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adsist-cms:feat/new-logic
adsist-cms:tracking
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6 changed files with 87 additions and 646 deletions
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291
app.py
View file

@ -29,12 +29,6 @@ app = FastAPI()
# "models" now holds a nested dict: { camera_id: { modelId: model_tree } }
models: Dict[str, Dict[str, Any]] = {}
streams: Dict[str, Dict[str, Any]] = {}
# Store session IDs per display
session_ids: Dict[str, int] = {}
# Track shared camera streams by camera URL
camera_streams: Dict[str, Dict[str, Any]] = {}
# Map subscriptions to their camera URL
subscription_to_camera: Dict[str, str] = {}
with open("config.json", "r") as f:
config = json.load(f)
@ -190,16 +184,9 @@ async def detect(websocket: WebSocket):
async def handle_detection(camera_id, stream, frame, websocket, model_tree, persistent_data):
try:
# Apply crop if specified
cropped_frame = frame
if all(coord is not None for coord in [stream.get("cropX1"), stream.get("cropY1"), stream.get("cropX2"), stream.get("cropY2")]):
cropX1, cropY1, cropX2, cropY2 = stream["cropX1"], stream["cropY1"], stream["cropX2"], stream["cropY2"]
cropped_frame = frame[cropY1:cropY2, cropX1:cropX2]
logger.debug(f"Applied crop coordinates ({cropX1}, {cropY1}, {cropX2}, {cropY2}) to frame for camera {camera_id}")
logger.debug(f"Processing frame for camera {camera_id} with model {stream['modelId']}")
start_time = time.time()
detection_result = run_pipeline(cropped_frame, model_tree)
detection_result = run_pipeline(frame, model_tree)
process_time = (time.time() - start_time) * 1000
logger.debug(f"Detection for camera {camera_id} completed in {process_time:.2f}ms")
@ -248,48 +235,22 @@ async def detect(websocket: WebSocket):
"box": [0, 0, 0, 0]
}
# Convert detection format to match protocol - flatten detection attributes
detection_dict = {}
# Handle different detection result formats
if isinstance(highest_confidence_detection, dict):
# Copy all fields from the detection result
for key, value in highest_confidence_detection.items():
if key not in ["box", "id"]: # Skip internal fields
detection_dict[key] = value
# Extract display identifier for session ID lookup
subscription_parts = stream["subscriptionIdentifier"].split(';')
display_identifier = subscription_parts[0] if subscription_parts else None
session_id = session_ids.get(display_identifier) if display_identifier else None
detection_data = {
"type": "imageDetection",
"subscriptionIdentifier": stream["subscriptionIdentifier"],
"timestamp": time.strftime("%Y-%m-%dT%H:%M:%S.%fZ", time.gmtime()),
"data": {
"detection": detection_dict,
"detection": highest_confidence_detection, # Send only the highest confidence detection
"modelId": stream["modelId"],
"modelName": stream["modelName"]
}
}
# Add session ID if available
if session_id is not None:
detection_data["sessionId"] = session_id
if highest_confidence_detection["class"] != "none":
logger.info(f"Camera {camera_id}: Detected {highest_confidence_detection['class']} with confidence {highest_confidence_detection['confidence']:.2f} using model {stream['modelName']}")
# Log session ID if available
subscription_parts = stream["subscriptionIdentifier"].split(';')
display_identifier = subscription_parts[0] if subscription_parts else None
session_id = session_ids.get(display_identifier) if display_identifier else None
if session_id:
logger.debug(f"Detection associated with session ID: {session_id}")
await websocket.send_json(detection_data)
logger.debug(f"Sent detection data to client for camera {camera_id}")
logger.debug(f"Sent detection data to client for camera {camera_id}:\n{json.dumps(detection_data, indent=2)}")
return persistent_data
except Exception as e:
logger.error(f"Error in handle_detection for camera {camera_id}: {str(e)}", exc_info=True)
@ -560,58 +521,50 @@ async def detect(websocket: WebSocket):
cropX2 = payload.get("cropX2")
cropY2 = payload.get("cropY2")
# Extract camera_id from subscriptionIdentifier (format: displayIdentifier;cameraIdentifier)
parts = subscriptionIdentifier.split(';')
if len(parts) != 2:
logger.error(f"Invalid subscriptionIdentifier format: {subscriptionIdentifier}")
continue
display_identifier, camera_identifier = parts
camera_id = subscriptionIdentifier # Use full subscriptionIdentifier as camera_id for mapping
camera_id = subscriptionIdentifier # Use subscriptionIdentifier as camera_id for mapping
if model_url:
with models_lock:
if (camera_id not in models) or (modelId not in models[camera_id]):
logger.info(f"Loading model from {model_url} for camera {camera_id}, modelId {modelId}")
extraction_dir = os.path.join("models", camera_identifier, str(modelId))
extraction_dir = os.path.join("models", camera_id, str(modelId))
os.makedirs(extraction_dir, exist_ok=True)
# If model_url is remote, download it first.
parsed = urlparse(model_url)
if parsed.scheme in ("http", "https"):
logger.info(f"Downloading remote .mpta file from {model_url}")
filename = os.path.basename(parsed.path) or f"model_{modelId}.mpta"
local_mpta = os.path.join(extraction_dir, filename)
logger.info(f"Downloading remote model from {model_url}")
local_mpta = os.path.join(extraction_dir, os.path.basename(parsed.path))
logger.debug(f"Download destination: {local_mpta}")
local_path = download_mpta(model_url, local_mpta)
if not local_path:
logger.error(f"Failed to download the remote .mpta file from {model_url}")
logger.error(f"Failed to download the remote mpta file from {model_url}")
error_response = {
"type": "error",
"subscriptionIdentifier": subscriptionIdentifier,
"cameraIdentifier": camera_id,
"error": f"Failed to download model from {model_url}"
}
await websocket.send_json(error_response)
continue
model_tree = load_pipeline_from_zip(local_path, extraction_dir)
else:
logger.info(f"Loading local .mpta file from {model_url}")
logger.info(f"Loading local model from {model_url}")
# Check if file exists before attempting to load
if not os.path.exists(model_url):
logger.error(f"Local .mpta file not found: {model_url}")
logger.error(f"Local model file not found: {model_url}")
logger.debug(f"Current working directory: {os.getcwd()}")
error_response = {
"type": "error",
"subscriptionIdentifier": subscriptionIdentifier,
"cameraIdentifier": camera_id,
"error": f"Model file not found: {model_url}"
}
await websocket.send_json(error_response)
continue
model_tree = load_pipeline_from_zip(model_url, extraction_dir)
if model_tree is None:
logger.error(f"Failed to load model {modelId} from .mpta file for camera {camera_id}")
logger.error(f"Failed to load model {modelId} from mpta file for camera {camera_id}")
error_response = {
"type": "error",
"subscriptionIdentifier": subscriptionIdentifier,
"cameraIdentifier": camera_id,
"error": f"Failed to load model {modelId}"
}
await websocket.send_json(error_response)
@ -620,80 +573,20 @@ async def detect(websocket: WebSocket):
models[camera_id] = {}
models[camera_id][modelId] = model_tree
logger.info(f"Successfully loaded model {modelId} for camera {camera_id}")
logger.debug(f"Model extraction directory: {extraction_dir}")
success_response = {
"type": "modelLoaded",
"cameraIdentifier": camera_id,
"modelId": modelId
}
await websocket.send_json(success_response)
if camera_id and (rtsp_url or snapshot_url):
with streams_lock:
# Determine camera URL for shared stream management
camera_url = snapshot_url if snapshot_url else rtsp_url
if camera_id not in streams and len(streams) < max_streams:
# Check if we already have a stream for this camera URL
shared_stream = camera_streams.get(camera_url)
if shared_stream:
# Reuse existing stream
logger.info(f"Reusing existing stream for camera URL: {camera_url}")
buffer = shared_stream["buffer"]
stop_event = shared_stream["stop_event"]
thread = shared_stream["thread"]
mode = shared_stream["mode"]
# Increment reference count
shared_stream["ref_count"] = shared_stream.get("ref_count", 0) + 1
else:
# Create new stream
buffer = queue.Queue(maxsize=1)
stop_event = threading.Event()
if snapshot_url and snapshot_interval:
logger.info(f"Creating new snapshot stream for camera {camera_id}: {snapshot_url}")
thread = threading.Thread(target=snapshot_reader, args=(camera_identifier, snapshot_url, snapshot_interval, buffer, stop_event))
thread.daemon = True
thread.start()
mode = "snapshot"
# Store shared stream info
shared_stream = {
"buffer": buffer,
"thread": thread,
"stop_event": stop_event,
"mode": mode,
"url": snapshot_url,
"snapshot_interval": snapshot_interval,
"ref_count": 1
}
camera_streams[camera_url] = shared_stream
elif rtsp_url:
logger.info(f"Creating new RTSP stream for camera {camera_id}: {rtsp_url}")
cap = cv2.VideoCapture(rtsp_url)
if not cap.isOpened():
logger.error(f"Failed to open RTSP stream for camera {camera_id}")
continue
thread = threading.Thread(target=frame_reader, args=(camera_identifier, cap, buffer, stop_event))
thread.daemon = True
thread.start()
mode = "rtsp"
# Store shared stream info
shared_stream = {
"buffer": buffer,
"thread": thread,
"stop_event": stop_event,
"mode": mode,
"url": rtsp_url,
"cap": cap,
"ref_count": 1
}
camera_streams[camera_url] = shared_stream
else:
logger.error(f"No valid URL provided for camera {camera_id}")
continue
# Create stream info for this subscription
buffer = queue.Queue(maxsize=1)
stop_event = threading.Event()
stream_info = {
"buffer": buffer,
"thread": thread,
"thread": None,
"stop_event": stop_event,
"modelId": modelId,
"modelName": modelName,
@ -701,25 +594,52 @@ async def detect(websocket: WebSocket):
"cropX1": cropX1,
"cropY1": cropY1,
"cropX2": cropX2,
"cropY2": cropY2,
"mode": mode,
"camera_url": camera_url
"cropY2": cropY2
}
if mode == "snapshot":
stream_info["snapshot_url"] = snapshot_url
stream_info["snapshot_interval"] = snapshot_interval
elif mode == "rtsp":
stream_info["rtsp_url"] = rtsp_url
stream_info["cap"] = shared_stream["cap"]
streams[camera_id] = stream_info
subscription_to_camera[camera_id] = camera_url
if snapshot_url and snapshot_interval:
logger.info(f"Using snapshot mode for camera {camera_id}: {snapshot_url}")
thread = threading.Thread(target=snapshot_reader, args=(camera_id, snapshot_url, snapshot_interval, buffer, stop_event))
thread.daemon = True
thread.start()
stream_info.update({
"snapshot_url": snapshot_url,
"snapshot_interval": snapshot_interval,
"mode": "snapshot"
})
stream_info["thread"] = thread
streams[camera_id] = stream_info
elif rtsp_url:
logger.info(f"Using RTSP mode for camera {camera_id}: {rtsp_url}")
cap = cv2.VideoCapture(rtsp_url)
if not cap.isOpened():
logger.error(f"Failed to open RTSP stream for camera {camera_id}")
continue
thread = threading.Thread(target=frame_reader, args=(camera_id, cap, buffer, stop_event))
thread.daemon = True
thread.start()
stream_info.update({
"cap": cap,
"rtsp_url": rtsp_url,
"mode": "rtsp"
})
stream_info["thread"] = thread
streams[camera_id] = stream_info
else:
logger.error(f"No valid URL provided for camera {camera_id}")
continue
elif camera_id and camera_id in streams:
# If already subscribed, unsubscribe first
logger.info(f"Resubscribing to camera {camera_id}")
# Note: Keep models in memory for reuse across subscriptions
stream = streams.pop(camera_id)
stream["stop_event"].set()
stream["thread"].join()
if "cap" in stream:
stream["cap"].release()
logger.info(f"Unsubscribed from camera {camera_id} for resubscription")
with models_lock:
if camera_id in models and modelId in models[camera_id]:
del models[camera_id][modelId]
if not models[camera_id]:
del models[camera_id]
elif msg_type == "unsubscribe":
payload = data.get("payload", {})
subscriptionIdentifier = payload.get("subscriptionIdentifier")
@ -727,25 +647,13 @@ async def detect(websocket: WebSocket):
with streams_lock:
if camera_id and camera_id in streams:
stream = streams.pop(camera_id)
camera_url = subscription_to_camera.pop(camera_id, None)
if camera_url and camera_url in camera_streams:
shared_stream = camera_streams[camera_url]
shared_stream["ref_count"] -= 1
# If no more references, stop the shared stream
if shared_stream["ref_count"] <= 0:
logger.info(f"Stopping shared stream for camera URL: {camera_url}")
shared_stream["stop_event"].set()
shared_stream["thread"].join()
if "cap" in shared_stream:
shared_stream["cap"].release()
del camera_streams[camera_url]
else:
logger.info(f"Shared stream for {camera_url} still has {shared_stream['ref_count']} references")
logger.info(f"Unsubscribed from camera {camera_id}")
# Note: Keep models in memory for potential reuse
stream["stop_event"].set()
stream["thread"].join()
if "cap" in stream:
stream["cap"].release()
with models_lock:
if camera_id in models:
del models[camera_id]
elif msg_type == "requestState":
cpu_usage = psutil.cpu_percent()
memory_usage = psutil.virtual_memory().percent
@ -776,37 +684,6 @@ async def detect(websocket: WebSocket):
"cameraConnections": camera_connections
}
await websocket.send_text(json.dumps(state_report))
elif msg_type == "setSessionId":
payload = data.get("payload", {})
display_identifier = payload.get("displayIdentifier")
session_id = payload.get("sessionId")
if display_identifier:
# Store session ID for this display
if session_id is None:
session_ids.pop(display_identifier, None)
logger.info(f"Cleared session ID for display {display_identifier}")
else:
session_ids[display_identifier] = session_id
logger.info(f"Set session ID {session_id} for display {display_identifier}")
elif msg_type == "patchSession":
session_id = data.get("sessionId")
patch_data = data.get("data", {})
# For now, just acknowledge the patch - actual implementation depends on backend requirements
response = {
"type": "patchSessionResult",
"payload": {
"sessionId": session_id,
"success": True,
"message": "Session patch acknowledged"
}
}
await websocket.send_json(response)
logger.info(f"Acknowledged patch for session {session_id}")
else:
logger.error(f"Unknown message type: {msg_type}")
except json.JSONDecodeError:
@ -829,23 +706,19 @@ async def detect(websocket: WebSocket):
stream_task.cancel()
await stream_task
with streams_lock:
# Clean up shared camera streams
for camera_url, shared_stream in camera_streams.items():
shared_stream["stop_event"].set()
shared_stream["thread"].join()
if "cap" in shared_stream:
shared_stream["cap"].release()
while not shared_stream["buffer"].empty():
for camera_id, stream in streams.items():
stream["stop_event"].set()
stream["thread"].join()
# Only release cap if it exists (RTSP mode)
if "cap" in stream:
stream["cap"].release()
while not stream["buffer"].empty():
try:
shared_stream["buffer"].get_nowait()
stream["buffer"].get_nowait()
except queue.Empty:
pass
logger.info(f"Released shared camera stream for {camera_url}")
logger.info(f"Released camera {camera_id} and cleaned up resources")
streams.clear()
camera_streams.clear()
subscription_to_camera.clear()
with models_lock:
models.clear()
session_ids.clear()
logger.info("WebSocket connection closed")

204
pympta.md
View file

@ -1,204 +0,0 @@
# pympta: Modular Pipeline Task Executor
`pympta` is a Python module designed to load and execute modular, multi-stage AI pipelines defined in a special package format (`.mpta`). It is primarily used within the detector worker to run complex computer vision tasks where the output of one model can trigger a subsequent model on a specific region of interest.
## Core Concepts
### 1. MPTA Package (`.mpta`)
An `.mpta` file is a standard `.zip` archive with a different extension. It bundles all the necessary components for a pipeline to run.
A typical `.mpta` file has the following structure:
```
my_pipeline.mpta/
├── pipeline.json
├── model1.pt
├── model2.pt
└── ...
```
- **`pipeline.json`**: (Required) The manifest file that defines the structure of the pipeline, the models to use, and the logic connecting them.
- **Model Files (`.pt`, etc.)**: The actual pre-trained model files (e.g., PyTorch, ONNX). The pipeline currently uses `ultralytics.YOLO` models.
### 2. Pipeline Structure
A pipeline is a tree-like structure of "nodes," defined in `pipeline.json`.
- **Root Node**: The entry point of the pipeline. It processes the initial, full-frame image.
- **Branch Nodes**: Child nodes that are triggered by specific detection results from their parent. For example, a root node might detect a "vehicle," which then triggers a branch node to detect a "license plate" within the vehicle's bounding box.
This modular structure allows for creating complex and efficient inference logic, avoiding the need to run every model on every frame.
## `pipeline.json` Specification
This file defines the entire pipeline logic. The root object contains a `pipeline` key for the pipeline definition and an optional `redis` key for Redis configuration.
### Top-Level Object Structure
| Key | Type | Required | Description |
| ---------- | ------ | -------- | ------------------------------------------------------- |
| `pipeline` | Object | Yes | The root node object of the pipeline. |
| `redis` | Object | No | Configuration for connecting to a Redis server. |
### Redis Configuration (`redis`)
| Key | Type | Required | Description |
| ---------- | ------ | -------- | ------------------------------------------------------- |
| `host` | String | Yes | The hostname or IP address of the Redis server. |
| `port` | Number | Yes | The port number of the Redis server. |
| `password` | String | No | The password for Redis authentication. |
| `db` | Number | No | The Redis database number to use. Defaults to `0`. |
### Node Object Structure
| Key | Type | Required | Description |
| ------------------- | ------------- | -------- | -------------------------------------------------------------------------------------------------------------------------------------- |
| `modelId` | String | Yes | A unique identifier for this model node (e.g., "vehicle-detector"). |
| `modelFile` | String | Yes | The path to the model file within the `.mpta` archive (e.g., "yolov8n.pt"). |
| `minConfidence` | Float | Yes | The minimum confidence score (0.0 to 1.0) required for a detection to be considered valid and potentially trigger a branch. |
| `triggerClasses` | Array<String> | Yes | A list of class names that, when detected by the parent, can trigger this node. For the root node, this lists all classes of interest. |
| `crop` | Boolean | No | If `true`, the image is cropped to the parent's detection bounding box before being passed to this node's model. Defaults to `false`. |
| `branches` | Array<Node> | No | A list of child node objects that can be triggered by this node's detections. |
| `actions` | Array<Action> | No | A list of actions to execute upon a successful detection in this node. |
### Action Object Structure
Actions allow the pipeline to interact with Redis. They are executed sequentially for a given detection.
#### Action Context & Dynamic Keys
All actions have access to a dynamic context for formatting keys and messages. The context is created for each detection event and includes:
- All key-value pairs from the detection result (e.g., `class`, `confidence`, `id`).
- `{timestamp_ms}`: The current Unix timestamp in milliseconds.
- `{uuid}`: A unique identifier (UUID4) for the detection event.
- `{image_key}`: If a `redis_save_image` action has already been executed for this event, this placeholder will be replaced with the key where the image was stored.
#### `redis_save_image`
Saves the current image frame (or cropped sub-image) to a Redis key.
| Key | Type | Required | Description |
| ---------------- | ------ | -------- | ------------------------------------------------------------------------------------------------------- |
| `type` | String | Yes | Must be `"redis_save_image"`. |
| `key` | String | Yes | The Redis key to save the image to. Can contain any of the dynamic placeholders. |
| `expire_seconds` | Number | No | If provided, sets an expiration time (in seconds) for the Redis key. |
#### `redis_publish`
Publishes a message to a Redis channel.
| Key | Type | Required | Description |
| --------- | ------ | -------- | ------------------------------------------------------------------------------------------------------- |
| `type` | String | Yes | Must be `"redis_publish"`. |
| `channel` | String | Yes | The Redis channel to publish the message to. |
| `message` | String | Yes | The message to publish. Can contain any of the dynamic placeholders, including `{image_key}`. |
### Example `pipeline.json` with Redis
This example demonstrates a pipeline that detects vehicles, saves a uniquely named image of each detection that expires in one hour, and then publishes a notification with the image key.
```json
{
"redis": {
"host": "redis.local",
"port": 6379,
"password": "your-super-secret-password"
},
"pipeline": {
"modelId": "vehicle-detector",
"modelFile": "vehicle_model.pt",
"minConfidence": 0.6,
"triggerClasses": ["car", "truck"],
"actions": [
{
"type": "redis_save_image",
"key": "detections:{class}:{timestamp_ms}:{uuid}",
"expire_seconds": 3600
},
{
"type": "redis_publish",
"channel": "vehicle_events",
"message": "{\"event\":\"new_detection\",\"class\":\"{class}\",\"confidence\":{confidence},\"image_key\":\"{image_key}\"}"
}
],
"branches": []
}
}
```
## API Reference
The `pympta` module exposes two main functions.
### `load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict`
Loads, extracts, and parses an `.mpta` file to build a pipeline tree in memory. It also establishes a Redis connection if configured in `pipeline.json`.
- **Parameters:**
- `zip_source` (str): The file path to the local `.mpta` zip archive.
- `target_dir` (str): A directory path where the archive's contents will be extracted.
- **Returns:**
- A dictionary representing the root node of the pipeline, ready to be used with `run_pipeline`. Returns `None` if loading fails.
### `run_pipeline(frame, node: dict, return_bbox: bool = False)`
Executes the inference pipeline on a single image frame.
- **Parameters:**
- `frame`: The input image frame (e.g., a NumPy array from OpenCV).
- `node` (dict): The pipeline node to execute (typically the root node returned by `load_pipeline_from_zip`).
- `return_bbox` (bool): If `True`, the function returns a tuple `(detection, bounding_box)`. Otherwise, it returns only the `detection`.
- **Returns:**
- The final detection result from the last executed node in the chain. A detection is a dictionary like `{'class': 'car', 'confidence': 0.95, 'id': 1}`. If no detection meets the criteria, it returns `None` (or `(None, None)` if `return_bbox` is `True`).
## Usage Example
This snippet, inspired by `pipeline_webcam.py`, shows how to use `pympta` to load a pipeline and process an image from a webcam.
```python
import cv2
from siwatsystem.pympta import load_pipeline_from_zip, run_pipeline
# 1. Define paths
MPTA_FILE = "path/to/your/pipeline.mpta"
CACHE_DIR = ".mptacache"
# 2. Load the pipeline from the .mpta file
# This reads pipeline.json and loads the YOLO models into memory.
model_tree = load_pipeline_from_zip(MPTA_FILE, CACHE_DIR)
if not model_tree:
print("Failed to load pipeline.")
exit()
# 3. Open a video source
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
break
# 4. Run the pipeline on the current frame
# The function will handle the entire logic tree (e.g., find a car, then find its license plate).
detection_result, bounding_box = run_pipeline(frame, model_tree, return_bbox=True)
# 5. Display the results
if detection_result:
print(f"Detected: {detection_result['class']} with confidence {detection_result['confidence']:.2f}")
if bounding_box:
x1, y1, x2, y2 = bounding_box
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(frame, detection_result['class'], (x1, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
cv2.imshow("Pipeline Output", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
```

1
requirements.txt
View file

@ -6,4 +6,3 @@ ultralytics
opencv-python
websockets
fastapi[standard]
redis

68
siwatsystem/pympta.py
View file

@ -7,16 +7,13 @@ import requests
import zipfile
import shutil
import traceback
import redis
import time
import uuid
from ultralytics import YOLO
from urllib.parse import urlparse
# Create a logger specifically for this module
logger = logging.getLogger("detector_worker.pympta")
def load_pipeline_node(node_config: dict, mpta_dir: str, redis_client) -> dict:
def load_pipeline_node(node_config: dict, mpta_dir: str) -> dict:
# Recursively load a model node from configuration.
model_path = os.path.join(mpta_dir, node_config["modelFile"])
if not os.path.exists(model_path):
@ -47,15 +44,13 @@ def load_pipeline_node(node_config: dict, mpta_dir: str, redis_client) -> dict:
"triggerClassIndices": trigger_class_indices,
"crop": node_config.get("crop", False),
"minConfidence": node_config.get("minConfidence", None),
"actions": node_config.get("actions", []),
"model": model,
"branches": [],
"redis_client": redis_client
"branches": []
}
logger.debug(f"Configured node {node_config['modelId']} with trigger classes: {node['triggerClasses']}")
for child in node_config.get("branches", []):
logger.debug(f"Loading branch for parent node {node_config['modelId']}")
node["branches"].append(load_pipeline_node(child, mpta_dir, redis_client))
node["branches"].append(load_pipeline_node(child, mpta_dir))
return node
def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
@ -163,26 +158,7 @@ def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
pipeline_config = json.load(f)
logger.info(f"Successfully loaded pipeline configuration from {pipeline_json_path}")
logger.debug(f"Pipeline config: {json.dumps(pipeline_config, indent=2)}")
# Establish Redis connection if configured
redis_client = None
if "redis" in pipeline_config:
redis_config = pipeline_config["redis"]
try:
redis_client = redis.Redis(
host=redis_config["host"],
port=redis_config["port"],
password=redis_config.get("password"),
db=redis_config.get("db", 0),
decode_responses=True
)
redis_client.ping()
logger.info(f"Successfully connected to Redis at {redis_config['host']}:{redis_config['port']}")
except redis.exceptions.ConnectionError as e:
logger.error(f"Failed to connect to Redis: {e}")
redis_client = None
return load_pipeline_node(pipeline_config["pipeline"], mpta_dir, redis_client)
return load_pipeline_node(pipeline_config["pipeline"], mpta_dir)
except json.JSONDecodeError as e:
logger.error(f"Error parsing pipeline.json: {str(e)}", exc_info=True)
return None
@ -193,39 +169,6 @@ def load_pipeline_from_zip(zip_source: str, target_dir: str) -> dict:
logger.error(f"Error loading pipeline.json: {str(e)}", exc_info=True)
return None
def execute_actions(node, frame, detection_result):
if not node["redis_client"] or not node["actions"]:
return
# Create a dynamic context for this detection event
action_context = {
**detection_result,
"timestamp_ms": int(time.time() * 1000),
"uuid": str(uuid.uuid4()),
}
for action in node["actions"]:
try:
if action["type"] == "redis_save_image":
key = action["key"].format(**action_context)
_, buffer = cv2.imencode('.jpg', frame)
expire_seconds = action.get("expire_seconds")
if expire_seconds:
node["redis_client"].setex(key, expire_seconds, buffer.tobytes())
logger.info(f"Saved image to Redis with key: {key} (expires in {expire_seconds}s)")
else:
node["redis_client"].set(key, buffer.tobytes())
logger.info(f"Saved image to Redis with key: {key}")
# Add the generated key to the context for subsequent actions
action_context["image_key"] = key
elif action["type"] == "redis_publish":
channel = action["channel"]
message = action["message"].format(**action_context)
node["redis_client"].publish(channel, message)
logger.info(f"Published message to Redis channel '{channel}': {message}")
except Exception as e:
logger.error(f"Error executing action {action['type']}: {e}")
def run_pipeline(frame, node: dict, return_bbox: bool=False):
"""
- For detection nodes (task != 'classify'):
@ -263,7 +206,6 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False):
"confidence": top1_conf,
"id": None
}
execute_actions(node, frame, det)
return (det, None) if return_bbox else det
@ -312,11 +254,9 @@ def run_pipeline(frame, node: dict, return_bbox: bool=False):
det2, _ = run_pipeline(sub, br, return_bbox=True)
if det2:
# return classification result + original bbox
execute_actions(br, sub, det2)
return (det2, best_box) if return_bbox else det2
# ─── No branch matched → return this detection ─────────────
execute_actions(node, frame, best_det)
return (best_det, best_box) if return_bbox else best_det
except Exception as e:

125
test_protocol.py
View file

@ -1,125 +0,0 @@
#!/usr/bin/env python3
"""
Test script to verify the worker implementation follows the protocol
"""
import json
import asyncio
import websockets
import time
async def test_protocol():
"""Test the worker protocol implementation"""
uri = "ws://localhost:8000"
try:
async with websockets.connect(uri) as websocket:
print("✓ Connected to worker")
# Test 1: Check if we receive heartbeat (stateReport)
print("\n1. Testing heartbeat...")
try:
message = await asyncio.wait_for(websocket.recv(), timeout=5)
data = json.loads(message)
if data.get("type") == "stateReport":
print("✓ Received stateReport heartbeat")
print(f" - CPU Usage: {data.get('cpuUsage', 'N/A')}%")
print(f" - Memory Usage: {data.get('memoryUsage', 'N/A')}%")
print(f" - Camera Connections: {len(data.get('cameraConnections', []))}")
else:
print(f"✗ Expected stateReport, got {data.get('type')}")
except asyncio.TimeoutError:
print("✗ No heartbeat received within 5 seconds")
# Test 2: Request state
print("\n2. Testing requestState...")
await websocket.send(json.dumps({"type": "requestState"}))
try:
message = await asyncio.wait_for(websocket.recv(), timeout=5)
data = json.loads(message)
if data.get("type") == "stateReport":
print("✓ Received stateReport response")
else:
print(f"✗ Expected stateReport, got {data.get('type')}")
except asyncio.TimeoutError:
print("✗ No response to requestState within 5 seconds")
# Test 3: Set session ID
print("\n3. Testing setSessionId...")
session_message = {
"type": "setSessionId",
"payload": {
"displayIdentifier": "display-001",
"sessionId": 12345
}
}
await websocket.send(json.dumps(session_message))
print("✓ Sent setSessionId message")
# Test 4: Test patchSession
print("\n4. Testing patchSession...")
patch_message = {
"type": "patchSession",
"sessionId": 12345,
"data": {
"currentCar": {
"carModel": "Civic",
"carBrand": "Honda"
}
}
}
await websocket.send(json.dumps(patch_message))
# Wait for patchSessionResult
try:
message = await asyncio.wait_for(websocket.recv(), timeout=5)
data = json.loads(message)
if data.get("type") == "patchSessionResult":
print("✓ Received patchSessionResult")
print(f" - Success: {data.get('payload', {}).get('success')}")
print(f" - Message: {data.get('payload', {}).get('message')}")
else:
print(f"✗ Expected patchSessionResult, got {data.get('type')}")
except asyncio.TimeoutError:
print("✗ No patchSessionResult received within 5 seconds")
# Test 5: Test subscribe message format (without actual camera)
print("\n5. Testing subscribe message format...")
subscribe_message = {
"type": "subscribe",
"payload": {
"subscriptionIdentifier": "display-001;cam-001",
"snapshotUrl": "http://example.com/snapshot.jpg",
"snapshotInterval": 5000,
"modelUrl": "http://example.com/model.mpta",
"modelName": "Test Model",
"modelId": 101,
"cropX1": 100,
"cropY1": 200,
"cropX2": 300,
"cropY2": 400
}
}
await websocket.send(json.dumps(subscribe_message))
print("✓ Sent subscribe message (will fail without actual camera/model)")
# Listen for a few more messages to catch any errors
print("\n6. Listening for additional messages...")
for i in range(3):
try:
message = await asyncio.wait_for(websocket.recv(), timeout=2)
data = json.loads(message)
msg_type = data.get("type")
print(f" - Received {msg_type}")
if msg_type == "error":
print(f" Error: {data.get('error')}")
except asyncio.TimeoutError:
break
print("\n✓ Protocol test completed successfully!")
except Exception as e:
print(f"✗ Connection failed: {e}")
print("Make sure the worker is running on localhost:8000")
if __name__ == "__main__":
asyncio.run(test_protocol())

42
worker.md
View file

@ -439,45 +439,3 @@ This section shows a typical sequence of messages between the backend and the wo
"cameraConnections": []
}
```
## 7. HTTP API: Image Retrieval
In addition to the WebSocket protocol, the worker exposes an HTTP endpoint for retrieving the latest image frame from a camera.
### Endpoint
```
GET /camera/{camera_id}/image
```
- **`camera_id`**: The full `subscriptionIdentifier` (e.g., `display-001;cam-001`).
### Response
- **Success (200):** Returns the latest JPEG image from the camera stream.
- `Content-Type: image/jpeg`
- Binary JPEG data.
- **Error (404):** If the camera is not found or no frame is available.
- JSON error response.
- **Error (500):** Internal server error.
### Example Request
```
GET /camera/display-001;cam-001/image
```
### Example Response
- **Headers:**
```
Content-Type: image/jpeg
```
- **Body:** Binary JPEG image.
### Notes
- The endpoint returns the most recent frame available for the specified camera subscription.
- If multiple displays share the same camera, each subscription has its own buffer; the endpoint uses the buffer for the given `camera_id`.
- This API is useful for debugging, monitoring, or integrating with external systems that require direct image access.