Refactor

* Implement multi queue pipelining
* Implement screenshot upload to S3
* Implement event insertion to Mongo
* Add SIGUSR1 handler to manually trigger screenshots

Dockerfile

@@ -15,7 +15,7 @@ RUN apt-get update && apt-get install -y \
     python3-flask \
     python3-pip \
  && apt-get clean
-RUN pip3 install boto3 prometheus_client pymongo==3.12.2 aiohttp jpeg2dct sanic==21.6.2 sanic_prometheus motor
+RUN pip3 install aioboto3 prometheus_client pymongo==3.12.2 aiohttp jpeg2dct sanic==21.6.2 sanic_prometheus motor
 COPY camdetect.py /app
 ENTRYPOINT /app/camdetect.py
 EXPOSE 5000

README.md

@@ -13,3 +13,16 @@ In a nutshell:
 - Exposes endpoint for distributing MJPEG stream inside the cluster,
   eg by the `camera-tiler`
 - Exposes endpoint for inspecting DCT blocks where motion has been detected
+
+# Developing
+
+Bundled `docker-compose.yml` brings up:
+
+* [Minio](http://localhost:9001/buckets/camdetect/browse)
+* [Mongoexpress](http://localhost:8081/db/default/eventlog)
+
+To manually trigger event:
+
+```
+docker kill -sUSR1 camera-motion-detect_camdetect_1
+```

camdetect.py

@@ -1,30 +1,39 @@
 #!/usr/bin/env python3
+import aioboto3
 import aiohttp
 import asyncio
 import cv2
+import hashlib
+import io
+import json
 import numpy as np
 import os
-import json
+import signal
 import socket
 import sys
-from datetime import datetime
+from datetime import datetime, timedelta
 from jpeg2dct.numpy import loads
+from motor.motor_asyncio import AsyncIOMotorClient
 from prometheus_client import Counter, Gauge
 from sanic import Sanic, response
 from sanic.response import stream
 from sanic_prometheus import monitor
-from time import time
 
 _, url = sys.argv
 
+AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID", "camdetect")
+AWS_SECRET_ACCESS_KEY = os.environ["AWS_SECRET_ACCESS_KEY"]
+S3_ENDPOINT_URL = os.environ["S3_ENDPOINT_URL"]
+S3_BUCKET_NAME = os.getenv("S3_BUCKET_NAME", "camdetect")
 MONGO_URI = os.getenv("MONGO_URI", "mongodb://127.0.0.1:27017/default")
-FQDN = socket.getfqdn()
+MONGO_COLLECTION = os.getenv("MONGO_COLLETION", "eventlog")
+SOURCE_NAME = os.environ["SOURCE_NAME"]
 SLIDE_WINDOW = 2
 DCT_BLOCK_SIZE = 8
 
-# How many blocks have changes to consider movement in frame
+# Percentage of blocks active to consider movement in whole frame
-THRESHOLD_BLOCKS = 20
+THRESHOLD_RATIO = int(os.getenv("THRESHOLD_RATIO", "5"))
-THRESHOLD_MOTION_START = 2
+
 CHUNK_BOUNDARY = b"\n--frame\nContent-Type: image/jpeg\n\n"
 
 counter_dropped_bytes = Counter(
@@ -55,32 +64,218 @@ counter_errors = Counter(
 counter_movement_frames = Counter(
     "camdetect_movement_frames",
     "Frames with movement detected in them")
+counter_uploaded_frames = Counter(
+    "camdetect_uploaded_frames",
+    "Frames uploaded via S3")
+counter_upload_errors = Counter(
+    "camdetect_upload_errors",
+    "Frames upload errors related to S3")
+counter_upload_dropped_frames = Counter(
+    "camdetect_upload_dropped_frames",
+    "Frames that were dropped due to S3 upload queue being full")
+counter_download_dropped_frames = Counter(
+    "camdetect_download_dropped_frames",
+    "Frames that were downloaded from camera, but not processed")
+
 gauge_last_frame = Gauge(
     "camdetect_last_frame",
     "Timestamp of last frame")
+gauge_frame_motion_detected = Gauge(
+    "camdetect_frame_motion_detected",
+    "Motion detected in frame")
+gauge_event_active = Gauge(
+    "camdetect_event_active",
+    "Motion event in progress")
 gauge_total_blocks = Gauge(
     "camdetect_total_blocks",
     "Total DCT blocks")
 gauge_active_blocks = Gauge(
     "camdetect_active_blocks",
     "Total active, threshold exceeding DCT blocks")
+gauge_upload_queue_size = Gauge(
+    "camdetect_upload_queue_size",
+    "Number of frames awaiting to be uploaded via S3")
+gauge_download_queue_size = Gauge(
+    "camdetect_download_queue_size",
+    "Number of frames awaiting to be processed by motion detection loop")
+
+# Reset some gauges
+gauge_frame_motion_detected.set(0)
+gauge_upload_queue_size.set(0)
+gauge_download_queue_size.set(0)
+
+assert SLIDE_WINDOW <= 8  # This is 256 frames which should be enough
 
 
-class Frame(object):
+async def upload(bucket, blob: bytes, event_id):
-    def __init__(self, blob):
+    """
-        self.blob = blob
+    Upload single JPEG blob to S3 bucket
-        self.y, self.cb, self.cr = loads(blob)
+    """
-        self.mask = np.int16(self.y[:, :, 0])
+
+    # Generate S3 path based on the JPEG blob SHA512 digest
+    fp = hashlib.sha512(blob).hexdigest()
+    path = "%s/%s/%s/%s.jpg" % (fp[:4], fp[4:8], fp[8:12], fp[12:])
+    await bucket.upload_fileobj(io.BytesIO(blob), path)
+
+    # Add screenshot path to the event
+    app.ctx.coll.update_one({
+        "_id": event_id
+    }, {
+        "$addToSet": {
+            "screenshots": path,
+        }
+    })
+
+    # TODO: Handle 16MB maximum document size
 
 
-async def client_connect(resp):
+async def uploader(queue):
+    """
+    Uploader task grabs JPEG blobs from upload queue and uploads them to S3
+    """
+    session = aioboto3.Session(
+        aws_access_key_id=AWS_ACCESS_KEY_ID,
+        aws_secret_access_key=AWS_SECRET_ACCESS_KEY)
+    async with session.resource("s3", endpoint_url=S3_ENDPOINT_URL) as s3:
+        bucket = await s3.Bucket(S3_BUCKET_NAME)
+        while True:
+            blob, event_id = await queue.get()
+            await upload(bucket, blob, event_id)
+            counter_uploaded_frames.inc()
+            gauge_upload_queue_size.set(queue.qsize())
+
+
+class ReferenceFrame():
+    """
+    ReferenceFrame keeps last 2 ^ size frames to infer the background scene
+    compared to which motion is detected
+
+    This is pretty much what background subtractor does in OpenCV,
+    only difference is that we want have better performance instead of
+    accuracy
+    """
+    class NotEnoughFrames(Exception):
+        pass
+
+    def __init__(self, size=SLIDE_WINDOW):
+        self.y = []
+        self.cumulative = None
+        self.size = size
+
+    def put(self, y):
+        if self.cumulative is None:
+            self.cumulative = np.copy(y)
+        else:
+            self.cumulative += y
+        self.y.append(y)
+        if len(self.y) > 2 ** self.size:
+            self.cumulative -= self.y[0]
+            self.y = self.y[1:]
+
+    def get(self):
+        if len(self.y) == 2 ** self.size:
+            return self.cumulative >> SLIDE_WINDOW
+        else:
+            raise self.NotEnoughFrames()
+
+
+async def motion_detector(reference_frame, download_queue, upload_queue):
+    """
+    Motion detector grabs JPEG blobs and Y channel coefficients
+    from download queue, performs motion detection and pushes relevant
+    JPEG blobs to upload queue going to S3
+    """
+    event_id = None
+    differing_blocks = []
+    while True:
+        dt, blob, y = await download_queue.get()
+        app.ctx.last_frame = blob
+
+        # Signal /bypass and /debug handlers about new frame
+        app.ctx.event_frame.set()
+        app.ctx.event_frame.clear()
+
+        # Update metrics
+        gauge_total_blocks.set(y.shape[0] * y.shape[1])
+        gauge_last_frame.set(dt.timestamp())
+
+        reference_frame.put(y)
+        try:
+            app.ctx.mask = cv2.inRange(cv2.absdiff(y,
+                                       reference_frame.get()), 25, 65535)
+        except ReferenceFrame.NotEnoughFrames:
+            app.ctx.mask = None
+            motion_detected = False
+        else:
+            # Implement dumb Kalman filter
+            active_blocks = np.count_nonzero(app.ctx.mask)
+            differing_blocks.append(active_blocks)
+            differing_blocks[:] = differing_blocks[-10:]
+            total_blocks = app.ctx.mask.shape[0] * app.ctx.mask.shape[1]
+            threshold_blocks = THRESHOLD_RATIO * total_blocks / 100
+            average_blocks = sum(differing_blocks) / len(differing_blocks)
+            motion_detected = average_blocks > threshold_blocks
+
+            # Update metrics
+            gauge_active_blocks.set(active_blocks)
+            gauge_total_blocks.set(total_blocks)
+
+        # Propagate SIGUSR1 signal handler
+        if app.ctx.manual_trigger:
+            print("Manually triggering event via SIGUSR1")
+            motion_detected = True
+            app.ctx.manual_trigger = False
+
+        # Handle event start
+        if motion_detected and not event_id:
+            result = await app.ctx.coll.insert_one({
+                "timestamp": dt,
+                "event": "motion-detected",
+                "started": dt,
+                "finished": dt + timedelta(minutes=2),
+                "component": "camdetect",
+                "source": SOURCE_NAME,
+                "screenshots": [],
+                "action": "event",
+            })
+            app.ctx.event_id = event_id = result.inserted_id
+            gauge_event_active.set(1)
+
+        # Handle image upload
+        if motion_detected and event_id:
+            counter_movement_frames.inc()
+            try:
+                # Push JPEG blob into upload queue
+                upload_queue.put_nowait((blob, event_id))
+            except asyncio.QueueFull:
+                counter_upload_dropped_frames.inc()
+            gauge_upload_queue_size.set(upload_queue.qsize())
+
+        # Handle event end
+        if not motion_detected and event_id:
+            app.ctx.coll.update_one({
+                "_id": event_id
+            }, {
+                "$set": {
+                    "finished": dt,
+                }
+            })
+            app.ctx.event_id = event_id = None
+            gauge_event_active.set(0)
+
+
+async def download(resp, queue):
+    """
+    This coroutine iterates over HTTP connection chunks
+    assembling the original JPEG blobs and decodes the
+    DCT coefficients of the frames
+    """
     buf = b""
     print("Upstream connection opened with status:", resp.status)
     async for data, end_of_http_chunk in resp.content.iter_chunks():
         counter_rx_bytes.inc(len(data))
         if end_of_http_chunk:
             counter_rx_chunks.inc()
-
         if buf:
             # seek end
             marker = data.find(b"\xff\xd9")
@@ -88,55 +283,20 @@ async def client_connect(resp):
                 buf += data
                 continue
             else:
-                app.ctx.last_frame = Frame(buf + data[:marker+2])
+                # Assemble JPEG blob
-                gauge_last_frame.set(time())
+                blob = buf + data[:marker+2]
 
-                reference = app.ctx.last_frame.mask
+                # Parse DCT coeffs and keep DCT coeffs only for Y channel
-                app.ctx.frames.append(reference)
+                y, _, _ = loads(blob)
-                if app.ctx.avg is None:
-                    app.ctx.avg = np.copy(reference)
-                else:
-                    app.ctx.avg += reference
-
-                if len(app.ctx.frames) > 2 ** SLIDE_WINDOW:
-                    app.ctx.avg -= app.ctx.frames[0]
-                    app.ctx.frames = app.ctx.frames[1:]
-
-                if len(app.ctx.frames) == 2 ** SLIDE_WINDOW:
-                    app.ctx.thresh = cv2.inRange(cv2.absdiff(
-                        app.ctx.last_frame.mask,
-                        app.ctx.avg >> SLIDE_WINDOW), 25, 65535)
-                else:
-                    app.ctx.thresh = None
-                gauge_total_blocks.set(app.ctx.last_frame.mask.shape[0] *
-                                       app.ctx.last_frame.mask.shape[1])
-
-                movement_detected = False
-                if app.ctx.thresh is not None:
-                    differing_blocks = np.count_nonzero(app.ctx.thresh)
-                    gauge_active_blocks.set(differing_blocks)
-                    if differing_blocks > THRESHOLD_BLOCKS:
-                        counter_movement_frames.inc()
-                        movement_detected = True
-
-                if movement_detected:
-                    if app.ctx.motion_frames < 30:
-                        app.ctx.motion_frames += 1
-                else:
-                    if app.ctx.motion_frames > 0:
-                        app.ctx.motion_frames -= 1
-
-                if app.ctx.motion_frames > 20:
-                    if not app.ctx.motion_start:
-                        app.ctx.motion_start = datetime.utcnow()
-                        print("Movement start")
-                elif app.ctx.motion_frames < 5:
-                    app.ctx.motion_start = None
-                    print("Movement end")
-
-                app.ctx.event_frame.set()
-                app.ctx.event_frame.clear()
 
+                try:
+                    # Convert Y component to 16 bit for easier handling
+                    queue.put_nowait((
+                        datetime.utcnow(),
+                        blob,
+                        np.int16(y[:, :, 0])))
+                except asyncio.QueueFull:
+                    counter_download_dropped_frames.inc()
                 data = data[marker+2:]
                 buf = b""
                 counter_rx_frames.inc()
@@ -149,23 +309,25 @@ async def client_connect(resp):
             counter_dropped_bytes.inc(len(data))
 
 
-async def client():
+async def downloader(queue: asyncio.Queue):
+    """
+    Downloader task connects to MJPEG source and
+    pushes the JPEG frames to a queue
+    """
     while True:
         to = aiohttp.ClientTimeout(connect=5, sock_read=2)
         async with aiohttp.ClientSession(timeout=to) as session:
             print("Opening upstream connection to %s" % url)
             try:
                 async with session.get(url) as resp:
-                    await client_connect(resp)
+                    await download(resp, queue)
             except (aiohttp.ClientError, asyncio.exceptions.TimeoutError) as e:
                 j = "%s.%s" % (e.__class__.__module__, e.__class__.__name__)
                 print("Caught exception %s" % j)
                 counter_errors.labels(exception=j).inc()
         await asyncio.sleep(1)
 
-
+app = Sanic("camdetect")
-app = Sanic("lease")
-app.config["WTF_CSRF_ENABLED"] = False
 
 
 @app.route("/bypass")
@@ -173,15 +335,13 @@ async def bypass_stream_wrapper(request):
     async def stream_camera(response):
         while True:
             await app.ctx.event_frame.wait()
-            data = CHUNK_BOUNDARY + app.ctx.last_frame.blob
+            data = CHUNK_BOUNDARY + app.ctx.last_frame
             await response.write(data)
             counter_tx_bytes.inc(len(data))
             counter_tx_frames.inc()
-
     return response.stream(
                 stream_camera,
-                content_type="multipart/x-mixed-replace; boundary=frame"
+                content_type="multipart/x-mixed-replace; boundary=frame")
-            )
 
 
 @app.route("/debug")
@@ -189,21 +349,30 @@ async def stream_wrapper(request):
     async def stream_camera(response):
         while True:
             await app.ctx.event_frame.wait()
-            arr = np.frombuffer(app.ctx.last_frame.blob, dtype=np.uint8)
-            img = cv2.imdecode(arr, cv2.IMREAD_UNCHANGED)
-            if len(app.ctx.frames) == 2 ** SLIDE_WINDOW:
-                for y in range(0, len(app.ctx.last_frame.mask)):
-                    for x in range(0, len(app.ctx.last_frame.mask[0])):
-                        if app.ctx.thresh[y][x] > 0:
-                            img[y*DCT_BLOCK_SIZE:(y+1)*DCT_BLOCK_SIZE,
-                                x*DCT_BLOCK_SIZE:(x+1)*DCT_BLOCK_SIZE, 2] = 255
 
+            # Parse JPEG blob
+            arr = np.frombuffer(app.ctx.last_frame, dtype=np.uint8)
+            img = cv2.imdecode(arr, cv2.IMREAD_UNCHANGED)
+
+            # Highlight green or red channel depending on whether
+            # motion event is in progress or not
+            channel = 2 if app.ctx.event_id else 1
+            if app.ctx.mask is not None:
+                for y in range(0, app.ctx.mask.shape[0]):
+                    for x in range(0, app.ctx.mask.shape[1]):
+                        if app.ctx.mask[y][x] > 0:
+                            img[y*DCT_BLOCK_SIZE:(y+1)*DCT_BLOCK_SIZE,
+                                x*DCT_BLOCK_SIZE:(x+1)*DCT_BLOCK_SIZE,
+                                channel] = 255
+
+            # Compress modified frame as JPEG frame
             _, jpeg = cv2.imencode(".jpg", img, (cv2.IMWRITE_JPEG_QUALITY, 80))
             data = CHUNK_BOUNDARY + jpeg.tobytes()
             await response.write(data)
             counter_tx_bytes.inc(len(data))
             counter_tx_frames.inc()
 
+    # Transmit as chunked MJPEG stream
     return response.stream(
         stream_camera,
         content_type="multipart/x-mixed-replace; boundary=frame"
@@ -212,7 +381,7 @@ async def stream_wrapper(request):
 
 @app.route("/readyz")
 async def ready_check(request):
-    if len(app.ctx.frames) == 2 ** SLIDE_WINDOW:
+    if app.ctx.mask is not None:
         return response.text("OK")
     return response.text("Not enough frames", status=503)
 
@@ -222,24 +391,41 @@ async def wrapper_stream_event(request):
     async def stream_event(response):
         while True:
             await app.ctx.event_frame.wait()
-            if len(app.ctx.frames) < 2 ** SLIDE_WINDOW:
+            if app.ctx.mask is not None:
                 continue
-            s = "data: " + json.dumps(app.ctx.thresh.tolist()) + "\r\n\r\n"
+            s = "data: " + json.dumps(app.ctx.mask.tolist()) + "\r\n\r\n"
             await response.write(s.encode())
             counter_tx_events.inc()
     return stream(stream_event, content_type="text/event-stream")
 
 
+def handler(signum, frame):
+    # SIGUSR1 handler for manually triggering an event
+    app.ctx.manual_trigger = True
+
+
 @app.listener("before_server_start")
 async def setup_db(app, loop):
+    app.ctx.db = AsyncIOMotorClient(MONGO_URI).get_default_database()
+    app.ctx.coll = app.ctx.db[MONGO_COLLECTION]
     app.ctx.last_frame = None
     app.ctx.event_frame = asyncio.Event()
-    app.ctx.frames = []
+    app.ctx.event_id = None
-    app.ctx.avg = None
+    app.ctx.manual_trigger = False
-    app.ctx.motion_frames = 0
+    signal.signal(signal.SIGUSR1, handler)
-    app.ctx.motion_start = None
+
-    app.ctx.motion_end = None
+    # Set up processing pipeline
-    asyncio.create_task(client())
+    download_queue = asyncio.Queue()
+    upload_queue = asyncio.Queue()
+    asyncio.create_task(uploader(
+        upload_queue))
+    asyncio.create_task(downloader(
+        download_queue))
+    asyncio.create_task(motion_detector(
+        ReferenceFrame(),
+        download_queue,
+        upload_queue))
+
 
 monitor(app).expose_endpoint()
 

docker-compose.yml

@@ -1,7 +1,6 @@
 version: '3.7'
 
 # All keys here are for dev instance only, do not put prod keys here
-# To override and use inventory from prod use .env file
 
 services:
   camdetect:
@@ -13,7 +12,9 @@ services:
     command: http://user:123456@127.0.0.1:8080?action=stream
     environment:
       - MJPEGSTREAMER_CREDENTIALS=user:123456
-    env_file: .env
+      - AWS_SECRET_ACCESS_KEY=2mSI6HdbJ8
+      - S3_ENDPOINT_URL=http://127.0.0.1:9000
+      - SOURCE_NAME=dummy
 
   mongoexpress:
     restart: always
@@ -42,16 +43,20 @@ services:
       - --config.file=/config/prometheus.yml
     volumes:
       - ./config:/config:ro
+    logging:
+      driver: none
 
   minio:
     restart: always
     network_mode: host
     image: bitnami/minio:latest
     environment:
-      - MINIO_ACCESS_KEY=kspace-mugshot
+      - MINIO_ACCESS_KEY=camdetect
       - MINIO_SECRET_KEY=2mSI6HdbJ8
-      - MINIO_DEFAULT_BUCKETS=kspace-mugshot:download
+      - MINIO_DEFAULT_BUCKETS=camdetect
       - MINIO_CONSOLE_PORT_NUMBER=9001
+    logging:
+      driver: none
 
   mjpg-streamer:
     network_mode: host