Add thumbnailing

master
Lauri Võsandi 12 months ago committed by Lauri Võsandi
parent a1699fa380
commit 8921c6112d
  1. 63
      camdetect.py

@ -9,7 +9,6 @@ import json
import numpy as np
import os
import signal
import socket
import sys
from datetime import datetime, timedelta
from jpeg2dct.numpy import loads
@ -107,7 +106,7 @@ 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):
async def upload(bucket, blob: bytes, thumb: bytes, event_id):
"""
Upload single JPEG blob to S3 bucket
"""
@ -115,6 +114,11 @@ async def upload(bucket, blob: bytes, event_id):
# 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:])
# First upload the thumbnail
await bucket.upload_fileobj(io.BytesIO(thumb), "thumb/%s" % path)
# Proceed to upload the original JPEG frame
await bucket.upload_fileobj(io.BytesIO(blob), path)
# Add screenshot path to the event
@ -139,8 +143,8 @@ async def uploader(queue):
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)
blob, thumb, event_id = await queue.get()
await upload(bucket, blob, thumb, event_id)
counter_uploaded_frames.inc()
gauge_upload_queue_size.set(queue.qsize())
@ -188,13 +192,16 @@ async def motion_detector(reference_frame, download_queue, upload_queue):
event_id = None
differing_blocks = []
while True:
dt, blob, y = await download_queue.get()
app.ctx.last_frame = blob
dt, blob, dct, thumb = await download_queue.get()
app.ctx.last_frame, app.ctx.dct = blob, dct
# Signal /bypass and /debug handlers about new frame
app.ctx.event_frame.set()
app.ctx.event_frame.clear()
# Separate most significant luma value for each DCT (8x8 pixel) block
y = np.int16(dct[0][:, :, 0])
# Update metrics
gauge_total_blocks.set(y.shape[0] * y.shape[1])
gauge_last_frame.set(dt.timestamp())
@ -246,7 +253,7 @@ async def motion_detector(reference_frame, download_queue, upload_queue):
counter_movement_frames.inc()
try:
# Push JPEG blob into upload queue
upload_queue.put_nowait((blob, event_id))
upload_queue.put_nowait((blob, thumb, event_id))
except asyncio.QueueFull:
counter_upload_dropped_frames.inc()
gauge_upload_queue_size.set(upload_queue.qsize())
@ -264,6 +271,41 @@ async def motion_detector(reference_frame, download_queue, upload_queue):
gauge_event_active.set(0)
def generate_thumbnail(dct):
"""
This is highly efficient and highly inaccurate function to generate
thumbnail based purely on JPEG coefficients
"""
y, cr, cb = dct
# Determine aspect ratio and minimum dimension for cropping
ar = y.shape[0] < y.shape[1]
dm = (y.shape[0] if ar else y.shape[1]) & 0xfffffff8
# Determine cropping slices to make it square
jl = ((y.shape[1] >> 1) - (dm >> 1) if ar else 0)
jt = (0 if ar else (y.shape[0] >> 1) - (dm >> 1))
jr = jl + dm
jb = jt + dm
# Do the actual crop
ty = y[jt:jb, jl:jr, 0]
tb = cb[jt >> 1:jb >> 1, jl >> 1:jr >> 1, 0]
tr = cr[jt >> 1:jb >> 1, jl >> 1:jr >> 1, 0]
# Upsample chroma, dummy convert first coeff and stack all channels
m = np.dstack((
np.array((ty >> 3) + 127, dtype=np.uint8),
np.array(
(tb.repeat(2, 1).repeat(2, 0) >> 3) + 127, dtype=np.uint8)[:dm],
np.array(
(tr.repeat(2, 1).repeat(2, 0) >> 3) + 127, dtype=np.uint8)[:dm]))
_, jpeg = cv2.imencode(".jpg",
cv2.cvtColor(m, cv2.COLOR_YCrCb2RGB),
(cv2.IMWRITE_JPEG_QUALITY, 80))
return jpeg
async def download(resp, queue):
"""
This coroutine iterates over HTTP connection chunks
@ -286,15 +328,16 @@ async def download(resp, queue):
# Assemble JPEG blob
blob = buf + data[:marker+2]
# Parse DCT coeffs and keep DCT coeffs only for Y channel
y, _, _ = loads(blob)
# Parse DCT coefficients
dct = loads(blob)
try:
# Convert Y component to 16 bit for easier handling
queue.put_nowait((
datetime.utcnow(),
blob,
np.int16(y[:, :, 0])))
dct,
generate_thumbnail(dct)))
except asyncio.QueueFull:
counter_download_dropped_frames.inc()
data = data[marker+2:]

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