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feat: implement real-time linescan motion detection pipeline

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- UDP packet reception on port 5000 with 16MB buffer
- Ring buffer implementation for real-time processing
- EMA-based motion detection with configurable thresholds
- Automatic AVIF image saving for detected motion segments
- Gradient metadata generation for web visualization
- Performance monitoring with latency warnings
- Debug mode for development troubleshooting
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wissotsky  
2025-11-16 00:50:45 +02:00
committed by ro
parent 1825bc79ba
commit 94633491b0
1 changed files with 216 additions and 0 deletions
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import numpy as np
import imageio.v3 as iio
from tqdm import tqdm
import concurrent.futures
import os
import time
# --- Configuration ---
DATA_SOURCE = "udp://10.81.2.120:5000"
OUTPUT_DIR = "output"
TARGET_FPS = 750
TARGET_FRAME_TIME_NS = int(1e9 / 1)
RING_BUFFER_SIZE = 32768 # must be power of two
RING_BUFFER_MASK = RING_BUFFER_SIZE - 1
COLUMN_HEIGHT, COLUMN_WIDTH, CHANNELS = 2456, 1, 3 # Example dimensions, adjust as needed
LIVE_DEBUG = False
os.makedirs(OUTPUT_DIR, exist_ok=True)
write_executor = concurrent.futures.ThreadPoolExecutor(max_workers=16)
futures = []
def img_to_gradient(input_image):
string = "linear-gradient(in oklab"
for i in np.array_split(input_image[:,0,:],11):
r,g,b = np.mean(i,axis=0).astype(np.uint8)
string += f",rgb({r},{g},{b})"
string += ")"
return string
#img_to_gradient(input_image)
def submit_write_job(ring_buffer, index, length, ring_index):
try:
# copy data so it doesnt run away
data_to_write = np.take(ring_buffer, range(ring_index-length, ring_index), axis=0, mode='wrap').copy()
presync_time = int(time.time())
gradient_string = img_to_gradient(np.rot90(data_to_write))
#append to metadata jsonl
with open(f"{OUTPUT_DIR}/metadata.jsonl", "a") as f:
f.write(f'{{"filename":"live-{presync_time}-{index-length}-{index}-{length}.avif","length":{length},"gradient":"{gradient_string}"}}\n')
except Exception as e:
print(f"Error taking data from ring buffer: {e}")
return None
def _write_task(data):
filename = f"{OUTPUT_DIR}/live-{presync_time}-{index-length}-{index}-{length}.avif"
iio.imwrite(filename, data)
future = write_executor.submit(_write_task, np.rot90(data_to_write))
return future
#props = iio.improps(DATA_SOURCE) # overwrite manually if live video source
#props = (None, COLUMN_HEIGHT, COLUMN_WIDTH, CHANNELS, np.uint8)
window_size = 12000
ema_mean = 1.2
ema_var = 0.4
baseline_alpha = 0.001
variance_alpha = 0.01
default_diff = 0.52
frame_mean = 0.0
stride=1
frameskip=1
postcutoff_variance_threshold = 2.5
cutoff_variance_threshold = 3.5
precutoff_variance_threshold = 0.07
is_recording = False
recording_length = 0
patience_default = 600
patience = patience_default
patience_length = 0
recorded_images = 0
frames_since_last_recording = 0
index = 0
ring_buffer = np.zeros((RING_BUFFER_SIZE, COLUMN_HEIGHT, CHANNELS), dtype=np.uint8)
sum_ring_buffer = np.zeros((RING_BUFFER_SIZE), dtype=np.uint64)
ema_var_ring = np.zeros((RING_BUFFER_SIZE), dtype=np.float32)
sum_buffer = np.zeros_like(sum_ring_buffer[0:window_size])
stride_indices = np.arange(0, COLUMN_HEIGHT, stride)
#for frame in tqdm(iio.imiter(DATA_SOURCE, plugin="pyav"), total=props.n_images):
#for frame in iio.imiter(DATA_SOURCE, plugin="pyav"):
import socket
"udp://10.81.2.120:5000"
#10.81.2.183
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
sock.setsockopt(socket.SOL_SOCKET, socket.SO_RCVBUF, 16777216) # 16MB buffer
sock.bind(("0.0.0.0", 5000))
while True:
data, addr = sock.recvfrom(65536) # buffer size is 65536 bytes
#print(f"Received {len(data)} bytes from {addr}")
frame = np.frombuffer(data, dtype=np.uint8).reshape((COLUMN_HEIGHT, COLUMN_WIDTH, CHANNELS))
frame = frame.transpose(1, 0, 2)[:, :, ::-1] # Convert BGR to RGB
#print(frame.shape)
loop_start_time = time.perf_counter_ns()
effective_window_size = min(index, window_size)
ring_index = index & RING_BUFFER_MASK
tia = np.arange(ring_index - effective_window_size, ring_index)
time_indices = tia & RING_BUFFER_MASK
sum_buffer[:len(time_indices)] = sum_ring_buffer[time_indices[:]]
# splayed out for perf debugging
s4 = frame[0:1,::stride,1]
frame_mean = np.divide(np.sum(s4),s4.size)
s2 = sum_buffer[:len(time_indices)]
s11 = np.sum(s2)
s1 = np.divide(s11,s2.size) if s2.size > 0 else 0 # Avoid division by zero
s0 = np.abs(frame_mean - s1)
s00 = np.mean(s0)
value = s00 if index > 0 else default_diff
if ema_mean is None:
ema_mean, ema_var = value, 1.0
deviation = value - ema_mean
ema_mean_temp = (1 - baseline_alpha) * ema_mean + baseline_alpha * value
ema_var_temp = (1 - variance_alpha) * ema_var + variance_alpha * (deviation ** 2)
ring_buffer[ring_index:ring_index+1, :, :] = frame[0:1,:,:]
sum_ring_buffer[ring_index] = frame_mean
if is_recording == False or abs(deviation) < 3 * np.sqrt(ema_var):
ema_var = ema_var_temp
ema_mean = ema_mean_temp
if (index > 1200 and is_recording == False and ema_var > cutoff_variance_threshold):
is_recording = True
quiet_indices = np.argwhere(np.take(ema_var_ring, range(ring_index-RING_BUFFER_SIZE, ring_index), mode='wrap') < precutoff_variance_threshold)
if quiet_indices.size > 0:
last_quiet_index = quiet_indices[-1].item()
recording_length = min(RING_BUFFER_SIZE - last_quiet_index + patience_default, frames_since_last_recording)
else:
recording_length = frames_since_last_recording
patience = patience_default
patience_length = 0
if is_recording:
if (ema_var < postcutoff_variance_threshold):
patience -= 1
patience_length += 1
if (ema_var >= postcutoff_variance_threshold):
recording_length += 1
patience_length = 0
patience = patience_default
if (patience == 0):
print(f"Dumping image starting at index {index-recording_length} ending at {index}, length {recording_length}")
future = submit_write_job(ring_buffer, index, recording_length, ring_index-patience_length)
if future:
futures.append(future)
ema_var_ring[ring_index-patience_length] = 0.0
recorded_images += 1
frames_since_last_recording = patience_length
patience_length = 0
is_recording = False
ema_var_ring[ring_index] = ema_var
index += 1
frames_since_last_recording += 1
loop_end_time = time.perf_counter_ns()
elapsed_ns = loop_end_time - loop_start_time
if elapsed_ns > TARGET_FRAME_TIME_NS:
print(f"Latency warning: processing time exceeded target by {elapsed_ns / TARGET_FRAME_TIME_NS} times")
# visualize the variables using tqdm
if "stats_bar" not in globals():
stats_bar = tqdm(total=0, leave=True)
stats_bar.update(1)
stats_bar.set_description(
f"rec={is_recording} idx={index} value={value:.4f} evr_mean = {np.mean(ema_var_ring[len(time_indices)]):.4f} ema_mean={ema_mean:.4f},{ema_mean_temp:.4f} ema_var={ema_var:.5f},{ema_var_temp:.5f} len={recording_length} pat={patience} frame_mean={frame_mean:.2f}"
)
if LIVE_DEBUG and index % 1000 == 0:
debug_image = np.take(ring_buffer, range(ring_index - effective_window_size, ring_index), axis=0, mode='wrap')
debug_filename = f"{OUTPUT_DIR}/live-debug-{index}.webp"
iio.imwrite(debug_filename, debug_image)
print(f"Wrote live debug image to {debug_filename}")
# handle final recording
if is_recording:
print(f"Dumping final image starting at index {index-recording_length} ending at {index}, length {recording_length}")
final_future = submit_write_job(ring_buffer, index, recording_length, ring_index-patience_length)
if final_future:
futures.append(final_future)
recorded_images += 1
# wait for image writes to complete
print("Processing complete. Waiting for all background writing tasks to finish...")
concurrent.futures.wait(futures)
write_executor.shutdown() # Cleanly release resources
print(f"All images written. Recorded {recorded_images} objects.")