From 3ed397bacd52a26763d558f735e279b2cc1b93f7 Mon Sep 17 00:00:00 2001 From: Scott Lamb Date: Mon, 13 Apr 2020 23:03:49 -0700 Subject: [PATCH] first step toward object detection (#30) When compiled with cargo build --features=analytics and enabled via moonfire-nvr run --object-detection, this runs object detection on every sub stream frame through an Edge TPU (a Coral USB accelerator) and logs the result. This is a very small step toward a working system. It doesn't actually record the result in the database or send it out on the live stream yet. It doesn't support running object detection at a lower frame rate than the sub streams come in at either. To address those problems, I need to do some refactoring. Currently moonfire_db::writer::Writer::Write is the only place that knows the duration of the frame it's about to flush, before it gets added to the index or sent out on the live stream. I don't want to do the detection from there; I'd prefer the moonfire_nvr crate. So I either need to introduce an analytics callback or move a bunch of that logic to the other crate. Once I do that, I need to add database support (although I have some experiments for that in moonfire-playground) and API support, then some kind of useful frontend. Note edgetpu.tflite is taken from the Apache 2.0-licensed https://github.com/google-coral/edgetpu, test_data/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite. The following page says it's fine to include Apache 2.0 stuff in GPLv3 projects: https://www.apache.org/licenses/GPL-compatibility.html --- Cargo.lock | 13 ++- Cargo.toml | 3 + src/analytics.rs | 254 +++++++++++++++++++++++++++++++++++++++++++++ src/cmds/run.rs | 15 ++- src/edgetpu.tflite | Bin 0 -> 6948536 bytes src/main.rs | 31 ++++++ src/stream.rs | 7 +- src/streamer.rs | 28 +++-- 8 files changed, 342 insertions(+), 9 deletions(-) create mode 100644 src/analytics.rs create mode 100644 src/edgetpu.tflite diff --git a/Cargo.lock b/Cargo.lock index 2685073..3b24c3d 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -1300,7 +1300,7 @@ dependencies = [ [[package]] name = "moonfire-ffmpeg" version = "0.0.1" -source = "git+https://github.com/scottlamb/moonfire-ffmpeg#c517aa782867c8b882524a2d21361753ca845f06" +source = "git+https://github.com/scottlamb/moonfire-ffmpeg#8082cc870334a6ef01420bf87b1d1ec6d14a9878" dependencies = [ "cc", "libc", @@ -1335,6 +1335,7 @@ dependencies = [ "moonfire-base", "moonfire-db", "moonfire-ffmpeg", + "moonfire-tflite", "mylog", "nix", "parking_lot", @@ -1355,6 +1356,16 @@ dependencies = [ "uuid", ] +[[package]] +name = "moonfire-tflite" +version = "0.0.1" +source = "git+https://github.com/scottlamb/moonfire-tflite#b86940ef5b3a5b2583e52c1d494477cae7dac7c8" +dependencies = [ + "cc", + "libc", + "log", +] + [[package]] name = "mylog" version = "0.1.0" diff --git a/Cargo.toml b/Cargo.toml index 435d7d8..4200e62 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -14,6 +14,8 @@ nightly = ["db/nightly", "parking_lot/nightly", "smallvec/union"] # native libraries where possible. bundled = ["rusqlite/bundled"] +analytics = ["moonfire-tflite", "ffmpeg/swscale"] + [workspace] members = ["base", "db"] @@ -51,6 +53,7 @@ rusqlite = "0.21.0" serde = { version = "1.0", features = ["derive"] } serde_json = "1.0" smallvec = "1.0" +moonfire-tflite = { git = "https://github.com/scottlamb/moonfire-tflite", features = ["edgetpu"], optional = true } time = "0.1" tokio = { version = "0.2.0", features = ["blocking", "macros", "rt-threaded", "signal"] } tokio-tungstenite = "0.10.1" diff --git a/src/analytics.rs b/src/analytics.rs new file mode 100644 index 0000000..49f6c97 --- /dev/null +++ b/src/analytics.rs @@ -0,0 +1,254 @@ +// This file is part of Moonfire NVR, a security camera network video recorder. +// Copyright (C) 2020 The Moonfire NVR Authors +// +// This program is free software: you can redistribute it and/or modify +// it under the terms of the GNU General Public License as published by +// the Free Software Foundation, either version 3 of the License, or +// (at your option) any later version. +// +// In addition, as a special exception, the copyright holders give +// permission to link the code of portions of this program with the +// OpenSSL library under certain conditions as described in each +// individual source file, and distribute linked combinations including +// the two. +// +// You must obey the GNU General Public License in all respects for all +// of the code used other than OpenSSL. If you modify file(s) with this +// exception, you may extend this exception to your version of the +// file(s), but you are not obligated to do so. If you do not wish to do +// so, delete this exception statement from your version. If you delete +// this exception statement from all source files in the program, then +// also delete it here. +// +// This program is distributed in the hope that it will be useful, +// but WITHOUT ANY WARRANTY; without even the implied warranty of +// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU General Public License +// along with this program. If not, see . + +//! Video analytics via TensorFlow Lite and an Edge TPU. +//! +//! Note this module is only compiled with `--features=analytics`. There's a stub implementation in +//! `src/main.rs` which is used otherwise. +//! +//! Currently results are only logged (rather spammily, on each frame), not persisted to the +//! database. This will change soon. +//! +//! Currently does object detection on every frame with a single hardcoded model: the 300x300 +//! MobileNet SSD v2 (COCO) from https://coral.ai/models/. Eventually analytics might include: +//! +//! * an object detection model retrained on surveillance images and/or larger input sizes +//! for increased accuracy. +//! * multiple invocations per image to improve resolution with current model sizes +//! (either fixed, overlapping subsets of the image or zooming in on full-frame detections to +//! increase confidence). +//! * support for other hardware setups (GPUs, other brands of NPUs). +//! * a motion detection model. +//! * H.264/H.265 decoding on every frame but performing object detection at a minimum pts +//! interval to cut down on expense. + +use cstr::*; +use failure::{Error, format_err}; +use ffmpeg; +use log::info; +use std::sync::Arc; + +static MODEL: &[u8] = include_bytes!("edgetpu.tflite"); + +//static MODEL_UUID: Uuid = Uuid::from_u128(0x02054a38_62cf_42ff_9ffa_04876a2970d0_u128); + +pub static MODEL_LABELS: [Option<&str>; 90] = [ + Some("person"), + Some("bicycle"), + Some("car"), + Some("motorcycle"), + Some("airplane"), + Some("bus"), + Some("train"), + Some("truck"), + Some("boat"), + Some("traffic light"), + Some("fire hydrant"), + None, + Some("stop sign"), + Some("parking meter"), + Some("bench"), + Some("bird"), + Some("cat"), + Some("dog"), + Some("horse"), + Some("sheep"), + Some("cow"), + Some("elephant"), + Some("bear"), + Some("zebra"), + Some("giraffe"), + None, + Some("backpack"), + Some("umbrella"), + None, + None, + Some("handbag"), + Some("tie"), + Some("suitcase"), + Some("frisbee"), + Some("skis"), + Some("snowboard"), + Some("sports ball"), + Some("kite"), + Some("baseball bat"), + Some("baseball glove"), + Some("skateboard"), + Some("surfboard"), + Some("tennis racket"), + Some("bottle"), + None, + Some("wine glass"), + Some("cup"), + Some("fork"), + Some("knife"), + Some("spoon"), + Some("bowl"), + Some("banana"), + Some("apple"), + Some("sandwich"), + Some("orange"), + Some("broccoli"), + Some("carrot"), + Some("hot dog"), + Some("pizza"), + Some("donut"), + Some("cake"), + Some("chair"), + Some("couch"), + Some("potted plant"), + Some("bed"), + None, + Some("dining table"), + None, + None, + Some("toilet"), + None, + Some("tv"), + Some("laptop"), + Some("mouse"), + Some("remote"), + Some("keyboard"), + Some("cell phone"), + Some("microwave"), + Some("oven"), + Some("toaster"), + Some("sink"), + Some("refrigerator"), + None, + Some("book"), + Some("clock"), + Some("vase"), + Some("scissors"), + Some("teddy bear"), + Some("hair drier"), + Some("toothbrush"), +]; + +pub struct ObjectDetector { + interpreter: parking_lot::Mutex>, + width: i32, + height: i32, +} + +impl ObjectDetector { + pub fn new(/*db: &db::LockedDatabase*/) -> Result, Error> { + let model = moonfire_tflite::Model::from_static(MODEL) + .map_err(|()| format_err!("TensorFlow Lite model initialization failed"))?; + let devices = moonfire_tflite::edgetpu::Devices::list(); + let device = devices.first().ok_or_else(|| format_err!("No Edge TPU device available"))?; + info!("Using device {:?}/{:?} for object detection", device.type_(), device.path()); + let mut builder = moonfire_tflite::Interpreter::builder(); + builder.add_owned_delegate(device.create_delegate() + .map_err(|()| format_err!("Unable to create delegate for {:?}/{:?}", + device.type_(), device.path()))?); + let interpreter = builder.build(&model) + .map_err(|()| format_err!("TensorFlow Lite initialization failed"))?; + Ok(Arc::new(Self { + interpreter: parking_lot::Mutex::new(interpreter), + width: 300, // TODO + height: 300, + })) + } +} + +pub struct ObjectDetectorStream { + decoder: ffmpeg::avcodec::DecodeContext, + frame: ffmpeg::avutil::VideoFrame, + scaler: ffmpeg::swscale::Scaler, + scaled: ffmpeg::avutil::VideoFrame, +} + +/// Copies from a RGB24 VideoFrame to a 1xHxWx3 Tensor. +fn copy(from: &ffmpeg::avutil::VideoFrame, to: &mut moonfire_tflite::Tensor) { + let from = from.plane(0); + let to = to.bytes_mut(); + let (w, h) = (from.width, from.height); + let mut from_i = 0; + let mut to_i = 0; + for _y in 0..h { + to[to_i..to_i+3*w].copy_from_slice(&from.data[from_i..from_i+3*w]); + from_i += from.linesize; + to_i += 3*w; + } +} + +const SCORE_THRESHOLD: f32 = 0.5; + +impl ObjectDetectorStream { + pub fn new(par: ffmpeg::avcodec::InputCodecParameters<'_>, + detector: &ObjectDetector) -> Result { + let mut dopt = ffmpeg::avutil::Dictionary::new(); + dopt.set(cstr!("refcounted_frames"), cstr!("0"))?; + let decoder = par.new_decoder(&mut dopt)?; + let scaled = ffmpeg::avutil::VideoFrame::owned(ffmpeg::avutil::ImageDimensions { + width: detector.width, + height: detector.height, + pix_fmt: ffmpeg::avutil::PixelFormat::rgb24(), + })?; + let frame = ffmpeg::avutil::VideoFrame::empty()?; + let scaler = ffmpeg::swscale::Scaler::new(par.dims(), scaled.dims())?; + Ok(Self { + decoder, + frame, + scaler, + scaled, + }) + } + + pub fn process_frame(&mut self, pkt: &ffmpeg::avcodec::Packet<'_>, + detector: &ObjectDetector) -> Result<(), Error> { + if !self.decoder.decode_video(pkt, &mut self.frame)? { + return Ok(()); + } + self.scaler.scale(&self.frame, &mut self.scaled); + let mut interpreter = detector.interpreter.lock(); + copy(&self.scaled, &mut interpreter.inputs()[0]); + interpreter.invoke().map_err(|()| format_err!("TFLite interpreter invocation failed"))?; + let outputs = interpreter.outputs(); + let classes = outputs[1].f32s(); + let scores = outputs[2].f32s(); + for (i, &score) in scores.iter().enumerate() { + if score < SCORE_THRESHOLD { + continue; + } + let class = classes[i] as usize; + if class >= MODEL_LABELS.len() { + continue; + } + let label = match MODEL_LABELS[class] { + None => continue, + Some(l) => l, + }; + info!("{}, score {}", label, score); + } + Ok(()) + } +} diff --git a/src/cmds/run.rs b/src/cmds/run.rs index 878e326..22a3b4f 100644 --- a/src/cmds/run.rs +++ b/src/cmds/run.rs @@ -81,6 +81,8 @@ Options: your proxy server is configured to set them and that no untrusted requests bypass the proxy server. You may want to specify --http-addr=127.0.0.1:8080. + --object-detection Perform object detection on SUB streams. + Note: requires compilation with --feature=analytics. "#; #[derive(Debug, Deserialize)] @@ -91,6 +93,7 @@ struct Args { flag_read_only: bool, flag_allow_unauthenticated_permissions: Option, flag_trust_forward_hdrs: bool, + flag_object_detection: bool, } fn trim_zoneinfo(p: &str) -> &str { @@ -176,6 +179,11 @@ pub async fn run() -> Result<(), Error> { let db = Arc::new(db::Database::new(clocks.clone(), conn, !args.flag_read_only).unwrap()); info!("Database is loaded."); + let object_detector = match args.flag_object_detection { + false => None, + true => Some(crate::analytics::ObjectDetector::new()?), + }; + { let mut l = db.lock(); let dirs_to_open: Vec<_> = @@ -252,10 +260,15 @@ pub async fn run() -> Result<(), Error> { }; let rotate_offset_sec = streamer::ROTATE_INTERVAL_SEC * i as i64 / streams as i64; let syncer = syncers.get(&sample_file_dir_id).unwrap(); + let object_detector = match stream.type_ { + db::StreamType::SUB => object_detector.as_ref().map(|a| Arc::clone(a)), + _ => None, + }; let mut streamer = 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zJ0zdL_Gj}+$xJ+y{x-Sz+^CqyGLs(OTX)`3t3HAHtIn)H@-dXD{ndb`u0K^jRkzO1EB5c;EOmTB4_`D)VNK+w?nY{5B|sE(80DN5WW`2_0E z=9AJN@lg6(>f*x{6IrI^d+*d09}A(ss?7T90h*Lae+R)=*Pp7Ns@oFDGf;n3;q<5Y z5}xxwSI3p(oJz!->U&u74s3tay{FON4Af2Ze=a^d6cc!gr1VET zl>X{KL$A*o7oY1U4<7%RieMN9jL#G^!lUM8H)bT#peYVt`iUf^(%&%`pI^H8(Atfa zl24%iDl+R&-PhwybgT_DbsW>xkJc>=gvHQ$0$SzK*OIMJ>2x}dJ$+(DIGrk4`Uu+e VpYWm85!U(()jujc2rt6o{{gq}IPU-e literal 0 HcmV?d00001 diff --git a/src/main.rs b/src/main.rs index b891a4a..b2ba361 100644 --- a/src/main.rs +++ b/src/main.rs @@ -33,6 +33,37 @@ use log::{error, info}; use serde::Deserialize; +#[cfg(feature = "analytics")] +mod analytics; + +/// Stub implementation of analytics module when not compiled with TensorFlow Lite. +#[cfg(not(feature = "analytics"))] +mod analytics { + use failure::{Error, bail}; + + pub struct ObjectDetector; + + impl ObjectDetector { + pub fn new() -> Result, Error> { + bail!("Recompile with --features=analytics for object detection."); + } + } + + pub struct ObjectDetectorStream; + + impl ObjectDetectorStream { + pub fn new(_par: ffmpeg::avcodec::InputCodecParameters<'_>, + _detector: &ObjectDetector) -> Result { + unimplemented!(); + } + + pub fn process_frame(&mut self, _pkt: &ffmpeg::avcodec::Packet<'_>, + _detector: &ObjectDetector) -> Result<(), Error> { + unimplemented!(); + } + } +} + mod body; mod cmds; mod h264; diff --git a/src/stream.rs b/src/stream.rs index 730172a..fc7de36 100644 --- a/src/stream.rs +++ b/src/stream.rs @@ -61,6 +61,7 @@ pub trait Opener : Sync { } pub trait Stream { + fn get_video_codecpar(&self) -> ffmpeg::avcodec::InputCodecParameters<'_>; fn get_extra_data(&self) -> Result; fn get_next<'p>(&'p mut self) -> Result, ffmpeg::Error>; } @@ -147,11 +148,15 @@ impl Opener for Ffmpeg { } pub struct FfmpegStream { - input: ffmpeg::avformat::InputFormatContext, + input: ffmpeg::avformat::InputFormatContext<'static>, video_i: usize, } impl Stream for FfmpegStream { + fn get_video_codecpar(&self) -> ffmpeg::avcodec::InputCodecParameters { + self.input.streams().get(self.video_i).codecpar() + } + fn get_extra_data(&self) -> Result { let video = self.input.streams().get(self.video_i); let tb = video.time_base(); diff --git a/src/streamer.rs b/src/streamer.rs index ad89ed1..178f7d7 100644 --- a/src/streamer.rs +++ b/src/streamer.rs @@ -63,13 +63,16 @@ pub struct Streamer<'a, C, S> where C: Clocks + Clone, S: 'a + stream::Stream { short_name: String, url: Url, redacted_url: Url, + detector: Option>, } impl<'a, C, S> Streamer<'a, C, S> where C: 'a + Clocks + Clone, S: 'a + stream::Stream { pub fn new<'b>(env: &Environment<'a, 'b, C, S>, dir: Arc, syncer_channel: writer::SyncerChannel<::std::fs::File>, stream_id: i32, c: &Camera, s: &Stream, rotate_offset_sec: i64, - rotate_interval_sec: i64) -> Result { + rotate_interval_sec: i64, + detector: Option>) + -> Result { let mut url = Url::parse(&s.rtsp_url)?; let mut redacted_url = url.clone(); if !c.username.is_empty() { @@ -80,16 +83,17 @@ impl<'a, C, S> Streamer<'a, C, S> where C: 'a + Clocks + Clone, S: 'a + stream:: } Ok(Streamer { shutdown: env.shutdown.clone(), - rotate_offset_sec: rotate_offset_sec, - rotate_interval_sec: rotate_interval_sec, + rotate_offset_sec, + rotate_interval_sec, db: env.db.clone(), dir, - syncer_channel: syncer_channel, + syncer_channel, opener: env.opener, - stream_id: stream_id, + stream_id, short_name: format!("{}-{}", c.short_name, s.type_.as_str()), url, redacted_url, + detector, }) } @@ -119,6 +123,11 @@ impl<'a, C, S> Streamer<'a, C, S> where C: 'a + Clocks + Clone, S: 'a + stream:: }; let realtime_offset = self.db.clocks().realtime() - clocks.monotonic(); // TODO: verify width/height. + let mut detector_stream = match self.detector.as_ref() { + None => None, + Some(od) => Some(crate::analytics::ObjectDetectorStream::new( + stream.get_video_codecpar(), &od)?), + }; let extra_data = stream.get_extra_data()?; let video_sample_entry_id = { let _t = TimerGuard::new(&clocks, || "inserting video sample entry"); @@ -144,6 +153,9 @@ impl<'a, C, S> Streamer<'a, C, S> where C: 'a + Clocks + Clone, S: 'a + stream:: debug!("{}: have first key frame", self.short_name); seen_key_frame = true; } + if let (Some(a_s), Some(a)) = (detector_stream.as_mut(), self.detector.as_ref()) { + a_s.process_frame(&pkt, &a)?; + } let frame_realtime = clocks.monotonic() + realtime_offset; let local_time = recording::Time::new(frame_realtime); rotate = if let Some(r) = rotate { @@ -273,6 +285,10 @@ mod tests { Ok(pkt) } + fn get_video_codecpar(&self) -> ffmpeg::avcodec::InputCodecParameters<'_> { + self.inner.get_video_codecpar() + } + fn get_extra_data(&self) -> Result { self.inner.get_extra_data() } } @@ -355,7 +371,7 @@ mod tests { let s = l.streams_by_id().get(&testutil::TEST_STREAM_ID).unwrap(); let dir = db.dirs_by_stream_id.get(&testutil::TEST_STREAM_ID).unwrap().clone(); stream = super::Streamer::new(&env, dir, db.syncer_channel.clone(), - testutil::TEST_STREAM_ID, camera, s, 0, 3).unwrap(); + testutil::TEST_STREAM_ID, camera, s, 0, 3, None).unwrap(); } stream.run(); assert!(opener.streams.lock().is_empty());

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