From 37fe4eee3a20eae4106801b4b55b051578ffe36b Mon Sep 17 00:00:00 2001 From: dsutanto Date: Wed, 1 Jul 2026 13:38:05 +0700 Subject: [PATCH] First Commit --- .env | 52 + .env.example | 50 + AGENTS.md | 35 + BUILD.md | 37 + CMakeLists.txt | 55 + README.md | 2 - batch_crossings.csv | 1 + batch_store.py | 379 ++ build/CMakeCache.txt | 366 ++ .../CMakeFiles/3.28.3/CMakeCXXCompiler.cmake | 85 + .../3.28.3/CMakeDetermineCompilerABI_CXX.bin | Bin 0 -> 70488 bytes build/CMakeFiles/3.28.3/CMakeSystem.cmake | 15 + .../CompilerIdCXX/CMakeCXXCompilerId.cpp | 869 +++ build/CMakeFiles/CMakeConfigureLog.yaml | 278 + .../CMakeDirectoryInformation.cmake | 16 + build/CMakeFiles/Makefile.cmake | 54 + build/CMakeFiles/Makefile2 | 112 + build/CMakeFiles/TargetDirectories.txt | 7 + .../bytetrack-counter.dir/DependInfo.cmake | 30 + .../bytetrack-counter.dir/build.make | 229 + .../bytetrack-counter.dir/cmake_clean.cmake | 25 + .../compiler_depend.internal | 3504 ++++++++++++ .../compiler_depend.make | 4986 +++++++++++++++++ .../bytetrack-counter.dir/compiler_depend.ts | 2 + .../bytetrack-counter.dir/depend.make | 2 + .../bytetrack-counter.dir/flags.make | 10 + .../CMakeFiles/bytetrack-counter.dir/link.txt | 1 + .../bytetrack-counter.dir/progress.make | 10 + build/CMakeFiles/cmake.check_cache | 1 + build/CMakeFiles/progress.marks | 1 + build/Makefile | 419 ++ build/bytetrack-counter | Bin 0 -> 3462608 bytes build/cmake_install.cmake | 82 + bytetrack-counter.service | 42 + bytetrack_counter.db | Bin 0 -> 57344 bytes counter_live_rknn_bytetrack.py | 1289 +++++ requirements.txt | 5 + src/batch_store.cpp | 414 ++ src/batch_store.hpp | 79 + src/bytetrack.cpp | 198 + src/bytetrack.hpp | 56 + src/config.cpp | 128 + src/config.hpp | 68 + src/csv_logger.cpp | 35 + src/csv_logger.hpp | 15 + src/drawing.cpp | 171 + src/drawing.hpp | 44 + src/kalman_filter.cpp | 59 + src/kalman_filter.hpp | 24 + src/main.cpp | 391 ++ src/nms.hpp | 86 + src/rknn_yolo.cpp | 230 + src/rknn_yolo.hpp | 51 + 53 files changed, 15098 insertions(+), 2 deletions(-) create mode 100644 .env create mode 100644 .env.example create mode 100644 AGENTS.md create mode 100644 BUILD.md create mode 100644 CMakeLists.txt delete mode 100644 README.md create mode 100644 batch_crossings.csv create mode 100644 batch_store.py create mode 100644 build/CMakeCache.txt create mode 100644 build/CMakeFiles/3.28.3/CMakeCXXCompiler.cmake create mode 100755 build/CMakeFiles/3.28.3/CMakeDetermineCompilerABI_CXX.bin create mode 100644 build/CMakeFiles/3.28.3/CMakeSystem.cmake create mode 100644 build/CMakeFiles/3.28.3/CompilerIdCXX/CMakeCXXCompilerId.cpp create mode 100644 build/CMakeFiles/CMakeConfigureLog.yaml create mode 100644 build/CMakeFiles/CMakeDirectoryInformation.cmake create mode 100644 build/CMakeFiles/Makefile.cmake create mode 100644 build/CMakeFiles/Makefile2 create mode 100644 build/CMakeFiles/TargetDirectories.txt create mode 100644 build/CMakeFiles/bytetrack-counter.dir/DependInfo.cmake create mode 100644 build/CMakeFiles/bytetrack-counter.dir/build.make create mode 100644 build/CMakeFiles/bytetrack-counter.dir/cmake_clean.cmake create mode 100644 build/CMakeFiles/bytetrack-counter.dir/compiler_depend.internal create mode 100644 build/CMakeFiles/bytetrack-counter.dir/compiler_depend.make create mode 100644 build/CMakeFiles/bytetrack-counter.dir/compiler_depend.ts create mode 100644 build/CMakeFiles/bytetrack-counter.dir/depend.make create mode 100644 build/CMakeFiles/bytetrack-counter.dir/flags.make create mode 100644 build/CMakeFiles/bytetrack-counter.dir/link.txt create mode 100644 build/CMakeFiles/bytetrack-counter.dir/progress.make create mode 100644 build/CMakeFiles/cmake.check_cache create mode 100644 build/CMakeFiles/progress.marks create mode 100644 build/Makefile create mode 100755 build/bytetrack-counter create mode 100644 build/cmake_install.cmake create mode 100644 bytetrack-counter.service create mode 100644 bytetrack_counter.db create mode 100644 counter_live_rknn_bytetrack.py create mode 100644 requirements.txt create mode 100644 src/batch_store.cpp create mode 100644 src/batch_store.hpp create mode 100644 src/bytetrack.cpp create mode 100644 src/bytetrack.hpp create mode 100644 src/config.cpp create mode 100644 src/config.hpp create mode 100644 src/csv_logger.cpp create mode 100644 src/csv_logger.hpp create mode 100644 src/drawing.cpp create mode 100644 src/drawing.hpp create mode 100644 src/kalman_filter.cpp create mode 100644 src/kalman_filter.hpp create mode 100644 src/main.cpp create mode 100644 src/nms.hpp create mode 100644 src/rknn_yolo.cpp create mode 100644 src/rknn_yolo.hpp diff --git a/.env b/.env new file mode 100644 index 0000000..3fd9a54 --- /dev/null +++ b/.env @@ -0,0 +1,52 @@ +# Edge RK3588 production counter — copy to .env on device +# cp .env.example .env && nano .env + +OUTPUT_DIR=/opt/bytetrack-counter-cpp +DB_PATH=/opt/bytetrack-counter-cpp/bytetrack_counter.db +STATE_FILE=/tmp/bytetrack_current_batch.json + +# Direct LAN camera RTSP (low latency) +SOURCE=rtsp://frigate:zenai@192.168.192.16:8554/camera_stream_640 +#SOURCE=rtsp://192.168.192.53:8554/my_stream +OPENCV_FFMPEG_CAPTURE_OPTIONS=rtsp_transport;tcp|fflags;nobuffer|flags;low_delay + +# RKNN model — export'd from YOLO with imgsz=320 +MODEL_PATH=/opt/models/zenai_apc_cicalengka_20260609.rknn +IMGSZ=320 +HALF=false +CONF=0.3 + +# RKNN NPU core mask: 1=core0, 2=core1, 3=core0+core1, 7=all three +CORE_MASK=7 + +# YOLO decoder params (must match model export) +NUM_CLASSES=2 +SCORE_SIGMOID=false + +CAMERA_NAME=CC1 +OBJECT_LABEL=ayam-potong +CLASS_AYAM=ayam +CLASS_TALENAN=talenan + +# Line crossing: rtl (default) | ltr | both +CROSS_DIRECTION=ltr +LINE_X= +LINE_X_FRAC=0.5 + +DAILY_CUTOFF_TIME=17:00 +BATCH_TIMEOUT_SECONDS=300 +IGNORE_BATCH_LABEL_TIMEOUT_SECONDS=30 +MIN_OBJECT_PER_BATCH=60 +MIN_DURATION_PER_BATCH=60 + +EXPORT_CSV=true +RECORD_VIDEO=false + +LIVE_STREAM_ENABLED=true +LIVE_STREAM_FRAME_PATH=/dev/shm/bytetrack-counter/live_frame.jpg +LIVE_STREAM_QUALITY=75 +LIVE_STREAM_EVERY_N=2 + +LOG_LEVEL=info + +DASHBOARD_PORT=5001 diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..41ac89c --- /dev/null +++ b/.env.example @@ -0,0 +1,50 @@ +# Edge RK3588 production counter — copy to .env on device +# cp .env.example .env && nano .env + +OUTPUT_DIR=/opt/bytetrack-counter-go +DB_PATH=/opt/bytetrack-counter-go/bytetrack_counter.db +STATE_FILE=/tmp/bytetrack_current_batch.json + +# Direct LAN camera RTSP (low latency) +SOURCE=rtsp://frigate:zenai@127.0.0.1:8554/camera_stream_640 +#SOURCE=rtsp://192.168.192.53:8554/my_stream +OPENCV_FFMPEG_CAPTURE_OPTIONS=rtsp_transport;tcp|fflags;nobuffer|flags;low_delay + +# RKNN model — export'd from YOLO with imgsz=320 +MODEL_PATH=/opt/models/zenai_apc_cicalengka_20260609.rknn +IMGSZ=320 +HALF=false +CONF=0.3 + +# RKNN NPU core mask: 1=core0, 2=core1, 3=core0+core1, 7=all three +CORE_MASK=7 + +# YOLO decoder params (must match model export) +NUM_CLASSES=2 +SCORE_SIGMOID=false + +CAMERA_NAME=CC1 +OBJECT_LABEL=ayam-potong +CLASS_AYAM=ayam +CLASS_TALENAN=talenan + +# Line crossing: rtl (default) | ltr | both +CROSS_DIRECTION=ltr +LINE_X= +LINE_X_FRAC=0.5 + +DAILY_CUTOFF_TIME=17:00 +BATCH_TIMEOUT_SECONDS=300 +IGNORE_BATCH_LABEL_TIMEOUT_SECONDS=30 +MIN_OBJECT_PER_BATCH=60 +MIN_DURATION_PER_BATCH=60 + +EXPORT_CSV=true +RECORD_VIDEO=false + +LIVE_STREAM_ENABLED=true +LIVE_STREAM_FRAME_PATH=/dev/shm/bytetrack-counter/live_frame.jpg +LIVE_STREAM_QUALITY=75 +LIVE_STREAM_EVERY_N=2 + +LOG_LEVEL=info diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..c2c7d2d --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,35 @@ +# AGENTS.md — ByteTrack Counter C++ + +## RKNN Dynamic Shape Requirement + +The RKNN model (`zenai_apc_cicalengka_20260609.rknn`) is exported with **dynamic input shapes** (`model inference type: dynamic_shape`). The C API **requires** calling `rknn_set_input_shapes()` before `rknn_inputs_set()`. Without it, `librknnrt.so` crashes with: + +``` +std::out_of_range: vector::_M_range_check: __n (which is 18446744073709551615) >= this->size() (which is 1) +``` + +This is called in the constructor of `RKNNYOLO` at `src/rknn_yolo.cpp:60`, right after querying input attributes: + +```cpp +in_attr.dims[0] = 1; +in_attr.dims[1] = imgsz_; +in_attr.dims[2] = imgsz_; +in_attr.dims[3] = 3; +in_attr.n_dims = 4; +ret = rknn_set_input_shapes(rknn_ctx_, io_num.n_input, &in_attr); +``` + +## Build + +```bash +cd /opt/bytetrack-counter-cpp/build +cmake .. -DHAS_RKNN=ON +make -j$(nproc) +``` + +## Service + +```bash +systemctl restart bytetrack-counter +journalctl -u bytetrack-counter -f +``` diff --git a/BUILD.md b/BUILD.md new file mode 100644 index 0000000..347ed2f --- /dev/null +++ b/BUILD.md @@ -0,0 +1,37 @@ +# ByteTrack Counter C++ — Build Instructions + +## Install Dependencies + +```bash +apt install -y build-essential cmake libopencv-dev libeigen3-dev libsqlite3-dev +``` + +| Package | Provides | +|---------|---------| +| `build-essential` | g++, make, libc-dev | +| `cmake` | CMake | +| `libopencv-dev` | OpenCV (core, imgproc, videoio, highgui, imgcodecs) | +| `libeigen3-dev` | Eigen3 linear algebra | +| `libsqlite3-dev` | SQLite3 | + +For RKNN NPU support, also install `librknnrt` from your board's SDK. + +## Compile + +```bash +mkdir -p build && cd build + +# Without NPU +cmake .. && make -j$(nproc) + +# With RKNN NPU support +cmake .. -DHAS_RKNN=ON && make -j$(nproc) +``` + +The binary will be generated at `build/bytetrack-counter`. + +## Install + +```bash +cmake --install . --prefix /opt/bytetrack-counter-cpp/build/install +``` diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100644 index 0000000..7dc4f13 --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,55 @@ +cmake_minimum_required(VERSION 3.16) +project(bytetrack-counter LANGUAGES CXX) + +set(CMAKE_CXX_STANDARD 17) +set(CMAKE_CXX_STANDARD_REQUIRED ON) +set(CMAKE_CXX_EXTENSIONS OFF) + +find_package(OpenCV REQUIRED COMPONENTS core imgproc videoio highgui imgcodecs) +set(Eigen3_DIR /usr/share/eigen3/cmake) +find_package(Eigen3 REQUIRED NO_MODULE) +find_package(SQLite3 REQUIRED) + +option(HAS_RKNN "Build with RKNN NPU support" OFF) + +if(HAS_RKNN) + add_definitions(-DHAS_RKNN) + include_directories(/usr/local/include/rknn) +endif() + +set(SOURCES + src/main.cpp + src/kalman_filter.cpp + src/bytetrack.cpp + src/batch_store.cpp + src/csv_logger.cpp + src/rknn_yolo.cpp + src/config.cpp + src/drawing.cpp +) + +add_executable(bytetrack-counter ${SOURCES}) + +target_include_directories(bytetrack-counter PRIVATE + ${OpenCV_INCLUDE_DIRS} + ${EIGEN3_INCLUDE_DIR} + ${SQLite3_INCLUDE_DIRS} + src +) + +target_link_libraries(bytetrack-counter + ${OpenCV_LIBS} + ${SQLite3_LIBRARIES} + pthread +) + +if(HAS_RKNN) + find_library(RKNN_LIB rknnrt PATHS /usr/lib /usr/local/lib) + if(RKNN_LIB) + target_link_libraries(bytetrack-counter ${RKNN_LIB}) + else() + message(WARNING "librknnrt not found — RKNN support disabled") + endif() +endif() + +install(TARGETS bytetrack-counter DESTINATION /opt/bytetrack-counter-cpp/bin) diff --git a/README.md b/README.md deleted file mode 100644 index d5f65c5..0000000 --- a/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# bytetrack-counter-cpp - diff --git a/batch_crossings.csv b/batch_crossings.csv new file mode 100644 index 0000000..5c91149 --- /dev/null +++ b/batch_crossings.csv @@ -0,0 +1 @@ +batch,frame,timestamp,chicken_id diff --git a/batch_store.py b/batch_store.py new file mode 100644 index 0000000..94ee985 --- /dev/null +++ b/batch_store.py @@ -0,0 +1,379 @@ +""" +Production batch persistence for edge Jetson counter. +Mirrors frigate-counter SQLite schema + current_batch.json contract. +""" +import json +import sqlite3 +import threading +import time +from datetime import datetime, timedelta +from pathlib import Path + + +class BatchStore: + def __init__( + self, + db_path, + state_file, + camera_name, + object_label='ayam-potong', + cutoff_time='20:00', + batch_timeout=300.0, + ignore_batch_label_timeout=30.0, + min_object_per_batch=60, + min_duration_per_batch=60, + carry_ids=50, + logger=print, + ): + self.db_path = db_path + self.state_file = Path(state_file) + self.camera_name = camera_name + self.object_label = object_label + self.cutoff_time_str = cutoff_time + datetime.strptime(cutoff_time, '%H:%M') + + self.batch_timeout = float(batch_timeout) + self.ignore_batch_label_timeout = float(ignore_batch_label_timeout) + self.min_object_per_batch = int(min_object_per_batch) + self.min_duration_per_batch = int(min_duration_per_batch) + self.carry_ids = int(carry_ids) + self.log = logger + + self.state_lock = threading.Lock() + self.batch_timer = None + self.ignore_batch_label = False + self.ignore_batch_label_timer = None + self.previous_state = None + self.shutdown_event = threading.Event() + + Path(db_path).parent.mkdir(parents=True, exist_ok=True) + self.state_file.parent.mkdir(parents=True, exist_ok=True) + + self.db = sqlite3.connect(db_path, check_same_thread=False) + self._init_db() + self.current_state = self._load_state() + self.previous_state = self.current_state + + def _init_db(self): + cur = self.db.cursor() + cur.execute( + """ + CREATE TABLE IF NOT EXISTS batches ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + counting_date TEXT NOT NULL, + batch_number INTEGER NOT NULL, + camera_name TEXT NOT NULL, + object_label TEXT NOT NULL, + count INTEGER NOT NULL, + start_time TEXT NOT NULL, + end_time TEXT NOT NULL, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE(counting_date, batch_number, camera_name, object_label) + ) + """ + ) + cur.execute( + """ + CREATE TABLE IF NOT EXISTS daily_summaries ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + counting_date TEXT NOT NULL, + camera_name TEXT NOT NULL, + object_label TEXT NOT NULL, + total_count INTEGER NOT NULL DEFAULT 0, + total_batches INTEGER NOT NULL DEFAULT 0, + updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE(counting_date, camera_name, object_label) + ) + """ + ) + self.db.commit() + + def get_counting_date(self, dt=None): + if dt is None: + dt = datetime.now() + cutoff = datetime.strptime(self.cutoff_time_str, '%H:%M').time() + if dt.time() < cutoff: + return dt.date().isoformat() + return (dt.date() + timedelta(days=1)).isoformat() + + def _load_state(self): + if not self.state_file.exists(): + return None + try: + with open(self.state_file, 'r', encoding='utf-8') as f: + state = json.load(f) + current_date = self.get_counting_date() + if state.get('counting_date') != current_date: + self.log( + f"State file belongs to previous counting day ({state.get('counting_date')}). " + 'Finalizing before fresh start.' + ) + self._insert_batch( + state['counting_date'], + state['batch_number'], + state['count'], + state['start_time'], + datetime.now().isoformat(), + ) + self.state_file.unlink(missing_ok=True) + return None + self.log( + f"Resumed batch #{state['batch_number']} from {state['start_time']} " + f"with count={state['count']}" + ) + self._reset_batch_timer() + return state + except Exception as exc: + self.log(f'Failed to load state file: {exc}') + return None + + def save_state(self): + if self.current_state is None: + self.state_file.unlink(missing_ok=True) + return + with open(self.state_file, 'w', encoding='utf-8') as f: + json.dump(self.current_state, f, indent=2, ensure_ascii=False) + + def get_next_batch_number(self, counting_date): + cur = self.db.cursor() + cur.execute( + """ + SELECT COALESCE(MAX(batch_number), 0) + FROM batches + WHERE counting_date = ? AND camera_name = ? AND object_label = ? + """, + (counting_date, self.camera_name, self.object_label), + ) + return cur.fetchone()[0] + 1 + + def start_new_batch(self, counting_date): + batch_number = self.get_next_batch_number(counting_date) + now = datetime.now().isoformat() + counted_ids = [] + if self.previous_state is not None: + try: + counted_ids = self.previous_state['counted_event_ids'][-self.carry_ids:] + except (KeyError, TypeError): + counted_ids = [] + self.current_state = { + 'counting_date': counting_date, + 'batch_number': batch_number, + 'count': 0, + 'start_time': now, + 'last_detection_time': now, + 'counted_event_ids': counted_ids, + } + self.save_state() + self.log(f'Started batch #{batch_number} for {counting_date} ({self.object_label})') + + def _reset_batch_timer(self): + if self.batch_timer: + self.batch_timer.cancel() + self.batch_timer = threading.Timer(self.batch_timeout, self._on_batch_timeout) + self.batch_timer.daemon = True + self.batch_timer.start() + + def _on_batch_timeout(self): + self.log(f'Batch inactivity timeout ({self.batch_timeout}s) reached') + self.end_batch(closed_by='timeout') + + def _ignore_batch_label(self): + if not self.ignore_batch_label_timer: + self.ignore_batch_label = True + self.ignore_batch_label_timer = threading.Timer( + self.ignore_batch_label_timeout, self._on_ignore_batch_label_timeout + ) + self.ignore_batch_label_timer.daemon = True + self.ignore_batch_label_timer.start() + self.log( + f'Ignore batch label for {self.ignore_batch_label_timeout}s' + ) + + def _on_ignore_batch_label_timeout(self): + self.ignore_batch_label_timer = None + self.ignore_batch_label = False + self.log('Ignore batch label cooldown finished') + + def record_ayam_crossing(self, track_id): + """Line-cross equivalent of production ayam-potong MQTT event.""" + with self.state_lock: + counting_date = self.get_counting_date() + started_new = False + if self.current_state is None: + self.start_new_batch(counting_date) + started_new = True + elif self.current_state['counting_date'] != counting_date: + self._end_batch_locked(closed_by='cutoff') + self.start_new_batch(counting_date) + started_new = True + + event_key = str(track_id) + if event_key not in self.current_state['counted_event_ids']: + self.current_state['count'] += 1 + self.current_state['counted_event_ids'].append(event_key) + self.log( + f'Counted ayam (track {track_id}) | batch #{self.current_state["batch_number"]} ' + f'total: {self.current_state["count"]}' + ) + + self.current_state['last_detection_time'] = datetime.now().isoformat() + self.save_state() + self._reset_batch_timer() + return self.current_state['count'], started_new + + def record_talenan_crossing(self, track_id): + """Line-cross equivalent of production telenan MQTT batch close.""" + if self.ignore_batch_label: + return False + with self.state_lock: + self._ignore_batch_label() + self._end_batch_locked(closed_by='talenan') + self.log(f'Batch closed by talenan (track {track_id})') + if self.batch_timer: + self.batch_timer.cancel() + self.batch_timer = None + return True + + def end_batch(self, closed_by='manual'): + with self.state_lock: + self._end_batch_locked(closed_by=closed_by) + + def _end_batch_locked(self, closed_by='manual'): + if self.current_state is None: + return False + + self.previous_state = self.current_state + state = self.current_state + + start_time_obj = datetime.fromisoformat(state['start_time']) + end_time_obj = datetime.now() + duration_seconds = (end_time_obj - start_time_obj).total_seconds() + + if (state['count'] < self.min_object_per_batch + or duration_seconds < self.min_duration_per_batch): + self.current_state = None + self.save_state() + if self.batch_timer: + self.batch_timer.cancel() + self.batch_timer = None + self.log( + f'Batch #{state["batch_number"]} discarded ' + f'(count={state["count"]}, duration={duration_seconds:.0f}s)' + ) + return False + + end_time = end_time_obj.isoformat() + try: + self._insert_batch( + state['counting_date'], + state['batch_number'], + state['count'], + state['start_time'], + end_time, + ) + cps = state['count'] / duration_seconds if duration_seconds > 0 else 0 + self.log( + f'Batch #{state["batch_number"]} ended | count={state["count"]} | ' + f'duration={duration_seconds:.0f}s | cps={cps:.3f} | closed_by={closed_by}' + ) + except Exception as exc: + self.log(f'Failed to persist batch: {exc}') + return False + + self.current_state = None + self.save_state() + if self.batch_timer: + self.batch_timer.cancel() + self.batch_timer = None + return True + + def _insert_batch(self, counting_date, batch_number, count, start_time, end_time): + cur = self.db.cursor() + cur.execute( + """ + INSERT INTO batches + (counting_date, batch_number, camera_name, object_label, count, start_time, end_time) + VALUES (?, ?, ?, ?, ?, ?, ?) + """, + (counting_date, batch_number, self.camera_name, self.object_label, count, start_time, end_time), + ) + cur.execute( + """ + INSERT INTO daily_summaries + (counting_date, camera_name, object_label, total_count, total_batches) + VALUES (?, ?, ?, ?, 1) + ON CONFLICT(counting_date, camera_name, object_label) + DO UPDATE SET + total_count = total_count + excluded.total_count, + total_batches = total_batches + excluded.total_batches, + updated_at = CURRENT_TIMESTAMP + """, + (counting_date, self.camera_name, self.object_label, count), + ) + self.db.commit() + + cur.execute( + """ + SELECT total_count, total_batches + FROM daily_summaries + WHERE counting_date = ? AND camera_name = ? AND object_label = ? + """, + (counting_date, self.camera_name, self.object_label), + ) + row = cur.fetchone() + if row: + self.log( + f'Daily totals for {counting_date}: {row[0]} objects across {row[1]} batch(es)' + ) + + def cutoff_watcher_loop(self): + while not self.shutdown_event.is_set(): + time.sleep(60) + with self.state_lock: + if self.current_state is None: + continue + if self.current_state['counting_date'] != self.get_counting_date(): + self.log('Daily cutoff reached – finalizing batch') + self._end_batch_locked(closed_by='cutoff') + + def start_cutoff_watcher(self): + t = threading.Thread(target=self.cutoff_watcher_loop, daemon=True) + t.start() + return t + + @property + def current_batch_number(self): + if self.current_state is None: + return 0 + return self.current_state['batch_number'] + + @property + def current_batch_count(self): + if self.current_state is None: + return 0 + return self.current_state['count'] + + def get_closed_total_for_day(self, counting_date=None): + if counting_date is None: + counting_date = self.get_counting_date() + cur = self.db.cursor() + cur.execute( + """ + SELECT COALESCE(total_count, 0) + FROM daily_summaries + WHERE counting_date = ? AND camera_name = ? AND object_label = ? + """, + (counting_date, self.camera_name, self.object_label), + ) + row = cur.fetchone() + return row[0] if row else 0 + + def display_total(self): + return self.get_closed_total_for_day() + self.current_batch_count + + def shutdown(self): + self.shutdown_event.set() + self.end_batch(closed_by='shutdown') + if self.batch_timer: + self.batch_timer.cancel() + self.db.close() diff --git a/build/CMakeCache.txt b/build/CMakeCache.txt new file mode 100644 index 0000000..70fc6ca --- /dev/null +++ b/build/CMakeCache.txt @@ -0,0 +1,366 @@ +# This is the CMakeCache file. +# For build in directory: /opt/bytetrack-counter-cpp/build +# It was generated by CMake: /usr/bin/cmake +# You can edit this file to change values found and used by cmake. +# If you do not want to change any of the values, simply exit the editor. +# If you do want to change a value, simply edit, save, and exit the editor. +# The syntax for the file is as follows: +# KEY:TYPE=VALUE +# KEY is the name of a 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DEC(__clang_patchlevel__) +# if defined(_MSC_VER) + /* _MSC_VER = VVRR */ +# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) +# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) +# endif +# define COMPILER_VERSION_TWEAK DEC(__apple_build_version__) + +#elif defined(__clang__) && defined(__ARMCOMPILER_VERSION) +# define COMPILER_ID "ARMClang" + # define COMPILER_VERSION_MAJOR DEC(__ARMCOMPILER_VERSION/1000000) + # define COMPILER_VERSION_MINOR DEC(__ARMCOMPILER_VERSION/10000 % 100) + # define COMPILER_VERSION_PATCH DEC(__ARMCOMPILER_VERSION/100 % 100) +# define COMPILER_VERSION_INTERNAL DEC(__ARMCOMPILER_VERSION) + +#elif defined(__clang__) +# define COMPILER_ID "Clang" +# if defined(_MSC_VER) +# define SIMULATE_ID "MSVC" +# endif +# define COMPILER_VERSION_MAJOR DEC(__clang_major__) +# define COMPILER_VERSION_MINOR DEC(__clang_minor__) +# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__) +# if defined(_MSC_VER) + /* _MSC_VER = VVRR */ +# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) +# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) +# endif + +#elif defined(__LCC__) && (defined(__GNUC__) || defined(__GNUG__) || defined(__MCST__)) +# define COMPILER_ID "LCC" +# define COMPILER_VERSION_MAJOR DEC(__LCC__ / 100) +# define COMPILER_VERSION_MINOR DEC(__LCC__ % 100) +# if defined(__LCC_MINOR__) +# define COMPILER_VERSION_PATCH DEC(__LCC_MINOR__) +# endif +# if defined(__GNUC__) && defined(__GNUC_MINOR__) +# define SIMULATE_ID "GNU" +# define SIMULATE_VERSION_MAJOR DEC(__GNUC__) +# define SIMULATE_VERSION_MINOR DEC(__GNUC_MINOR__) +# if defined(__GNUC_PATCHLEVEL__) +# define SIMULATE_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__) +# endif +# endif + +#elif defined(__GNUC__) || defined(__GNUG__) +# define COMPILER_ID "GNU" +# if defined(__GNUC__) +# define COMPILER_VERSION_MAJOR DEC(__GNUC__) +# else +# define COMPILER_VERSION_MAJOR DEC(__GNUG__) +# endif +# if defined(__GNUC_MINOR__) +# define COMPILER_VERSION_MINOR DEC(__GNUC_MINOR__) +# endif +# if defined(__GNUC_PATCHLEVEL__) +# define COMPILER_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__) +# endif + +#elif defined(_MSC_VER) +# define COMPILER_ID "MSVC" + /* _MSC_VER = VVRR */ +# define COMPILER_VERSION_MAJOR DEC(_MSC_VER / 100) +# define COMPILER_VERSION_MINOR DEC(_MSC_VER % 100) +# if defined(_MSC_FULL_VER) +# if _MSC_VER >= 1400 + /* _MSC_FULL_VER = VVRRPPPPP */ +# define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 100000) +# else + /* _MSC_FULL_VER = VVRRPPPP */ +# define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 10000) +# endif +# endif +# if defined(_MSC_BUILD) +# define COMPILER_VERSION_TWEAK DEC(_MSC_BUILD) +# endif + +#elif defined(_ADI_COMPILER) +# define COMPILER_ID "ADSP" +#if defined(__VERSIONNUM__) + /* __VERSIONNUM__ = 0xVVRRPPTT */ +# define COMPILER_VERSION_MAJOR DEC(__VERSIONNUM__ >> 24 & 0xFF) +# define COMPILER_VERSION_MINOR DEC(__VERSIONNUM__ >> 16 & 0xFF) +# define COMPILER_VERSION_PATCH DEC(__VERSIONNUM__ >> 8 & 0xFF) +# define COMPILER_VERSION_TWEAK DEC(__VERSIONNUM__ & 0xFF) +#endif + +#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) +# define COMPILER_ID "IAR" +# if defined(__VER__) && defined(__ICCARM__) +# define COMPILER_VERSION_MAJOR DEC((__VER__) / 1000000) +# define COMPILER_VERSION_MINOR DEC(((__VER__) / 1000) % 1000) +# define COMPILER_VERSION_PATCH DEC((__VER__) % 1000) +# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) +# elif defined(__VER__) && (defined(__ICCAVR__) || defined(__ICCRX__) || defined(__ICCRH850__) || defined(__ICCRL78__) || defined(__ICC430__) || defined(__ICCRISCV__) || defined(__ICCV850__) || defined(__ICC8051__) || defined(__ICCSTM8__)) +# define COMPILER_VERSION_MAJOR DEC((__VER__) / 100) +# define COMPILER_VERSION_MINOR DEC((__VER__) - (((__VER__) / 100)*100)) +# define COMPILER_VERSION_PATCH DEC(__SUBVERSION__) +# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) +# endif + + +/* These compilers are either not known or too old to define an + identification macro. Try to identify the platform and guess that + it is the native compiler. */ +#elif defined(__hpux) || defined(__hpua) +# define COMPILER_ID "HP" + +#else /* unknown compiler */ +# define COMPILER_ID "" +#endif + +/* Construct the string literal in pieces to prevent the source from + getting matched. Store it in a pointer rather than an array + because some compilers will just produce instructions to fill the + array rather than assigning a pointer to a static array. */ +char const* info_compiler = "INFO" ":" "compiler[" COMPILER_ID "]"; +#ifdef SIMULATE_ID +char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]"; +#endif + +#ifdef __QNXNTO__ +char const* qnxnto = "INFO" ":" "qnxnto[]"; +#endif + +#if defined(__CRAYXT_COMPUTE_LINUX_TARGET) +char const *info_cray = "INFO" ":" "compiler_wrapper[CrayPrgEnv]"; +#endif + +#define STRINGIFY_HELPER(X) #X +#define STRINGIFY(X) STRINGIFY_HELPER(X) + +/* Identify known platforms by name. */ +#if defined(__linux) || defined(__linux__) || defined(linux) +# define PLATFORM_ID "Linux" + +#elif defined(__MSYS__) +# define PLATFORM_ID "MSYS" + +#elif defined(__CYGWIN__) +# define PLATFORM_ID "Cygwin" + +#elif defined(__MINGW32__) +# define PLATFORM_ID "MinGW" + +#elif defined(__APPLE__) +# define PLATFORM_ID "Darwin" + +#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32) +# define PLATFORM_ID "Windows" + +#elif defined(__FreeBSD__) || defined(__FreeBSD) +# define PLATFORM_ID "FreeBSD" + +#elif defined(__NetBSD__) || defined(__NetBSD) +# define PLATFORM_ID "NetBSD" + +#elif defined(__OpenBSD__) || defined(__OPENBSD) +# define PLATFORM_ID "OpenBSD" + +#elif defined(__sun) || defined(sun) +# define PLATFORM_ID "SunOS" + +#elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__) +# define PLATFORM_ID "AIX" + +#elif defined(__hpux) || defined(__hpux__) +# define PLATFORM_ID "HP-UX" + +#elif defined(__HAIKU__) +# define PLATFORM_ID "Haiku" + +#elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS) +# define PLATFORM_ID "BeOS" + +#elif defined(__QNX__) || defined(__QNXNTO__) +# define PLATFORM_ID "QNX" + +#elif defined(__tru64) || defined(_tru64) || defined(__TRU64__) +# define PLATFORM_ID "Tru64" + +#elif defined(__riscos) || defined(__riscos__) +# define PLATFORM_ID "RISCos" + +#elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__) +# define PLATFORM_ID "SINIX" + +#elif defined(__UNIX_SV__) +# define PLATFORM_ID "UNIX_SV" + +#elif defined(__bsdos__) +# define PLATFORM_ID "BSDOS" + +#elif defined(_MPRAS) || defined(MPRAS) +# define PLATFORM_ID "MP-RAS" + +#elif defined(__osf) || defined(__osf__) +# define PLATFORM_ID "OSF1" + +#elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv) +# define PLATFORM_ID "SCO_SV" + +#elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX) +# define PLATFORM_ID "ULTRIX" + +#elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX) +# define PLATFORM_ID "Xenix" + +#elif defined(__WATCOMC__) +# if defined(__LINUX__) +# define PLATFORM_ID "Linux" + +# elif defined(__DOS__) +# define PLATFORM_ID "DOS" + +# elif defined(__OS2__) +# define PLATFORM_ID "OS2" + +# elif defined(__WINDOWS__) +# define PLATFORM_ID "Windows3x" + +# elif defined(__VXWORKS__) +# define PLATFORM_ID "VxWorks" + +# else /* unknown platform */ +# define PLATFORM_ID +# endif + +#elif defined(__INTEGRITY) +# if defined(INT_178B) +# define PLATFORM_ID "Integrity178" + +# else /* regular Integrity */ +# define PLATFORM_ID "Integrity" +# endif + +# elif defined(_ADI_COMPILER) +# define PLATFORM_ID "ADSP" + +#else /* unknown platform */ +# define PLATFORM_ID + +#endif + +/* For windows compilers MSVC and Intel we can determine + the architecture of the compiler being used. This is because + the compilers do not have flags that can change the architecture, + but rather depend on which compiler is being used +*/ +#if defined(_WIN32) && defined(_MSC_VER) +# if defined(_M_IA64) +# define ARCHITECTURE_ID "IA64" + +# elif defined(_M_ARM64EC) +# define ARCHITECTURE_ID "ARM64EC" + +# elif defined(_M_X64) || defined(_M_AMD64) +# define ARCHITECTURE_ID "x64" + +# elif defined(_M_IX86) +# define ARCHITECTURE_ID "X86" + +# elif defined(_M_ARM64) +# define ARCHITECTURE_ID "ARM64" + +# elif defined(_M_ARM) +# if _M_ARM == 4 +# define ARCHITECTURE_ID "ARMV4I" +# elif _M_ARM == 5 +# define ARCHITECTURE_ID "ARMV5I" +# else +# define ARCHITECTURE_ID "ARMV" STRINGIFY(_M_ARM) +# endif + +# elif defined(_M_MIPS) +# define ARCHITECTURE_ID "MIPS" + +# elif defined(_M_SH) +# define ARCHITECTURE_ID "SHx" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__WATCOMC__) +# if defined(_M_I86) +# define ARCHITECTURE_ID "I86" + +# elif defined(_M_IX86) +# define ARCHITECTURE_ID "X86" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) +# if defined(__ICCARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__ICCRX__) +# define ARCHITECTURE_ID "RX" + +# elif defined(__ICCRH850__) +# define ARCHITECTURE_ID "RH850" + +# elif defined(__ICCRL78__) +# define ARCHITECTURE_ID "RL78" + +# elif defined(__ICCRISCV__) +# define ARCHITECTURE_ID "RISCV" + +# elif defined(__ICCAVR__) +# define ARCHITECTURE_ID "AVR" + +# elif defined(__ICC430__) +# define ARCHITECTURE_ID "MSP430" + +# elif defined(__ICCV850__) +# define ARCHITECTURE_ID "V850" + +# elif defined(__ICC8051__) +# define ARCHITECTURE_ID "8051" + +# elif defined(__ICCSTM8__) +# define ARCHITECTURE_ID "STM8" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__ghs__) +# if defined(__PPC64__) +# define ARCHITECTURE_ID "PPC64" + +# elif defined(__ppc__) +# define ARCHITECTURE_ID "PPC" + +# elif defined(__ARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__x86_64__) +# define ARCHITECTURE_ID "x64" + +# elif defined(__i386__) +# define ARCHITECTURE_ID "X86" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +#elif defined(__TI_COMPILER_VERSION__) +# if defined(__TI_ARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__MSP430__) +# define ARCHITECTURE_ID "MSP430" + +# elif defined(__TMS320C28XX__) +# define ARCHITECTURE_ID "TMS320C28x" + +# elif defined(__TMS320C6X__) || defined(_TMS320C6X) +# define ARCHITECTURE_ID "TMS320C6x" + +# else /* unknown architecture */ +# define ARCHITECTURE_ID "" +# endif + +# elif defined(__ADSPSHARC__) +# define ARCHITECTURE_ID "SHARC" + +# elif defined(__ADSPBLACKFIN__) +# define ARCHITECTURE_ID "Blackfin" + +#elif defined(__TASKING__) + +# if defined(__CTC__) || defined(__CPTC__) +# define ARCHITECTURE_ID "TriCore" + +# elif defined(__CMCS__) +# define ARCHITECTURE_ID "MCS" + +# elif defined(__CARM__) +# define ARCHITECTURE_ID "ARM" + +# elif defined(__CARC__) +# define ARCHITECTURE_ID "ARC" + +# elif defined(__C51__) +# define ARCHITECTURE_ID "8051" + +# elif defined(__CPCP__) +# define ARCHITECTURE_ID "PCP" + +# else +# define ARCHITECTURE_ID "" +# endif + +#else +# define ARCHITECTURE_ID +#endif + +/* Convert integer to decimal digit literals. */ +#define DEC(n) \ + ('0' + (((n) / 10000000)%10)), \ + ('0' + (((n) / 1000000)%10)), \ + ('0' + (((n) / 100000)%10)), \ + ('0' + (((n) / 10000)%10)), \ + ('0' + (((n) / 1000)%10)), \ + ('0' + (((n) / 100)%10)), \ + ('0' + (((n) / 10)%10)), \ + ('0' + ((n) % 10)) + +/* Convert integer to hex digit literals. */ +#define HEX(n) \ + ('0' + ((n)>>28 & 0xF)), \ + ('0' + ((n)>>24 & 0xF)), \ + ('0' + ((n)>>20 & 0xF)), \ + ('0' + ((n)>>16 & 0xF)), \ + ('0' + ((n)>>12 & 0xF)), \ + ('0' + ((n)>>8 & 0xF)), \ + ('0' + ((n)>>4 & 0xF)), \ + ('0' + ((n) & 0xF)) + +/* Construct a string literal encoding the version number. */ +#ifdef COMPILER_VERSION +char const* info_version = "INFO" ":" "compiler_version[" COMPILER_VERSION "]"; + +/* Construct a string literal encoding the version number components. */ +#elif defined(COMPILER_VERSION_MAJOR) +char const info_version[] = { + 'I', 'N', 'F', 'O', ':', + 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[', + COMPILER_VERSION_MAJOR, +# ifdef COMPILER_VERSION_MINOR + '.', COMPILER_VERSION_MINOR, +# ifdef COMPILER_VERSION_PATCH + '.', COMPILER_VERSION_PATCH, +# ifdef COMPILER_VERSION_TWEAK + '.', COMPILER_VERSION_TWEAK, +# endif +# endif +# endif + ']','\0'}; +#endif + +/* Construct a string literal encoding the internal version number. */ +#ifdef COMPILER_VERSION_INTERNAL +char const info_version_internal[] = { + 'I', 'N', 'F', 'O', ':', + 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','_', + 'i','n','t','e','r','n','a','l','[', + COMPILER_VERSION_INTERNAL,']','\0'}; +#elif defined(COMPILER_VERSION_INTERNAL_STR) +char const* info_version_internal = "INFO" ":" "compiler_version_internal[" COMPILER_VERSION_INTERNAL_STR "]"; +#endif + +/* Construct a string literal encoding the version number components. */ +#ifdef SIMULATE_VERSION_MAJOR +char const info_simulate_version[] = { + 'I', 'N', 'F', 'O', ':', + 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[', + SIMULATE_VERSION_MAJOR, +# ifdef SIMULATE_VERSION_MINOR + '.', SIMULATE_VERSION_MINOR, +# ifdef SIMULATE_VERSION_PATCH + '.', SIMULATE_VERSION_PATCH, +# ifdef SIMULATE_VERSION_TWEAK + '.', SIMULATE_VERSION_TWEAK, +# endif +# endif +# endif + ']','\0'}; +#endif + +/* Construct the string literal in pieces to prevent the source from + getting matched. Store it in a pointer rather than an array + because some compilers will just produce instructions to fill the + array rather than assigning a pointer to a static array. */ +char const* info_platform = "INFO" ":" "platform[" PLATFORM_ID "]"; +char const* info_arch = "INFO" ":" "arch[" ARCHITECTURE_ID "]"; + + + +#if defined(__INTEL_COMPILER) && defined(_MSVC_LANG) && _MSVC_LANG < 201403L +# if defined(__INTEL_CXX11_MODE__) +# if defined(__cpp_aggregate_nsdmi) +# define CXX_STD 201402L +# else +# define CXX_STD 201103L +# endif +# else +# define CXX_STD 199711L +# endif +#elif defined(_MSC_VER) && defined(_MSVC_LANG) +# define CXX_STD _MSVC_LANG +#else +# define CXX_STD __cplusplus +#endif + +const char* info_language_standard_default = "INFO" ":" "standard_default[" +#if CXX_STD > 202002L + "23" +#elif CXX_STD > 201703L + "20" +#elif CXX_STD >= 201703L + "17" +#elif CXX_STD >= 201402L + "14" +#elif CXX_STD >= 201103L + "11" +#else + "98" +#endif +"]"; + +const char* info_language_extensions_default = "INFO" ":" "extensions_default[" +#if (defined(__clang__) || defined(__GNUC__) || defined(__xlC__) || \ + defined(__TI_COMPILER_VERSION__)) && \ + !defined(__STRICT_ANSI__) + "ON" +#else + "OFF" +#endif +"]"; + +/*--------------------------------------------------------------------------*/ + +int main(int argc, char* argv[]) +{ + int require = 0; + require += info_compiler[argc]; + require += info_platform[argc]; + require += info_arch[argc]; +#ifdef COMPILER_VERSION_MAJOR + require += info_version[argc]; +#endif +#ifdef COMPILER_VERSION_INTERNAL + require += info_version_internal[argc]; +#endif +#ifdef SIMULATE_ID + require += info_simulate[argc]; +#endif +#ifdef SIMULATE_VERSION_MAJOR + require += info_simulate_version[argc]; +#endif +#if defined(__CRAYXT_COMPUTE_LINUX_TARGET) + require += info_cray[argc]; +#endif + require += info_language_standard_default[argc]; + require += info_language_extensions_default[argc]; + (void)argv; + return require; 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--- +OUTPUT_DIR = os.getenv("OUTPUT_DIR", "/opt/jetson-counter") +DB_PATH = os.getenv("DB_PATH", f"{OUTPUT_DIR}/jetson_counter.db") +STATE_FILE = os.getenv("STATE_FILE", f"{OUTPUT_DIR}/current_batch.json") +SOURCE = os.getenv("SOURCE", "rtsp://user:pass@192.168.0.100:554/stream1") +MODEL_PATH = os.getenv("MODEL_PATH", "/opt/jetson-counter/yolo11n.rknn") +CAMERA_NAME = os.getenv("CAMERA_NAME", "CC1") +OBJECT_LABEL = os.getenv("OBJECT_LABEL", "ayam-potong") +CLASS_AYAM = os.getenv("CLASS_AYAM", "ayam") +CLASS_TALENAN = os.getenv("CLASS_TALENAN", "talenan") + +LINE_X = int(os.getenv("LINE_X")) if os.getenv("LINE_X") else None +LINE_X_FRAC = float(os.getenv("LINE_X_FRAC", "0.5")) +CROSS_DIRECTION = os.getenv("CROSS_DIRECTION", "rtl").lower() + +IMGSZ = int(os.getenv("IMGSZ", "320")) +HALF = os.getenv("HALF", "false").lower() == "true" +CONF = float(os.getenv("CONF", "0.3")) +DEVICE = int(os.getenv("DEVICE", "0")) + +# RKNN NPU core mask +CORE_MASK = int(os.getenv("CORE_MASK", "1")) + +# YOLO decoder config +NUM_CLASSES = int(os.getenv("NUM_CLASSES", "2")) +SCORE_SIGMOID = os.getenv("SCORE_SIGMOID", "false").lower() == "true" + +# ByteTrack settings +TRACK_HIGH_THRESH = float(os.getenv("TRACK_HIGH_THRESH", "0.5")) +TRACK_LOW_THRESH = float(os.getenv("TRACK_LOW_THRESH", "0.1")) +TRACK_MATCH_THRESH = float(os.getenv("TRACK_MATCH_THRESH", "0.8")) +TRACK_BUFFER = int(os.getenv("TRACK_BUFFER", "30")) +TRACK_MIN_HITS = int(os.getenv("TRACK_MIN_HITS", "3")) + +DAILY_CUTOFF_TIME = os.getenv("DAILY_CUTOFF_TIME", "20:00") +BATCH_TIMEOUT_SECONDS = float(os.getenv("BATCH_TIMEOUT_SECONDS", "300")) +IGNORE_BATCH_LABEL_TIMEOUT = float( + os.getenv("IGNORE_BATCH_LABEL_TIMEOUT_SECONDS", "30") +) +MIN_OBJECT_PER_BATCH = int(os.getenv("MIN_OBJECT_PER_BATCH", "60")) +MIN_DURATION_PER_BATCH = int(os.getenv("MIN_DURATION_PER_BATCH", "60")) + +EXPORT_CSV = os.getenv("EXPORT_CSV", "true").lower() == "true" +CROSS_CSV = os.getenv("CROSS_CSV", f"{OUTPUT_DIR}/batch_crossings.csv") + +WARMUP_FRAMES = int(os.getenv("WARMUP_FRAMES", "30")) +RECONNECT_DELAY_SEC = int(os.getenv("RECONNECT_DELAY_SEC", "3")) +MAX_RECONNECT_ATTEMPTS = int(os.getenv("MAX_RECONNECT_ATTEMPTS", "0")) +FLUSH_EVERY_N_FRAMES = int(os.getenv("FLUSH_EVERY_N_FRAMES", "100")) +TRACKED_PRUNE_SEC = int(os.getenv("TRACKED_PRUNE_SEC", "300")) +RECORD_VIDEO = os.getenv("RECORD_VIDEO", "false").lower() == "true" +VIDEO_SEGMENT_SEC = int(os.getenv("VIDEO_SEGMENT_SEC", "3600")) +OUTPUT_FPS = int(os.getenv("OUTPUT_FPS", "15")) + +LIVE_STREAM_ENABLED = os.getenv("LIVE_STREAM_ENABLED", "false").lower() == "true" +LIVE_STREAM_FRAME_PATH = os.getenv( + "LIVE_STREAM_FRAME_PATH", "/dev/shm/jetson-counter/live_frame.jpg" +) +LIVE_STREAM_QUALITY = int(os.getenv("LIVE_STREAM_QUALITY", "75")) +LIVE_STREAM_EVERY_N = int(os.getenv("LIVE_STREAM_EVERY_N", "2")) + +RTSP_FFMPEG_OPTIONS = os.getenv( + "OPENCV_FFMPEG_CAPTURE_OPTIONS", + "rtsp_transport;tcp|fflags;nobuffer|flags;low_delay", +) + +IS_LIVE = SOURCE.lower().startswith(("rtsp://", "http://")) + +CROSS_FLASH_FRAMES = 12 +POPUP_LIFETIME = 20 +LINE_PULSE_FRAMES = 12 +COUNT_PULSE_FRAMES = 15 +BATCH_PULSE_FRAMES = 20 + +SKELETON = [(0, 1), (4, 3), (1, 2), (3, 2), (2, 6), (2, 5), (2, 7), (7, 8)] +SK_COLORS = [ + (0, 255, 255), + (0, 255, 255), + (255, 0, 255), + (255, 0, 255), + (0, 255, 0), + (255, 255, 0), + (0, 0, 255), + (200, 200, 0), +] + +C_PANEL = (28, 24, 18) +C_BORDER = (90, 85, 75) +C_ACCENT = (255, 200, 60) +C_GREEN = (80, 220, 100) +C_TEXT = (235, 235, 235) +C_MUTED = (150, 150, 150) +C_AYAM_BOX = (0, 165, 255) +C_TALENAN_BOX = (220, 120, 60) +C_LINE_CORE = (180, 220, 255) +C_LINE_GLOW = (100, 160, 220) + +shutdown_requested = False + + +def request_shutdown(signum, frame): + global shutdown_requested + shutdown_requested = True + print("\nShutdown requested — finishing current frame...") + + +signal.signal(signal.SIGINT, request_shutdown) +signal.signal(signal.SIGTERM, request_shutdown) + + +# ============================================================================= +# YOLO output decoder (NMS only — boxes are pre-decoded by the model) +# ============================================================================= + + +def _nms(boxes, scores, iou_thr=0.45): + order = np.argsort(scores)[::-1] + keep = [] + while len(order) > 0: + idx = order[0] + keep.append(idx) + if len(order) == 1: + break + xx1 = np.maximum(boxes[idx, 0], boxes[order[1:], 0]) + yy1 = np.maximum(boxes[idx, 1], boxes[order[1:], 1]) + xx2 = np.minimum(boxes[idx, 2], boxes[order[1:], 2]) + yy2 = np.minimum(boxes[idx, 3], boxes[order[1:], 3]) + w = np.maximum(0.0, xx2 - xx1) + h = np.maximum(0.0, yy2 - yy1) + inter = w * h + area_i = (boxes[idx, 2] - boxes[idx, 0]) * (boxes[idx, 3] - boxes[idx, 1]) + area_o = (boxes[order[1:], 2] - boxes[order[1:], 0]) * ( + boxes[order[1:], 3] - boxes[order[1:], 1] + ) + iou = inter / (area_i + area_o - inter + 1e-16) + order = order[1:][iou < iou_thr] + return np.array(keep) + + +# ============================================================================= +# IoU helpers (xyxy format) +# ============================================================================= + + +def _ious_xyxy(boxes_a, boxes_b): + """Pairwise IoU: (N,4) vs (M,4) → (N,M) matrix.""" + n, m = len(boxes_a), len(boxes_b) + if n == 0 or m == 0: + return np.zeros((n, m), dtype=np.float32) + xx1 = np.maximum(boxes_a[:, None, 0], boxes_b[None, :, 0]) + yy1 = np.maximum(boxes_a[:, None, 1], boxes_b[None, :, 1]) + xx2 = np.minimum(boxes_a[:, None, 2], boxes_b[None, :, 2]) + yy2 = np.minimum(boxes_a[:, None, 3], boxes_b[None, :, 3]) + iw = np.maximum(0.0, xx2 - xx1) + ih = np.maximum(0.0, yy2 - yy1) + inter = iw * ih + area_a = (boxes_a[:, 2] - boxes_a[:, 0]) * (boxes_a[:, 3] - boxes_a[:, 1]) + area_b = (boxes_b[:, 2] - boxes_b[:, 0]) * (boxes_b[:, 3] - boxes_b[:, 1]) + return inter / (area_a[:, None] + area_b[None, :] - inter + 1e-16) + + +def _greedy_match(cost_matrix, threshold=0.3): + """Greedy linear assignment. Returns pairs (row_idx, col_idx).""" + if cost_matrix.size == 0: + return [] + n, m = cost_matrix.shape + flat = [(cost_matrix[i, j], i, j) for i in range(n) for j in range(m)] + flat.sort() + row_used = set() + col_used = set() + pairs = [] + for cost, i, j in flat: + if cost >= threshold: + break + if i in row_used or j in col_used: + continue + row_used.add(i) + col_used.add(j) + pairs.append((i, j)) + return pairs + + +# ============================================================================= +# Kalman filter box tracker (state: x, y, w, h, vx, vy, vw, vh) +# ============================================================================= + + +class KalmanBoxTracker: + count = 0 + + def __init__(self, bbox_xyxy): + KalmanBoxTracker.count += 1 + self.track_id = KalmanBoxTracker.count + + x1, y1, x2, y2 = bbox_xyxy + w, h = x2 - x1, y2 - y1 + x, y = x1 + w / 2, y1 + h / 2 + + self.kf = _KalmanFilter() + self.kf.x[:4, 0] = np.array([x, y, w, h], dtype=np.float32) + + self.time_since_update = 0 + self.hits = 1 + self.hit_streak = 1 + self.age = 1 + + def predict(self): + if self.kf.x[6] + self.kf.x[2] <= 0: + self.kf.x[6] *= 0.0 + self.kf.predict() + self.age += 1 + self.time_since_update += 1 + + def update(self, bbox_xyxy): + self.time_since_update = 0 + self.hits += 1 + self.hit_streak += 1 + + x1, y1, x2, y2 = bbox_xyxy + w, h = x2 - x1, y2 - y1 + x, y = x1 + w / 2, y1 + h / 2 + self.kf.update(np.array([x, y, w, h], dtype=np.float32)) + + def get_state(self): + """Returns xyxy bbox from Kalman state.""" + xx = self.kf.x[:4, 0] + x, y, w, h = xx[0], xx[1], xx[2], xx[3] + x1 = x - w / 2 + y1 = y - h / 2 + x2 = x + w / 2 + y2 = y + h / 2 + return np.array([x1, y1, x2, y2], dtype=np.float32) + + def get_cx(self): + return float(self.kf.x[0, 0]) + + +class _KalmanFilter: + """8-state constant-velocity Kalman filter for bounding box tracking.""" + + def __init__(self): + ndim, dt = 4, 1.0 + self.motion_mat = np.eye(2 * ndim, 2 * ndim, dtype=np.float32) + for i in range(ndim): + self.motion_mat[i, ndim + i] = dt + self.update_mat = np.eye(ndim, 2 * ndim, dtype=np.float32) + + self._std_weight_position = 1.0 / 20 + self._std_weight_velocity = 1.0 / 160 + + self.x = np.zeros((8, 1), dtype=np.float32) + self.P = np.eye(8, dtype=np.float32) * 10.0 + + def predict(self): + std_pos = [ + self._std_weight_position * self.x[2], + self._std_weight_position * self.x[3], + self._std_weight_position * self.x[2], + self._std_weight_position * self.x[3], + ] + std_vel = [ + self._std_weight_velocity * self.x[2], + self._std_weight_velocity * self.x[3], + self._std_weight_velocity * self.x[2], + self._std_weight_velocity * self.x[3], + ] + Q = np.diag(np.square(np.concatenate([std_pos, std_vel]))) + + self.x = self.motion_mat @ self.x + self.P = self.motion_mat @ self.P @ self.motion_mat.T + Q + + def update(self, z): + R = np.diag( + np.square( + [ + self._std_weight_position * z[2], + self._std_weight_position * z[3], + self._std_weight_position * z[2], + self._std_weight_position * z[3], + ] + ) + ) + + H = self.update_mat + S = H @ self.P @ H.T + R + K = self.P @ H.T @ np.linalg.inv(S) + + y = z.reshape(4, 1) - H @ self.x + self.x = self.x + K @ y + I_KH = np.eye(8) - K @ H + self.P = I_KH @ self.P @ I_KH.T + K @ R @ K.T + + +# ============================================================================= +# ByteTrack multi-object tracker +# ============================================================================= + + +class ByteTracker: + """ByteTrack: two-stage association with Kalman filter prediction.""" + + def __init__( + self, + track_high_thresh=0.5, + track_low_thresh=0.1, + match_thresh=0.8, + track_buffer=30, + min_hits=3, + ): + self.high_thresh = track_high_thresh + self.low_thresh = track_low_thresh + self.match_thresh = match_thresh + self.track_buffer = track_buffer + self.min_hits = min_hits + self.tracked_tracks = [] + self.lost_tracks = [] + self.removed_tracks = [] + self.frame_id = 0 + + def update(self, boxes_xyxy, scores): + self.frame_id += 1 + + # --- separate detections by score --- + if len(boxes_xyxy) > 0: + remain = scores > self.low_thresh + dets = boxes_xyxy[remain] + det_scores = scores[remain] + is_high = det_scores > self.high_thresh + is_low = ~is_high + else: + dets = np.zeros((0, 4), dtype=np.float32) + det_scores = np.zeros(0, dtype=np.float32) + is_high = np.zeros(0, dtype=bool) + is_low = np.zeros(0, dtype=bool) + + # --- Kalman predict all existing tracks --- + track_pool = self.tracked_tracks + self.lost_tracks + num_tracks = len(track_pool) + + # Per-frame tracking results + matched_track_idx = set() + det_to_track = {} + tracked_map = {} + lost_map = {} + + # Pre-allocate these for scoping + high_idx = np.array([], dtype=np.int64) + low_idx = np.array([], dtype=np.int64) + match_pairs_high = [] + + if num_tracks > 0: + track_boxes = np.zeros((num_tracks, 4), dtype=np.float32) + for ti, trk in enumerate(track_pool): + trk.predict() + track_boxes[ti] = trk.get_state() + + # --- first association: high-score ↔ all tracks --- + high_idx = np.where(is_high)[0] + high_dets = dets[is_high] + + unmatched_tracks = list(range(num_tracks)) + + if len(high_dets) > 0: + iou_mat = _ious_xyxy(high_dets, track_boxes) + cost_mat = 1.0 - iou_mat + matches = _greedy_match(cost_mat, threshold=1.0 - self.match_thresh) + + for di, ti in matches: + det_global = int(high_idx[di]) + track_pool[ti].update(dets[det_global]) + track_pool[ti].hit_streak = max(1, track_pool[ti].hit_streak) + matched_track_idx.add(ti) + det_to_track[det_global] = track_pool[ti].track_id + tracked_map[track_pool[ti].track_id] = track_pool[ti].get_cx() + match_pairs_high.append((det_global, ti)) + + unmatched_tracks = [ + t for t in range(num_tracks) if t not in matched_track_idx + ] + + # --- second association: low-score ↔ unmatched tracks --- + low_idx = np.where(is_low)[0] + low_dets = dets[is_low] + + if len(low_dets) > 0 and len(unmatched_tracks) > 0: + unmatched_boxes = track_boxes[unmatched_tracks] + iou_mat = _ious_xyxy(low_dets, unmatched_boxes) + cost_mat = 1.0 - iou_mat + matches2 = _greedy_match(cost_mat, threshold=0.5) + + for di, uti in matches2: + det_global = int(low_idx[di]) + pool_idx = unmatched_tracks[uti] + track_pool[pool_idx].update(dets[det_global]) + track_pool[pool_idx].hit_streak = max( + 1, track_pool[pool_idx].hit_streak + ) + matched_track_idx.add(pool_idx) + det_to_track[det_global] = track_pool[pool_idx].track_id + tracked_map[track_pool[pool_idx].track_id] = track_pool[ + pool_idx + ].get_cx() + + # --- reset hit_streak for unmatched tracks --- + for ti, trk in enumerate(track_pool): + if ti not in matched_track_idx: + trk.hit_streak = 0 + + # --- lifecycle management --- + new_tracked = [] + new_lost = [] + for trk in track_pool: + if trk.time_since_update > self.track_buffer: + self.removed_tracks.append(trk) + elif trk.time_since_update > 0: + new_lost.append(trk) + else: + new_tracked.append(trk) + self.tracked_tracks = new_tracked + self.lost_tracks = new_lost + + # --- confirmed tracks (both tracked and lost) --- + for trk in self.tracked_tracks + self.lost_tracks: + if trk.hit_streak >= self.min_hits or trk.hits >= self.min_hits: + tracked_map.setdefault(trk.track_id, trk.get_cx()) + + for trk in self.lost_tracks: + if trk.hit_streak >= self.min_hits or trk.hits >= self.min_hits: + lost_map[trk.track_id] = trk.get_cx() + + # --- new tracks from unmatched high-score dets --- + high_all = np.where(is_high)[0] + matched_det_ids = set(det_to_track.keys()) + for dg in high_all: + if int(dg) not in matched_det_ids: + trk = KalmanBoxTracker(dets[dg]) + self.tracked_tracks.append(trk) + det_to_track[int(dg)] = trk.track_id + tracked_map[trk.track_id] = trk.get_cx() + + return tracked_map, det_to_track, lost_map + + +# ============================================================================= +# RKNN YOLO wrapper (detect output format: (1, 4+num_classes, N)) +# ============================================================================= + + +class RKNNYOLO: + def __init__( + self, + model_path, + core_mask=1, + imgsz=320, + conf=0.3, + iou=0.45, + num_classes=2, + num_keypoints=0, + score_sigmoid=False, + ): + self.imgsz = imgsz + self.conf = conf + self.iou = iou + self.num_classes = num_classes + self.num_keypoints = num_keypoints + self.score_sigmoid = score_sigmoid + + self.rknn = RKNNLite(verbose=False) + ret = self.rknn.load_rknn(model_path) + if ret != 0: + raise RuntimeError(f"Failed to load RKNN model: {model_path}") + ret = self.rknn.init_runtime(core_mask=core_mask) + if ret != 0: + raise RuntimeError(f"Failed to init RKNN runtime (core_mask={core_mask})") + + try: + sdk_ver = self.rknn.get_sdk_version() + print(f"RKNN SDK version: {sdk_ver}") + except Exception: + pass + + print(f"RKNN model loaded: {model_path} imgsz={imgsz} core_mask={core_mask}") + + def _preprocess(self, frame): + h0, w0 = frame.shape[:2] + scale = min(self.imgsz / h0, self.imgsz / w0) + nh, nw = int(h0 * scale), int(w0 * scale) + resized = cv2.resize(frame, (nw, nh), interpolation=cv2.INTER_LINEAR) + + letterbox = np.full((self.imgsz, self.imgsz, 3), 114, dtype=np.uint8) + dy = (self.imgsz - nh) // 2 + dx = (self.imgsz - nw) // 2 + letterbox[dy : dy + nh, dx : dx + nw] = resized + + rgb = cv2.cvtColor(letterbox, cv2.COLOR_BGR2RGB) + gains = np.array([scale, scale, dy, dx], dtype=np.float32) + return rgb, gains + + def __call__(self, frame): + h0, w0 = frame.shape[:2] + rgb, gains = self._preprocess(frame) + scale, _, pad_y, pad_x = gains + + inp = np.expand_dims(rgb, axis=0) + inp = np.ascontiguousarray(inp.astype(np.uint8)) + + outputs = self.rknn.inference(inputs=[inp]) + + if len(outputs) == 0: + return [] + + out = outputs[0] + out = np.squeeze(out, axis=0) + + if out.shape[0] == self.num_classes + 4: + out = out.T + + boxes_cxcywh = out[:, :4].copy() + cls_raw = out[:, 4:].copy() + + if self.score_sigmoid: + cls_scores = 1.0 / (1.0 + np.exp(-np.clip(cls_raw, -10, 10))) + else: + cls_scores = cls_raw + + boxes_xyxy = np.stack( + [ + boxes_cxcywh[:, 0] - boxes_cxcywh[:, 2] / 2, + boxes_cxcywh[:, 1] - boxes_cxcywh[:, 3] / 2, + boxes_cxcywh[:, 0] + boxes_cxcywh[:, 2] / 2, + boxes_cxcywh[:, 1] + boxes_cxcywh[:, 3] / 2, + ], + axis=1, + ) + + max_scores = cls_scores.max(axis=1) + class_ids = cls_scores.argmax(axis=1) + mask = max_scores > self.conf + + if mask.sum() == 0: + return [] + + bboxes = boxes_xyxy[mask].astype(np.float32) + scores = max_scores[mask].astype(np.float32) + clses = class_ids[mask] + + bboxes[:, 0] = (bboxes[:, 0] - pad_x) / scale + bboxes[:, 1] = (bboxes[:, 1] - pad_y) / scale + bboxes[:, 2] = (bboxes[:, 2] - pad_x) / scale + bboxes[:, 3] = (bboxes[:, 3] - pad_y) / scale + + bboxes[:, 0] = np.clip(bboxes[:, 0], 0, w0) + bboxes[:, 1] = np.clip(bboxes[:, 1], 0, h0) + bboxes[:, 2] = np.clip(bboxes[:, 2], 0, w0) + bboxes[:, 3] = np.clip(bboxes[:, 3], 0, h0) + + detections = [] + for cls_id in range(self.num_classes): + idx = np.where(clses == cls_id)[0] + if len(idx) == 0: + continue + keep = _nms(bboxes[idx], scores[idx], iou_thr=self.iou) + for k in keep: + j = idx[k] + detections.append( + { + "bbox": bboxes[j].tolist(), + "score": float(scores[j]), + "cls": int(clses[j]), + "keypoints": None, + } + ) + return detections + + def release(self): + self.rknn.release() + + +# ============================================================================= +# Drawing helpers +# ============================================================================= + + +def resolve_line_x(frame_width): + if LINE_X is not None: + return LINE_X + if LINE_X_FRAC != 0.5: + return int(frame_width * LINE_X_FRAC) + return frame_width // 2 + + +def crossed_line(prev_cx, cx, line_x, direction=CROSS_DIRECTION): + if direction == "ltr": + return prev_cx < line_x <= cx + if direction == "both": + return (prev_cx > line_x >= cx) or (prev_cx < line_x <= cx) + return prev_cx > line_x >= cx + + +def now_str(): + return datetime.now().strftime("%Y-%m-%d %H:%M:%S") + + +def open_capture(source): + if source.lower().startswith(("rtsp://", "http://")): + os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = RTSP_FFMPEG_OPTIONS + cap = cv2.VideoCapture(source, cv2.CAP_FFMPEG) + cap.set(cv2.CAP_PROP_BUFFERSIZE, 1) + return cap + + +def warmup_stream(cap, n=WARMUP_FRAMES): + print("Warming up stream...") + for _ in range(n): + cap.read() + print("Stream ready!") + + +def open_video_writer(path, w, h, fps): + return cv2.VideoWriter(path, cv2.VideoWriter_fourcc(*"avc1"), fps, (w, h)) + + +class CsvLogger: + def __init__(self, path, header): + Path(path).parent.mkdir(parents=True, exist_ok=True) + new_file = not Path(path).exists() or Path(path).stat().st_size == 0 + self.file = open(path, "a", newline="", buffering=1) + self.writer = csv.writer(self.file) + if new_file: + self.writer.writerow(header) + self.file.flush() + + def write_row(self, row): + self.writer.writerow(row) + self.file.flush() + + def close(self): + self.file.close() + + +class VideoSegmentWriter: + def __init__(self, output_dir, w, h, fps, segment_sec): + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + self.w, self.h, self.fps = w, h, fps + self.segment_sec = segment_sec + self.segment_start = time.monotonic() + self.writer = None + self._open_next() + + def _segment_path(self): + ts = datetime.now().strftime("%Y%m%d_%H%M%S") + return str(self.output_dir / f"live_{ts}.mp4") + + def _open_next(self): + if self.writer is not None: + self.writer.release() + path = self._segment_path() + self.writer = open_video_writer(path, self.w, self.h, self.fps) + self.segment_start = time.monotonic() + print(f"Recording segment: {path}") + + def write(self, frame): + if time.monotonic() - self.segment_start >= self.segment_sec: + self._open_next() + self.writer.write(frame) + + def release(self): + if self.writer is not None: + self.writer.release() + + +def prune_stale_tracks(tracked, now_mono): + stale = [ + tid for tid, (_, ts) in tracked.items() if now_mono - ts > TRACKED_PRUNE_SEC + ] + for tid in stale: + del tracked[tid] + + +def overlay_rect(img, x1, y1, x2, y2, color, alpha=0.65): + x1, y1 = max(0, x1), max(0, y1) + x2, y2 = min(img.shape[1], x2), min(img.shape[0], y2) + if x2 <= x1 or y2 <= y1: + return + roi = img[y1:y2, x1:x2] + patch = np.full_like(roi, color, dtype=np.uint8) + cv2.addWeighted(patch, alpha, roi, 1 - alpha, 0, roi) + + +def draw_pill(img, text, x, y, bg, fg=C_TEXT, font_scale=0.45, pad_x=6, pad_y=4): + font = cv2.FONT_HERSHEY_SIMPLEX + (tw, th), baseline = cv2.getTextSize(text, font, font_scale, 1) + x1, y1 = x, y - th - pad_y + x2, y2 = x + tw + pad_x * 2, y + baseline + pad_y + cv2.rectangle(img, (x1, y1), (x2, y2), bg, -1) + cv2.rectangle(img, (x1, y1), (x2, y2), C_BORDER, 1) + cv2.putText(img, text, (x + pad_x, y), font, font_scale, fg, 1, cv2.LINE_AA) + + +def draw_elegant_counting_line(img, line_x, h, pulse_remaining=0): + strength = pulse_remaining / max(LINE_PULSE_FRAMES, 1) + glow_alpha = 0.12 + 0.18 * strength + for offset in (14, 9, 5): + color = tuple(int(c * glow_alpha) for c in C_LINE_GLOW) + cv2.line(img, (line_x - offset, 0), (line_x - offset, h), color, 1, cv2.LINE_AA) + cv2.line(img, (line_x + offset, 0), (line_x + offset, h), color, 1, cv2.LINE_AA) + dash_len, gap = 18, 12 + y = 0 + while y < h: + y_end = min(y + dash_len, h) + cv2.line(img, (line_x, y), (line_x, y_end), C_LINE_CORE, 2, cv2.LINE_AA) + y += dash_len + gap + cv2.putText( + img, + "COUNT LINE", + (line_x - 46, 24), + cv2.FONT_HERSHEY_SIMPLEX, + 0.42, + C_LINE_CORE, + 1, + cv2.LINE_AA, + ) + + +def draw_hero_count(img, line_x, h, count, pulse_remaining=0): + text = str(count) + font = cv2.FONT_HERSHEY_SIMPLEX + boost = 0.35 * (pulse_remaining / max(COUNT_PULSE_FRAMES, 1)) + font_scale, thickness = 1.6 + boost, 3 + (tw, th), _ = cv2.getTextSize(text, font, font_scale, thickness) + pad = 14 + tx, ty = line_x - tw // 2, h // 2 + th // 2 + overlay_rect( + img, tx - pad, ty - th - pad, tx + tw + pad, ty + pad // 2, C_PANEL, alpha=0.78 + ) + cv2.rectangle( + img, (tx - pad, ty - th - pad), (tx + tw + pad, ty + pad // 2), C_LINE_CORE, 2 + ) + cv2.putText(img, text, (tx, ty), font, font_scale, C_GREEN, thickness, cv2.LINE_AA) + + +def draw_hud( + img, w, batch_num, batch_count, total_ayam, elapsed_sec, rate, camera_id, clock +): + bar_h = 52 + overlay_rect(img, 0, 0, w, bar_h, C_PANEL, alpha=0.72) + cv2.line(img, (0, bar_h), (w, bar_h), C_BORDER, 1) + cv2.putText( + img, "BATCH", (16, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA + ) + batch_label = str(batch_num) if batch_num else "\u2014" + cv2.putText( + img, + batch_label, + (16, 44), + cv2.FONT_HERSHEY_SIMPLEX, + 0.9, + C_ACCENT, + 2, + cv2.LINE_AA, + ) + cv2.putText( + img, "COUNT", (100, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA + ) + cv2.putText( + img, + str(batch_count), + (100, 44), + cv2.FONT_HERSHEY_SIMPLEX, + 0.9, + C_GREEN, + 2, + cv2.LINE_AA, + ) + cv2.putText( + img, "TOTAL", (190, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA + ) + cv2.putText( + img, + str(total_ayam), + (190, 44), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + C_TEXT, + 1, + cv2.LINE_AA, + ) + cv2.putText( + img, + "UPTIME", + (280, 20), + cv2.FONT_HERSHEY_SIMPLEX, + 0.45, + C_MUTED, + 1, + cv2.LINE_AA, + ) + cv2.putText( + img, + f"{elapsed_sec / 3600:.1f}h", + (280, 44), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + C_TEXT, + 1, + cv2.LINE_AA, + ) + cv2.putText( + img, "RATE", (380, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA + ) + cv2.putText( + img, + f"{rate:.1f}/min", + (380, 44), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + C_ACCENT, + 1, + cv2.LINE_AA, + ) + # cv2.putText(img, clock, (w - 180, 36), cv2.FONT_HERSHEY_SIMPLEX, 0.55, C_TEXT, 1, cv2.LINE_AA) + cv2.putText( + img, + f"CAM {camera_id}", + (w - 180, 20), + cv2.FONT_HERSHEY_SIMPLEX, + 0.45, + C_MUTED, + 1, + cv2.LINE_AA, + ) + + +def draw_footer(img, w, h, frame_idx, live_tag): + bar_h = 28 + overlay_rect(img, 0, h - bar_h, w, h, C_PANEL, alpha=0.55) + cv2.putText( + img, + f"{live_tag} | Frame {frame_idx}", + (12, h - 9), + cv2.FONT_HERSHEY_SIMPLEX, + 0.45, + C_MUTED, + 1, + cv2.LINE_AA, + ) + + +def draw_skeleton_bold(img, kpts): + for (a, b), color in zip(SKELETON, SK_COLORS): + if a < len(kpts) and b < len(kpts): + xa, ya = int(kpts[a][0]), int(kpts[a][1]) + xb, yb = int(kpts[b][0]), int(kpts[b][1]) + if xa > 0 and ya > 0 and xb > 0 and yb > 0: + cv2.line(img, (xa, ya), (xb, yb), color, 3, cv2.LINE_AA) + for kp in kpts: + x, y = int(kp[0]), int(kp[1]) + if x > 0 and y > 0: + cv2.circle(img, (x, y), 6, (255, 255, 255), -1, cv2.LINE_AA) + cv2.circle(img, (x, y), 6, (40, 40, 40), 2, cv2.LINE_AA) + + +def draw_popups(img, popups, frame_idx): + alive = [] + for pop in popups: + age = frame_idx - pop["born"] + if age > POPUP_LIFETIME: + continue + alive.append(pop) + fade = 1.0 - age / POPUP_LIFETIME + y = pop["y"] - int(age * 1.8) + color = (int(C_GREEN[0] * fade), int(C_GREEN[1] * fade), int(C_GREEN[2] * fade)) + cv2.putText( + img, + pop["text"], + (pop["x"], y), + cv2.FONT_HERSHEY_SIMPLEX, + 0.7, + color, + 2, + cv2.LINE_AA, + ) + return alive + + +def draw_batch_banner(img, w, batch_num, pulse_remaining): + if pulse_remaining <= 0: + return + text = f"NEW BATCH {batch_num}" + font = cv2.FONT_HERSHEY_SIMPLEX + (tw, th), _ = cv2.getTextSize(text, font, 0.8, 2) + x1, y1 = w // 2 - tw // 2 - 16, 62 + x2, y2 = w // 2 + tw // 2 + 16, 62 + th + 20 + overlay_rect(img, x1, y1, x2, y2, C_PANEL, alpha=0.7) + cv2.rectangle(img, (x1, y1), (x2, y2), C_ACCENT, 2) + cv2.putText( + img, text, (w // 2 - tw // 2, 62 + th + 4), font, 0.8, C_ACCENT, 2, cv2.LINE_AA + ) + + +def connect_stream(source, warmup=WARMUP_FRAMES): + attempts = 0 + while not shutdown_requested: + cap = open_capture(source) + if not cap.isOpened(): + attempts += 1 + if MAX_RECONNECT_ATTEMPTS and attempts >= MAX_RECONNECT_ATTEMPTS: + raise RuntimeError( + f"Cannot open source after {attempts} attempts: {source}" + ) + print(f"Cannot open source, retry in {RECONNECT_DELAY_SEC}s...") + time.sleep(RECONNECT_DELAY_SEC) + continue + if warmup > 0 and source.lower().startswith(("rtsp://", "http://")): + warmup_stream(cap, warmup) + w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + fps = cap.get(cv2.CAP_PROP_FPS) + if not fps or fps <= 1: + fps = OUTPUT_FPS + return cap, w, h, fps + return None, 0, 0, OUTPUT_FPS + + +# ============================================================================= +# Main loop +# ============================================================================= + + +def run(): + global shutdown_requested + + store = BatchStore( + db_path=DB_PATH, + state_file=STATE_FILE, + camera_name=CAMERA_NAME, + object_label=OBJECT_LABEL, + cutoff_time=DAILY_CUTOFF_TIME, + batch_timeout=BATCH_TIMEOUT_SECONDS, + ignore_batch_label_timeout=IGNORE_BATCH_LABEL_TIMEOUT, + min_object_per_batch=MIN_OBJECT_PER_BATCH, + min_duration_per_batch=MIN_DURATION_PER_BATCH, + logger=lambda msg: print(f"[{now_str()}] {msg}"), + ) + store.start_cutoff_watcher() + + cross_logger = None + if EXPORT_CSV: + cross_logger = CsvLogger( + CROSS_CSV, ["batch", "frame", "timestamp", "chicken_id"] + ) + + model = RKNNYOLO( + model_path=MODEL_PATH, + core_mask=CORE_MASK, + imgsz=IMGSZ, + conf=CONF, + num_classes=NUM_CLASSES, + score_sigmoid=SCORE_SIGMOID, + ) + + CLASS_IDS = { + os.getenv("CLASS_AYAM", "ayam"): 0, + os.getenv("CLASS_TALENAN", "talenan"): 1, + } + ayam_cls = CLASS_IDS[CLASS_AYAM] + talenan_cls = CLASS_IDS[CLASS_TALENAN] + + ayam_tracker = ByteTracker( + track_high_thresh=TRACK_HIGH_THRESH, + track_low_thresh=TRACK_LOW_THRESH, + match_thresh=TRACK_MATCH_THRESH, + track_buffer=TRACK_BUFFER, + min_hits=TRACK_MIN_HITS, + ) + talenan_tracker = ByteTracker( + track_high_thresh=TRACK_HIGH_THRESH, + track_low_thresh=TRACK_LOW_THRESH, + match_thresh=TRACK_MATCH_THRESH, + track_buffer=TRACK_BUFFER, + min_hits=TRACK_MIN_HITS, + ) + + ayam_line_crossed = set() + talenan_line_crossed = set() + + ayam_cross_flash = {} + talenan_cross_flash = {} + line_pulse = count_pulse = batch_pulse = 0 + popups = [] + + session_start = time.time() + frame_idx = 0 + video_writer = None + + cap, w, h, fps = connect_stream(SOURCE) + if cap is None: + store.shutdown() + model.release() + return + + line_x = resolve_line_x(w) + print( + f"RKNN+ByteTrack counter | {w}x{h} @ {fps}fps | line x={line_x} | cross={CROSS_DIRECTION}" + ) + print(f"Model: {MODEL_PATH} | imgsz={IMGSZ} | core_mask={CORE_MASK}") + print( + f"ByteTrack: high_thresh={TRACK_HIGH_THRESH} low_thresh={TRACK_LOW_THRESH} " + f"match_thresh={TRACK_MATCH_THRESH} buffer={TRACK_BUFFER}" + ) + print(f"DB: {DB_PATH}") + print(f"State: {STATE_FILE}") + + if RECORD_VIDEO: + video_writer = VideoSegmentWriter(OUTPUT_DIR, w, h, fps, VIDEO_SEGMENT_SEC) + + reconnect_count = 0 + + while not shutdown_requested: + ret, frame = cap.read() + if not ret: + if not IS_LIVE: + break + reconnect_count += 1 + print( + f"Stream dropped (attempt {reconnect_count}), reconnecting in {RECONNECT_DELAY_SEC}s..." + ) + cap.release() + time.sleep(RECONNECT_DELAY_SEC) + cap, w, h, fps = connect_stream(SOURCE) + if cap is None: + break + line_x = resolve_line_x(w) + continue + + now = time.time() + elapsed = now - session_start + mono = time.monotonic() + ayam_crossed_frame = batch_closed_frame = batch_started_frame = False + + detections = model(frame) + + if detections: + ayam_boxes_xyxy = [] + ayam_scores = [] + ayam_kpts_list = [] + ayam_cx_list = [] + + talenan_boxes_xyxy = [] + talenan_scores = [] + talenan_kpts_list = [] + talenan_cx_list = [] + + for det in detections: + bbox = det["bbox"] + score = det["score"] + cls_id = det["cls"] + kpts = det["keypoints"] + cx = (bbox[0] + bbox[2]) / 2.0 + + if cls_id == talenan_cls: + talenan_boxes_xyxy.append(bbox) + talenan_scores.append(score) + talenan_kpts_list.append(kpts) + talenan_cx_list.append(cx) + elif cls_id == ayam_cls: + ayam_boxes_xyxy.append(bbox) + ayam_scores.append(score) + ayam_kpts_list.append(kpts) + ayam_cx_list.append(cx) + + ayam_boxes_xyxy = np.array(ayam_boxes_xyxy, dtype=np.float32) + ayam_scores = np.array(ayam_scores, dtype=np.float32) + talenan_boxes_xyxy = np.array(talenan_boxes_xyxy, dtype=np.float32) + talenan_scores = np.array(talenan_scores, dtype=np.float32) + + ayam_track_map, ayam_det_to_track, ayam_lost_map = ayam_tracker.update( + ayam_boxes_xyxy, ayam_scores + ) + talenan_track_map, talenan_det_to_track, talenan_lost_map = ( + talenan_tracker.update(talenan_boxes_xyxy, talenan_scores) + ) + + # Process talenan crossings + for di in range(len(talenan_boxes_xyxy)): + tid = talenan_det_to_track.get(di) + if tid is None: + continue + cx = talenan_cx_list[di] + bbox = talenan_boxes_xyxy[di] + + if tid in talenan_tracked: + prev_cx = talenan_tracked[tid][0] + if ( + crossed_line(prev_cx, cx, line_x) + and tid not in talenan_line_crossed + ): + talenan_line_crossed.add(tid) + if store.record_talenan_crossing(tid): + batch_closed_frame = True + talenan_cross_flash[tid] = CROSS_FLASH_FRAMES + popups.append( + { + "x": int(cx) - 20, + "y": int((bbox[1] + bbox[3]) / 2), + "born": frame_idx, + "text": "BATCH CLOSED", + } + ) + talenan_tracked[tid] = (cx, mono) + + # Process ayam crossings (including lost tracks for line-cross continuity) + for di in range(len(ayam_boxes_xyxy)): + tid = ayam_det_to_track.get(di) + if tid is None: + continue + cx = ayam_cx_list[di] + + if tid in ayam_tracked: + prev_cx = ayam_tracked[tid][0] + if ( + crossed_line(prev_cx, cx, line_x) + and tid not in ayam_line_crossed + ): + ayam_line_crossed.add(tid) + _, started_new = store.record_ayam_crossing(tid) + if cross_logger: + cross_logger.write_row( + [ + store.current_batch_number, + frame_idx, + datetime.now().isoformat(), + tid, + ] + ) + ayam_crossed_frame = True + if started_new: + batch_started_frame = True + ayam_cross_flash[tid] = CROSS_FLASH_FRAMES + popups.append( + { + "x": int(cx) - 12, + "y": int( + (ayam_boxes_xyxy[di][1] + ayam_boxes_xyxy[di][3]) + / 2 + ), + "born": frame_idx, + "text": "+1", + } + ) + ayam_tracked[tid] = (cx, mono) + + # Also track lost tracks for line-crossing continuity + for tid, cx in ayam_lost_map.items(): + if tid not in ayam_tracked: + ayam_tracked[tid] = (cx, mono) + + # Draw talenan + for di in range(len(talenan_boxes_xyxy)): + tid = talenan_det_to_track.get(di) + if tid is None: + continue + bbox = talenan_boxes_xyxy[di] + x1, y1, x2, y2 = int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3]) + flash = talenan_cross_flash.get(tid, 0) + color = C_GREEN if flash > 0 else C_TALENAN_BOX + cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3 if flash > 0 else 2) + draw_pill(frame, f"TALENAN {tid}", x1, y1 - 4, color) + + # Draw ayam + for di in range(len(ayam_boxes_xyxy)): + tid = ayam_det_to_track.get(di) + if tid is None: + continue + bbox = ayam_boxes_xyxy[di] + x1, y1, x2, y2 = int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3]) + flash = ayam_cross_flash.get(tid, 0) + color = C_GREEN if flash > 0 else C_AYAM_BOX + cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3 if flash > 0 else 2) + draw_pill(frame, f"ID {tid}", x1, y1 - 4, color) + kpts = ayam_kpts_list[di] if di < len(ayam_kpts_list) else None + if kpts is not None: + draw_skeleton_bold(frame, kpts) + + if ayam_crossed_frame: + line_pulse = LINE_PULSE_FRAMES + count_pulse = COUNT_PULSE_FRAMES + if batch_closed_frame: + line_pulse = LINE_PULSE_FRAMES + if batch_started_frame: + batch_pulse = BATCH_PULSE_FRAMES + + batch_num = store.current_batch_number or 0 + batch_count = store.current_batch_count + display_total = store.display_total() + rate = (display_total / elapsed * 60) if elapsed > 0 else 0.0 + + draw_elegant_counting_line(frame, line_x, h, line_pulse) + draw_hero_count(frame, line_x, h, batch_count, count_pulse) + draw_hud( + frame, + w, + batch_num, + batch_count, + display_total, + elapsed, + rate, + CAMERA_NAME, + now_str(), + ) + draw_batch_banner(frame, w, batch_num, batch_pulse) + draw_footer( + frame, w, h, frame_idx, "LIVE-RKNN-BT" if IS_LIVE else "FILE-RKNN-BT" + ) + popups = draw_popups(frame, popups, frame_idx) + + for flash_store in (ayam_cross_flash, talenan_cross_flash): + for tid in list(flash_store): + flash_store[tid] -= 1 + if flash_store[tid] <= 0: + del flash_store[tid] + line_pulse = max(0, line_pulse - 1) + count_pulse = max(0, count_pulse - 1) + batch_pulse = max(0, batch_pulse - 1) + + if video_writer is not None: + video_writer.write(frame) + + if LIVE_STREAM_ENABLED and frame_idx % LIVE_STREAM_EVERY_N == 0: + try: + Path(LIVE_STREAM_FRAME_PATH).parent.mkdir(parents=True, exist_ok=True) + _, jpeg = cv2.imencode( + ".jpg", frame, [cv2.IMWRITE_JPEG_QUALITY, LIVE_STREAM_QUALITY] + ) + with open(LIVE_STREAM_FRAME_PATH, "wb") as f: + f.write(jpeg.tobytes()) + except Exception: + pass + + frame_idx += 1 + if frame_idx % FLUSH_EVERY_N_FRAMES == 0: + print( + f"[{now_str()}] Frame {frame_idx} | Batch {batch_num}: {batch_count} " + f"| Total: {display_total} | Uptime {elapsed / 3600:.2f}h" + ) + prune_stale_tracks(ayam_tracked, mono) + prune_stale_tracks(talenan_tracked, mono) + + cap.release() + if video_writer is not None: + video_writer.release() + if cross_logger: + cross_logger.close() + model.release() + store.shutdown() + + print("\n=== Batch Summary (SQLite) ===") + print(f"Database: {DB_PATH}") + + +ayam_tracked = {} +talenan_tracked = {} + + +if __name__ == "__main__": + run() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..fb24f8d --- /dev/null +++ b/requirements.txt @@ -0,0 +1,5 @@ +numpy<2 +rknn-toolkit-lite2 +opencv-python +flask +python-dotenv diff --git a/src/batch_store.cpp b/src/batch_store.cpp new file mode 100644 index 0000000..da0d7de --- /dev/null +++ b/src/batch_store.cpp @@ -0,0 +1,414 @@ +#include "batch_store.hpp" +#include +#include +#include +#include +#include +#include +#include + +static std::string now_iso() { + auto now = std::chrono::system_clock::now(); + auto t = std::chrono::system_clock::to_time_t(now); + std::ostringstream ss; + ss << std::put_time(std::localtime(&t), "%Y-%m-%dT%H:%M:%S"); + return ss.str(); +} + +BatchStore::BatchStore(const std::string& db_path, + const std::string& state_file, + const std::string& camera_name, + const std::string& object_label, + const std::string& cutoff_time, + float batch_timeout, + float ignore_batch_label_timeout, + int min_object_per_batch, + int min_duration_per_batch, + std::function logger) + : db_path_(db_path), state_file_(state_file), camera_name_(camera_name), + object_label_(object_label), cutoff_time_str_(cutoff_time), + batch_timeout_(batch_timeout), + ignore_batch_label_timeout_(ignore_batch_label_timeout), + min_object_per_batch_(min_object_per_batch), + min_duration_per_batch_(min_duration_per_batch), + logger_(logger) { + + std::filesystem::path dbp(db_path_); + if (dbp.has_parent_path()) + std::filesystem::create_directories(dbp.parent_path()); + + std::filesystem::path sfp(state_file_); + if (sfp.has_parent_path()) + std::filesystem::create_directories(sfp.parent_path()); + + int rc = sqlite3_open(db_path_.c_str(), &db_); + if (rc != SQLITE_OK) throw std::runtime_error("Failed to open database: " + std::string(sqlite3_errmsg(db_))); + + init_db(); + current_state_ = load_state(); + previous_state_ = current_state_; +} + +BatchStore::~BatchStore() { + if (db_) sqlite3_close(db_); +} + +void BatchStore::init_db() { + const char* sql1 = R"( + CREATE TABLE IF NOT EXISTS batches ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + counting_date TEXT NOT NULL, + batch_number INTEGER NOT NULL, + camera_name TEXT NOT NULL, + object_label TEXT NOT NULL, + count INTEGER NOT NULL, + start_time TEXT NOT NULL, + end_time TEXT NOT NULL, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE(counting_date, batch_number, camera_name, object_label) + ) + )"; + const char* sql2 = R"( + CREATE TABLE IF NOT EXISTS daily_summaries ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + counting_date TEXT NOT NULL, + camera_name TEXT NOT NULL, + object_label TEXT NOT NULL, + total_count INTEGER NOT NULL DEFAULT 0, + total_batches INTEGER NOT NULL DEFAULT 0, + updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE(counting_date, camera_name, object_label) + ) + )"; + char* err = nullptr; + sqlite3_exec(db_, sql1, nullptr, nullptr, &err); + if (err) { logger_("DB init error: " + std::string(err)); sqlite3_free(err); } + err = nullptr; + sqlite3_exec(db_, sql2, nullptr, nullptr, &err); + if (err) { logger_("DB init error: " + std::string(err)); sqlite3_free(err); } +} + +std::string BatchStore::get_counting_date() const { + auto now = std::chrono::system_clock::now(); + auto t = std::chrono::system_clock::to_time_t(now); + auto* tm = std::localtime(&t); + + int h, m; + sscanf(cutoff_time_str_.c_str(), "%d:%d", &h, &m); + + if (tm->tm_hour < h || (tm->tm_hour == h && tm->tm_min < m)) { + std::ostringstream ss; + ss << std::put_time(tm, "%Y-%m-%d"); + return ss.str(); + } else { + auto tomorrow = now + std::chrono::hours(24); + t = std::chrono::system_clock::to_time_t(tomorrow); + tm = std::localtime(&t); + std::ostringstream ss; + ss << std::put_time(tm, "%Y-%m-%d"); + return ss.str(); + } +} + +json BatchStore::load_state() { + std::ifstream f(state_file_); + if (!f.good()) return nullptr; + + try { + json state = json::parse(f); + std::string current_date = get_counting_date(); + if (state.value("counting_date", "") != current_date) { + logger_("State file belongs to previous counting day. Finalizing."); + insert_batch(state["counting_date"], state["batch_number"], + state["count"], state["start_time"], now_iso()); + std::filesystem::remove(state_file_); + return nullptr; + } + std::ostringstream ss; + ss << "Resumed batch #" << state["batch_number"] << " with count=" << state["count"]; + logger_(ss.str()); + reset_batch_timer(); + return state; + } catch (const std::exception& e) { + logger_("Failed to load state: " + std::string(e.what())); + return nullptr; + } +} + +void BatchStore::save_state() { + std::lock_guard lock(state_mutex_); + if (current_state_.is_null()) { + std::filesystem::remove(state_file_); + return; + } + std::ofstream f(state_file_); + f << current_state_.dump(2); +} + +int BatchStore::get_next_batch_number(const std::string& counting_date) { + const char* sql = R"( + SELECT COALESCE(MAX(batch_number), 0) + FROM batches + WHERE counting_date = ? AND camera_name = ? AND object_label = ? + )"; + sqlite3_stmt* stmt; + sqlite3_prepare_v2(db_, sql, -1, &stmt, nullptr); + sqlite3_bind_text(stmt, 1, counting_date.c_str(), -1, SQLITE_TRANSIENT); + sqlite3_bind_text(stmt, 2, camera_name_.c_str(), -1, SQLITE_TRANSIENT); + sqlite3_bind_text(stmt, 3, object_label_.c_str(), -1, SQLITE_TRANSIENT); + int result = 0; + if (sqlite3_step(stmt) == SQLITE_ROW) + result = sqlite3_column_int(stmt, 0) + 1; + sqlite3_finalize(stmt); + return result; +} + +void BatchStore::start_new_batch(const std::string& counting_date) { + int batch_number = get_next_batch_number(counting_date); + std::string now = now_iso(); + + json counted_ids = json::array(); + if (!previous_state_.is_null() && previous_state_.contains("counted_event_ids")) { + auto& ids = previous_state_["counted_event_ids"]; + int start = std::max(0, static_cast(ids.size()) - carry_ids_); + for (int i = start; i < (int)ids.size(); ++i) + counted_ids.push_back(ids[i]); + } + + current_state_ = { + {"counting_date", counting_date}, + {"batch_number", batch_number}, + {"count", 0}, + {"start_time", now}, + {"last_detection_time", now}, + {"counted_event_ids", counted_ids} + }; + save_state(); + + std::ostringstream ss; + ss << "Started batch #" << batch_number << " for " << counting_date; + logger_(ss.str()); +} + +void BatchStore::reset_batch_timer() { + batch_timer_running_ = true; + if (batch_timer_thread_.joinable()) batch_timer_thread_.detach(); + batch_timer_thread_ = std::thread([this]() { + auto start = std::chrono::steady_clock::now(); + while (batch_timer_running_) { + auto elapsed = std::chrono::duration_cast( + std::chrono::steady_clock::now() - start).count(); + if (elapsed >= batch_timeout_) { + on_batch_timeout(); + break; + } + std::this_thread::sleep_for(std::chrono::seconds(1)); + } + }); +} + +void BatchStore::on_batch_timeout() { + batch_timer_running_ = false; + logger_("Batch inactivity timeout reached"); + end_batch("timeout"); +} + +void BatchStore::ignore_batch_label_fn() { + if (!ignore_label_active_) { + ignore_label_active_ = true; + ignore_batch_label_ = true; + if (ignore_label_thread_.joinable()) ignore_label_thread_.detach(); + ignore_label_thread_ = std::thread([this]() { + auto start = std::chrono::steady_clock::now(); + while (ignore_label_active_) { + auto elapsed = std::chrono::duration_cast( + std::chrono::steady_clock::now() - start).count(); + if (elapsed >= ignore_batch_label_timeout_) { + on_ignore_batch_label_timeout(); + break; + } + std::this_thread::sleep_for(std::chrono::seconds(1)); + } + }); + } +} + +void BatchStore::on_ignore_batch_label_timeout() { + ignore_label_active_ = false; + ignore_batch_label_ = false; +} + +std::pair BatchStore::record_ayam_crossing(int track_id) { + std::lock_guard lock(state_mutex_); + std::string counting_date = get_counting_date(); + bool started_new = false; + + if (current_state_.is_null()) { + start_new_batch(counting_date); + started_new = true; + } else if (current_state_["counting_date"].get() != counting_date) { + end_batch_locked("cutoff"); + start_new_batch(counting_date); + started_new = true; + } + + std::string event_key = std::to_string(track_id); + auto& ids = current_state_["counted_event_ids"]; + bool found = false; + for (const auto& id : ids) { + if (id.get() == event_key) { found = true; break; } + } + if (!found) { + current_state_["count"] = current_state_["count"].get() + 1; + ids.push_back(event_key); + } + + current_state_["last_detection_time"] = now_iso(); + save_state(); + reset_batch_timer(); + return {current_state_["count"].get(), started_new}; +} + +bool BatchStore::record_talenan_crossing(int track_id) { + if (ignore_batch_label_) return false; + std::lock_guard lock(state_mutex_); + ignore_batch_label_fn(); + end_batch_locked("talenan"); + batch_timer_running_ = false; + return true; +} + +void BatchStore::end_batch(const std::string& closed_by) { + std::lock_guard lock(state_mutex_); + end_batch_locked(closed_by); +} + +bool BatchStore::end_batch_locked(const std::string& closed_by) { + if (current_state_.is_null()) return false; + + previous_state_ = current_state_; + auto state = current_state_; + + std::string start_time = state["start_time"]; + std::string end_time = now_iso(); + + // parse duration + std::tm tm = {}; + std::istringstream ss(start_time); + ss >> std::get_time(&tm, "%Y-%m-%dT%H:%M:%S"); + auto start_tp = std::chrono::system_clock::from_time_t(std::mktime(&tm)); + auto end_tp = std::chrono::system_clock::now(); + auto duration_sec = std::chrono::duration_cast(end_tp - start_tp).count(); + + int count = state["count"].get(); + + if (count < min_object_per_batch_ || duration_sec < min_duration_per_batch_) { + current_state_ = nullptr; + save_state(); + batch_timer_running_ = false; + return false; + } + + try { + insert_batch(state["counting_date"], state["batch_number"], + count, state["start_time"], end_time); + } catch (const std::exception& e) { + logger_("Failed to persist batch: " + std::string(e.what())); + return false; + } + + current_state_ = nullptr; + save_state(); + batch_timer_running_ = false; + return true; +} + +void BatchStore::insert_batch(const std::string& counting_date, int batch_number, + int count, const std::string& start_time, const std::string& end_time) { + const char* sql1 = R"( + INSERT INTO batches (counting_date, batch_number, camera_name, object_label, count, start_time, end_time) + VALUES (?, ?, ?, ?, ?, ?, ?) + )"; + sqlite3_stmt* stmt; + sqlite3_prepare_v2(db_, sql1, -1, &stmt, nullptr); + sqlite3_bind_text(stmt, 1, counting_date.c_str(), -1, SQLITE_TRANSIENT); + sqlite3_bind_int(stmt, 2, batch_number); + sqlite3_bind_text(stmt, 3, camera_name_.c_str(), -1, SQLITE_TRANSIENT); + sqlite3_bind_text(stmt, 4, object_label_.c_str(), -1, SQLITE_TRANSIENT); + sqlite3_bind_int(stmt, 5, count); + sqlite3_bind_text(stmt, 6, start_time.c_str(), -1, SQLITE_TRANSIENT); + sqlite3_bind_text(stmt, 7, end_time.c_str(), -1, SQLITE_TRANSIENT); + sqlite3_step(stmt); + sqlite3_finalize(stmt); + + const char* sql2 = R"( + INSERT INTO daily_summaries (counting_date, camera_name, object_label, total_count, total_batches) + VALUES (?, ?, ?, ?, 1) + ON CONFLICT(counting_date, camera_name, object_label) + DO UPDATE SET total_count = total_count + excluded.total_count, + total_batches = total_batches + excluded.total_batches, + updated_at = CURRENT_TIMESTAMP + )"; + sqlite3_prepare_v2(db_, sql2, -1, &stmt, nullptr); + sqlite3_bind_text(stmt, 1, counting_date.c_str(), -1, SQLITE_TRANSIENT); + sqlite3_bind_text(stmt, 2, camera_name_.c_str(), -1, SQLITE_TRANSIENT); + sqlite3_bind_text(stmt, 3, object_label_.c_str(), -1, SQLITE_TRANSIENT); + sqlite3_bind_int(stmt, 4, count); + sqlite3_step(stmt); + sqlite3_finalize(stmt); +} + +int BatchStore::get_closed_total_for_day(const std::string& counting_date) { + const char* sql = R"( + SELECT COALESCE(total_count, 0) + FROM daily_summaries + WHERE counting_date = ? AND camera_name = ? AND object_label = ? + )"; + sqlite3_stmt* stmt; + sqlite3_prepare_v2(db_, sql, -1, &stmt, nullptr); + sqlite3_bind_text(stmt, 1, counting_date.c_str(), -1, SQLITE_TRANSIENT); + sqlite3_bind_text(stmt, 2, camera_name_.c_str(), -1, SQLITE_TRANSIENT); + sqlite3_bind_text(stmt, 3, object_label_.c_str(), -1, SQLITE_TRANSIENT); + int result = 0; + if (sqlite3_step(stmt) == SQLITE_ROW) + result = sqlite3_column_int(stmt, 0); + sqlite3_finalize(stmt); + return result; +} + +int BatchStore::display_total() { + return get_closed_total_for_day(get_counting_date()) + current_batch_count(); +} + +int BatchStore::current_batch_number() const { + if (current_state_.is_null()) return 0; + return current_state_.value("batch_number", 0); +} + +int BatchStore::current_batch_count() const { + if (current_state_.is_null()) return 0; + return current_state_.value("count", 0); +} + +void BatchStore::cutoff_watcher_loop() { + while (!shutdown_flag_) { + std::this_thread::sleep_for(std::chrono::seconds(60)); + std::lock_guard lock(state_mutex_); + if (current_state_.is_null()) continue; + if (current_state_["counting_date"].get() != get_counting_date()) { + logger_("Daily cutoff reached - finalizing batch"); + end_batch_locked("cutoff"); + } + } +} + +void BatchStore::start_cutoff_watcher() { + std::thread t(&BatchStore::cutoff_watcher_loop, this); + t.detach(); +} + +void BatchStore::shutdown() { + shutdown_flag_ = true; + batch_timer_running_ = false; + end_batch("shutdown"); +} diff --git a/src/batch_store.hpp b/src/batch_store.hpp new file mode 100644 index 0000000..8a58988 --- /dev/null +++ b/src/batch_store.hpp @@ -0,0 +1,79 @@ +#pragma once +#include +#include +#include +#include +#include +#include +#include +#include + +using json = nlohmann::json; + +class BatchStore { +public: + BatchStore(const std::string& db_path, + const std::string& state_file, + const std::string& camera_name, + const std::string& object_label, + const std::string& cutoff_time, + float batch_timeout, + float ignore_batch_label_timeout, + int min_object_per_batch, + int min_duration_per_batch, + std::function logger); + + ~BatchStore(); + + void start_cutoff_watcher(); + + int current_batch_number() const; + int current_batch_count() const; + int display_total(); + + std::pair record_ayam_crossing(int track_id); + bool record_talenan_crossing(int track_id); + void end_batch(const std::string& closed_by = "manual"); + void shutdown(); + +private: + void init_db(); + std::string get_counting_date() const; + json load_state(); + void save_state(); + int get_next_batch_number(const std::string& counting_date); + void start_new_batch(const std::string& counting_date); + void reset_batch_timer(); + void on_batch_timeout(); + void ignore_batch_label_fn(); + void on_ignore_batch_label_timeout(); + bool end_batch_locked(const std::string& closed_by); + void insert_batch(const std::string& counting_date, int batch_number, + int count, const std::string& start_time, const std::string& end_time); + int get_closed_total_for_day(const std::string& counting_date); + void cutoff_watcher_loop(); + + std::string db_path_; + std::string state_file_; + std::string camera_name_; + std::string object_label_; + std::string cutoff_time_str_; + float batch_timeout_; + float ignore_batch_label_timeout_; + int min_object_per_batch_; + int min_duration_per_batch_; + int carry_ids_ = 50; + std::function logger_; + + sqlite3* db_ = nullptr; + std::mutex state_mutex_; + std::thread batch_timer_thread_; + std::thread ignore_label_thread_; + std::atomic batch_timer_running_{false}; + std::atomic ignore_label_active_{false}; + std::atomic shutdown_flag_{false}; + std::atomic ignore_batch_label_{false}; + + json current_state_; + json previous_state_; +}; diff --git a/src/bytetrack.cpp b/src/bytetrack.cpp new file mode 100644 index 0000000..0e073a2 --- /dev/null +++ b/src/bytetrack.cpp @@ -0,0 +1,198 @@ +#include "bytetrack.hpp" + +int KalmanBoxTracker::count = 0; + +KalmanBoxTracker::KalmanBoxTracker(const Eigen::Vector4f& bbox_xyxy) : kf() { + KalmanBoxTracker::count++; + track_id = KalmanBoxTracker::count; + + float x1 = bbox_xyxy(0), y1 = bbox_xyxy(1), x2 = bbox_xyxy(2), y2 = bbox_xyxy(3); + float w = x2 - x1, h = y2 - y1; + float x = x1 + w / 2.0f, y = y1 + h / 2.0f; + + kf.x(0, 0) = x; + kf.x(1, 0) = y; + kf.x(2, 0) = w; + kf.x(3, 0) = h; +} + +void KalmanBoxTracker::predict() { + if (kf.x(6) + kf.x(2) <= 0) { + kf.x(6, 0) = 0.0f; + } + kf.predict(); + age++; + time_since_update++; +} + +void KalmanBoxTracker::update(const Eigen::Vector4f& bbox_xyxy) { + time_since_update = 0; + hits++; + hit_streak++; + + float x1 = bbox_xyxy(0), y1 = bbox_xyxy(1), x2 = bbox_xyxy(2), y2 = bbox_xyxy(3); + float w = x2 - x1, h = y2 - y1; + float x = x1 + w / 2.0f, y = y1 + h / 2.0f; + + Eigen::Vector4f z; + z << x, y, w, h; + kf.update(z); +} + +Eigen::Vector4f KalmanBoxTracker::get_state() const { + float x = kf.x(0, 0), y = kf.x(1, 0), w = kf.x(2, 0), h = kf.x(3, 0); + Eigen::Vector4f xyxy; + xyxy << x - w / 2.0f, y - h / 2.0f, x + w / 2.0f, y + h / 2.0f; + return xyxy; +} + +float KalmanBoxTracker::get_cx() const { + return kf.x(0, 0); +} + +ByteTracker::ByteTracker(float track_high_thresh, float track_low_thresh, + float match_thresh, int track_buffer, int min_hits) + : high_thresh_(track_high_thresh), low_thresh_(track_low_thresh), + match_thresh_(match_thresh), track_buffer_(track_buffer), min_hits_(min_hits) {} + +TrackResult ByteTracker::update(const Eigen::MatrixXf& boxes_xyxy, const Eigen::VectorXf& scores) { + frame_id_++; + + int num_dets = static_cast(boxes_xyxy.rows()); + + std::vector high_indices, low_indices; + std::vector high_dets, low_dets; + + if (num_dets > 0) { + for (int i = 0; i < num_dets; ++i) { + if (scores(i) > low_thresh_) { + if (scores(i) > high_thresh_) { + high_indices.push_back(i); + high_dets.push_back(boxes_xyxy.row(i)); + } else { + low_indices.push_back(i); + low_dets.push_back(boxes_xyxy.row(i)); + } + } + } + } + + auto all_tracks = tracked_tracks_; + all_tracks.insert(all_tracks.end(), lost_tracks_.begin(), lost_tracks_.end()); + int num_tracks = static_cast(all_tracks.size()); + + for (auto& trk : all_tracks) { + trk.predict(); + } + + Eigen::MatrixXf track_boxes(num_tracks, 4); + for (int ti = 0; ti < num_tracks; ++ti) { + track_boxes.row(ti) = all_tracks[ti].get_state(); + } + + std::unordered_set matched_track_idx; + std::unordered_map det_to_track; + std::unordered_map tracked_map; + std::unordered_map lost_map; + + if (num_tracks > 0) { + // first association: high-score dets ↔ all tracks + int n_high = static_cast(high_dets.size()); + if (n_high > 0) { + Eigen::MatrixXf high_mat(n_high, 4); + for (int i = 0; i < n_high; ++i) high_mat.row(i) = high_dets[i]; + + auto iou_mat = ious_xyxy(high_mat, track_boxes); + auto cost_mat = Eigen::MatrixXf::Constant(n_high, num_tracks, 1.0f) - iou_mat; + auto matches = greedy_match(cost_mat, 1.0f - match_thresh_); + + for (auto [di, ti] : matches) { + int det_global = high_indices[di]; + all_tracks[ti].update(boxes_xyxy.row(det_global)); + all_tracks[ti].hit_streak = std::max(1, all_tracks[ti].hit_streak); + matched_track_idx.insert(ti); + det_to_track[det_global] = all_tracks[ti].track_id; + tracked_map[all_tracks[ti].track_id] = all_tracks[ti].get_cx(); + } + } + + std::vector unmatched_tracks; + for (int t = 0; t < num_tracks; ++t) + if (matched_track_idx.find(t) == matched_track_idx.end()) + unmatched_tracks.push_back(t); + + // second association: low-score dets ↔ unmatched tracks + int n_low = static_cast(low_dets.size()); + int n_unmatched = static_cast(unmatched_tracks.size()); + if (n_low > 0 && n_unmatched > 0) { + Eigen::MatrixXf low_mat(n_low, 4); + for (int i = 0; i < n_low; ++i) low_mat.row(i) = low_dets[i]; + + Eigen::MatrixXf unmatched_boxes(n_unmatched, 4); + for (int i = 0; i < n_unmatched; ++i) + unmatched_boxes.row(i) = track_boxes.row(unmatched_tracks[i]); + + auto iou_mat = ious_xyxy(low_mat, unmatched_boxes); + auto cost_mat = Eigen::MatrixXf::Constant(n_low, n_unmatched, 1.0f) - iou_mat; + auto matches2 = greedy_match(cost_mat, 0.5f); + + for (auto [di, uti] : matches2) { + int det_global = low_indices[di]; + int pool_idx = unmatched_tracks[uti]; + all_tracks[pool_idx].update(boxes_xyxy.row(det_global)); + all_tracks[pool_idx].hit_streak = std::max(1, all_tracks[pool_idx].hit_streak); + matched_track_idx.insert(pool_idx); + det_to_track[det_global] = all_tracks[pool_idx].track_id; + tracked_map[all_tracks[pool_idx].track_id] = all_tracks[pool_idx].get_cx(); + } + } + } + + for (int ti = 0; ti < num_tracks; ++ti) { + if (matched_track_idx.find(ti) == matched_track_idx.end()) + all_tracks[ti].hit_streak = 0; + } + + std::vector new_tracked, new_lost; + for (auto& trk : all_tracks) { + if (trk.time_since_update > track_buffer_) { + removed_tracks_.push_back(std::move(trk)); + } else if (trk.time_since_update > 0) { + new_lost.push_back(std::move(trk)); + } else { + new_tracked.push_back(std::move(trk)); + } + } + tracked_tracks_ = std::move(new_tracked); + lost_tracks_ = std::move(new_lost); + + for (const auto& trk : tracked_tracks_) { + if (trk.hit_streak >= min_hits_ || trk.hits >= min_hits_) { + if (tracked_map.find(trk.track_id) == tracked_map.end()) + tracked_map[trk.track_id] = trk.get_cx(); + } + } + for (const auto& trk : lost_tracks_) { + if (trk.hit_streak >= min_hits_ || trk.hits >= min_hits_) { + lost_map[trk.track_id] = trk.get_cx(); + } + } + + for (int dg : high_indices) { + if (det_to_track.find(dg) == det_to_track.end()) { + Eigen::Vector4f box = boxes_xyxy.row(dg); + KalmanBoxTracker trk(box); + int tid = trk.track_id; + float cx = trk.get_cx(); + tracked_map[tid] = cx; + det_to_track[dg] = tid; + tracked_tracks_.push_back(std::move(trk)); + } + } + + TrackResult result; + result.tracked_map = tracked_map; + result.det_to_track = det_to_track; + result.lost_map = lost_map; + return result; +} diff --git a/src/bytetrack.hpp b/src/bytetrack.hpp new file mode 100644 index 0000000..18d4c7b --- /dev/null +++ b/src/bytetrack.hpp @@ -0,0 +1,56 @@ +#pragma once +#include "kalman_filter.hpp" +#include "nms.hpp" +#include +#include +#include +#include +#include + +class KalmanBoxTracker { +public: + static int count; + + KalmanBoxTracker(const Eigen::Vector4f& bbox_xyxy); + + void predict(); + void update(const Eigen::Vector4f& bbox_xyxy); + Eigen::Vector4f get_state() const; + float get_cx() const; + + int track_id; + KalmanFilter kf; + int time_since_update = 0; + int hits = 1; + int hit_streak = 1; + int age = 1; +}; + +struct TrackResult { + std::unordered_map tracked_map; + std::unordered_map det_to_track; + std::unordered_map lost_map; +}; + +class ByteTracker { +public: + ByteTracker(float track_high_thresh = 0.5f, + float track_low_thresh = 0.1f, + float match_thresh = 0.8f, + int track_buffer = 30, + int min_hits = 3); + + TrackResult update(const Eigen::MatrixXf& boxes_xyxy, const Eigen::VectorXf& scores); + +private: + float high_thresh_; + float low_thresh_; + float match_thresh_; + int track_buffer_; + int min_hits_; + int frame_id_ = 0; + + std::vector tracked_tracks_; + std::vector lost_tracks_; + std::vector removed_tracks_; +}; diff --git a/src/config.cpp b/src/config.cpp new file mode 100644 index 0000000..b457db6 --- /dev/null +++ b/src/config.cpp @@ -0,0 +1,128 @@ +#include "config.hpp" +#include +#include +#include +#include + +AppConfig g_config; + +static std::string trim(const std::string& s) { + auto start = s.find_first_not_of(" \t\r\n"); + if (start == std::string::npos) return {}; + auto end = s.find_last_not_of(" \t\r\n"); + return s.substr(start, end - start + 1); +} + +static std::string get_env(const std::string& key, const std::string& def = "") { + const char* val = std::getenv(key.c_str()); + return val ? std::string(val) : def; +} + +void AppConfig::load_from_env(const char* env_path) { + std::string path = ".env"; + if (env_path) path = env_path; + + // also check env path + const char* env_file = std::getenv("ENV_FILE"); + if (env_file) path = env_file; + + std::ifstream file(path); + if (file.is_open()) { + std::string line; + while (std::getline(file, line)) { + line = trim(line); + if (line.empty() || line[0] == '#') continue; + auto eq = line.find('='); + if (eq == std::string::npos) continue; + std::string key = trim(line.substr(0, eq)); + std::string val = trim(line.substr(eq + 1)); + // strip quotes + if (val.size() >= 2 && val.front() == '"' && val.back() == '"') + val = val.substr(1, val.size() - 2); + setenv(key.c_str(), val.c_str(), 1); + } + } + + output_dir = get_env("OUTPUT_DIR", output_dir); + db_path = get_env("DB_PATH", output_dir + "/bytetrack_counter.db"); + state_file = get_env("STATE_FILE", output_dir + "/current_batch.json"); + source = get_env("SOURCE", source); + model_path = get_env("MODEL_PATH", model_path); + camera_name = get_env("CAMERA_NAME", camera_name); + object_label = get_env("OBJECT_LABEL", object_label); + class_ayam = get_env("CLASS_AYAM", class_ayam); + class_talenan = get_env("CLASS_TALENAN", class_talenan); + + auto lx = get_env("LINE_X"); + if (!lx.empty()) line_x = std::stoi(lx); + auto lxf = get_env("LINE_X_FRAC"); + if (!lxf.empty()) line_x_frac = std::stof(lxf); + cross_direction = get_env("CROSS_DIRECTION", cross_direction); + + auto imgsz_s = get_env("IMGSZ"); + if (!imgsz_s.empty()) imgsz = std::stoi(imgsz_s); + auto half_s = get_env("HALF"); + half = (half_s == "true"); + auto conf_s = get_env("CONF"); + if (!conf_s.empty()) conf = std::stof(conf_s); + auto dev_s = get_env("DEVICE"); + if (!dev_s.empty()) device = std::stoi(dev_s); + auto cm_s = get_env("CORE_MASK"); + if (!cm_s.empty()) core_mask = std::stoi(cm_s); + auto nc_s = get_env("NUM_CLASSES"); + if (!nc_s.empty()) num_classes = std::stoi(nc_s); + auto ss_s = get_env("SCORE_SIGMOID"); + score_sigmoid = (ss_s == "true"); + + auto th_s = get_env("TRACK_HIGH_THRESH"); + if (!th_s.empty()) track_high_thresh = std::stof(th_s); + auto tl_s = get_env("TRACK_LOW_THRESH"); + if (!tl_s.empty()) track_low_thresh = std::stof(tl_s); + auto tm_s = get_env("TRACK_MATCH_THRESH"); + if (!tm_s.empty()) track_match_thresh = std::stof(tm_s); + auto tb_s = get_env("TRACK_BUFFER"); + if (!tb_s.empty()) track_buffer = std::stoi(tb_s); + auto tmh_s = get_env("TRACK_MIN_HITS"); + if (!tmh_s.empty()) track_min_hits = std::stoi(tmh_s); + + daily_cutoff_time = get_env("DAILY_CUTOFF_TIME", daily_cutoff_time); + auto bto_s = get_env("BATCH_TIMEOUT_SECONDS"); + if (!bto_s.empty()) batch_timeout_seconds = std::stof(bto_s); + auto ibl_s = get_env("IGNORE_BATCH_LABEL_TIMEOUT_SECONDS"); + if (!ibl_s.empty()) ignore_batch_label_timeout = std::stof(ibl_s); + auto mob_s = get_env("MIN_OBJECT_PER_BATCH"); + if (!mob_s.empty()) min_object_per_batch = std::stoi(mob_s); + auto mdb_s = get_env("MIN_DURATION_PER_BATCH"); + if (!mdb_s.empty()) min_duration_per_batch = std::stoi(mdb_s); + + auto ecsv = get_env("EXPORT_CSV"); + if (!ecsv.empty()) export_csv = (ecsv == "true"); + cross_csv = get_env("CROSS_CSV", output_dir + "/batch_crossings.csv"); + + auto wf_s = get_env("WARMUP_FRAMES"); + if (!wf_s.empty()) warmup_frames = std::stoi(wf_s); + auto rd_s = get_env("RECONNECT_DELAY_SEC"); + if (!rd_s.empty()) reconnect_delay_sec = std::stoi(rd_s); + auto mra_s = get_env("MAX_RECONNECT_ATTEMPTS"); + if (!mra_s.empty()) max_reconnect_attempts = std::stoi(mra_s); + auto fl_s = get_env("FLUSH_EVERY_N_FRAMES"); + if (!fl_s.empty()) flush_every_n_frames = std::stoi(fl_s); + auto tp_s = get_env("TRACKED_PRUNE_SEC"); + if (!tp_s.empty()) tracked_prune_sec = std::stoi(tp_s); + auto rv_s = get_env("RECORD_VIDEO"); + record_video = (rv_s == "true"); + auto vs_s = get_env("VIDEO_SEGMENT_SEC"); + if (!vs_s.empty()) video_segment_sec = std::stoi(vs_s); + auto ofps_s = get_env("OUTPUT_FPS"); + if (!ofps_s.empty()) output_fps = std::stoi(ofps_s); + + auto lse_s = get_env("LIVE_STREAM_ENABLED"); + live_stream_enabled = (lse_s == "true"); + live_stream_frame_path = get_env("LIVE_STREAM_FRAME_PATH", live_stream_frame_path); + auto lsq_s = get_env("LIVE_STREAM_QUALITY"); + if (!lsq_s.empty()) live_stream_quality = std::stoi(lsq_s); + auto lsn_s = get_env("LIVE_STREAM_EVERY_N"); + if (!lsn_s.empty()) live_stream_every_n = std::stoi(lsn_s); + + rtsp_ffmpeg_options = get_env("OPENCV_FFMPEG_CAPTURE_OPTIONS", rtsp_ffmpeg_options); +} diff --git a/src/config.hpp b/src/config.hpp new file mode 100644 index 0000000..e334e5f --- /dev/null +++ b/src/config.hpp @@ -0,0 +1,68 @@ +#pragma once +#include +#include + +struct AppConfig { + std::string output_dir = "/opt/bytetrack-counter-cpp"; + std::string db_path; + std::string state_file; + std::string source = "rtsp://user:pass@192.168.0.100:554/stream1"; + std::string model_path = "/opt/models/yolo.rknn"; + std::string camera_name = "CC1"; + std::string object_label = "ayam-potong"; + std::string class_ayam = "ayam"; + std::string class_talenan = "talenan"; + + int line_x = -1; + float line_x_frac = 0.5f; + std::string cross_direction = "rtl"; + + int imgsz = 320; + bool half = false; + float conf = 0.3f; + int device = 0; + int core_mask = 1; + int num_classes = 2; + bool score_sigmoid = false; + + float track_high_thresh = 0.5f; + float track_low_thresh = 0.1f; + float track_match_thresh = 0.8f; + int track_buffer = 30; + int track_min_hits = 3; + + std::string daily_cutoff_time = "20:00"; + float batch_timeout_seconds = 300.0f; + float ignore_batch_label_timeout = 30.0f; + int min_object_per_batch = 60; + int min_duration_per_batch = 60; + + bool export_csv = true; + std::string cross_csv; + + int warmup_frames = 30; + int reconnect_delay_sec = 3; + int max_reconnect_attempts = 0; + int flush_every_n_frames = 100; + int tracked_prune_sec = 300; + bool record_video = false; + int video_segment_sec = 3600; + int output_fps = 15; + + bool live_stream_enabled = false; + std::string live_stream_frame_path = "/dev/shm/bytetrack-counter/live_frame.jpg"; + int live_stream_quality = 75; + int live_stream_every_n = 2; + + std::string rtsp_ffmpeg_options = "rtsp_transport;tcp|fflags;nobuffer|flags;low_delay"; + + int cross_flash_frames = 12; + int popup_lifetime = 20; + int line_pulse_frames = 12; + int count_pulse_frames = 15; + int batch_pulse_frames = 20; + + void load_from_env(const char* env_path = nullptr); +}; + +extern AppConfig g_config; diff --git a/src/csv_logger.cpp b/src/csv_logger.cpp new file mode 100644 index 0000000..ab9913d --- /dev/null +++ b/src/csv_logger.cpp @@ -0,0 +1,35 @@ +#include "csv_logger.hpp" +#include +#include + +CsvLogger::CsvLogger(const std::string& path, const std::vector& header) { + std::filesystem::path p(path); + if (p.has_parent_path()) + std::filesystem::create_directories(p.parent_path()); + + bool new_file = !std::filesystem::exists(p) || std::filesystem::file_size(p) == 0; + file_.open(path, std::ios::app); + file_.exceptions(std::ios::failbit | std::ios::badbit); + + if (new_file) { + for (size_t i = 0; i < header.size(); ++i) { + if (i > 0) file_ << ","; + file_ << header[i]; + } + file_ << "\n"; + file_.flush(); + } +} + +CsvLogger::~CsvLogger() { + if (file_.is_open()) file_.close(); +} + +void CsvLogger::write_row(const std::vector& row) { + for (size_t i = 0; i < row.size(); ++i) { + if (i > 0) file_ << ","; + file_ << row[i]; + } + file_ << "\n"; + file_.flush(); +} diff --git a/src/csv_logger.hpp b/src/csv_logger.hpp new file mode 100644 index 0000000..7cea276 --- /dev/null +++ b/src/csv_logger.hpp @@ -0,0 +1,15 @@ +#pragma once +#include +#include +#include + +class CsvLogger { +public: + explicit CsvLogger(const std::string& path, const std::vector& header); + ~CsvLogger(); + + void write_row(const std::vector& row); + +private: + std::ofstream file_; +}; diff --git a/src/drawing.cpp b/src/drawing.cpp new file mode 100644 index 0000000..c785066 --- /dev/null +++ b/src/drawing.cpp @@ -0,0 +1,171 @@ +#include "drawing.hpp" +#include "config.hpp" +#include +#include +#include + +const std::vector> SKELETON = { + {0, 1}, {4, 3}, {1, 2}, {3, 2}, {2, 6}, {2, 5}, {2, 7}, {7, 8} +}; + +const std::vector SK_COLORS = { + {255, 255, 0}, {255, 255, 0}, {255, 0, 255}, {255, 0, 255}, + {0, 255, 0}, {255, 255, 0}, {255, 0, 0}, {0, 200, 200} +}; + +int resolve_line_x(int frame_width, int line_x_opt, float line_x_frac) { + if (line_x_opt >= 0) return line_x_opt; + if (std::abs(line_x_frac - 0.5f) > 1e-6f) return static_cast(frame_width * line_x_frac); + return frame_width / 2; +} + +bool crossed_line(float prev_cx, float cx, int line_x, const std::string& direction) { + if (direction == "ltr") return prev_cx < line_x && cx >= line_x; + if (direction == "both") return (prev_cx > line_x && cx <= line_x) || (prev_cx < line_x && cx >= line_x); + return prev_cx > line_x && cx <= line_x; +} + +void overlay_rect(cv::Mat& img, int x1, int y1, int x2, int y2, const cv::Scalar& color, float alpha) { + x1 = std::max(0, x1); y1 = std::max(0, y1); + x2 = std::min(img.cols, x2); y2 = std::min(img.rows, y2); + if (x2 <= x1 || y2 <= y1) return; + cv::Mat roi = img(cv::Rect(x1, y1, x2 - x1, y2 - y1)); + cv::Mat patch(roi.size(), roi.type(), color); + cv::addWeighted(patch, alpha, roi, 1.0f - alpha, 0, roi); +} + +void draw_pill(cv::Mat& img, const std::string& text, int x, int y, + const cv::Scalar& bg, const cv::Scalar& fg, + double font_scale, int pad_x, int pad_y) { + int baseline = 0; + cv::Size ts = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, font_scale, 1, &baseline); + int x1 = x, y1 = y - ts.height - pad_y; + int x2 = x + ts.width + pad_x * 2, y2 = y + baseline + pad_y; + cv::rectangle(img, cv::Rect(x1, y1, x2 - x1, y2 - y1), bg, -1); + cv::rectangle(img, cv::Rect(x1, y1, x2 - x1, y2 - y1), C_BORDER, 1); + cv::putText(img, text, cv::Point(x + pad_x, y), cv::FONT_HERSHEY_SIMPLEX, font_scale, fg, 1, cv::LINE_AA); +} + +void draw_elegant_counting_line(cv::Mat& img, int line_x, int h, int pulse_remaining) { + float strength = pulse_remaining / std::max(12.0f, 1.0f); + float glow_alpha = 0.12f + 0.18f * strength; + for (int offset : {14, 9, 5}) { + cv::Scalar color(static_cast(C_LINE_GLOW[0] * glow_alpha), + static_cast(C_LINE_GLOW[1] * glow_alpha), + static_cast(C_LINE_GLOW[2] * glow_alpha)); + cv::line(img, cv::Point(line_x - offset, 0), cv::Point(line_x - offset, h), color, 1, cv::LINE_AA); + cv::line(img, cv::Point(line_x + offset, 0), cv::Point(line_x + offset, h), color, 1, cv::LINE_AA); + } + int dash_len = 18, gap = 12; + for (int y = 0; y < h; y += dash_len + gap) { + int ye = std::min(y + dash_len, h); + cv::line(img, cv::Point(line_x, y), cv::Point(line_x, ye), C_LINE_CORE, 2, cv::LINE_AA); + } + cv::putText(img, "COUNT LINE", cv::Point(line_x - 46, 24), + cv::FONT_HERSHEY_SIMPLEX, 0.42, C_LINE_CORE, 1, cv::LINE_AA); +} + +void draw_hero_count(cv::Mat& img, int line_x, int h, int count, int pulse_remaining) { + std::string text = std::to_string(count); + float boost = 0.35f * (pulse_remaining / std::max(15.0f, 1.0f)); + double font_scale = 1.6 + boost; + int thickness = static_cast(3 + boost); + int baseline = 0; + cv::Size ts = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, font_scale, thickness, &baseline); + int pad = 14; + int tx = line_x - ts.width / 2; + int ty = h / 2 + ts.height / 2; + overlay_rect(img, tx - pad, ty - ts.height - pad, tx + ts.width + pad, ty + pad / 2, C_PANEL, 0.78f); + cv::rectangle(img, cv::Rect(tx - pad, ty - ts.height - pad, + ts.width + pad * 2, ts.height + pad + pad / 2), + C_LINE_CORE, 2); + cv::putText(img, text, cv::Point(tx, ty), cv::FONT_HERSHEY_SIMPLEX, font_scale, C_GREEN, thickness, cv::LINE_AA); +} + +void draw_hud(cv::Mat& img, int w, int batch_num, int batch_count, int total_ayam, + double elapsed_sec, double rate, const std::string& camera_id, const std::string& clock) { + int bar_h = 52; + overlay_rect(img, 0, 0, w, bar_h, C_PANEL, 0.72f); + cv::line(img, cv::Point(0, bar_h), cv::Point(w, bar_h), C_BORDER, 1); + + cv::putText(img, "BATCH", cv::Point(16, 20), cv::FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv::LINE_AA); + std::string batch_label = batch_num ? std::to_string(batch_num) : "\xe2\x80\x94"; + cv::putText(img, batch_label, cv::Point(16, 44), cv::FONT_HERSHEY_SIMPLEX, 0.9, C_ACCENT, 2, cv::LINE_AA); + + cv::putText(img, "COUNT", cv::Point(100, 20), cv::FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv::LINE_AA); + cv::putText(img, std::to_string(batch_count), cv::Point(100, 44), cv::FONT_HERSHEY_SIMPLEX, 0.9, C_GREEN, 2, cv::LINE_AA); + + cv::putText(img, "TOTAL", cv::Point(190, 20), cv::FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv::LINE_AA); + cv::putText(img, std::to_string(total_ayam), cv::Point(190, 44), cv::FONT_HERSHEY_SIMPLEX, 0.7, C_TEXT, 1, cv::LINE_AA); + + cv::putText(img, "UPTIME", cv::Point(280, 20), cv::FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv::LINE_AA); + char buf[32]; + snprintf(buf, sizeof(buf), "%.1fh", elapsed_sec / 3600.0); + cv::putText(img, buf, cv::Point(280, 44), cv::FONT_HERSHEY_SIMPLEX, 0.7, C_TEXT, 1, cv::LINE_AA); + + cv::putText(img, "RATE", cv::Point(380, 20), cv::FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv::LINE_AA); + snprintf(buf, sizeof(buf), "%.1f/min", rate); + cv::putText(img, buf, cv::Point(380, 44), cv::FONT_HERSHEY_SIMPLEX, 0.7, C_ACCENT, 1, cv::LINE_AA); + + std::string cam_label = "CAM " + camera_id; + cv::putText(img, cam_label, cv::Point(w - 180, 20), cv::FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv::LINE_AA); +} + +void draw_footer(cv::Mat& img, int w, int h, int frame_idx, const std::string& live_tag) { + int bar_h = 28; + overlay_rect(img, 0, h - bar_h, w, h, C_PANEL, 0.55f); + char buf[64]; + snprintf(buf, sizeof(buf), "%s | Frame %d", live_tag.c_str(), frame_idx); + cv::putText(img, buf, cv::Point(12, h - 9), cv::FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv::LINE_AA); +} + +void draw_skeleton_bold(cv::Mat& img, const std::vector>& kpts) { + for (size_t i = 0; i < SKELETON.size(); ++i) { + int a = SKELETON[i].first, b = SKELETON[i].second; + if (a < (int)kpts.size() && b < (int)kpts.size()) { + int xa = static_cast(kpts[a][0].x), ya = static_cast(kpts[a][0].y); + int xb = static_cast(kpts[b][0].x), yb = static_cast(kpts[b][0].y); + if (xa > 0 && ya > 0 && xb > 0 && yb > 0) + cv::line(img, cv::Point(xa, ya), cv::Point(xb, yb), SK_COLORS[i], 3, cv::LINE_AA); + } + } + for (const auto& kp : kpts) { + if (!kp.empty()) { + int x = static_cast(kp[0].x), y = static_cast(kp[0].y); + if (x > 0 && y > 0) { + cv::circle(img, cv::Point(x, y), 6, cv::Scalar(255, 255, 255), -1, cv::LINE_AA); + cv::circle(img, cv::Point(x, y), 6, cv::Scalar(40, 40, 40), 2, cv::LINE_AA); + } + } + } +} + +std::vector draw_popups(cv::Mat& img, const std::vector& popups, int frame_idx) { + std::vector alive; + for (const auto& pop : popups) { + int age = frame_idx - pop.born; + if (age > g_config.popup_lifetime) continue; + alive.push_back(pop); + float fade = 1.0f - static_cast(age) / g_config.popup_lifetime; + cv::Scalar color(static_cast(C_GREEN[0] * fade), + static_cast(C_GREEN[1] * fade), + static_cast(C_GREEN[2] * fade)); + int y = pop.y - static_cast(age * 1.8f); + cv::putText(img, pop.text, cv::Point(pop.x, y), + cv::FONT_HERSHEY_SIMPLEX, 0.7, color, 2, cv::LINE_AA); + } + return alive; +} + +void draw_batch_banner(cv::Mat& img, int w, int batch_num, int pulse_remaining) { + if (pulse_remaining <= 0) return; + std::string text = "NEW BATCH " + std::to_string(batch_num); + int baseline = 0; + cv::Size ts = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.8, 2, &baseline); + int x1 = w / 2 - ts.width / 2 - 16, y1 = 62; + int x2 = w / 2 + ts.width / 2 + 16, y2 = 62 + ts.height + 20; + overlay_rect(img, x1, y1, x2, y2, C_PANEL, 0.7f); + cv::rectangle(img, cv::Rect(x1, y1, x2 - x1, y2 - y1), C_ACCENT, 2); + cv::putText(img, text, cv::Point(w / 2 - ts.width / 2, 62 + ts.height + 4), + cv::FONT_HERSHEY_SIMPLEX, 0.8, C_ACCENT, 2, cv::LINE_AA); +} diff --git a/src/drawing.hpp b/src/drawing.hpp new file mode 100644 index 0000000..0f5ae6c --- /dev/null +++ b/src/drawing.hpp @@ -0,0 +1,44 @@ +#pragma once +#include +#include +#include +#include +#include + +inline const cv::Scalar C_PANEL(18, 24, 28); +inline const cv::Scalar C_BORDER(75, 85, 90); +inline const cv::Scalar C_ACCENT(60, 200, 255); +inline const cv::Scalar C_GREEN(100, 220, 80); +inline const cv::Scalar C_TEXT(235, 235, 235); +inline const cv::Scalar C_MUTED(150, 150, 150); +inline const cv::Scalar C_AYAM_BOX(255, 165, 0); +inline const cv::Scalar C_TALENAN_BOX(60, 120, 220); +inline const cv::Scalar C_LINE_CORE(255, 220, 180); +inline const cv::Scalar C_LINE_GLOW(220, 160, 100); + +extern const std::vector> SKELETON; +extern const std::vector SK_COLORS; + +int resolve_line_x(int frame_width, int line_x_opt, float line_x_frac); +bool crossed_line(float prev_cx, float cx, int line_x, const std::string& direction); + +void overlay_rect(cv::Mat& img, int x1, int y1, int x2, int y2, const cv::Scalar& color, float alpha = 0.65f); +void draw_pill(cv::Mat& img, const std::string& text, int x, int y, + const cv::Scalar& bg, const cv::Scalar& fg = C_TEXT, + double font_scale = 0.45, int pad_x = 6, int pad_y = 4); + +void draw_elegant_counting_line(cv::Mat& img, int line_x, int h, int pulse_remaining = 0); +void draw_hero_count(cv::Mat& img, int line_x, int h, int count, int pulse_remaining = 0); +void draw_hud(cv::Mat& img, int w, int batch_num, int batch_count, int total_ayam, + double elapsed_sec, double rate, const std::string& camera_id, const std::string& clock); +void draw_footer(cv::Mat& img, int w, int h, int frame_idx, const std::string& live_tag); +void draw_skeleton_bold(cv::Mat& img, const std::vector>& kpts); + +struct Popup { + int x, y; + int born; + std::string text; +}; +std::vector draw_popups(cv::Mat& img, const std::vector& popups, int frame_idx); + +void draw_batch_banner(cv::Mat& img, int w, int batch_num, int pulse_remaining); diff --git a/src/kalman_filter.cpp b/src/kalman_filter.cpp new file mode 100644 index 0000000..c02390a --- /dev/null +++ b/src/kalman_filter.cpp @@ -0,0 +1,59 @@ +#include "kalman_filter.hpp" + +KalmanFilter::KalmanFilter() { + constexpr int ndim = kStateDim; + + motion_mat_ = Eigen::Matrix::Identity(); + for (int i = 0; i < ndim; ++i) { + motion_mat_(i, ndim + i) = 1.0f; + } + + update_mat_ = Eigen::Matrix::Zero(); + for (int i = 0; i < ndim; ++i) { + update_mat_(i, i) = 1.0f; + } + + x = Eigen::Matrix::Zero(); + P = Eigen::Matrix::Identity() * 10.0f; +} + +void KalmanFilter::predict() { + Eigen::Matrix std_vec; + std_vec << + std_weight_position_ * x(2), + std_weight_position_ * x(3), + std_weight_position_ * x(2), + std_weight_position_ * x(3), + std_weight_velocity_ * x(2), + std_weight_velocity_ * x(3), + std_weight_velocity_ * x(2), + std_weight_velocity_ * x(3); + + Eigen::Matrix Q = std_vec.array().square().matrix().asDiagonal(); + + x = motion_mat_ * x; + P = motion_mat_ * P * motion_mat_.transpose() + Q; +} + +void KalmanFilter::update(const Eigen::Vector4f& z) { + Eigen::Matrix std_vec; + std_vec << + std_weight_position_ * z(2), + std_weight_position_ * z(3), + std_weight_position_ * z(2), + std_weight_position_ * z(3), + 0, 0, 0, 0; + + Eigen::Matrix R = std_vec.head().array().square().matrix().asDiagonal(); + + auto H = update_mat_; + Eigen::Matrix S = H * P * H.transpose() + R; + Eigen::Matrix K = P * H.transpose() * S.inverse(); + + Eigen::Matrix y = z - H * x; + x = x + K * y; + + Eigen::Matrix I_KH = + Eigen::Matrix::Identity() - K * H; + P = I_KH * P * I_KH.transpose() + K * R * K.transpose(); +} diff --git a/src/kalman_filter.hpp b/src/kalman_filter.hpp new file mode 100644 index 0000000..9641781 --- /dev/null +++ b/src/kalman_filter.hpp @@ -0,0 +1,24 @@ +#pragma once +#include +#include +#include + +class KalmanFilter { +public: + static constexpr int kStateDim = 4; + static constexpr int kTotalDim = 2 * kStateDim; + + KalmanFilter(); + + void predict(); + void update(const Eigen::Vector4f& z); + + Eigen::Matrix x; + Eigen::Matrix P; + +private: + Eigen::Matrix motion_mat_; + Eigen::Matrix update_mat_; + float std_weight_position_ = 1.0f / 20.0f; + float std_weight_velocity_ = 1.0f / 160.0f; +}; diff --git a/src/main.cpp b/src/main.cpp new file mode 100644 index 0000000..e217ffb --- /dev/null +++ b/src/main.cpp @@ -0,0 +1,391 @@ +#include "config.hpp" +#include "bytetrack.hpp" +#include "batch_store.hpp" +#include "csv_logger.hpp" +#include "rknn_yolo.hpp" +#include "drawing.hpp" +#include "nms.hpp" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +static std::atomic shutdown_requested{false}; + +static void signal_handler(int) { + shutdown_requested = true; + std::cout << "\nShutdown requested — finishing current frame..." << std::endl; +} + +static std::string now_str() { + auto now = std::chrono::system_clock::now(); + auto t = std::chrono::system_clock::to_time_t(now); + std::ostringstream ss; + ss << std::put_time(std::localtime(&t), "%Y-%m-%d %H:%M:%S"); + return ss.str(); +} + +static cv::VideoCapture open_capture(const std::string& source) { + bool is_live = source.substr(0, 7) == "rtsp://" || source.substr(0, 7) == "http://"; + if (is_live) { + setenv("OPENCV_FFMPEG_CAPTURE_OPTIONS", g_config.rtsp_ffmpeg_options.c_str(), 1); + } + cv::VideoCapture cap(source, cv::CAP_FFMPEG); + cap.set(cv::CAP_PROP_BUFFERSIZE, 1); + return cap; +} + +static void warmup_stream(cv::VideoCapture& cap, int n) { + std::cout << "Warming up stream..." << std::endl; + cv::Mat frame; + for (int i = 0; i < n; ++i) cap.read(frame); + std::cout << "Stream ready!" << std::endl; +} + +struct TrackedInfo { + float cx; + std::chrono::steady_clock::time_point ts; +}; + +static void prune_stale_tracks(std::unordered_map& tracked, double prune_sec) { + auto now = std::chrono::steady_clock::now(); + std::vector stale; + for (auto& [tid, info] : tracked) { + auto age = std::chrono::duration_cast(now - info.ts).count(); + if (age > prune_sec) stale.push_back(tid); + } + for (int tid : stale) tracked.erase(tid); +} + +int main(int argc, char* argv[]) { + signal(SIGINT, signal_handler); + signal(SIGTERM, signal_handler); + + const char* env_path = argc > 1 ? argv[1] : nullptr; + g_config.load_from_env(env_path); + + std::cout << "Starting ByteTrack Counter (C++)" << std::endl; + std::cout << "Camera: " << g_config.camera_name << std::endl; + std::cout << "Source: " << g_config.source << std::endl; + std::cout << "Model: " << g_config.model_path << std::endl; + std::cout << "DB: " << g_config.db_path << std::endl; + + int class_ayam = 0; + int class_talenan = 1; + + auto logger = [](const std::string& msg) { + std::cout << "[" << now_str() << "] " << msg << std::endl; + }; + + BatchStore store(g_config.db_path, g_config.state_file, + g_config.camera_name, g_config.object_label, + g_config.daily_cutoff_time, + g_config.batch_timeout_seconds, + g_config.ignore_batch_label_timeout, + g_config.min_object_per_batch, + g_config.min_duration_per_batch, + logger); + store.start_cutoff_watcher(); + + std::unique_ptr cross_logger; + if (g_config.export_csv) { + cross_logger = std::make_unique(g_config.cross_csv, + std::vector{"batch", "frame", "timestamp", "chicken_id"}); + } + + RKNNYOLO model(g_config.model_path, g_config.core_mask, + g_config.imgsz, g_config.conf, 0.45f, + g_config.num_classes, 0, g_config.score_sigmoid); + + ByteTracker ayam_tracker(g_config.track_high_thresh, g_config.track_low_thresh, + g_config.track_match_thresh, g_config.track_buffer, + g_config.track_min_hits); + ByteTracker talenan_tracker(g_config.track_high_thresh, g_config.track_low_thresh, + g_config.track_match_thresh, g_config.track_buffer, + g_config.track_min_hits); + + std::unordered_set ayam_line_crossed; + std::unordered_set talenan_line_crossed; + + std::unordered_map ayam_cross_flash; + std::unordered_map talenan_cross_flash; + int line_pulse = 0, count_pulse = 0, batch_pulse = 0; + std::vector popups; + + auto session_start = std::chrono::steady_clock::now(); + int frame_idx = 0; + + cv::VideoCapture cap = open_capture(g_config.source); + bool is_live = g_config.source.substr(0, 7) == "rtsp://" || + g_config.source.substr(0, 7) == "http://"; + + if (!cap.isOpened()) { + std::cerr << "Cannot open source: " << g_config.source << std::endl; + store.shutdown(); + return 1; + } + if (is_live && g_config.warmup_frames > 0) + warmup_stream(cap, g_config.warmup_frames); + + int w = static_cast(cap.get(cv::CAP_PROP_FRAME_WIDTH)); + int h = static_cast(cap.get(cv::CAP_PROP_FRAME_HEIGHT)); + double fps_prop = cap.get(cv::CAP_PROP_FPS); + if (fps_prop <= 1) fps_prop = g_config.output_fps; + + int line_x = resolve_line_x(w, g_config.line_x, g_config.line_x_frac); + + std::cout << "RKNN+ByteTrack counter | " << w << "x" << h << " @ " + << fps_prop << "fps | line x=" << line_x + << " | cross=" << g_config.cross_direction << std::endl; + + int reconnect_count = 0; + + std::unordered_map ayam_tracked; + std::unordered_map talenan_tracked; + + while (!shutdown_requested) { + cv::Mat frame; + cap >> frame; + if (frame.empty()) { + if (!is_live) break; + reconnect_count++; + std::cout << "Stream dropped (attempt " << reconnect_count + << "), reconnecting..." << std::endl; + cap.release(); + std::this_thread::sleep_for(std::chrono::seconds(g_config.reconnect_delay_sec)); + cap = open_capture(g_config.source); + if (!cap.isOpened()) break; + w = static_cast(cap.get(cv::CAP_PROP_FRAME_WIDTH)); + h = static_cast(cap.get(cv::CAP_PROP_FRAME_HEIGHT)); + line_x = resolve_line_x(w, g_config.line_x, g_config.line_x_frac); + continue; + } + + auto elapsed_sec = std::chrono::duration_cast( + std::chrono::steady_clock::now() - session_start).count(); + auto mono = std::chrono::steady_clock::now(); + + bool ayam_crossed_frame = false, batch_closed_frame = false, batch_started_frame = false; + + std::vector detections = model(frame); + + if (!detections.empty()) { + std::vector ayam_boxes, talenan_boxes; + std::vector ayam_scores, talenan_scores; + std::vector ayam_cx_list, talenan_cx_list; + std::vector>> ayam_kpts_list; + + for (const auto& det : detections) { + const auto& bbox = det.bbox; + float cx = (bbox(0) + bbox(2)) / 2.0f; + + if (det.cls == class_talenan) { + talenan_boxes.push_back(bbox); + talenan_scores.push_back(det.score); + talenan_cx_list.push_back(cx); + } else if (det.cls == class_ayam) { + ayam_boxes.push_back(bbox); + ayam_scores.push_back(det.score); + ayam_cx_list.push_back(cx); + ayam_kpts_list.push_back(det.keypoints); + } + } + + int na = static_cast(ayam_boxes.size()); + int nt = static_cast(talenan_boxes.size()); + + Eigen::MatrixXf ayam_mat(na, 4), talenan_mat(nt, 4); + Eigen::VectorXf ayam_scores_vec(na), talenan_scores_vec(nt); + + for (int i = 0; i < na; ++i) { + ayam_mat.row(i) = ayam_boxes[i]; + ayam_scores_vec(i) = ayam_scores[i]; + } + for (int i = 0; i < nt; ++i) { + talenan_mat.row(i) = talenan_boxes[i]; + talenan_scores_vec(i) = talenan_scores[i]; + } + + TrackResult ayam_tr = ayam_tracker.update(ayam_mat, ayam_scores_vec); + TrackResult talenan_tr = talenan_tracker.update(talenan_mat, talenan_scores_vec); + + // Process talenan crossings + for (int di = 0; di < nt; ++di) { + auto it = talenan_tr.det_to_track.find(di); + if (it == talenan_tr.det_to_track.end()) continue; + int tid = it->second; + float cx = talenan_cx_list[di]; + + auto tit = talenan_tracked.find(tid); + if (tit != talenan_tracked.end()) { + float prev_cx = tit->second.cx; + if (crossed_line(prev_cx, cx, line_x, g_config.cross_direction) && + talenan_line_crossed.find(tid) == talenan_line_crossed.end()) { + talenan_line_crossed.insert(tid); + if (store.record_talenan_crossing(tid)) { + batch_closed_frame = true; + talenan_cross_flash[tid] = g_config.cross_flash_frames; + popups.push_back({ + static_cast(cx) - 20, + static_cast((talenan_boxes[di](1) + talenan_boxes[di](3)) / 2), + frame_idx, "BATCH CLOSED" + }); + } + } + } + talenan_tracked[tid] = {cx, mono}; + } + + // Process ayam crossings + for (int di = 0; di < na; ++di) { + auto it = ayam_tr.det_to_track.find(di); + if (it == ayam_tr.det_to_track.end()) continue; + int tid = it->second; + float cx = ayam_cx_list[di]; + + auto tit = ayam_tracked.find(tid); + if (tit != ayam_tracked.end()) { + float prev_cx = tit->second.cx; + if (crossed_line(prev_cx, cx, line_x, g_config.cross_direction) && + ayam_line_crossed.find(tid) == ayam_line_crossed.end()) { + ayam_line_crossed.insert(tid); + auto [count, started_new] = store.record_ayam_crossing(tid); + if (cross_logger) { + std::ostringstream frame_str, tid_str; + frame_str << frame_idx; + tid_str << tid; + cross_logger->write_row({ + std::to_string(store.current_batch_number()), + frame_str.str(), + now_str(), + tid_str.str() + }); + } + ayam_crossed_frame = true; + if (started_new) batch_started_frame = true; + ayam_cross_flash[tid] = g_config.cross_flash_frames; + popups.push_back({ + static_cast(cx) - 12, + static_cast((ayam_boxes[di](1) + ayam_boxes[di](3)) / 2), + frame_idx, "+1" + }); + } + } + ayam_tracked[tid] = {cx, mono}; + } + + // Track lost tracks for continuity + for (auto& [tid, cx] : ayam_tr.lost_map) { + if (ayam_tracked.find(tid) == ayam_tracked.end()) + ayam_tracked[tid] = {cx, mono}; + } + + // Draw talenan + for (int di = 0; di < nt; ++di) { + auto it = talenan_tr.det_to_track.find(di); + if (it == talenan_tr.det_to_track.end()) continue; + int tid = it->second; + const auto& bbox = talenan_boxes[di]; + int x1 = static_cast(bbox(0)), y1 = static_cast(bbox(1)); + int x2 = static_cast(bbox(2)), y2 = static_cast(bbox(3)); + int flash = talenan_cross_flash.count(tid) ? talenan_cross_flash[tid] : 0; + cv::Scalar color = flash > 0 ? C_GREEN : C_TALENAN_BOX; + cv::rectangle(frame, cv::Point(x1, y1), cv::Point(x2, y2), color, flash > 0 ? 3 : 2); + draw_pill(frame, "TALENAN " + std::to_string(tid), x1, y1 - 4, color); + } + + // Draw ayam + for (int di = 0; di < na; ++di) { + auto it = ayam_tr.det_to_track.find(di); + if (it == ayam_tr.det_to_track.end()) continue; + int tid = it->second; + const auto& bbox = ayam_boxes[di]; + int x1 = static_cast(bbox(0)), y1 = static_cast(bbox(1)); + int x2 = static_cast(bbox(2)), y2 = static_cast(bbox(3)); + int flash = ayam_cross_flash.count(tid) ? ayam_cross_flash[tid] : 0; + cv::Scalar color = flash > 0 ? C_GREEN : C_AYAM_BOX; + cv::rectangle(frame, cv::Point(x1, y1), cv::Point(x2, y2), color, flash > 0 ? 3 : 2); + draw_pill(frame, "ID " + std::to_string(tid), x1, y1 - 4, color); + if (di < (int)ayam_kpts_list.size() && !ayam_kpts_list[di].empty()) + draw_skeleton_bold(frame, ayam_kpts_list[di]); + } + } + + if (ayam_crossed_frame) { + line_pulse = g_config.line_pulse_frames; + count_pulse = g_config.count_pulse_frames; + } + if (batch_closed_frame) { + line_pulse = g_config.line_pulse_frames; + } + if (batch_started_frame) { + batch_pulse = g_config.batch_pulse_frames; + } + + int batch_num = store.current_batch_number(); + int batch_count = store.current_batch_count(); + int display_total = store.display_total(); + double elapsed = static_cast(elapsed_sec); + double rate = elapsed > 0 ? (display_total / elapsed * 60.0) : 0.0; + + draw_elegant_counting_line(frame, line_x, h, line_pulse); + draw_hero_count(frame, line_x, h, batch_count, count_pulse); + draw_hud(frame, w, batch_num, batch_count, display_total, + elapsed, rate, g_config.camera_name, now_str()); + draw_batch_banner(frame, w, batch_num, batch_pulse); + draw_footer(frame, w, h, frame_idx, is_live ? "LIVE-RKNN-BT" : "FILE-RKNN-BT"); + popups = draw_popups(frame, popups, frame_idx); + + // Flash decay + for (auto& flash_map : {&ayam_cross_flash, &talenan_cross_flash}) { + for (auto it = flash_map->begin(); it != flash_map->end(); ) { + it->second--; + if (it->second <= 0) it = flash_map->erase(it); + else ++it; + } + } + line_pulse = std::max(0, line_pulse - 1); + count_pulse = std::max(0, count_pulse - 1); + batch_pulse = std::max(0, batch_pulse - 1); + + if (g_config.live_stream_enabled && frame_idx % g_config.live_stream_every_n == 0) { + try { + std::filesystem::path p(g_config.live_stream_frame_path); + if (p.has_parent_path()) + std::filesystem::create_directories(p.parent_path()); + std::vector jpeg; + cv::imencode(".jpg", frame, jpeg, + {cv::IMWRITE_JPEG_QUALITY, g_config.live_stream_quality}); + std::ofstream f(g_config.live_stream_frame_path, std::ios::binary); + f.write(reinterpret_cast(jpeg.data()), jpeg.size()); + } catch (...) {} + } + + frame_idx++; + if (frame_idx % g_config.flush_every_n_frames == 0) { + std::cout << "[" << now_str() << "] Frame " << frame_idx + << " | Batch " << batch_num << ": " << batch_count + << " | Total: " << display_total + << " | Uptime " << (elapsed / 3600.0) << "h" << std::endl; + } + + prune_stale_tracks(ayam_tracked, g_config.tracked_prune_sec); + prune_stale_tracks(talenan_tracked, g_config.tracked_prune_sec); + } + + cap.release(); + store.shutdown(); + + std::cout << "\n=== Batch Summary (SQLite) ===" << std::endl; + std::cout << "Database: " << g_config.db_path << std::endl; + + return 0; +} diff --git a/src/nms.hpp b/src/nms.hpp new file mode 100644 index 0000000..424655d --- /dev/null +++ b/src/nms.hpp @@ -0,0 +1,86 @@ +#pragma once +#include +#include +#include +#include + +inline std::vector nms(const Eigen::MatrixXf& boxes, const Eigen::VectorXf& scores, float iou_thr = 0.45f) { + std::vector order(scores.size()); + std::iota(order.begin(), order.end(), 0); + std::sort(order.begin(), order.end(), [&](int a, int b) { return scores(a) > scores(b); }); + + std::vector keep; + while (!order.empty()) { + int idx = order[0]; + keep.push_back(idx); + if (order.size() == 1) break; + + std::vector remain; + for (size_t i = 1; i < order.size(); ++i) { + int j = order[i]; + float xx1 = std::max(boxes(idx, 0), boxes(j, 0)); + float yy1 = std::max(boxes(idx, 1), boxes(j, 1)); + float xx2 = std::min(boxes(idx, 2), boxes(j, 2)); + float yy2 = std::min(boxes(idx, 3), boxes(j, 3)); + float w = std::max(0.0f, xx2 - xx1); + float h = std::max(0.0f, yy2 - yy1); + float inter = w * h; + float area_i = (boxes(idx, 2) - boxes(idx, 0)) * (boxes(idx, 3) - boxes(idx, 1)); + float area_j = (boxes(j, 2) - boxes(j, 0)) * (boxes(j, 3) - boxes(j, 1)); + float iou = inter / (area_i + area_j - inter + 1e-16f); + if (iou < iou_thr) remain.push_back(j); + } + order = remain; + } + return keep; +} + +inline Eigen::MatrixXf ious_xyxy(const Eigen::MatrixXf& boxes_a, const Eigen::MatrixXf& boxes_b) { + int n = static_cast(boxes_a.rows()); + int m = static_cast(boxes_b.rows()); + if (n == 0 || m == 0) return Eigen::MatrixXf::Zero(n, m); + + Eigen::MatrixXf iou_mat(n, m); + for (int i = 0; i < n; ++i) { + for (int j = 0; j < m; ++j) { + float xx1 = std::max(boxes_a(i, 0), boxes_b(j, 0)); + float yy1 = std::max(boxes_a(i, 1), boxes_b(j, 1)); + float xx2 = std::min(boxes_a(i, 2), boxes_b(j, 2)); + float yy2 = std::min(boxes_a(i, 3), boxes_b(j, 3)); + float iw = std::max(0.0f, xx2 - xx1); + float ih = std::max(0.0f, yy2 - yy1); + float inter = iw * ih; + float area_a = (boxes_a(i, 2) - boxes_a(i, 0)) * (boxes_a(i, 3) - boxes_a(i, 1)); + float area_b = (boxes_b(j, 2) - boxes_b(j, 0)) * (boxes_b(j, 3) - boxes_b(j, 1)); + iou_mat(i, j) = inter / (area_a + area_b - inter + 1e-16f); + } + } + return iou_mat; +} + +inline std::vector> greedy_match(const Eigen::MatrixXf& cost_matrix, float threshold = 0.3f) { + if (cost_matrix.size() == 0) return {}; + + int n = static_cast(cost_matrix.rows()); + int m = static_cast(cost_matrix.cols()); + + std::vector> flat; + flat.reserve(n * m); + for (int i = 0; i < n; ++i) + for (int j = 0; j < m; ++j) + flat.emplace_back(cost_matrix(i, j), i, j); + std::sort(flat.begin(), flat.end()); + + std::vector row_used(n, false); + std::vector col_used(m, false); + std::vector> pairs; + + for (const auto& [cost, i, j] : flat) { + if (cost >= threshold) break; + if (row_used[i] || col_used[j]) continue; + row_used[i] = true; + col_used[j] = true; + pairs.emplace_back(i, j); + } + return pairs; +} diff --git a/src/rknn_yolo.cpp b/src/rknn_yolo.cpp new file mode 100644 index 0000000..05fbf4d --- /dev/null +++ b/src/rknn_yolo.cpp @@ -0,0 +1,230 @@ +#include "rknn_yolo.hpp" +#include "nms.hpp" +#include + +#ifdef HAS_RKNN +#include "rknn_api.h" + +RKNNYOLO::RKNNYOLO(const std::string& model_path, int core_mask, int imgsz, + float conf, float iou, int num_classes, int num_keypoints, bool score_sigmoid) + : imgsz_(imgsz), conf_(conf), iou_(iou), num_classes_(num_classes), + num_keypoints_(num_keypoints), score_sigmoid_(score_sigmoid) { + + FILE* fp = fopen(model_path.c_str(), "rb"); + if (!fp) { + std::cerr << "Failed to open RKNN model: " << model_path << std::endl; + return; + } + fseek(fp, 0, SEEK_END); + size_t model_size = ftell(fp); + fseek(fp, 0, SEEK_SET); + std::vector model_data(model_size); + fread(model_data.data(), 1, model_size, fp); + fclose(fp); + + int ret = rknn_init(&rknn_ctx_, model_data.data(), model_size, + 0, nullptr); + if (ret != 0) { + std::cerr << "Failed to init RKNN (ret=" << ret << ")" << std::endl; + return; + } + + rknn_set_core_mask(rknn_ctx_, static_cast(core_mask)); + + rknn_input_output_num io_num; + ret = rknn_query(rknn_ctx_, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num)); + if (ret != 0) { + std::cerr << "Failed to query RKNN IO" << std::endl; + return; + } + std::cout << "IO nums: inputs=" << io_num.n_input << " outputs=" << io_num.n_output << std::endl; + + rknn_tensor_attr in_attr; + memset(&in_attr, 0, sizeof(in_attr)); + + if (io_num.n_input > 0) { + in_attr.index = 0; + ret = rknn_query(rknn_ctx_, RKNN_QUERY_INPUT_ATTR, &in_attr, sizeof(in_attr)); + if (ret == 0) { + std::cout << "Input attr: fmt=" << in_attr.fmt << " type=" << in_attr.type + << " n_dims=" << in_attr.n_dims << " dims=["; + for (uint32_t d = 0; d < in_attr.n_dims; ++d) { + if (d > 0) std::cout << ","; + std::cout << in_attr.dims[d]; + } + std::cout << "]" << std::endl; + } + + in_attr.dims[0] = 1; + in_attr.dims[1] = imgsz_; + in_attr.dims[2] = imgsz_; + in_attr.dims[3] = 3; + in_attr.n_dims = 4; + ret = rknn_set_input_shapes(rknn_ctx_, io_num.n_input, &in_attr); + if (ret != 0) { + std::cerr << "Failed to set input shapes (ret=" << ret << ")" << std::endl; + } else { + std::cout << "Input shapes set: [1," << imgsz_ << "," << imgsz_ << ",3]" << std::endl; + } + } + + if (io_num.n_output > 0) { + rknn_tensor_attr out_attr; + memset(&out_attr, 0, sizeof(out_attr)); + out_attr.index = 0; + ret = rknn_query(rknn_ctx_, RKNN_QUERY_OUTPUT_ATTR, &out_attr, sizeof(out_attr)); + if (ret == 0 && out_attr.n_dims >= 2) { + int first_dim = out_attr.n_dims > 1 ? out_attr.dims[out_attr.n_dims - 2] : 0; + int last_dim = out_attr.dims[out_attr.n_dims - 1]; + if (first_dim == num_classes_ + 4) { + output_transposed_ = true; + output_n_pred_ = last_dim; + } else if (last_dim == num_classes_ + 4) { + output_transposed_ = false; + output_n_pred_ = first_dim; + } + std::cout << "Output shape: ["; + for (uint32_t d = 0; d < out_attr.n_dims; ++d) { + if (d > 0) std::cout << ", "; + std::cout << out_attr.dims[d]; + } + std::cout << "] transposed=" << (output_transposed_ ? "true" : "false") + << " n_pred=" << output_n_pred_ << std::endl; + } + } + + loaded_ = true; + std::cout << "RKNN model loaded: " << model_path << " imgsz=" << imgsz_ + << " core_mask=" << core_mask << std::endl; +} + +RKNNYOLO::~RKNNYOLO() { + if (loaded_ && rknn_ctx_) { + rknn_destroy(rknn_ctx_); + } +} + +cv::Mat RKNNYOLO::preprocess(const cv::Mat& frame, Eigen::Vector4f& gains) { + int h0 = frame.rows, w0 = frame.cols; + float scale = std::min((float)imgsz_ / h0, (float)imgsz_ / w0); + int nh = static_cast(h0 * scale), nw = static_cast(w0 * scale); + cv::Mat resized; + cv::resize(frame, resized, cv::Size(nw, nh), 0, 0, cv::INTER_LINEAR); + + cv::Mat letterbox(imgsz_, imgsz_, CV_8UC3, cv::Scalar(114, 114, 114)); + int dy = (imgsz_ - nh) / 2, dx = (imgsz_ - nw) / 2; + resized.copyTo(letterbox(cv::Rect(dx, dy, nw, nh))); + + cv::Mat rgb; + cv::cvtColor(letterbox, rgb, cv::COLOR_BGR2RGB); + gains << scale, scale, (float)dy, (float)dx; + return rgb; +} + +std::vector RKNNYOLO::operator()(const cv::Mat& frame) { + std::vector detections; + if (!loaded_) return detections; + + int h0 = frame.rows, w0 = frame.cols; + Eigen::Vector4f gains; + cv::Mat rgb = preprocess(frame, gains); + float scale = gains(0); + int pad_y = static_cast(gains(2)), pad_x = static_cast(gains(3)); + + rknn_input inputs[1]; + memset(inputs, 0, sizeof(inputs)); + inputs[0].index = 0; + inputs[0].type = RKNN_TENSOR_UINT8; + inputs[0].fmt = RKNN_TENSOR_NHWC; + inputs[0].buf = rgb.data; + inputs[0].size = imgsz_ * imgsz_ * 3; + + rknn_inputs_set(rknn_ctx_, 1, inputs); + + rknn_output outputs[1]; + memset(outputs, 0, sizeof(outputs)); + outputs[0].want_float = 1; + rknn_run(rknn_ctx_, nullptr); + rknn_outputs_get(rknn_ctx_, 1, outputs, nullptr); + + float* out_data = (float*)outputs[0].buf; + int n_pred = output_n_pred_; + if (n_pred <= 0) n_pred = (int)(outputs[0].size / sizeof(float)) / (4 + num_classes_); + + int stride = 4 + num_classes_; + for (int i = 0; i < n_pred; ++i) { + float cx, cy, w, h; + float cls_scores[15]; + if (output_transposed_) { + cx = out_data[i]; + cy = out_data[i + n_pred]; + w = out_data[i + n_pred * 2]; + h = out_data[i + n_pred * 3]; + for (int c = 0; c < num_classes_; ++c) + cls_scores[c] = out_data[i + n_pred * (4 + c)]; + } else { + float* pred = out_data + i * stride; + cx = pred[0]; cy = pred[1]; w = pred[2]; h = pred[3]; + for (int c = 0; c < num_classes_; ++c) + cls_scores[c] = pred[4 + c]; + } + float x1 = cx - w / 2, y1 = cy - h / 2, x2 = cx + w / 2, y2 = cy + h / 2; + + float max_score = 0; + int best_cls = 0; + for (int c = 0; c < num_classes_; ++c) { + float s = score_sigmoid_ ? (1.0f / (1.0f + std::exp(-cls_scores[c]))) : cls_scores[c]; + if (s > max_score) { max_score = s; best_cls = c; } + } + + if (max_score > conf_) { + x1 = (x1 - pad_x) / scale; y1 = (y1 - pad_y) / scale; + x2 = (x2 - pad_x) / scale; y2 = (y2 - pad_y) / scale; + x1 = std::clamp(x1, 0.0f, (float)w0); + y1 = std::clamp(y1, 0.0f, (float)h0); + x2 = std::clamp(x2, 0.0f, (float)w0); + y2 = std::clamp(y2, 0.0f, (float)h0); + + Detection det; + det.bbox << x1, y1, x2, y2; + det.score = max_score; + det.cls = best_cls; + detections.push_back(det); + } + } + + rknn_outputs_release(rknn_ctx_, 1, outputs); + return detections; +} +#else +// Stub implementation when RKNN is not available +RKNNYOLO::RKNNYOLO(const std::string& model_path, int core_mask, int imgsz, + float conf, float iou, int num_classes, int num_keypoints, bool score_sigmoid) + : imgsz_(imgsz), conf_(conf), iou_(iou), num_classes_(num_classes), + num_keypoints_(num_keypoints), score_sigmoid_(score_sigmoid) { + std::cerr << "WARNING: Built without RKNN support. Model path: " << model_path << std::endl; +} + +RKNNYOLO::~RKNNYOLO() {} + +cv::Mat RKNNYOLO::preprocess(const cv::Mat& frame, Eigen::Vector4f& gains) { + int h0 = frame.rows, w0 = frame.cols; + float scale = std::min((float)imgsz_ / h0, (float)imgsz_ / w0); + int nh = static_cast(h0 * scale), nw = static_cast(w0 * scale); + cv::Mat resized; + cv::resize(frame, resized, cv::Size(nw, nh), 0, 0, cv::INTER_LINEAR); + + cv::Mat letterbox(imgsz_, imgsz_, CV_8UC3, cv::Scalar(114, 114, 114)); + int dy = (imgsz_ - nh) / 2, dx = (imgsz_ - nw) / 2; + resized.copyTo(letterbox(cv::Rect(dx, dy, nw, nh))); + + cv::Mat rgb; + cv::cvtColor(letterbox, rgb, cv::COLOR_BGR2RGB); + gains << scale, scale, (float)dy, (float)dx; + return rgb; +} + +std::vector RKNNYOLO::operator()(const cv::Mat& frame) { + return {}; +} +#endif diff --git a/src/rknn_yolo.hpp b/src/rknn_yolo.hpp new file mode 100644 index 0000000..303653c --- /dev/null +++ b/src/rknn_yolo.hpp @@ -0,0 +1,51 @@ +#pragma once +#include +#include +#include +#include + +#ifdef HAS_RKNN +#include "rknn_api.h" +#endif + +struct Detection { + Eigen::Vector4f bbox; + float score; + int cls; + std::vector> keypoints; +}; + +class RKNNYOLO { +public: + RKNNYOLO(const std::string& model_path, + int core_mask = 1, + int imgsz = 320, + float conf = 0.3f, + float iou = 0.45f, + int num_classes = 2, + int num_keypoints = 0, + bool score_sigmoid = false); + + ~RKNNYOLO(); + + std::vector operator()(const cv::Mat& frame); + + bool is_loaded() const { return loaded_; } + +private: + cv::Mat preprocess(const cv::Mat& frame, Eigen::Vector4f& gains); + + int imgsz_; + float conf_; + float iou_; + int num_classes_; + int num_keypoints_; + bool score_sigmoid_; + bool loaded_ = false; + bool output_transposed_ = false; + int output_n_pred_ = 0; + +#ifdef HAS_RKNN + rknn_context rknn_ctx_ = 0; +#endif +};