Merge pull request 'Optimize python biar bisa jalan di Jetson Orin Nano' (#1) from dsutanto/chicken-counting-sukawarna-det:main into main

Reviewed-on: zakaria/chicken-counting-sukawarna-det#1

.gitignore

@@ -0,0 +1,220 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#   Usually these files are written by a python script from a template
+#   before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py.cover
+*.lcov
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+# Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+# uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+# poetry.lock
+# poetry.toml
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#   pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
+#   https://pdm-project.org/en/latest/usage/project/#working-with-version-control
+# pdm.lock
+# pdm.toml
+.pdm-python
+.pdm-build/
+
+# pixi
+#   Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
+# pixi.lock
+#   Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
+#   in the .venv directory. It is recommended not to include this directory in version control.
+.pixi/*
+!.pixi/config.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule*
+celerybeat.pid
+
+# Redis
+*.rdb
+*.aof
+*.pid
+
+# RabbitMQ
+mnesia/
+rabbitmq/
+rabbitmq-data/
+
+# ActiveMQ
+activemq-data/
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.envrc
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#   JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#   be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#   and can be added to the global gitignore or merged into this file.  For a more nuclear
+#   option (not recommended) you can uncomment the following to ignore the entire idea folder.
+# .idea/
+
+# Abstra
+#   Abstra is an AI-powered process automation framework.
+#   Ignore directories containing user credentials, local state, and settings.
+#   Learn more at https://abstra.io/docs
+.abstra/
+
+# Visual Studio Code
+#   Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
+#   that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#   and can be added to the global gitignore or merged into this file. However, if you prefer, 
+#   you could uncomment the following to ignore the entire vscode folder
+# .vscode/
+# Temporary file for partial code execution
+tempCodeRunnerFile.py
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Marimo
+marimo/_static/
+marimo/_lsp/
+__marimo__/
+
+# Streamlit
+.streamlit/secrets.toml

RUN.md

@@ -0,0 +1,11 @@
+# Counter
+## Pakai virtual env di /media/jetson/DATA/karung-sukawarna/venv
+
+alias chicken-counter='PYTHONPATH={fullpath git clone folder} /media/jetson/DATA/karung-sukawarna/venv/bin/python -m chicken_counter.cli'
+
+chicken-counter batch --config configs/cycle7_batch.yaml --date 2026-06-10 --no-video --progress-bar
+
+# Dashboard
+## Pakai virtual env di /media/jetson/DATA/karung-sukawarna/venv
+source /media/jetson/DATA/karung-sukawarna/venv/bin/source
+python dashboard.py --port 8080

configs/cycle7_batch.yaml

@@ -1,5 +1,5 @@
 batch:
-  root_dir: /home/nvidia-admin/VIDEOS/cycle7/kandang-atas
+  root_dir: /media/jetson/DATA/.Codes/VIDEOS/cycle7/kandang-atas
   camera_glob: "kandang_*_camera_{num}_*.mp4"
   output_subdir: output
   compress_max_mb: 200
@@ -8,7 +8,7 @@ batch:
 
 defaults:
   detection:
-    model_path: /home/nvidia-admin/LABS/try-weight-estimator/try-chicken-sukawarna/chicken-detection-model-v26n-300e-best-2026-05-02-NEW.pt
+    model_path: /media/jetson/DATA/.Codes/chicken-counting-sukawarna-det/chicken-detection-model-v26n-300e-best-2026-05-02-NEW.engine
     classes: [0]
     ignored_classes: [1, 2]
     conf: 0.35
@@ -23,7 +23,7 @@ defaults:
     buffer_below_px: 250
     show_in_overlay: true
   tracker:
-    tracker_config_path: /home/nvidia-admin/LABS/try-weight-estimator/try-chicken-sukawarna/configs/trackers/botsort_chicken.yaml
+    tracker_config_path: /media/jetson/DATA/.Codes/chicken-counting-sukawarna-det/configs/trackers/botsort_chicken.yaml
     persist: true
     track_buffer: 75
   gate:
@@ -66,6 +66,10 @@ defaults:
     half: false
     overlay_buffer_reuse: true
     inference_stride: 2
+  stream:
+    enabled: true
+    shm_dir: /dev/shm
+    interval_frames: 5
   feedback:
     enabled: true
     every_n_frames: 3000

dashboard.py

@@ -0,0 +1,261 @@
+#!/usr/bin/env python3
+"""Standalone live dashboard for chicken-counter pipeline.
+
+Serve from project root:
+    PYTHONPATH=src python3 dashboard.py [--port 8080]
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+from http.server import HTTPServer, SimpleHTTPRequestHandler
+from pathlib import Path
+from socketserver import ThreadingMixIn
+from urllib.parse import unquote, urlparse
+
+
+class ThreadingHTTPServer(ThreadingMixIn, HTTPServer):
+    daemon_threads = True
+
+DEFAULT_SHM_DIR = "/dev/shm"
+DEFAULT_PORT = 8080
+
+DASHBOARD_HTML = r"""<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width,initial-scale=1">
+<title>Chicken Counter — Live Dashboard</title>
+<style>
+*{margin:0;padding:0;box-sizing:border-box}
+body{font-family:system-ui,monospace;background:#0f0f14;color:#e0e0e0;overflow:hidden}
+#app{display:flex;height:100vh}
+#sidebar{width:260px;background:#16161e;padding:16px;overflow-y:auto;flex-shrink:0}
+#sidebar h1{font-size:18px;color:#80dc5a;margin-bottom:16px}
+#sidebar .stat{margin-bottom:12px}
+#sidebar .stat label{display:block;font-size:11px;color:#888;text-transform:uppercase;letter-spacing:1px}
+#sidebar .stat .value{font-size:22px;font-weight:700;color:#e0e0e0}
+#sidebar .stat .value.warn{color:#ff9f43}
+#sidebar .stat .value.good{color:#80dc5a}
+#cam-list{list-style:none;margin-top:16px}
+#cam-list li{padding:8px 10px;margin:2px 0;border-radius:6px;cursor:pointer;font-size:13px;transition:background .2s}
+#cam-list li:hover{background:#222}
+#cam-list li.active{background:#1a3a2a;color:#80dc5a;font-weight:700}
+#cam-list li .cam-badge{float:right;font-size:10px;padding:1px 6px;border-radius:8px;background:#222;color:#888}
+#cam-list li.active .cam-badge{background:#2a5a3a;color:#80dc5a}
+#main{flex:1;display:flex;flex-direction:column}
+#frame-container{flex:1;display:flex;align-items:center;justify-content:center;background:#000;position:relative}
+#frame-img{max-width:100%;max-height:100%;object-fit:contain}
+#no-frame{color:#555;font-size:18px}
+#top-bar{display:flex;justify-content:space-between;align-items:center;padding:10px 16px;background:#16161e;font-size:12px}
+#top-bar .refresh{color:#888}
+#top-bar .status-dot{display:inline-block;width:8px;height:8px;border-radius:50%;margin-right:6px}
+#top-bar .status-dot.online{background:#80dc5a;box-shadow:0 0 6px #80dc5a}
+#top-bar .status-dot.offline{background:#555}
+.refresh-btn{padding:4px 12px;border-radius:4px;background:#222;border:1px solid #444;color:#ccc;cursor:pointer;font-size:11px}
+.refresh-btn:hover{background:#333}
+</style>
+</head>
+<body>
+<div id="app">
+<div id="sidebar">
+<h1>&#x1f414; Chicken Counter</h1>
+<div class="stat"><label>Total Entered</label><div class="value good" id="stat-total">--</div></div>
+<div class="stat"><label>Inside Box</label><div class="value" id="stat-inside">--</div></div>
+<div class="stat"><label>Tracks</label><div class="value" id="stat-tracks">--</div></div>
+<div class="stat"><label>Frame</label><div class="value" id="stat-frame">--</div></div>
+<div class="stat"><label>Motion Speed</label><div class="value" id="stat-speed">--</div></div>
+<div class="stat"><label>Status</label><div class="value" id="stat-status">--</div></div>
+<ul id="cam-list"></ul>
+</div>
+<div id="main">
+<div id="top-bar">
+<span><span class="status-dot" id="status-dot"></span><span id="status-text">waiting for pipeline...</span></span>
+<span><span class="refresh" id="refresh-counter"></span> ago &nbsp;
+<button class="refresh-btn" onclick="load()">&#x21bb; Refresh</button></span>
+</div>
+<div id="frame-container">
+<img id="frame-img" alt="live stream">
+<div id="no-frame"></div>
+</div>
+</div>
+</div>
+<script>
+var POLL_MS = %%POLL_MS%%;
+var SHM = "%%SHM_DIR%%";
+var cameras = [];
+var activeCam = null;
+var lastUpdate = 0;
+var img = document.getElementById("frame-img");
+var noFrame = document.getElementById("no-frame");
+
+function loadCameras() {{
+    fetch("/api/cameras").then(r => r.json()).then(data => {{
+        cameras = data.cameras || [];
+        renderCamList();
+        if (cameras.length && !activeCam) selectCam(cameras[0]);
+        if (!cameras.length) {{ noFrame.textContent = "No cameras found in " + SHM; img.style.display = "none"; }}
+    }});
+}}
+
+function renderCamList() {{
+    var ul = document.getElementById("cam-list");
+    ul.innerHTML = cameras.map(function(c) {{
+        return '<li class="' + (c === activeCam ? "active" : "") + '" onclick="selectCam(\'' + c + '\')">' +
+            c + '<span class="cam-badge">&#x25b6;</span></li>';
+    }}).join("");
+}}
+
+function selectCam(id) {{
+    activeCam = id;
+    renderCamList();
+    load();
+}}
+
+function load() {{
+    if (!activeCam) return;
+    var t = Date.now();
+    img.src = "/shm/" + activeCam + "/frame.jpg?t=" + t;
+    fetch("/shm/" + activeCam + "/stats.json?t=" + t).then(function(r) {{
+        if (!r.ok) {{ setOffline(); return; }}
+        return r.json();
+    }}).then(function(s) {{
+        if (!s) return;
+        lastUpdate = Date.now();
+        document.getElementById("stat-total").textContent = s.total_entered_count;
+        document.getElementById("stat-inside").textContent = s.inside_box_count;
+        document.getElementById("stat-tracks").textContent = s.track_count;
+        document.getElementById("stat-frame").textContent = s.frame_index;
+        document.getElementById("stat-speed").textContent = s.smoothed_speed;
+        document.getElementById("stat-status").textContent = s.backward_active ? "BACKWARD STOP" : "RUNNING";
+        var el = document.getElementById("stat-status");
+        el.className = "value" + (s.backward_active ? " warn" : " good");
+        document.getElementById("status-dot").className = "status-dot online";
+        document.getElementById("status-text").textContent = activeCam + " \u2022 frame " + s.frame_index;
+    }});
+}}
+
+function setOffline() {{
+    document.getElementById("status-dot").className = "status-dot offline";
+    document.getElementById("status-text").textContent = activeCam + " \u2022 offline";
+}}
+
+function updateRefresh() {{
+    var ago = Math.round((Date.now() - lastUpdate) / 1000);
+    document.getElementById("refresh-counter").textContent = ago + "s";
+}}
+
+img.onerror = function() {{ img.style.display = "none"; noFrame.style.display = "block"; noFrame.textContent = "Waiting for frame..."; }};
+img.onload = function() {{ img.style.display = "block"; noFrame.style.display = "none"; }};
+
+setInterval(function() {{ load(); }}, POLL_MS);
+setInterval(loadCameras, 3000);
+setInterval(updateRefresh, 1000);
+loadCameras();
+</script>
+</body>
+</html>"""
+
+
+class DashboardHandler(SimpleHTTPRequestHandler):
+    shm_dir = DEFAULT_SHM_DIR
+    poll_ms = 500
+
+    def log_message(self, format, *args):
+        pass
+
+    def do_GET(self):
+        try:
+            self._handle_request()
+        except (BrokenPipeError, ConnectionResetError):
+            pass
+
+    def _handle_request(self):
+        parsed = urlparse(self.path)
+        path = unquote(parsed.path)
+
+        if path == "/" or path == "/index.html":
+            html = DASHBOARD_HTML.replace("%%POLL_MS%%", str(self.poll_ms)).replace("%%SHM_DIR%%", self.shm_dir)
+            self._send_html(html)
+            return
+
+        if path == "/api/cameras":
+            cameras = self._discover_cameras()
+            self._send_json({"cameras": cameras})
+            return
+
+        if path.startswith("/shm/"):
+            rel = path[len("/shm/"):]
+            parts = rel.split("/", 1)
+            if len(parts) >= 1:
+                parts[0] = f"chicken_counter_{parts[0]}"
+                rel = "/".join(parts)
+            shm_path = Path(self.shm_dir) / rel
+            resolved = shm_path.resolve()
+            if not str(resolved).startswith(str(Path(self.shm_dir).resolve())):
+                self.send_error(403)
+                return
+            if not resolved.exists():
+                self.send_error(404)
+                return
+            ct = "image/jpeg" if resolved.suffix in (".jpg", ".jpeg") else "application/json"
+            self.send_response(200)
+            self.send_header("Content-Type", ct)
+            self.send_header("Cache-Control", "no-cache, no-store, must-revalidate")
+            self.end_headers()
+            self.wfile.write(resolved.read_bytes())
+            return
+
+        self.send_error(404)
+
+    def _discover_cameras(self):
+        shm = Path(self.shm_dir)
+        cameras = []
+        if shm.is_dir():
+            for entry in sorted(shm.iterdir()):
+                if entry.is_dir() and entry.name.startswith("chicken_counter_"):
+                    cam_id = entry.name[len("chicken_counter_"):]
+                    cameras.append(cam_id)
+        return cameras
+
+    def _send_html(self, html: str):
+        data = html.encode("utf-8")
+        self.send_response(200)
+        self.send_header("Content-Type", "text/html; charset=utf-8")
+        self.send_header("Content-Length", str(len(data)))
+        self.end_headers()
+        self.wfile.write(data)
+
+    def _send_json(self, obj):
+        data = json.dumps(obj).encode("utf-8")
+        self.send_response(200)
+        self.send_header("Content-Type", "application/json")
+        self.send_header("Content-Length", str(len(data)))
+        self.end_headers()
+        self.wfile.write(data)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Chicken Counter live dashboard")
+    parser.add_argument("--port", type=int, default=DEFAULT_PORT, help=f"HTTP port (default: {DEFAULT_PORT})")
+    parser.add_argument("--shm-dir", default=DEFAULT_SHM_DIR, help=f"Shared memory directory (default: {DEFAULT_SHM_DIR})")
+    parser.add_argument("--poll-ms", type=int, default=500, help="Image poll interval in ms (default: 500)")
+    args = parser.parse_args()
+
+    DashboardHandler.shm_dir = args.shm_dir
+    DashboardHandler.poll_ms = args.poll_ms
+
+    server = ThreadingHTTPServer(("0.0.0.0", args.port), DashboardHandler)
+    print(f"[dashboard] serving at http://0.0.0.0:{args.port}")
+    print(f"[dashboard] shm_dir={args.shm_dir}  poll={args.poll_ms}ms")
+    try:
+        server.serve_forever()
+    except KeyboardInterrupt:
+        print("\n[dashboard] stopped")
+        server.server_close()
+
+
+if __name__ == "__main__":
+    main()

src/chicken_counter/batch_runner.py

@@ -14,7 +14,7 @@ from chicken_counter.tracking import DetectionTracker
 from chicken_counter.types import CameraBatchResult
 
 
-def run_daily_batch(settings: BatchSettings, date: str | None = None) -> Path:
+def run_daily_batch(settings: BatchSettings, date: str | None = None, *, verbose: bool = False, no_video: bool = False, show_progress: bool = False) -> Path:
     run_date = date or date_type.today().isoformat()
     day_dir = Path(settings.batch.root_dir) / run_date
     output_dir = day_dir / settings.batch.output_subdir
@@ -23,6 +23,8 @@ def run_daily_batch(settings: BatchSettings, date: str | None = None) -> Path:
     print(f"[batch] starting daily run for {run_date}")
     print(f"[batch] input folder: {day_dir}")
     print(f"[batch] output folder: {output_dir}")
+    if no_video:
+        print("[batch] --no-video: skipping video output, overlay, and compression")
 
     discovery = discover_camera_videos(day_dir, settings)
     camera_order = sorted(settings.cameras.items(), key=lambda item: item[1].camera_num)
@@ -31,11 +33,12 @@ def run_daily_batch(settings: BatchSettings, date: str | None = None) -> Path:
         camera_id for camera_id, _preset in camera_order if camera_id in discovery.found
     )
     first_source = discovery.found[first_camera_id]
+    init_output_path = output_dir / f"{first_camera_id}_vis.mp4" if not no_video else None
     init_config = build_camera_config_from_batch(
         settings,
         first_camera_id,
         source=first_source,
-        output_path=output_dir / f"{first_camera_id}_vis.mp4",
+        output_path=init_output_path,
         checkpoint_dir=output_dir / "checkpoints" / first_camera_id,
     )
     shared_tracker = DetectionTracker(init_config)
@@ -58,7 +61,7 @@ def run_daily_batch(settings: BatchSettings, date: str | None = None) -> Path:
             continue
 
         source_path = discovery.found[camera_id]
-        vis_path = output_dir / f"{camera_id}_vis.mp4"
+        vis_path = output_dir / f"{camera_id}_vis.mp4" if not no_video else None
         checkpoint_dir = output_dir / "checkpoints" / camera_id
 
         print(f"[batch] processing {camera_id} from {source_path.name}")
@@ -69,7 +72,8 @@ def run_daily_batch(settings: BatchSettings, date: str | None = None) -> Path:
             output_path=vis_path,
             checkpoint_dir=checkpoint_dir,
         )
-        pipeline_result = run_pipeline(camera_config, tracker=shared_tracker)
+        camera_config.performance.verbose = verbose
+        pipeline_result = run_pipeline(camera_config, tracker=shared_tracker, show_progress=show_progress)
         camera_results.append(
             CameraBatchResult(
                 camera_id=camera_id,
@@ -82,6 +86,14 @@ def run_daily_batch(settings: BatchSettings, date: str | None = None) -> Path:
         )
         persist_batch_reports(run_date, camera_results, output_dir)
 
+    if no_video:
+        report = build_batch_report(run_date, camera_results, output_dir=output_dir)
+        print(
+            f"[batch] complete for {run_date}: total_entered_sum={report.total_entered_sum} "
+            f"report={report_path}"
+        )
+        return report_path
+
     print("[batch] all cameras complete; starting compression")
     for item in camera_results:
         if item.skipped or item.pipeline is None:

src/chicken_counter/cli.py

@@ -16,6 +16,8 @@ def build_parser() -> argparse.ArgumentParser:
     run_parser = subparsers.add_parser("run", help="Run a single camera pipeline.")
     run_parser.add_argument("--config", required=True, help="Path to camera config YAML/JSON.")
     run_parser.add_argument("--camera-id", help="Camera ID when using a multi-camera config file.")
+    run_parser.add_argument("--verbose", action="store_true", help="Enable debug-level logging.")
+    run_parser.add_argument("--progress-bar", action="store_true", help="Show a terminal progress bar.")
 
     batch_parser = subparsers.add_parser("batch", help="Run the daily Cycle7 multi-camera batch.")
     batch_parser.add_argument("--config", required=True, help="Path to batch config YAML/JSON.")
@@ -23,6 +25,9 @@ def build_parser() -> argparse.ArgumentParser:
         "--date",
         help="Processing date folder in YYYY-MM-DD format. Defaults to today.",
     )
+    batch_parser.add_argument("--verbose", action="store_true", help="Enable debug-level logging.")
+    batch_parser.add_argument("--no-video", action="store_true", help="Skip video output and compression for speed.")
+    batch_parser.add_argument("--progress-bar", action="store_true", help="Show a terminal progress bar.")
 
     parser.add_argument("--config", help=argparse.SUPPRESS)
     parser.add_argument("--camera-id", help=argparse.SUPPRESS)
@@ -35,12 +40,13 @@ def main() -> None:
 
     if args.command == "batch":
         settings = load_batch_config(args.config)
-        run_daily_batch(settings, date=args.date)
+        run_daily_batch(settings, date=args.date, verbose=args.verbose, no_video=args.no_video, show_progress=args.progress_bar)
         return
 
     if args.command == "run":
         config = load_camera_config(args.config, args.camera_id)
-        result = run_pipeline(config)
+        config.performance.verbose = args.verbose
+        result = run_pipeline(config, show_progress=args.progress_bar)
         print(
             f"[done] camera={result.camera_id} total_entered={result.total_entered_count} "
             f"frames={result.frames_processed} reason={result.stopped_reason}"
@@ -49,7 +55,8 @@ def main() -> None:
 
     if args.config:
         config = load_camera_config(args.config, args.camera_id)
-        result = run_pipeline(config)
+        config.performance.verbose = args.verbose
+        result = run_pipeline(config, show_progress=args.progress_bar)
         print(
             f"[done] camera={result.camera_id} total_entered={result.total_entered_count} "
             f"frames={result.frames_processed} reason={result.stopped_reason}"

src/chicken_counter/config.py

@@ -168,6 +168,14 @@ class PerformanceConfig:
     half: bool = False
     overlay_buffer_reuse: bool = True
     inference_stride: int = 1
+    verbose: bool = False
+
+
+@dataclass
+class StreamConfig:
+    enabled: bool = False
+    shm_dir: str = "/dev/shm"
+    interval_frames: int = 5
 
 
 @dataclass
@@ -193,6 +201,7 @@ class CameraConfig:
     performance: PerformanceConfig
     feedback: FeedbackConfig
     detection_zone: DetectionZoneConfig = field(default_factory=DetectionZoneConfig)
+    stream: StreamConfig = field(default_factory=StreamConfig)
 
 
 @dataclass
@@ -269,6 +278,7 @@ def _build_camera_config(raw: dict[str, Any]) -> CameraConfig:
         performance=PerformanceConfig(**raw.get("performance", {})),
         feedback=FeedbackConfig(**raw.get("feedback", {})),
         detection_zone=DetectionZoneConfig(**raw.get("detection_zone", {})),
+        stream=StreamConfig(**raw.get("stream", {})),
     )
 
 
@@ -338,7 +348,7 @@ def build_camera_config_from_batch(
     camera_id: str,
     *,
     source: str | Path,
-    output_path: str | Path,
+    output_path: str | Path | None,
     checkpoint_dir: str | Path,
 ) -> CameraConfig:
     if camera_id not in settings.cameras:
@@ -378,13 +388,13 @@ def build_camera_config_from_batch(
         raw["overlay"]["count_anchor"] = list(preset.count_anchor)
 
     raw.setdefault("display", {})
-    raw["display"]["output_path"] = str(output_path)
+    raw["display"]["output_path"] = str(output_path) if output_path is not None else None
     raw["display"]["show_window"] = False
 
     raw.setdefault("feedback", {})
     raw["feedback"]["enabled"] = True
     raw["feedback"]["every_n_frames"] = settings.batch.checkpoint_every_n_frames
-    raw["feedback"]["save_images"] = True
+    raw["feedback"]["save_images"] = output_path is not None
     raw["feedback"]["image_output_dir"] = str(checkpoint_dir)
     raw["feedback"]["log_to_terminal"] = True
 

src/chicken_counter/counting.py

@@ -20,6 +20,8 @@ class CountingZone:
         track_buffer: int,
         min_box_area_px: int = 0,
         validate_while_inside: bool = True,
+        *,
+        verbose: bool = False,
     ) -> None:
         self.roi = roi
         self.gate = gate
@@ -28,6 +30,7 @@ class CountingZone:
         self.min_box_area_px = min_box_area_px
         self.min_overlap_ratio = roi.min_overlap_ratio
         self.validate_while_inside = validate_while_inside
+        self.verbose = verbose
         self.inside_box_count = 0
         self.total_entered_count = 0
         self.histories: dict[int, deque[tuple[int, int]]] = defaultdict(lambda: deque(maxlen=trail_length))
@@ -86,6 +89,15 @@ class CountingZone:
                         sequence_number=self.sequence_numbers_by_track_id[track.track_id],
                     )
                 )
+                if self.verbose:
+                    x1, y1, x2, y2 = track.bbox_xyxy
+                    bbox_area = max(0, x2 - x1) * max(0, y2 - y1)
+                    overlap = self._bbox_overlap_ratio(track)
+                    print(
+                        f"[count] track={track.track_id} seq=#{self.total_entered_count} "
+                        f"frame={frame_index} area={bbox_area} overlap={overlap:.2f} "
+                        f"conf={track.confidence:.2f} centroid={track.centroid}"
+                    )
 
         self.inside_box_count = len(inside_ids)
         self.current_inside_ids = inside_ids

src/chicken_counter/motion.py

@@ -10,12 +10,14 @@ from chicken_counter.types import MotionState, TrackObservation
 
 
 class BackwardMotionDetector:
-    def __init__(self, config: MotionConfig, roi: RoiConfig) -> None:
+    def __init__(self, config: MotionConfig, roi: RoiConfig, *, verbose: bool = False) -> None:
         self.config = config
         self.roi = roi
         self.previous_gray: np.ndarray | None = None
         self.state = MotionState()
         self._roi_bounds = self._compute_roi_bounds()
+        self.verbose = verbose
+        self._update_count = 0
 
     def _compute_roi_bounds(self) -> tuple[int, int, int, int]:
         x_values = [point[0] for point in self.roi.points]
@@ -94,6 +96,25 @@ class BackwardMotionDetector:
             self.state.backward_active = False
 
         if self.state.consecutive_reverse_frames >= self.config.debounce_frames:
+            was_active = self.state.backward_active
             self.state.backward_active = True
+            if self.verbose and not was_active:
+                print(
+                    f"[motion #{self._update_count}] BACKWARD TRIGGERED! "
+                    f"smoothed_speed={self.state.smoothed_speed:.1f} "
+                    f"consecutive={self.state.consecutive_reverse_frames}"
+                )
+
+        if self.verbose:
+            self._update_count += 1
+            features_found = len(valid_prev) if points is not None and self.previous_gray is not None else 0
+            print(
+                f"[motion #{self._update_count}] "
+                f"features={features_found} "
+                f"median_speed={median_axis_speed:.1f} "
+                f"smoothed_speed={self.state.smoothed_speed:.1f} "
+                f"consecutive_rev={self.state.consecutive_reverse_frames} "
+                f"backward={self.state.backward_active}"
+            )
 
         return self.state

src/chicken_counter/pipeline.py

@@ -2,6 +2,9 @@
 
 from __future__ import annotations
 
+import json
+import shutil
+import sys
 import time
 from dataclasses import dataclass
 from pathlib import Path
@@ -19,6 +22,62 @@ from chicken_counter.types import FrameResult, PipelineResult, TrackObservation
 from chicken_counter.video_writer import make_video_writer
 
 
+class _ProgressBar:
+    def __init__(self, total: int | None, width: int = 30) -> None:
+        self._total = total
+        self._width = width
+        self._last_render = 0.0
+        self._last_line_len = 0
+        self._checkpoint_msg = ""
+
+    def _build_checkpoint_suffix(self) -> str:
+        if not self._checkpoint_msg:
+            return ""
+        msg = self._checkpoint_msg
+        self._checkpoint_msg = ""
+        return f"  [{msg}]"
+
+    def render(self, frame_index: int, elapsed: float, fps: float, inside: int, total_entered: int, backward: bool) -> None:
+        now = time.monotonic()
+        if now - self._last_render < 0.2 and frame_index > 1 and not self._checkpoint_msg:
+            return
+        self._last_render = now
+
+        elapsed_str = _format_duration(elapsed)
+        checkpoint_suffix = self._build_checkpoint_suffix()
+        if self._total:
+            pct = min(100, frame_index * 100 // self._total)
+            filled = self._width * pct // 100
+            bar = "[" + "=" * filled + ">" + " " * (self._width - filled) + "]"
+            eta_seconds = (self._total - frame_index) / fps if fps > 0 else 0.0
+            eta_str = _format_duration(eta_seconds)
+            status = "backward" if backward else "running"
+            line = (
+                f"\r{bar} {pct:3d}%  {frame_index}/{self._total}  "
+                f"{elapsed_str}  eta={eta_str}  {fps:.1f}fps  "
+                f"count={inside}/{total_entered}  {status}{checkpoint_suffix}"
+            )
+        else:
+            status = "backward" if backward else "running"
+            line = (
+                f"\rframe={frame_index}  {elapsed_str}  {fps:.1f}fps  "
+                f"count={inside}/{total_entered}  {status}{checkpoint_suffix}"
+            )
+
+        pad = max(0, self._last_line_len - len(line))
+        self._last_line_len = len(line)
+        sys.stderr.write(line + " " * pad)
+        sys.stderr.flush()
+
+    def emit(self, message: str) -> None:
+        self._checkpoint_msg = message
+        self._last_render = 0.0
+
+    def finish(self) -> None:
+        sys.stderr.write("\n")
+        sys.stderr.flush()
+
+
 @dataclass
 class PipelineArtifacts:
     capture: cv2.VideoCapture
@@ -48,8 +107,9 @@ def build_pipeline(
         track_buffer=config.tracker.track_buffer,
         min_box_area_px=config.detection.min_box_area_px,
         validate_while_inside=config.detection.validate_while_inside,
+        verbose=config.performance.verbose,
     )
-    motion_detector = BackwardMotionDetector(config.motion, config.roi)
+    motion_detector = BackwardMotionDetector(config.motion, config.roi, verbose=config.performance.verbose)
 
     width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
     height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
@@ -80,6 +140,12 @@ def build_pipeline(
             codec_preference=config.display.codec_preference,
         )
 
+    if config.stream.enabled:
+        cam_dir = Path(config.stream.shm_dir) / f"chicken_counter_{config.camera_id}"
+        if cam_dir.exists():
+            shutil.rmtree(str(cam_dir))
+            print(f"[stream] cleaned {cam_dir}")
+
     return PipelineArtifacts(
         capture=capture,
         tracker=tracker,
@@ -97,6 +163,8 @@ def build_pipeline(
 def run_pipeline(
     config: CameraConfig,
     tracker: DetectionTracker | None = None,
+    *,
+    show_progress: bool = False,
 ) -> PipelineResult:
     if tracker is not None:
         tracker.config = config
@@ -114,26 +182,51 @@ def run_pipeline(
     last_tracks: list[TrackObservation] = []
     stopped_reason = "eof"
     user_quit = False
-
+    verbose = config.performance.verbose
+    cumulative_timings: dict[str, float] = {"read": 0.0, "infer": 0.0, "motion": 0.0, "count": 0.0, "overlay": 0.0, "write": 0.0}
+    timed_frames = 0
+    verbose_interval = max(1, inference_stride * 30)
+    progress = _ProgressBar(artifacts.total_source_frames) if show_progress else None
+    
     try:
         while True:
+            if verbose:
+                t0 = time.monotonic()
+
             ok, frame = artifacts.capture.read()
             if not ok:
                 break
 
             frame_index += 1
+
+            if verbose:
+                t_read = time.monotonic()
+
             if frame_index % inference_stride == 0 or not last_tracks:
                 last_tracks = artifacts.tracker.infer(
                     frame,
                     crop_rect=artifacts.detection_zone_rect,
                 )
+
+            if verbose and frame_index % inference_stride == 0:
+                t_infer = time.monotonic()
+
             tracks = last_tracks
             motion_state = artifacts.motion_detector.update(frame, tracks, frame_index)
+
+            if verbose:
+                t_motion = time.monotonic()
+
             count_events = artifacts.counting_zone.update(
                 tracks,
                 frame_index,
                 counting_paused=motion_state.backward_active,
             )
+
+            if verbose:
+                t_count = time.monotonic()
+
+            needs_overlay = config.display.show_window or artifacts.writer is not None or config.stream.enabled
             annotated = draw_overlay(
                 frame,
                 config,
@@ -142,7 +235,10 @@ def run_pipeline(
                 motion_state,
                 frame_index=frame_index,
                 buffer=artifacts.overlay_buffer,
-            )
+            ) if needs_overlay else frame
+
+            if verbose:
+                t_overlay = time.monotonic()
 
             result = FrameResult(
                 frame_index=frame_index,
@@ -152,12 +248,58 @@ def run_pipeline(
                 motion_state=motion_state,
                 count_events=count_events,
             )
-            _consume_result(config, artifacts, annotated, result)
+            _consume_result(config, artifacts, annotated, result, progress)
             last_annotated = annotated
 
+            if config.stream.enabled and frame_index % max(1, config.stream.interval_frames) == 0:
+                _write_stream_frame(config.stream.shm_dir, config.camera_id, annotated, result)
+
+            if verbose:
+                t_write = time.monotonic()
+                if frame_index % inference_stride == 0:
+                    cumulative_timings["read"] += (t_read - t0) * 1000
+                    cumulative_timings["infer"] += (t_infer - t_read) * 1000
+                    cumulative_timings["motion"] += (t_motion - t_infer) * 1000
+                    cumulative_timings["count"] += (t_count - t_motion) * 1000
+                    cumulative_timings["overlay"] += (t_overlay - t_count) * 1000
+                    cumulative_timings["write"] += (t_write - t_overlay) * 1000
+                    timed_frames += 1
+
+                if frame_index % verbose_interval == 0 and timed_frames > 0:
+                    n = timed_frames
+                    print(
+                        f"[debug ~{verbose_interval}f avg ms] "
+                        f"read={cumulative_timings['read']/n:.1f} "
+                        f"infer={cumulative_timings['infer']/n:.1f} "
+                        f"motion={cumulative_timings['motion']/n:.1f} "
+                        f"count={cumulative_timings['count']/n:.1f} "
+                        f"overlay={cumulative_timings['overlay']/n:.1f} "
+                        f"write={cumulative_timings['write']/n:.1f} "
+                        f"tracks={len(tracks)} "
+                        f"inside={artifacts.counting_zone.inside_box_count} "
+                        f"total={artifacts.counting_zone.total_entered_count} "
+                        f"motion_speed={motion_state.smoothed_speed:.1f} "
+                        f"backward={motion_state.backward_active}"
+                    )
+                    cumulative_timings = {k: 0.0 for k in cumulative_timings}
+                    timed_frames = 0
+
+            if progress is not None:
+                elapsed = time.monotonic() - artifacts.run_start_time
+                fps = frame_index / elapsed if elapsed > 0 else 0.0
+                progress.render(
+                    frame_index, elapsed, fps,
+                    artifacts.counting_zone.inside_box_count,
+                    artifacts.counting_zone.total_entered_count,
+                    motion_state.backward_active,
+                )
+
             if motion_state.backward_active:
                 stopped_reason = "backward"
-                print(f"[stop] backward detection confirmed at frame={frame_index}; ending pipeline")
+                if progress is not None:
+                    progress.emit(f"[stop] backward detection confirmed at frame={frame_index}; ending pipeline")
+                else:
+                    print(f"[stop] backward detection confirmed at frame={frame_index}; ending pipeline")
                 break
 
             if config.display.max_frames and frame_index >= config.display.max_frames:
@@ -183,6 +325,9 @@ def run_pipeline(
     if user_quit:
         stopped_reason = "user_quit"
 
+    if progress is not None:
+        progress.finish()
+
     return PipelineResult(
         camera_id=config.camera_id,
         total_entered_count=artifacts.counting_zone.total_entered_count,
@@ -199,6 +344,7 @@ def _consume_result(
     artifacts: PipelineArtifacts,
     annotated,
     result: FrameResult,
+    progress: _ProgressBar | None = None,
 ) -> None:
     if config.display.show_window:
         cv2.imshow(config.display.window_name, annotated)
@@ -206,13 +352,42 @@ def _consume_result(
         artifacts.writer.write(annotated)
 
     for event in result.count_events:
-        print(
-            f"[frame {event.frame_index}] counted track={event.track_id} "
-            f"inside_box={result.inside_box_count} total_entered={event.total_entered_after_event}"
-        )
+        if config.performance.verbose:
+            msg = (
+                f"[frame {event.frame_index}] counted track={event.track_id} "
+                f"inside_box={result.inside_box_count} total_entered={event.total_entered_after_event}"
+            )
+            if progress is not None:
+                progress.emit(msg)
+            else:
+                print(msg)
 
     if _should_emit_feedback(config, result.frame_index):
-        _emit_periodic_feedback(config, artifacts, annotated, result)
+        _emit_periodic_feedback(config, artifacts, annotated, result, progress)
+
+
+def _write_stream_frame(shm_dir: str, camera_id: str, frame: np.ndarray, result: FrameResult) -> None:
+    cam_dir = Path(shm_dir) / f"chicken_counter_{camera_id}"
+    cam_dir.mkdir(parents=True, exist_ok=True)
+
+    jpg_path = cam_dir / "frame.jpg"
+    tmp_path = cam_dir / ".frame_tmp.jpg"
+    cv2.imwrite(str(tmp_path), frame, [cv2.IMWRITE_JPEG_QUALITY, 75])
+    tmp_path.replace(jpg_path)
+
+    stats = {
+        "frame_index": result.frame_index,
+        "inside_box_count": result.inside_box_count,
+        "total_entered_count": result.total_entered_count,
+        "track_count": len(result.tracks),
+        "backward_active": result.motion_state.backward_active,
+        "smoothed_speed": round(result.motion_state.smoothed_speed, 1),
+        "count_events": len(result.count_events),
+    }
+    stats_path = cam_dir / "stats.json"
+    stats_tmp = cam_dir / ".stats_tmp.json"
+    stats_tmp.write_text(json.dumps(stats), encoding="utf-8")
+    stats_tmp.replace(stats_path)
 
 
 def _should_emit_feedback(config: CameraConfig, frame_index: int) -> bool:
@@ -238,15 +413,16 @@ def _emit_periodic_feedback(
     artifacts: PipelineArtifacts,
     annotated,
     result: FrameResult,
+    progress: _ProgressBar | None = None,
 ) -> None:
     if config.feedback.log_to_terminal:
         elapsed = time.monotonic() - artifacts.run_start_time
         fps = result.frame_index / elapsed if elapsed > 0 else 0.0
         status = "backward_stop" if result.motion_state.backward_active else "running"
 
-        progress = f"frame={result.frame_index}"
+        progress_text = f"frame={result.frame_index}"
         if artifacts.total_source_frames:
-            progress = f"frame={result.frame_index}/{artifacts.total_source_frames}"
+            progress_text = f"frame={result.frame_index}/{artifacts.total_source_frames}"
 
         eta_text = ""
         if artifacts.total_source_frames and fps > 0:
@@ -254,13 +430,17 @@ def _emit_periodic_feedback(
             eta_seconds = remaining_frames / fps
             eta_text = f" eta={_format_duration(eta_seconds)}"
 
-        print(
-            f"[checkpoint] {progress} elapsed={_format_duration(elapsed)} "
+        msg = (
+            f"[checkpoint] {progress_text} elapsed={_format_duration(elapsed)} "
             f"fps={fps:.1f} inside_box={result.inside_box_count} "
             f"total_entered={result.total_entered_count} "
             f"backward_active={result.motion_state.backward_active} "
             f"status={status}{eta_text}"
         )
+        if progress is not None:
+            progress.emit(msg)
+        else:
+            print(f"\r\033[K{msg}")
 
     if config.feedback.save_images:
         output_dir = Path(config.feedback.image_output_dir)

src/chicken_counter/tracking.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import time
 from pathlib import Path
 
 import numpy as np
@@ -18,6 +19,8 @@ class DetectionTracker:
         self.model_kind = model_path.suffix.lower().lstrip(".") or "unknown"
         self.model = YOLO(config.detection.model_path)
         self.tracker_config_path = str(Path(config.tracker.tracker_config_path))
+        self.verbose = config.performance.verbose
+        self._infer_count = 0
         print(
             f"[model] loaded {self.model_kind} from {model_path} "
             f"(imgsz={config.detection.imgsz}, device={config.detection.device})"
@@ -57,9 +60,18 @@ class DetectionTracker:
         if self.model_kind != "engine" and self.config.performance.half:
             track_kwargs["half"] = True
 
+        if self.verbose:
+            t_start = time.monotonic()
+
         results = self.model.track(**track_kwargs)
 
+        if self.verbose:
+            t_track = time.monotonic()
+            self._infer_count += 1
+
         if not results:
+            if self.verbose:
+                print(f"[tracker #{self._infer_count}] no detections (infer={t_track - t_start:.1f}ms)")
             return []
 
         result = results[0]
@@ -115,6 +127,17 @@ class DetectionTracker:
                 )
             )
 
+        if self.verbose:
+            unique_ids = sorted(set(t.track_id for t in tracks))
+            confs = [t.confidence for t in tracks] if tracks else [0]
+            print(
+                f"[tracker #{self._infer_count}] "
+                f"det={len(tracks)} unique={len(unique_ids)} "
+                f"conf=[{min(confs):.2f}..{max(confs):.2f}] "
+                f"ids={unique_ids[:10]}{'+' if len(unique_ids) > 10 else ''} "
+                f"infer={t_track - t_start:.1f}ms"
+            )
+
         return tracks
 
     @staticmethod