let sleep_duration = self.target_interval - average_duration; 501 | if sleep_duration > 0.0 { 502 | std::thread::sleep(Duration::from_secs_f32(sleep_duration)); 503 | } 504 | 505 | // calculate current frequency from the tick interval (workload + sleeping) 506 | let tick_interval = Instant::now() - last_iteration; 507 | self.current_frequency = 1.0f32 / tick_interval.as_secs_f32(); 508 | } 509 | self.last_tick = Some(Instant::now()); 510 | } 511 | 512 | fn current_frequency(&self) -> f32 { 513 | self.current_frequency 514 | } 515 | } 516 | 517 | trait Watchdog { 518 | fn reset(&self) -> (); 519 | } 520 | 521 | struct ThreadedWatchdog { 522 | tx: mpsc::Sender<()>, 523 | } 524 | 525 | impl ThreadedWatchdog { 526 | fn new(timeout: Duration) -> ThreadedWatchdog { 527 | let (tx, rx) = mpsc::channel(); 528 | 529 | std::thread::spawn(move || loop { 530 | if let Err(mpsc::RecvTimeoutError::Timeout) = rx.recv_timeout(timeout) { 531 | error!("Watchdog expired, terminating."); 532 | std::process::exit(1); 533 | } 534 | }); 535 | 536 | ThreadedWatchdog { tx } 537 | } 538 | } 539 | 540 | impl Watchdog for ThreadedWatchdog { 541 | fn reset(&self) -> () { 542 | self.tx.send(()).unwrap() 543 | } 544 | } 545 | -------------------------------------------------------------------------------- /src/ml.rs: -------------------------------------------------------------------------------- 1 | use std::collections::HashMap; 2 | use std::hash::{Hash, Hasher}; 3 | 4 | use opencv::core::{Mat, MatTraitConst, MatTraitConstManual, Rect, Vector, CV_8U}; 5 | use opencv::dnn::{ 6 | blob_from_image, nms_boxes, read_net, DictValue, LayerTraitConst, Net, NetTrait, NetTraitConst, 7 | }; 8 | use opencv::types::{VectorOfMat, VectorOfRect}; 9 | 10 | #[derive(Clone, Debug)] 11 | pub struct Detection { 12 | pub confidence: f32, 13 | pub class_id: i32, 14 | pub bounding_box: Rect, 15 | } 16 | 17 | impl Detection { 18 | fn confidence_pct(&self) -> u8 { 19 | (self.confidence * 100.0) as u8 20 | } 21 | } 22 | 23 | impl Hash for Detection { 24 | fn hash(&self, state: &mut H) { 25 | state.write_u8(self.confidence_pct()); 26 | state.write_i32(self.class_id); 27 | state.write_i32(self.bounding_box.width); 28 | state.write_i32(self.bounding_box.height); 29 | state.write_i32(self.bounding_box.x); 30 | state.write_i32(self.bounding_box.y); 31 | } 32 | } 33 | 34 | impl PartialEq for Detection { 35 | fn eq(&self, other: &Self) -> bool { 36 | self.confidence_pct() == other.confidence_pct() 37 | && self.class_id == other.class_id 38 | && self.bounding_box == other.bounding_box 39 | } 40 | } 41 | 42 | impl Eq for Detection {} 43 | 44 | pub struct YoloV4Tiny { 45 | net: Net, 46 | confidence_threshold: f32, 47 | nms_threshold: f32, 48 | size: u32, 49 | 50 | out_names: Vector, 51 | } 52 | 53 | impl YoloV4Tiny { 54 | pub fn new(confidence_threshold: f32, size: u32, use_cuda: bool) -> opencv::Result { 55 | let mut net = read_net("yolov4-tiny.weights", "yolov4-tiny.cfg", "")?; 56 | if use_cuda { 57 | net.set_preferable_target(opencv::dnn::DNN_TARGET_CUDA)?; 58 | net.set_preferable_backend(opencv::dnn::DNN_BACKEND_CUDA)?; 59 | } else { 60 | net.set_preferable_target(opencv::dnn::DNN_TARGET_CPU)?; 61 | net.set_preferable_backend(opencv::dnn::DNN_BACKEND_OPENCV)?; 62 | } 63 | 64 | let out_names = net.get_unconnected_out_layers_names()?; 65 | let out_layers = net.get_unconnected_out_layers()?; 66 | let out_layer_type = net 67 | .get_layer(DictValue::from_i32(out_layers.get(0).unwrap())?) 68 | .unwrap() 69 | .typ(); 70 | assert_eq!(out_layer_type, "Region"); 71 | 72 | Ok(YoloV4Tiny { 73 | net, 74 | size, 75 | out_names, 76 | confidence_threshold, 77 | nms_threshold: 0.4, 78 | }) 79 | } 80 | 81 | pub fn infer(&mut self, image: &Mat) -> opencv::Result> { 82 | let size = self.size as i32; 83 | let size = (size, size); 84 | let mean = (0.0, 0.0, 0.0); 85 | let blob = blob_from_image(&image, 1.0, size.into(), mean.into(), false, false, CV_8U)?; 86 | let scale = 1.0 / 255.0; 87 | self.net.set_input(&blob, "", scale, mean.into())?; 88 | 89 | let outs = { 90 | let mut outs = VectorOfMat::new(); 91 | self.net.forward(&mut outs, &self.out_names)?; 92 | outs 93 | }; 94 | 95 | let image_width = image.cols() as f32; 96 | let image_height = image.rows() as f32; 97 | 98 | let detections: Vec = outs 99 | .iter() 100 | .map(|out| { 101 | // Network produces output blob with a shape NxC where N is a number of 102 | // detected objects and C is a number of classes + 4 where the first 4 103 | // numbers are [center_x, center_y, width, height] 104 | 105 | (0..out.rows()) 106 | .map(move |i| { 107 | let row = out.at_row::(i).unwrap(); 108 | 109 | let get_bounding_box = |row: &[f32]| -> Rect { 110 | let (center_x, center_y) = (row[0], row[1]); 111 | let (width, height) = (row[2], row[3]); 112 | 113 | let center_x = (center_x * image_width) as i32; 114 | let center_y = (center_y * image_height) as i32; 115 | let width = (width * image_width) as i32; 116 | let height = (height * image_height) as i32; 117 | 118 | let left_edge = (center_x - width / 2).max(0); 119 | let top_edge = (center_y - height / 2).max(0); 120 | 121 | Rect::new(left_edge, top_edge, width, height) 122 | }; 123 | 124 | let get_class = |row: &[f32]| { 125 | let class = row[4..] 126 | .iter() 127 | //.cloned() 128 | .zip(1..) // 1.. for 1-based class index, 0.. for 0-based 129 | .max_by(|a, b| a.0.partial_cmp(&b.0).unwrap()); 130 | let (&confidence, class_id) = class.unwrap(); 131 | (confidence, class_id) 132 | }; 133 | 134 | let (confidence, class_id) = get_class(row); 135 | let bounding_box = get_bounding_box(row); 136 | 137 | Detection { 138 | confidence, 139 | class_id, 140 | bounding_box, 141 | } 142 | }) 143 | .filter(|detection| detection.confidence >= self.confidence_threshold) 144 | }) 145 | .flatten() 146 | .collect(); 147 | 148 | // Perform NMS filtering 149 | let mut class2detections: HashMap> = HashMap::new(); 150 | for detection in &detections { 151 | let dets = class2detections 152 | .entry(detection.class_id) 153 | .or_insert_with(Vec::new); 154 | dets.push(&detection); 155 | } 156 | 157 | let mut nms_detections = vec![]; 158 | 159 | for (_, detections) in &class2detections { 160 | let bounding_boxes: VectorOfRect = 161 | detections.iter().map(|det| det.bounding_box).collect(); 162 | let confidences: Vector = detections.iter().map(|det| det.confidence).collect(); 163 | let mut chosen_indices = Vector::new(); 164 | nms_boxes( 165 | &bounding_boxes, 166 | &confidences, 167 | self.confidence_threshold, 168 | self.nms_threshold, 169 | &mut chosen_indices, 170 | 1.0, 171 | 0, 172 | )?; 173 | 174 | for index in chosen_indices { 175 | nms_detections.push(detections[index as usize].clone()); 176 | } 177 | } 178 | 179 | Ok(nms_detections) 180 | } 181 | } 182 | -------------------------------------------------------------------------------- /src/vio.rs: -------------------------------------------------------------------------------- 1 | use std::io::Read; 2 | use std::path::Path; 3 | use std::process::{Child, Command, Stdio}; 4 | 5 | use anyhow::{anyhow, Result}; 6 | use opencv::core::{Mat, MatTraitManual}; 7 | use serde::Deserialize; 8 | 9 | #[derive(Debug, Deserialize, Eq, PartialEq)] 10 | struct ProbeOutput { 11 | streams: Vec, 12 | } 13 | 14 | #[derive(Debug, Deserialize, Eq, PartialEq)] 15 | pub struct VideoProperties { 16 | codec_name: String, 17 | avg_frame_rate: String, 18 | pub width: u32, 19 | pub height: u32, 20 | } 21 | 22 | impl VideoProperties { 23 | pub fn get_fps(&self) -> f32 { 24 | let (a, b) = self.avg_frame_rate.split_once('/').unwrap(); 25 | let (a, b) = (a.parse::().unwrap(), (b.parse::().unwrap())); 26 | a / b 27 | } 28 | } 29 | 30 | impl ToString for VideoProperties { 31 | fn to_string(&self) -> String { 32 | format!( 33 | "{}x{} {:.1} fps ({})", 34 | self.width, 35 | self.height, 36 | self.get_fps(), 37 | self.codec_name 38 | ) 39 | } 40 | } 41 | 42 | pub fn properties(path: &Path) -> Result { 43 | let output = Command::new("ffprobe") 44 | .args([ 45 | "-v", 46 | "error", 47 | "-print_format", 48 | "json", 49 | "-select_streams", 50 | "v:0", 51 | "-show_streams", 52 | ]) 53 | .arg(path) 54 | .output()?; 55 | if !output.status.success() { 56 | return Err(anyhow!( 57 | "ffprobe failed: {}", 58 | String::from_utf8_lossy(&output.stderr) 59 | )); 60 | } 61 | let output = String::from_utf8(output.stdout)?; 62 | let mut output: ProbeOutput = serde_json::from_str(&output)?; 63 | Ok(output.streams.remove(0)) 64 | } 65 | 66 | pub struct ImageStream { 67 | width: u32, 68 | height: u32, 69 | ffmpeg: Child, 70 | } 71 | 72 | impl Iterator for ImageStream { 73 | type Item = Mat; 74 | 75 | fn next(&mut self) -> Option { 76 | let mut mat = Mat::new_size_with_default( 77 | (self.width as i32, self.height as i32).into(), 78 | opencv::core::CV_8UC3, 79 | 0.into(), 80 | ) 81 | .ok()?; 82 | let mut slice = mat.data_bytes_mut().expect("Got an non-continuous Mat for some reason?"); 83 | let stdout = self.ffmpeg.stdout.as_mut()?; 84 | stdout.read_exact(&mut slice).ok()?; 85 | return Some(mat); 86 | } 87 | } 88 | 89 | pub fn stream_file(path: &Path, width: u32, height: u32, framerate: f32) -> Result { 90 | let video_size = format!("{}x{}", width, height); 91 | let framerate = framerate.to_string(); 92 | let ffmpeg = Command::new("ffmpeg") 93 | .args(["-v", "error", "-i"]) 94 | .arg(path) 95 | .args([ 96 | "-f", 97 | "rawvideo", 98 | "-pix_fmt", 99 | "rgb24", 100 | "-s:v", 101 | &video_size, 102 | "-sws_flags", 103 | "neighbor", 104 | "-r", 105 | &framerate, 106 | "-", 107 | ]) 108 | .stdout(Stdio::piped()) 109 | .spawn()?; 110 | 111 | Ok(ImageStream { 112 | width, 113 | height, 114 | ffmpeg, 115 | }) 116 | } 117 | 118 | #[cfg(test)] 119 | mod tests { 120 | use super::*; 121 | use anyhow::Result; 122 | 123 | #[test] 124 | fn test_parse_ffprobe() -> Result<()> { 125 | let ffprobe = r#" 126 | { 127 | "streams": [ 128 | { 129 | "index": 0, 130 | "codec_name": "h264", 131 | "codec_long_name": "H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10", 132 | "profile": "High", 133 | "codec_type": "video", 134 | "codec_time_base": "7491/449600", 135 | "codec_tag_string": "avc1", 136 | "codec_tag": "0x31637661", 137 | "width": 1920, 138 | "height": 1080, 139 | "coded_width": 1920, 140 | "coded_height": 1088, 141 | "closed_captions": 0, 142 | "has_b_frames": 0, 143 | "pix_fmt": "yuvj420p", 144 | "level": 42, 145 | "color_range": "pc", 146 | "color_space": "bt470bg", 147 | "color_transfer": "bt709", 148 | "color_primaries": "bt470bg", 149 | "chroma_location": "left", 150 | "refs": 1, 151 | "is_avc": "true", 152 | "nal_length_size": "4", 153 | "r_frame_rate": "100/1", 154 | "avg_frame_rate": "2248/74", 155 | "time_base": "1/90000", 156 | "start_pts": 0, 157 | "start_time": "0.000000", 158 | "duration_ts": 6741900, 159 | "duration": "74.910000", 160 | "bit_rate": "3046762", 161 | "bits_per_raw_sample": "8", 162 | "disposition": { 163 | "default": 1, 164 | "dub": 0, 165 | "original": 0, 166 | "comment": 0, 167 | "lyrics": 0, 168 | "karaoke": 0, 169 | "forced": 0, 170 | "hearing_impaired": 0, 171 | "visual_impaired": 0, 172 | "clean_effects": 0, 173 | "attached_pic": 0, 174 | "timed_thumbnails": 0 175 | }, 176 | "tags": { 177 | "language": "und", 178 | "handler_name": "VideoHandler" 179 | } 180 | } 181 | ] 182 | }"#; 183 | 184 | assert_eq!( 185 | serde_json::from_str::(&ffprobe)?, 186 | ProbeOutput { 187 | streams: vec![VideoProperties { 188 | codec_name: "h264".to_string(), 189 | avg_frame_rate: "2248/74".to_string(), 190 | width: 1920, 191 | height: 1080, 192 | }] 193 | } 194 | ); 195 | Ok(()) 196 | } 197 | } 198 | -------------------------------------------------------------------------------- /src/zoneminder.rs: -------------------------------------------------------------------------------- 1 | use std::collections::HashMap; 2 | use std::fs::{self, File, OpenOptions}; 3 | use std::mem::size_of; 4 | use std::os::unix::fs::{FileExt, MetadataExt}; 5 | use std::time::Duration; 6 | 7 | use anyhow::{anyhow, Context, Result}; 8 | use libc::timeval; 9 | use log::error; 10 | use opencv::core::{Mat, MatTraitConst, MatTraitManual}; 11 | 12 | use crate::zoneminder::db::MonitorSettings; 13 | 14 | pub mod db; 15 | mod shm; 16 | 17 | pub trait MonitorTrait<'this> { 18 | // for lack of a better term 19 | type ImageIterator: Iterator>; 20 | 21 | fn stream_images(&'this self) -> Result; 22 | 23 | fn is_idle(&self) -> Result; 24 | 25 | fn trigger(&self, cause: &str, description: &str, score: u32) -> Result; 26 | 27 | fn id(&self) -> u32; 28 | } 29 | 30 | pub struct Monitor<'zmconf> { 31 | monitor_id: u32, 32 | zm_conf: &'zmconf ZoneMinderConf, 33 | 34 | mmap_path: String, 35 | ino: u64, 36 | shm: shm::MonitorShm, 37 | } 38 | 39 | impl<'this> MonitorTrait<'this> for Monitor<'this> { 40 | type ImageIterator = ImageStream<'this>; 41 | 42 | fn stream_images(&'this self) -> Result { 43 | let state = self.read()?; 44 | let settings = MonitorSettings::query(self.zm_conf, self.monitor_id)?; 45 | let image_buffer_count = settings.image_buffer_count; 46 | 47 | // now that we have the image buffer size we can figure the dynamic offsets out 48 | let shared_timestamps_offset = self.shm.read_field::(shm::ShmField::SHARED_SIZE)? 49 | + self.shm.read_field::(shm::ShmField::TRIGGER_SIZE)? 50 | + self.shm.read_field::(shm::ShmField::VIDEOSTORE_SIZE)?; 51 | let shared_images_offset = 52 | shared_timestamps_offset as usize + image_buffer_count as usize * size_of::(); 53 | let shared_images_offset = shared_images_offset + 64 - (shared_images_offset % 64); 54 | 55 | Ok(ImageStream { 56 | width: settings.width, 57 | height: settings.height, 58 | image_buffer_count, 59 | monitor: self, 60 | last_read_index: image_buffer_count, 61 | image_size: state.imagesize, 62 | format: state.format, 63 | shared_images_offset: shared_images_offset as u64, 64 | }) 65 | } 66 | 67 | fn is_idle(&self) -> Result { 68 | Ok(self.read()?.state == shm::MonitorState::Idle) 69 | } 70 | 71 | /// Mark at least one frame as an alarm frame with the given score. Wait for event to be created, 72 | /// then return event ID. Does not necessarily cause creation of a new event. 73 | fn trigger(&self, cause: &str, description: &str, score: u32) -> Result { 74 | let poll_interval = 10; 75 | self.set_trigger(cause, description, score)?; 76 | for n in 0.. { 77 | let state = self.read()?.state; 78 | // Alarm sorta implies that we just triggered an alarm frame, while 79 | // Alert sorta implies there's an on-going event. 80 | // Wait for Alarm state to become active so that the frame is marked. 81 | if state == shm::MonitorState::Alarm { 82 | break; 83 | } 84 | std::thread::sleep(Duration::from_millis(poll_interval)); 85 | if n > 500 { 86 | error!("Waited {} ms for zoneminder to notice our bulgy wulgy, giving up and canceling it :c", n * poll_interval); 87 | } 88 | } 89 | self.reset_trigger()?; 90 | Ok(self.read()?.last_event_id) 91 | } 92 | 93 | fn id(&self) -> u32 { 94 | self.monitor_id 95 | } 96 | } 97 | 98 | impl Monitor<'_> { 99 | pub fn connect(zm_conf: &ZoneMinderConf, monitor_id: u32) -> Result { 100 | let mmap_path = format!("{}/zm.mmap.{}", zm_conf.mmap_path, monitor_id); 101 | let file = OpenOptions::new() 102 | .read(true) 103 | .write(true) 104 | .open(&mmap_path) 105 | .with_context(|| { 106 | format!( 107 | "Failed to open mmap file {} for monitor {}", 108 | mmap_path, monitor_id 109 | ) 110 | })?; 111 | 112 | Ok(Monitor { 113 | monitor_id, 114 | zm_conf, 115 | mmap_path, 116 | ino: file.metadata()?.ino(), 117 | shm: shm::MonitorShm::new(file)?, 118 | }) 119 | } 120 | 121 | fn set_trigger(&self, cause: &str, description: &str, score: u32) -> Result<()> { 122 | self.shm.write_string(shm::ShmField::TRIGGER_CAUSE, cause)?; 123 | self.shm 124 | .write_string(shm::ShmField::TRIGGER_TEXT, description)?; 125 | self.shm.write_string(shm::ShmField::TRIGGER_SHOWTEXT, "")?; 126 | self.shm.write_field(shm::ShmField::TRIGGER_SCORE, &score)?; 127 | // all of this is terribly racy but pwritin' the data before the state change should reduce the odds of problems 128 | self.shm 129 | .write_field(shm::ShmField::TRIGGER_STATE, &shm::TriggerState::TriggerOn) 130 | } 131 | 132 | fn reset_trigger(&self) -> Result<()> { 133 | self.shm.write_string(shm::ShmField::TRIGGER_CAUSE, "")?; 134 | self.shm.write_string(shm::ShmField::TRIGGER_TEXT, "")?; 135 | self.shm.write_string(shm::ShmField::TRIGGER_SHOWTEXT, "")?; 136 | self.shm.write_field(shm::ShmField::TRIGGER_SCORE, &0)?; 137 | self.shm.write_field( 138 | shm::ShmField::TRIGGER_STATE, 139 | &shm::TriggerState::TriggerCancel, 140 | ) 141 | } 142 | 143 | fn read(&self) -> Result { 144 | if self.shm.read_field::(shm::ShmField::VALID)? == 0 { 145 | return Err(anyhow!("Monitor shm is not valid")); 146 | } 147 | self.check_file_stale()?; 148 | 149 | Ok(MonitorState { 150 | last_write_index: self.shm.read_field(shm::ShmField::LAST_WRITE_INDEX)?, 151 | state: self.shm.read_field(shm::ShmField::STATE)?, 152 | last_event_id: self.shm.read_field(shm::ShmField::LAST_EVENT_ID)?, 153 | format: self.shm.read_field(shm::ShmField::FORMAT)?, 154 | imagesize: self.shm.read_field(shm::ShmField::IMAGESIZE)?, 155 | }) 156 | } 157 | 158 | fn check_file_stale(&self) -> Result<()> { 159 | // Additional sanity check, if the file-on-tmpfs is now a different file, we're definitely listening to a stranger. 160 | // ZM seems to be quite good about ensuring shared_data.valid gets flipped to 0 even when zmc crashes though. 161 | if fs::metadata(&self.mmap_path) 162 | .with_context(|| format!("Monitor mmap file {} does not exist", self.mmap_path))? 163 | .ino() 164 | != self.ino 165 | { 166 | return Err(anyhow!("Monitor shm fd is stale, must reconnect")); 167 | } 168 | Ok(()) 169 | } 170 | } 171 | 172 | fn zm_format_to_cv_format(format: shm::SubpixelOrder) -> i32 { 173 | match format { 174 | shm::SubpixelOrder::NONE => opencv::core::CV_8UC1, 175 | shm::SubpixelOrder::RGB => opencv::core::CV_8UC3, 176 | shm::SubpixelOrder::BGR => opencv::core::CV_8UC3, 177 | shm::SubpixelOrder::BGRA => opencv::core::CV_8UC4, 178 | shm::SubpixelOrder::RGBA => opencv::core::CV_8UC4, 179 | shm::SubpixelOrder::ABGR => opencv::core::CV_8UC4, 180 | shm::SubpixelOrder::ARGB => opencv::core::CV_8UC4, 181 | } 182 | } 183 | 184 | pub struct Image { 185 | image: Mat, 186 | format: shm::SubpixelOrder, 187 | } 188 | 189 | impl Image { 190 | pub fn convert_to_rgb24(self) -> Result { 191 | let conversion = match self.format { 192 | shm::SubpixelOrder::NONE => Some(opencv::imgproc::COLOR_GRAY2RGB), 193 | shm::SubpixelOrder::RGB => None, 194 | shm::SubpixelOrder::BGR => Some(opencv::imgproc::COLOR_BGR2RGB), 195 | shm::SubpixelOrder::BGRA => Some(opencv::imgproc::COLOR_BGRA2RGB), 196 | shm::SubpixelOrder::RGBA => Some(opencv::imgproc::COLOR_RGBA2RGB), 197 | _ => panic!("Unsupported pixel format: {:?}", self.format), 198 | }; 199 | self.convert(conversion) 200 | } 201 | 202 | #[allow(dead_code)] 203 | pub fn convert_to_rgb32(self) -> Result { 204 | let conversion = match self.format { 205 | shm::SubpixelOrder::NONE => Some(opencv::imgproc::COLOR_GRAY2RGBA), 206 | shm::SubpixelOrder::RGB => Some(opencv::imgproc::COLOR_RGB2RGBA), 207 | shm::SubpixelOrder::BGR => Some(opencv::imgproc::COLOR_BGR2RGBA), 208 | shm::SubpixelOrder::BGRA => Some(opencv::imgproc::COLOR_BGRA2RGBA), 209 | shm::SubpixelOrder::RGBA => None, 210 | _ => panic!("Unsupported pixel format: {:?}", self.format), 211 | }; 212 | self.convert(conversion) 213 | } 214 | 215 | #[allow(dead_code)] 216 | pub fn convert_to_gray(self) -> Result { 217 | let conversion = match self.format { 218 | shm::SubpixelOrder::NONE => None, 219 | shm::SubpixelOrder::RGB => Some(opencv::imgproc::COLOR_RGB2GRAY), 220 | shm::SubpixelOrder::BGR => Some(opencv::imgproc::COLOR_BGR2GRAY), 221 | shm::SubpixelOrder::BGRA => Some(opencv::imgproc::COLOR_BGRA2GRAY), 222 | shm::SubpixelOrder::RGBA => Some(opencv::imgproc::COLOR_RGBA2GRAY), 223 | _ => panic!("Unsupported pixel format: {:?}", self.format), 224 | }; 225 | self.convert(conversion) 226 | } 227 | 228 | fn convert(self, conversion: Option) -> Result { 229 | if let Some(conversion) = conversion { 230 | let mut rgb_image = Mat::default(); 231 | // You could do this in-place as well, though it's probably not worth it 232 | opencv::imgproc::cvt_color(&self.image, &mut rgb_image, conversion, 0)?; 233 | return Ok(rgb_image); 234 | } 235 | Ok(self.image) 236 | } 237 | } 238 | 239 | pub struct ImageStream<'mon> { 240 | monitor: &'mon Monitor<'mon>, 241 | last_read_index: u32, 242 | width: u32, 243 | height: u32, 244 | image_size: u32, 245 | format: shm::SubpixelOrder, 246 | image_buffer_count: u32, 247 | shared_images_offset: u64, 248 | } 249 | 250 | impl ImageStream<'_> { 251 | fn wait_for_image(&mut self) -> Result { 252 | loop { 253 | let state = self.monitor.read()?; 254 | let last_write_index = state.last_write_index as u32; 255 | if last_write_index != self.last_read_index 256 | && last_write_index != self.image_buffer_count 257 | { 258 | self.last_read_index = last_write_index; 259 | let image = self.read_image(last_write_index)?; 260 | return Ok(Image { 261 | image, 262 | format: self.format, 263 | }); 264 | } 265 | std::thread::sleep(Duration::from_millis(5)); 266 | } 267 | } 268 | 269 | fn read_image(&self, index: u32) -> Result { 270 | assert_eq!(self.width * self.height * 4, self.image_size); 271 | let mut mat = Mat::new_size_with_default( 272 | (self.width as i32, self.height as i32).into(), 273 | zm_format_to_cv_format(self.format), 274 | 0.into(), 275 | )?; 276 | self.read_image_into(index, &mut mat)?; 277 | Ok(mat) 278 | } 279 | 280 | fn read_image_into(&self, index: u32, mat: &mut Mat) -> Result<()> { 281 | assert_eq!(self.width * self.height, mat.total() as u32); 282 | assert_eq!(mat.typ(), zm_format_to_cv_format(self.format)); 283 | self.monitor.check_file_stale()?; 284 | let mut slice = mat.data_bytes_mut()?; 285 | let image_offset = self.shared_images_offset as u64 + self.image_size as u64 * index as u64; 286 | self.monitor 287 | .shm 288 | .file 289 | .read_exact_at(&mut slice, image_offset) 290 | .with_context(|| "Failed to read image")?; 291 | Ok(()) 292 | } 293 | } 294 | 295 | impl Iterator for ImageStream<'_> { 296 | type Item = Result; 297 | 298 | fn next(&mut self) -> Option { 299 | Some(self.wait_for_image()) 300 | } 301 | } 302 | 303 | struct MonitorState { 304 | pub last_write_index: i32, 305 | pub state: shm::MonitorState, 306 | pub last_event_id: u64, 307 | pub format: shm::SubpixelOrder, 308 | pub imagesize: u32, 309 | } 310 | 311 | #[derive(Debug)] 312 | pub struct ZoneMinderConf { 313 | db_host: String, 314 | db_name: String, 315 | db_user: String, 316 | db_password: String, 317 | mmap_path: String, 318 | } 319 | 320 | impl ZoneMinderConf { 321 | fn parse_zm_conf(zm_conf_contents: &str) -> ZoneMinderConf { 322 | let keys: HashMap<&str, &str> = zm_conf_contents 323 | .lines() 324 | .map(|line| line.trim()) 325 | .filter(|line| line.starts_with("ZM_")) 326 | .filter_map(|line| line.split_once('=')) 327 | .collect(); 328 | 329 | ZoneMinderConf { 330 | db_host: keys["ZM_DB_HOST"].to_string(), 331 | db_name: keys["ZM_DB_NAME"].to_string(), 332 | db_user: keys["ZM_DB_USER"].to_string(), 333 | db_password: keys["ZM_DB_PASS"].to_string(), 334 | mmap_path: keys["ZM_PATH_MAP"].to_string(), 335 | } 336 | } 337 | 338 | pub fn parse_default() -> Result { 339 | let zm_conf = "/etc/zm/zm.conf"; 340 | let zm_conf_d = "/etc/zm/conf.d"; 341 | let contents = fs::read_to_string(zm_conf).with_context(|| { 342 | format!("Failed to parse Zoneminder configuration file {}", zm_conf) 343 | })?; 344 | let contents = contents 345 | + "\n" 346 | + &fs::read_dir(zm_conf_d) 347 | .with_context(|| format!("Failed to read Zoneminder overrides from {}", zm_conf_d))? 348 | .filter_map(Result::ok) 349 | .map(|entry| fs::read_to_string(entry.path())) 350 | .filter_map(Result::ok) 351 | .fold(String::new(), |a, b| a + "\n" + &b); // O(n**2) 352 | 353 | Ok(Self::parse_zm_conf(&contents)) 354 | } 355 | } 356 | 357 | #[cfg(test)] 358 | mod tests { 359 | use super::*; 360 | 361 | #[test] 362 | fn test_parse_zm_conf() { 363 | let conf = "# ZoneMinder database hostname or ip address and optionally port or unix socket 364 | # Acceptable formats include hostname[:port], ip_address[:port], or 365 | # localhost:/path/to/unix_socket 366 | ZM_DB_HOST=localhost 367 | 368 | # ZoneMinder database name 369 | ZM_DB_NAME=zm 370 | 371 | # ZoneMinder database user 372 | ZM_DB_USER=zmuser 373 | 374 | # ZoneMinder database password 375 | ZM_DB_PASS=zmpass 376 | 377 | ZM_PATH_MAP=/dev/shm 378 | "; 379 | 380 | let parsed = ZoneMinderConf::parse_zm_conf(conf); 381 | assert_eq!(parsed.db_host, "localhost"); 382 | assert_eq!(parsed.db_name, "zm"); 383 | assert_eq!(parsed.db_user, "zmuser"); 384 | assert_eq!(parsed.db_password, "zmpass"); 385 | assert_eq!(parsed.mmap_path, "/dev/shm"); 386 | } 387 | } 388 | -------------------------------------------------------------------------------- /src/zoneminder/db.rs: -------------------------------------------------------------------------------- 1 | use std::collections::HashMap; 2 | use std::path::PathBuf; 3 | 4 | use anyhow::{anyhow, Result}; 5 | use mysql::params; 6 | use mysql::prelude::Queryable; 7 | use opencv::core::Rect; 8 | 9 | use crate::zoneminder::ZoneMinderConf; 10 | 11 | trait ZoneMinderDB { 12 | fn connect_db(&self) -> mysql::Result; 13 | } 14 | 15 | impl ZoneMinderDB for ZoneMinderConf { 16 | fn connect_db(&self) -> mysql::Result { 17 | let builder = mysql::OptsBuilder::new() 18 | .ip_or_hostname(Some(&self.db_host)) 19 | .db_name(Some(&self.db_name)) 20 | .user(Some(&self.db_user)) 21 | .pass(Some(&self.db_password)); 22 | mysql::Conn::new(builder) 23 | } 24 | } 25 | 26 | pub fn update_event_notes(zm_conf: &ZoneMinderConf, event_id: u64, notes: &str) -> Result<()> { 27 | let mut db = zm_conf.connect_db()?; 28 | Ok(db.exec_drop( 29 | "UPDATE Events SET Notes = :notes WHERE Id = :id", 30 | params! { 31 | "id" => event_id, 32 | "notes" => notes, 33 | }, 34 | )?) 35 | } 36 | 37 | #[derive(Debug)] 38 | pub struct MonitorSettings { 39 | pub name: String, 40 | pub storage_id: u32, 41 | pub enabled: bool, 42 | pub width: u32, 43 | pub height: u32, 44 | pub colours: u32, 45 | pub image_buffer_count: u32, 46 | pub analysis_fps_limit: Option, 47 | } 48 | 49 | impl MonitorSettings { 50 | pub fn query(zm_conf: &ZoneMinderConf, monitor_id: u32) -> Result { 51 | let mut db = zm_conf.connect_db()?; 52 | Ok(db.exec_map("SELECT Name, StorageId, Enabled, Width, Height, Colours, ImageBufferCount, AnalysisFPSLimit FROM Monitors WHERE Id = :id", 53 | params! { "id" => monitor_id }, 54 | |(name, storage_id, enabled, width, height, colours, image_buffer_count, analysis_fps_limit)| { 55 | MonitorSettings { 56 | name, 57 | storage_id, 58 | enabled, 59 | width, 60 | height, 61 | colours, 62 | image_buffer_count, 63 | analysis_fps_limit, 64 | } 65 | } 66 | )?.remove(0)) 67 | } 68 | } 69 | 70 | #[derive(Debug)] 71 | pub struct Event { 72 | pub id: u64, 73 | pub monitor_id: u32, 74 | pub name: String, 75 | pub max_score: u32, 76 | pub avg_score: u32, 77 | pub total_score: u32, 78 | default_video: String, 79 | start_datetime: String, // local time, 2022-01-27 18:45:59 80 | 81 | storage: Storage, 82 | } 83 | 84 | impl Event { 85 | pub fn query(zm_conf: &ZoneMinderConf, event_id: u64) -> Result { 86 | let mut db = zm_conf.connect_db()?; 87 | 88 | let storage_id = db.exec_first( 89 | "SELECT StorageId FROM Events WHERE Id = :id", 90 | params! { "id" => event_id }, 91 | )?; 92 | let storage = get_storage_by_id(&mut db, storage_id.unwrap())?; 93 | 94 | // the "date time" handling here is janky af but sufficient for what's needed (only used to derive the file name) 95 | Ok(db.exec_map("SELECT Name, MonitorId, MaxScore, AvgScore, TotScore, DefaultVideo, CAST(StartDateTime AS CHAR) FROM Events WHERE Id = :id", 96 | params! { "id" => event_id }, 97 | |(name, monitor_id, max_score, avg_score, total_score, default_video, start_datetime)| { 98 | Event { 99 | id: event_id, 100 | name, 101 | monitor_id, 102 | max_score, 103 | avg_score, 104 | total_score, 105 | default_video, 106 | start_datetime, 107 | storage: storage.clone(), 108 | } 109 | } 110 | )?.remove(0)) 111 | } 112 | 113 | pub fn video_path(&self) -> Result { 114 | if self.storage.storage_type != "local" { 115 | return Err(anyhow!( 116 | "Unsupported storage type {} for event {}", 117 | self.storage.storage_type, 118 | self.id 119 | )); 120 | } 121 | 122 | let event_path = match self.storage.scheme { 123 | StorageScheme::Deep => { 124 | let re = regex::Regex::new("[-: ]").unwrap(); 125 | format!("{}/{}", re.replace_all(&self.start_datetime, "/"), self.id) 126 | } 127 | StorageScheme::Medium => format!( 128 | "{}/{}", 129 | self.start_datetime.split_once(" ").unwrap().0, 130 | self.id 131 | ), 132 | StorageScheme::Shallow => format!("{}", self.id), 133 | }; 134 | 135 | let monitor_path = self.monitor_id.to_string(); 136 | 137 | let path: PathBuf = [ 138 | &self.storage.path, 139 | &monitor_path, 140 | &event_path, 141 | &self.default_video, 142 | ] 143 | .iter() 144 | .collect(); 145 | Ok(path) 146 | } 147 | } 148 | 149 | #[derive(Debug, Copy, Clone)] 150 | enum StorageScheme { 151 | Deep, 152 | Medium, 153 | Shallow, 154 | } 155 | 156 | impl TryFrom<&str> for StorageScheme { 157 | type Error = anyhow::Error; 158 | 159 | fn try_from(input: &str) -> std::result::Result { 160 | Ok(match input { 161 | "Deep" => StorageScheme::Deep, 162 | "Medium" => StorageScheme::Medium, 163 | "Shallow" => StorageScheme::Shallow, 164 | _ => return Err(anyhow!("Invalid/unknown storage scheme {}", input)), 165 | }) 166 | } 167 | } 168 | 169 | #[derive(Debug, Clone)] 170 | struct Storage { 171 | #[allow(dead_code)] 172 | id: u64, 173 | #[allow(dead_code)] 174 | name: String, 175 | path: String, 176 | storage_type: String, 177 | scheme: StorageScheme, 178 | } 179 | 180 | fn get_storage_by_id(db: &mut mysql::Conn, storage_id: u64) -> Result { 181 | //let mut db = zm_conf.connect_db()?; 182 | Ok(db 183 | .exec_map( 184 | "SELECT Name, Path, Type, Scheme FROM Storage WHERE Id = :id", 185 | params! { "id" => storage_id }, 186 | |(name, path, storage_type, scheme)| -> Result { 187 | let scheme: String = scheme; 188 | Ok(Storage { 189 | id: storage_id, 190 | name, 191 | path, 192 | storage_type, 193 | scheme: StorageScheme::try_from(scheme.as_str())?, 194 | }) 195 | }, 196 | )? 197 | .remove(0)?) 198 | } 199 | 200 | pub type ZoneShape = Vec<(i32, i32)>; 201 | 202 | pub trait Bounding { 203 | fn bounding_box(&self) -> Rect; 204 | } 205 | 206 | impl Bounding for ZoneShape { 207 | fn bounding_box(&self) -> Rect { 208 | let min_x = self.iter().map(|xy| xy.0).min().unwrap(); 209 | let min_y = self.iter().map(|xy| xy.1).min().unwrap(); 210 | let max_x = self.iter().map(|xy| xy.0).max().unwrap(); 211 | let max_y = self.iter().map(|xy| xy.1).max().unwrap(); 212 | 213 | let width = max_x - min_x; 214 | let height = max_y - min_y; 215 | Rect { 216 | x: min_x, 217 | y: min_y, 218 | width, 219 | height, 220 | } 221 | } 222 | } 223 | 224 | #[derive(Debug)] 225 | pub struct ZoneConfig { 226 | pub size: Option, 227 | pub threshold: Option, 228 | pub shape: ZoneShape, 229 | pub trigger: Option, 230 | pub fps: Option, 231 | pub min_area: Option, 232 | } 233 | 234 | impl ZoneConfig { 235 | pub fn get_zone_config(zm_conf: &ZoneMinderConf, monitor_id: u32) -> Result { 236 | let mut db = zm_conf.connect_db()?; 237 | let dbzone = db.exec_first( 238 | "SELECT Name, Type, Coords FROM Zones WHERE MonitorId = :id AND Name LIKE \"aidect%\"", 239 | params! { "id" => monitor_id }, 240 | )?; 241 | let dbzone: mysql::Row = 242 | dbzone.ok_or(anyhow!("No aidect zone found for monitor {}", monitor_id))?; 243 | 244 | Ok(ZoneConfig::parse( 245 | &dbzone.get::("Name").unwrap(), 246 | &dbzone.get::("Coords").unwrap(), 247 | )) 248 | } 249 | 250 | fn parse(name: &str, coords: &str) -> ZoneConfig { 251 | ZoneConfig { 252 | shape: Self::parse_zone_coords(coords), 253 | ..Self::parse_zone_name(name) 254 | } 255 | } 256 | 257 | fn parse_zone_name(zone_name: &str) -> ZoneConfig { 258 | let keys: HashMap<&str, &str> = zone_name 259 | .split_ascii_whitespace() 260 | .skip(1) 261 | .map(|item| item.split_once('=')) 262 | .filter_map(|x| x) 263 | .collect(); 264 | 265 | let get_int = |key| keys.get(key).and_then(|v| v.trim().parse::().ok()); 266 | let get_f32 = |key| keys.get(key).and_then(|v| v.trim().parse::().ok()); 267 | 268 | ZoneConfig { 269 | shape: Vec::new(), 270 | threshold: keys 271 | .get("Threshold") 272 | .and_then(|v| v.trim().parse::().ok()) 273 | .map(|v| v / 100.0), 274 | size: get_int("Size"), 275 | trigger: get_int("Trigger"), 276 | fps: get_f32("FPS"), 277 | min_area: get_int("MinArea"), 278 | } 279 | } 280 | 281 | fn parse_zone_coords(coords: &str) -> ZoneShape { 282 | let parse = |v: &str| v.trim().parse::().unwrap(); 283 | coords 284 | .split_ascii_whitespace() 285 | .map(|point| point.split_once(',')) 286 | .filter_map(|v| v) 287 | .map(|(x, y)| (parse(x), parse(y))) 288 | .collect() 289 | } 290 | } 291 | 292 | #[cfg(test)] 293 | mod tests { 294 | use super::*; 295 | 296 | #[test] 297 | fn test_parse_zone_name_basic() { 298 | let zone_name = "aidect"; 299 | let parsed = ZoneConfig::parse_zone_name(zone_name); 300 | assert_eq!(parsed.shape.len(), 0); 301 | assert_eq!(parsed.threshold, None); 302 | assert_eq!(parsed.size, None); 303 | } 304 | 305 | #[test] 306 | fn test_parse_zone_name() { 307 | let zone_name = "aidect Size=128 Threshold=50"; 308 | let parsed = ZoneConfig::parse_zone_name(zone_name); 309 | assert_eq!(parsed.shape.len(), 0); 310 | assert_eq!(parsed.threshold, Some(0.5)); 311 | assert_eq!(parsed.size, Some(128)); 312 | } 313 | 314 | #[test] 315 | fn test_parse_zone_coords() { 316 | let coords = "123,56 899,41 687,425"; 317 | let parsed = ZoneConfig::parse_zone_coords(coords); 318 | assert_eq!(parsed, vec![(123, 56), (899, 41), (687, 425)]); 319 | } 320 | } 321 | -------------------------------------------------------------------------------- /src/zoneminder/shm.rs: -------------------------------------------------------------------------------- 1 | use std::io::Read; 2 | use std::mem::{align_of, size_of}; 3 | use std::os::unix::fs::FileExt; 4 | use std::slice; 5 | 6 | use anyhow::{anyhow, Context, Result}; 7 | use lazy_static::lazy_static; 8 | use libc::time_t; 9 | use regex::Regex; 10 | 11 | // TODO: panic! wrapper which adds a bit that this requires maintainer attention 12 | 13 | #[derive(Debug, Eq, PartialEq)] 14 | struct Type { 15 | size: usize, 16 | alignment: usize, 17 | } 18 | 19 | impl Type { 20 | fn new() -> Type { 21 | Type { 22 | size: size_of::(), 23 | alignment: align_of::(), 24 | } 25 | } 26 | 27 | fn array_of(&self, num_elements: usize) -> Type { 28 | Type { 29 | size: self.size * num_elements, 30 | alignment: self.alignment, 31 | } 32 | } 33 | } 34 | 35 | fn parse_basic_typename(typename: &str) -> Type { 36 | match typename { 37 | "uint8" => Type::new::(), 38 | "int8" => Type::new::(), 39 | "uint32" => Type::new::(), 40 | "int32" => Type::new::(), 41 | "uint64" => Type::new::(), 42 | "int64" => Type::new::(), 43 | "float" => Type::new::(), 44 | "double" => Type::new::(), 45 | "time_t64" => Type::new::(), 46 | _ => panic!( 47 | "Unhandled ABI type in Memory.pm shm definition: {}", 48 | typename 49 | ), 50 | } 51 | } 52 | 53 | fn parse_typename(typename: &str) -> Type { 54 | match typename.split_once('[') { 55 | None => parse_basic_typename(typename), 56 | Some((basic_typename, array_size)) => { 57 | let t = parse_basic_typename(basic_typename); 58 | assert!(array_size.ends_with(']')); 59 | let array_size = &array_size[0..array_size.len() - 1]; 60 | let elements = array_size 61 | .parse::() 62 | .with_context(|| { 63 | format!( 64 | "Could not parse array size in Memory.pm shm definition: {}", 65 | typename 66 | ) 67 | }) 68 | .unwrap(); 69 | t.array_of(elements) 70 | } 71 | } 72 | } 73 | 74 | #[derive(Debug, Eq, PartialEq)] 75 | struct ParsedField { 76 | name: String, 77 | typ: Type, 78 | } 79 | 80 | fn parse_field_definition(line: &str) -> ParsedField { 81 | lazy_static! { 82 | static ref RE: Regex = Regex::new(r"(\w+)\s+=> \{ type=>'([a-z0-9_\[\]]+)'").unwrap(); 83 | } 84 | let m = RE 85 | .captures(line) 86 | .ok_or(anyhow!( 87 | "Could not parse field definition in Memory.pm shm definition: {:?}", 88 | line 89 | )) 90 | .unwrap(); 91 | ParsedField { 92 | name: m[1].to_string(), 93 | typ: parse_typename(&m[2]), 94 | } 95 | } 96 | 97 | #[derive(Debug, Eq, PartialEq)] 98 | struct ParsedStruct { 99 | name: String, 100 | fields: Vec, 101 | } 102 | 103 | #[derive(Debug, Eq, PartialEq)] 104 | struct Field { 105 | name: String, 106 | offset: usize, 107 | typ: Type, 108 | } 109 | 110 | #[derive(Debug, Eq, PartialEq)] 111 | struct Struct { 112 | name: String, 113 | size: usize, 114 | fields: Vec, 115 | } 116 | 117 | impl ParsedStruct { 118 | fn calculate_offsets(self) -> Struct { 119 | let mut offset = 0; 120 | let mut fields = vec![]; 121 | 122 | for ParsedField { name, typ } in self.fields { 123 | let field = Field { 124 | offset: align_to(offset, typ.alignment), 125 | name, 126 | typ, 127 | }; 128 | offset = field.offset + field.typ.size; 129 | fields.push(field); 130 | } 131 | 132 | Struct { 133 | name: self.name, 134 | size: offset, 135 | fields, 136 | } 137 | } 138 | } 139 | 140 | fn align_to(offset: usize, alignment: usize) -> usize { 141 | if offset % alignment == 0 { 142 | offset 143 | } else { 144 | offset + alignment - (offset % alignment) 145 | } 146 | } 147 | 148 | fn parse_struct_definition(input: &mut std::str::Lines) -> Option { 149 | lazy_static! { 150 | static ref RE: Regex = 151 | Regex::new(r"\w+\s+=> \{ type=>'(\w+)', seq=>\$mem_seq\+\+, '?contents'?").unwrap(); 152 | } 153 | let struct_def = input.next().expect("Empty struct definition in Memory.pm"); 154 | if struct_def.trim_start().starts_with("end =>") { 155 | return None; 156 | } 157 | 158 | let m = RE 159 | .captures(struct_def) 160 | .ok_or(anyhow!( 161 | "Could not parse struct definition in Memory.pm shm definition: {:?}", 162 | struct_def 163 | )) 164 | .unwrap(); 165 | 166 | let mut fields = vec![]; 167 | loop { 168 | let line = input.next().expect("Unexpected EOR in Memory.pm"); 169 | let line = line.trim_start(); 170 | if line == "}" { 171 | continue; 172 | } 173 | if line == "}," { 174 | break; 175 | } 176 | fields.push(parse_field_definition(line)); 177 | } 178 | 179 | Some(ParsedStruct { 180 | name: m[1].to_string(), 181 | fields, 182 | }) 183 | } 184 | 185 | fn parse_memory_pm(input: &str) -> ParsedStruct { 186 | let re = Regex::new(r"(?ms)our \$mem_data = \{\n(.*?)};").unwrap(); 187 | let m = re 188 | .captures(input) 189 | .expect("No shm definitions found in Memory.pm"); 190 | 191 | let mut lines = m[1].lines(); 192 | let mut fields = vec![]; 193 | loop { 194 | if let Some(s) = parse_struct_definition(&mut lines) { 195 | fields.extend(s.fields.into_iter().map(|f| ParsedField { 196 | name: format!("{}::{}", s.name, f.name), 197 | ..f 198 | })); 199 | } else { 200 | break; 201 | } 202 | } 203 | 204 | // Memory.pm does not define this struct, but we need to read this field to calculate 205 | // the offset of the timestamps and the shared image buffer. 206 | fields.push(ParsedField { 207 | name: "VideoStoreData::size".into(), 208 | typ: Type::new::(), 209 | }); 210 | 211 | ParsedStruct { 212 | name: "memory".into(), 213 | fields, 214 | } 215 | } 216 | 217 | fn read_memory_pm(mut input: T) -> Result { 218 | let input = { 219 | let mut contents = String::new(); 220 | input.read_to_string(&mut contents)?; 221 | contents 222 | }; 223 | Ok(parse_memory_pm(&input).calculate_offsets()) 224 | } 225 | 226 | lazy_static! { 227 | static ref LAYOUT: Struct = { 228 | let file = std::fs::File::open("/usr/share/perl5/ZoneMinder/Memory.pm").expect("Failed to open ZoneMinder Memory.pm - ZM not installed or installed in unknown location."); 229 | read_memory_pm(file).unwrap() 230 | }; 231 | } 232 | 233 | #[non_exhaustive] 234 | pub struct MonitorShm { 235 | pub file: T, 236 | pub videostore_size: u32, 237 | } 238 | 239 | impl MonitorShm { 240 | pub fn new(file: File) -> Result> { 241 | let mut mshm = MonitorShm { 242 | file, 243 | videostore_size: 0, 244 | }; 245 | mshm.videostore_size = mshm.read_field("VideoStoreData::size")?; 246 | Ok(mshm) 247 | } 248 | 249 | fn lookup_field(&self, name: &str) -> &Field { 250 | for field in LAYOUT.fields.iter() { 251 | if field.name == name { 252 | return field; 253 | } 254 | } 255 | panic!("Field not found in Memory.pm: {name}"); 256 | } 257 | 258 | fn typecheck(&self, field: &Field) { 259 | let typ = Type::new::(); 260 | if field.typ != typ { 261 | panic!( 262 | "Mismatched field type for {} (wanted: {typ:?}, got: {:?}", 263 | field.name, field.typ 264 | ); 265 | } 266 | } 267 | 268 | pub fn read_field(&self, name: &str) -> Result { 269 | let field = self.lookup_field(name); 270 | self.typecheck::(field); 271 | self.pread(field.offset) 272 | } 273 | 274 | pub fn write_field(&self, name: &str, value: &T) -> Result<()> { 275 | let field = self.lookup_field(name); 276 | self.typecheck::(field); 277 | self.pwrite(field.offset, value) 278 | } 279 | 280 | pub fn write_string(&self, name: &str, value: &str) -> Result<()> { 281 | let field = self.lookup_field(name); 282 | let terminated_len = value.len() + 1; 283 | assert!(field.typ.size >= terminated_len); 284 | let mut s = String::with_capacity(terminated_len); 285 | s.push_str(value); 286 | s.push('\0'); 287 | self.file.write_all_at(s.as_bytes(), field.offset as u64)?; 288 | Ok(()) 289 | } 290 | 291 | fn pread(&self, offset: usize) -> Result { 292 | let mut buf = Vec::new(); 293 | buf.resize(size_of::(), 0); 294 | self.file.read_exact_at(&mut buf, offset as u64)?; 295 | unsafe { Ok(std::ptr::read(buf.as_ptr() as *const _)) } 296 | } 297 | 298 | fn pwrite(&self, offset: usize, data: &T) -> Result<()> { 299 | let data = unsafe { slice::from_raw_parts(data as *const T as *const u8, size_of::()) }; 300 | self.file.write_all_at(data, offset as u64)?; 301 | Ok(()) 302 | } 303 | } 304 | 305 | #[non_exhaustive] 306 | pub(super) struct ShmField; 307 | 308 | // TODO: This should be an enum and we should associate the name and expected type internally, 309 | // TODO: so that all fields we may can be validated after parsing Memory.pm 310 | impl ShmField { 311 | pub const LAST_WRITE_INDEX: &'static str = "SharedData::last_write_index"; 312 | pub const STATE: &'static str = "SharedData::state"; 313 | pub const LAST_EVENT_ID: &'static str = "SharedData::last_event"; 314 | pub const VALID: &'static str = "SharedData::valid"; 315 | pub const FORMAT: &'static str = "SharedData::format"; 316 | pub const IMAGESIZE: &'static str = "SharedData::imagesize"; 317 | 318 | pub const TRIGGER_STATE: &'static str = "TriggerData::trigger_state"; 319 | pub const TRIGGER_SCORE: &'static str = "TriggerData::trigger_score"; 320 | pub const TRIGGER_CAUSE: &'static str = "TriggerData::trigger_cause"; 321 | pub const TRIGGER_TEXT: &'static str = "TriggerData::trigger_text"; 322 | pub const TRIGGER_SHOWTEXT: &'static str = "TriggerData::trigger_showtext"; 323 | 324 | pub const SHARED_SIZE: &'static str = "SharedData::size"; 325 | pub const TRIGGER_SIZE: &'static str = "TriggerData::size"; 326 | pub const VIDEOSTORE_SIZE: &'static str = "VideoStoreData::size"; 327 | } 328 | 329 | #[cfg(test)] 330 | mod tests { 331 | use super::*; 332 | 333 | #[test] 334 | fn test_parse_typename() { 335 | assert_eq!(parse_typename("int32"), Type::new::()); 336 | assert_eq!(parse_typename("int32[44]"), Type::new::().array_of(44)); 337 | } 338 | 339 | #[test] 340 | #[should_panic] 341 | fn test_parse_typename_panic() { 342 | parse_typename("int32[44x]"); 343 | } 344 | 345 | #[test] 346 | #[should_panic] 347 | fn test_parse_typename_panic2() { 348 | parse_typename("int32[44"); 349 | } 350 | 351 | #[test] 352 | fn test_parse_field_definition() { 353 | assert_eq!( 354 | parse_field_definition(" size => { type=>'uint32', seq=>$mem_seq++ },"), 355 | ParsedField { 356 | name: "size".into(), 357 | typ: Type::new::(), 358 | } 359 | ); 360 | assert_eq!( 361 | parse_field_definition(" size => { type=>'uint32[5]', seq=>$mem_seq++ },"), 362 | ParsedField { 363 | name: "size".into(), 364 | typ: Type::new::().array_of(5), 365 | } 366 | ); 367 | } 368 | 369 | #[test] 370 | fn test_parse_struct_definition() { 371 | assert_eq!( 372 | parse_struct_definition( 373 | &mut r#"trigger_data => { type=>'TriggerData', seq=>$mem_seq++, 'contents'=> { 374 | size => { type=>'uint32', seq=>$mem_seq++ }, 375 | trigger_cause => { type=>'int8[32]', seq=>$mem_seq++ }, 376 | } 377 | },"# 378 | .lines() 379 | ) 380 | .unwrap(), 381 | ParsedStruct { 382 | name: "TriggerData".into(), 383 | fields: vec![ 384 | ParsedField { 385 | name: "size".into(), 386 | typ: Type::new::() 387 | }, 388 | ParsedField { 389 | name: "trigger_cause".into(), 390 | typ: Type::new::().array_of(32) 391 | }, 392 | ], 393 | } 394 | ); 395 | } 396 | 397 | const INPUT: &str = "our $mem_seq = 0; 398 | 399 | our $mem_data = { 400 | shared_data => { type=>'SharedData', seq=>$mem_seq++, contents=> { 401 | size => { type=>'uint32', seq=>$mem_seq++ }, 402 | startup_time => { type=>'time_t64', seq=>$mem_seq++ }, 403 | audio_fifo => { type=>'int8[64]', seq=>$mem_seq++ }, 404 | } 405 | }, 406 | trigger_data => { type=>'TriggerData', seq=>$mem_seq++, 'contents'=> { 407 | size => { type=>'uint32', seq=>$mem_seq++ }, 408 | trigger_cause => { type=>'int8[32]', seq=>$mem_seq++ }, 409 | } 410 | }, 411 | end => { seq=>$mem_seq++, size=>0 } 412 | }; 413 | 414 | our $mem_size = 0; 415 | 416 | sub zmMemInit { 417 | "; 418 | 419 | #[test] 420 | fn test_parse_memory_pm() { 421 | assert_eq!( 422 | parse_memory_pm(INPUT), 423 | ParsedStruct { 424 | name: "memory".into(), 425 | fields: vec![ 426 | ParsedField { 427 | name: "SharedData::size".into(), 428 | typ: Type::new::() 429 | }, 430 | ParsedField { 431 | name: "SharedData::startup_time".into(), 432 | typ: Type::new::() 433 | }, 434 | ParsedField { 435 | name: "SharedData::audio_fifo".into(), 436 | typ: Type::new::().array_of(64) 437 | }, 438 | ParsedField { 439 | name: "TriggerData::size".into(), 440 | typ: Type::new::() 441 | }, 442 | ParsedField { 443 | name: "TriggerData::trigger_cause".into(), 444 | typ: Type::new::().array_of(32) 445 | }, 446 | ParsedField { 447 | name: "VideoStoreData::size".into(), 448 | typ: Type::new::() 449 | }, 450 | ], 451 | }, 452 | ); 453 | } 454 | 455 | #[test] 456 | fn test_align_to() { 457 | assert_eq!(align_to(6, 8), 8); 458 | assert_eq!(align_to(8, 8), 8); 459 | assert_eq!(align_to(0, 8), 0); 460 | assert_eq!(align_to(7, 1), 7); 461 | } 462 | 463 | #[test] 464 | fn test_read_memory_pm() { 465 | assert_eq!( 466 | read_memory_pm(INPUT.as_bytes()).unwrap(), 467 | Struct { 468 | name: "memory".into(), 469 | size: 4 + 4 + 8 + 64 + 4 + 32 + 4, 470 | fields: vec![ 471 | Field { 472 | name: "SharedData::size".into(), 473 | typ: Type::new::(), 474 | offset: 0, 475 | }, 476 | Field { 477 | name: "SharedData::startup_time".into(), 478 | typ: Type::new::(), 479 | offset: align_of::(), 480 | }, 481 | Field { 482 | name: "SharedData::audio_fifo".into(), 483 | typ: Type::new::().array_of(64), 484 | offset: align_of::() + 8, 485 | }, 486 | Field { 487 | name: "TriggerData::size".into(), 488 | typ: Type::new::(), 489 | offset: align_of::() + 8 + 64, 490 | }, 491 | Field { 492 | name: "TriggerData::trigger_cause".into(), 493 | typ: Type::new::().array_of(32), 494 | offset: align_of::() + 8 + 64 + 4, 495 | }, 496 | Field { 497 | name: "VideoStoreData::size".into(), 498 | typ: Type::new::(), 499 | offset: align_of::() + 8 + 64 + 4 + 32, 500 | }, 501 | ], 502 | }, 503 | ); 504 | } 505 | } 506 | 507 | #[derive(Copy, Clone, Debug, PartialEq, Eq)] 508 | #[repr(u32)] 509 | #[allow(dead_code)] 510 | pub(super) enum MonitorState { 511 | Unknown = 0, 512 | Idle, 513 | Prealarm, // Likely when there are alarm frames but not enough to trigger an event 514 | Alarm, // I believe "current" frame is an alarm frame 515 | Alert, // Current frame is not an alarm frame, but we're still in an alarmed state 516 | Tape, // I think this is the idle state of Mocord and Record 517 | } 518 | 519 | // zm_rgb.h 520 | 521 | #[derive(Copy, Clone, Debug)] 522 | #[repr(u32)] 523 | #[allow(dead_code)] 524 | pub(super) enum ColourType { 525 | GRAY8 = 1, 526 | RGB24 = 3, 527 | RGB32 = 4, 528 | } 529 | 530 | #[derive(Copy, Clone, Debug)] 531 | #[repr(u8)] 532 | #[allow(dead_code)] 533 | pub(super) enum SubpixelOrder { 534 | NONE = 2, // grayscale 535 | RGB = 6, 536 | BGR = 5, 537 | BGRA = 7, 538 | RGBA = 8, 539 | ABGR = 9, 540 | ARGB = 10, 541 | } 542 | 543 | #[derive(Copy, Clone, Debug)] 544 | #[repr(u32)] 545 | #[allow(dead_code)] 546 | pub(super) enum TriggerState { 547 | TriggerCancel, 548 | TriggerOn, 549 | TriggerOff, 550 | } 551 | -------------------------------------------------------------------------------- /yolov4-tiny.cfg: -------------------------------------------------------------------------------- 1 | [net] 2 | # Testing 3 | #batch=1 4 | #subdivisions=1 5 | # Training 6 | batch=64 7 | subdivisions=1 8 | width=416 9 | height=416 10 | channels=3 11 | momentum=0.9 12 | decay=0.0005 13 | angle=0 14 | saturation = 1.5 15 | exposure = 1.5 16 | hue=.1 17 | 18 | learning_rate=0.00261 19 | burn_in=1000 20 | 21 | max_batches = 2000200 22 | policy=steps 23 | steps=1600000,1800000 24 | scales=.1,.1 25 | 26 | 27 | #weights_reject_freq=1001 28 | #ema_alpha=0.9998 29 | #equidistant_point=1000 30 | #num_sigmas_reject_badlabels=3 31 | #badlabels_rejection_percentage=0.2 32 | 33 | 34 | [convolutional] 35 | batch_normalize=1 36 | filters=32 37 | size=3 38 | stride=2 39 | pad=1 40 | activation=leaky 41 | 42 | [convolutional] 43 | batch_normalize=1 44 | filters=64 45 | size=3 46 | stride=2 47 | pad=1 48 | activation=leaky 49 | 50 | [convolutional] 51 | batch_normalize=1 52 | filters=64 53 | size=3 54 | stride=1 55 | pad=1 56 | activation=leaky 57 | 58 | [route] 59 | layers=-1 60 | groups=2 61 | group_id=1 62 | 63 | [convolutional] 64 | batch_normalize=1 65 | filters=32 66 | size=3 67 | stride=1 68 | pad=1 69 | activation=leaky 70 | 71 | [convolutional] 72 | batch_normalize=1 73 | filters=32 74 | size=3 75 | stride=1 76 | pad=1 77 | activation=leaky 78 | 79 | [route] 80 | layers = -1,-2 81 | 82 | [convolutional] 83 | batch_normalize=1 84 | filters=64 85 | size=1 86 | stride=1 87 | pad=1 88 | activation=leaky 89 | 90 | [route] 91 | layers = -6,-1 92 | 93 | [maxpool] 94 | size=2 95 | stride=2 96 | 97 | [convolutional] 98 | batch_normalize=1 99 | filters=128 100 | size=3 101 | stride=1 102 | pad=1 103 | activation=leaky 104 | 105 | [route] 106 | layers=-1 107 | groups=2 108 | group_id=1 109 | 110 | [convolutional] 111 | batch_normalize=1 112 | filters=64 113 | size=3 114 | stride=1 115 | pad=1 116 | activation=leaky 117 | 118 | [convolutional] 119 | batch_normalize=1 120 | filters=64 121 | size=3 122 | stride=1 123 | pad=1 124 | activation=leaky 125 | 126 | [route] 127 | layers = -1,-2 128 | 129 | [convolutional] 130 | batch_normalize=1 131 | filters=128 132 | size=1 133 | stride=1 134 | pad=1 135 | activation=leaky 136 | 137 | [route] 138 | layers = -6,-1 139 | 140 | [maxpool] 141 | size=2 142 | stride=2 143 | 144 | [convolutional] 145 | batch_normalize=1 146 | filters=256 147 | size=3 148 | stride=1 149 | pad=1 150 | activation=leaky 151 | 152 | [route] 153 | layers=-1 154 | groups=2 155 | group_id=1 156 | 157 | [convolutional] 158 | batch_normalize=1 159 | filters=128 160 | size=3 161 | stride=1 162 | pad=1 163 | activation=leaky 164 | 165 | [convolutional] 166 | batch_normalize=1 167 | filters=128 168 | size=3 169 | stride=1 170 | pad=1 171 | activation=leaky 172 | 173 | [route] 174 | layers = -1,-2 175 | 176 | [convolutional] 177 | batch_normalize=1 178 | filters=256 179 | size=1 180 | stride=1 181 | pad=1 182 | activation=leaky 183 | 184 | [route] 185 | layers = -6,-1 186 | 187 | [maxpool] 188 | size=2 189 | stride=2 190 | 191 | [convolutional] 192 | batch_normalize=1 193 | filters=512 194 | size=3 195 | stride=1 196 | pad=1 197 | activation=leaky 198 | 199 | ################################## 200 | 201 | [convolutional] 202 | batch_normalize=1 203 | filters=256 204 | size=1 205 | stride=1 206 | pad=1 207 | activation=leaky 208 | 209 | [convolutional] 210 | batch_normalize=1 211 | filters=512 212 | size=3 213 | stride=1 214 | pad=1 215 | activation=leaky 216 | 217 | [convolutional] 218 | size=1 219 | stride=1 220 | pad=1 221 | filters=255 222 | activation=linear 223 | 224 | 225 | 226 | [yolo] 227 | mask = 3,4,5 228 | anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 229 | classes=80 230 | num=6 231 | jitter=.3 232 | scale_x_y = 1.05 233 | cls_normalizer=1.0 234 | iou_normalizer=0.07 235 | iou_loss=ciou 236 | ignore_thresh = .7 237 | truth_thresh = 1 238 | random=0 239 | resize=1.5 240 | nms_kind=greedynms 241 | beta_nms=0.6 242 | #new_coords=1 243 | #scale_x_y = 2.0 244 | 245 | [route] 246 | layers = -4 247 | 248 | [convolutional] 249 | batch_normalize=1 250 | filters=128 251 | size=1 252 | stride=1 253 | pad=1 254 | activation=leaky 255 | 256 | [upsample] 257 | stride=2 258 | 259 | [route] 260 | layers = -1, 23 261 | 262 | [convolutional] 263 | batch_normalize=1 264 | filters=256 265 | size=3 266 | stride=1 267 | pad=1 268 | activation=leaky 269 | 270 | [convolutional] 271 | size=1 272 | stride=1 273 | pad=1 274 | filters=255 275 | activation=linear 276 | 277 | [yolo] 278 | mask = 1,2,3 279 | anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 280 | classes=80 281 | num=6 282 | jitter=.3 283 | scale_x_y = 1.05 284 | cls_normalizer=1.0 285 | iou_normalizer=0.07 286 | iou_loss=ciou 287 | ignore_thresh = .7 288 | truth_thresh = 1 289 | random=0 290 | resize=1.5 291 | nms_kind=greedynms 292 | beta_nms=0.6 293 | #new_coords=1 294 | #scale_x_y = 2.0 295 | -------------------------------------------------------------------------------- /yolov4-tiny.weights: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/enkore/zm-aidect/e26dd5207e5730de8159afffa9b7429410ca4029/yolov4-tiny.weights -------------------------------------------------------------------------------- /zm-aidect@.service: -------------------------------------------------------------------------------- 1 | [Service] 2 | Type=simple 3 | ExecStart=/zm-aidect/zm-aidect run %i 4 | Restart=always 5 | RestartSec=1 6 | User=www-data 7 | Group=www-data 8 | 9 | [Unit] 10 | StartLimitIntervalSec=0 11 | 12 | [Install] 13 | WantedBy=multi-user.target 14 | --------------------------------------------------------------------------------