#!/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 signal
import socket
import sys
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

_, 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")
MONGO_COLLECTION = os.getenv("MONGO_COLLETION", "eventlog")
SOURCE_NAME = os.environ["SOURCE_NAME"]
SLIDE_WINDOW = 2
DCT_BLOCK_SIZE = 8

# Percentage of blocks active to consider movement in whole frame
THRESHOLD_RATIO = int(os.getenv("THRESHOLD_RATIO", "5"))

CHUNK_BOUNDARY = b"\n--frame\nContent-Type: image/jpeg\n\n"

counter_dropped_bytes = Counter(
    "camdetect_dropped_bytes",
    "Bytes that were not not handled or part of actual JPEG frames")
counter_rx_bytes = Counter(
    "camdetect_rx_bytes",
    "Bytes received over HTTP stream")
counter_tx_bytes = Counter(
    "camdetect_tx_bytes",
    "Bytes transmitted over HTTP streams")
counter_rx_frames = Counter(
    "camdetect_rx_frames",
    "Frames received")
counter_tx_frames = Counter(
    "camdetect_tx_frames",
    "Frames transmitted")
counter_tx_events = Counter(
    "camdetect_tx_events",
    "Events emitted")
counter_rx_chunks = Counter(
    "camdetect_rx_chunks",
    "HTTP chunks received")
counter_errors = Counter(
    "camdetect_errors",
    "Upstream connection errors",
    ["exception"])
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


async def upload(bucket, blob: bytes, event_id):
    """
    Upload single JPEG blob to S3 bucket
    """

    # 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 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")
            if marker < 0:
                buf += data
                continue
            else:
                # Assemble JPEG blob
                blob = buf + data[:marker+2]

                # Parse DCT coeffs and keep DCT coeffs only for Y channel
                y, _, _ = loads(blob)

                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()

        # seek begin
        marker = data.find(b"\xff\xd8")
        if marker >= 0:
            buf = data[marker:]
        else:
            counter_dropped_bytes.inc(len(data))


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 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.route("/bypass")
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
            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")


@app.route("/debug")
async def stream_wrapper(request):
    async def stream_camera(response):
        while True:
            await app.ctx.event_frame.wait()

            # 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"
    )


@app.route("/readyz")
async def ready_check(request):
    if app.ctx.mask is not None:
        return response.text("OK")
    return response.text("Not enough frames", status=503)


@app.route("/event")
async def wrapper_stream_event(request):
    async def stream_event(response):
        while True:
            await app.ctx.event_frame.wait()
            if app.ctx.mask is not None:
                continue
            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.event_id = None
    app.ctx.manual_trigger = False
    signal.signal(signal.SIGUSR1, handler)

    # Set up processing pipeline
    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()

try:
    app.run(host="0.0.0.0", port=5000)
except KeyboardInterrupt:
    asyncio.get_event_loop().close()