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Proper res, low framerate due to JPEG encoding

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Tempest 2025-12-01 09:59:22 +07:00
parent 7af789a1d6
commit 19fcdd6c9f
1 changed files with 77 additions and 30 deletions
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107
src/detectionSoftware/run.py
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@ -1,22 +1,58 @@
import sys
import subprocess
import threading
import gi
import logging
from flask import Flask, Response, render_template_string
# GStreamer dependencies
# --- PART 1: ROBUST DETECTION (Unchanged) ---
def detect_camera_resolution_isolated():
print("--- Spawning isolated process for detection ---")
detection_script = """
import sys
try:
from pypylon import pylon
tl_factory = pylon.TlFactory.GetInstance()
devices = tl_factory.EnumerateDevices()
if not devices:
print("0,0")
else:
cam = pylon.InstantCamera(tl_factory.CreateDevice(devices[0]))
cam.Open()
print(f"{cam.Width.GetValue()},{cam.Height.GetValue()}")
cam.Close()
except Exception:
print("0,0")
"""
try:
result = subprocess.run(
[sys.executable, "-c", detection_script],
capture_output=True, text=True, check=True
)
parts = result.stdout.strip().split(',')
w, h = int(parts[0]), int(parts[1])
if w == 0: return 1920, 1080
print(f"Isolated Detection Success: {w}x{h}")
return w, h
except Exception as e:
print(f"Subprocess detection failed: {e}")
return 1920, 1080
CAM_W, CAM_H = detect_camera_resolution_isolated()
# Calculate Grid
STREAM_WIDTH = CAM_W
STREAM_HEIGHT = CAM_H
TILED_WIDTH = CAM_W * 2
TILED_HEIGHT = CAM_H
# --- PART 2: FLASK & GSTREAMER ---
import gi
gi.require_version('Gst', '1.0')
from gi.repository import Gst, GLib
# --- Configuration ---
CAMERA_1_SERIAL = "40650847"
CAMERA_2_SERIAL = "40653314"
STREAM_WIDTH = 1920
STREAM_HEIGHT = 1080
TILED_WIDTH = 1920
TILED_HEIGHT = 1080
app = Flask(__name__)
frame_buffer = None
buffer_lock = threading.Lock()
@ -44,65 +80,76 @@ class GStreamerPipeline(threading.Thread):
buffer = sample.get_buffer()
success, map_info = buffer.map(Gst.MapFlags.READ)
if not success: return Gst.FlowReturn.ERROR
global frame_buffer
with buffer_lock:
# Copy memory from GPU buffer to CPU for Flask
frame_buffer = bytes(map_info.data)
buffer.unmap(map_info)
return Gst.FlowReturn.OK
def build_pipeline(self):
# FIX: Added 'compute-hw=1' to nvvideoconvert.
# This forces the conversion to happen on the GPU (CUDA) instead of the VIC,
# which fixes the "RGB/BGR not supported" error.
# PERFORMANCE TUNING:
# 1. PacketSize=8192: Critical for GigE cameras to reduce CPU interrupts (Enable Jumbo Frames on NIC!)
# 2. FrameRate: Capped at 30 to prevent saturating the USB/GigE bus.
cam_settings = (
"cam::TriggerMode=Off "
"cam::AcquisitionFrameRateEnable=true cam::AcquisitionFrameRate=30.0 "
"cam::ExposureAuto=Continuous "
"cam::GainAuto=Continuous "
)
# Source 1
# Source 1: Mono -> CPU Convert (Light) -> GPU Upload
src1 = (
f"pylonsrc device-serial-number={CAMERA_1_SERIAL} "
"cam::TriggerMode=Off cam::AcquisitionFrameRateEnable=true cam::AcquisitionFrameRate=30.0 ! "
"videoconvert ! "
"nvvideoconvert compute-hw=1 ! "
f"pylonsrc device-serial-number={CAMERA_1_SERIAL} {cam_settings} ! "
"video/x-raw,format=GRAY8 ! "
"videoconvert ! " # Light CPU load: Gray -> I420
"video/x-raw,format=I420 ! "
"nvvideoconvert compute-hw=1 ! " # Upload to GPU Memory (NVMM)
"video/x-raw(memory:NVMM) ! " # Explicitly state we want NVMM
"m.sink_0 "
)
# Source 2
src2 = (
f"pylonsrc device-serial-number={CAMERA_2_SERIAL} "
"cam::TriggerMode=Off cam::AcquisitionFrameRateEnable=true cam::AcquisitionFrameRate=30.0 ! "
f"pylonsrc device-serial-number={CAMERA_2_SERIAL} {cam_settings} ! "
"video/x-raw,format=GRAY8 ! "
"videoconvert ! "
"video/x-raw,format=I420 ! "
"nvvideoconvert compute-hw=1 ! "
"video/x-raw(memory:NVMM) ! "
"m.sink_1 "
)
# Muxer -> Tiler -> Output
# Muxer -> Tiler -> Hardware Encoder
# nvjpegenc is the key here. It uses the dedicated JPEG block on the Jetson.
processing = (
f"nvstreammux name=m batch-size=2 width={STREAM_WIDTH} height={STREAM_HEIGHT} live-source=1 ! "
f"nvmultistreamtiler width={TILED_WIDTH} height={TILED_HEIGHT} rows=2 columns=1 ! "
"nvvideoconvert ! "
"video/x-raw, format=I420 ! "
"jpegenc quality=85 ! "
f"nvmultistreamtiler width={TILED_WIDTH} height={TILED_HEIGHT} rows=1 columns=2 ! "
"nvvideoconvert compute-hw=1 ! " # Ensure we are ready for encoding
"video/x-raw(memory:NVMM) ! " # Keep in NVMM
"nvjpegenc quality=85 ! " # HARDWARE ENCODE
"appsink name=sink emit-signals=True sync=False max-buffers=1 drop=True"
)
pipeline_str = f"{src1} {src2} {processing}"
print(f"Launching Pipeline (GPU Mode)...")
print(f"Launching CUDA-Accelerated Pipeline...")
self.pipeline = Gst.parse_launch(pipeline_str)
appsink = self.pipeline.get_by_name("sink")
appsink.connect("new-sample", self.on_new_sample)
# --- Flask ---
# --- Flask Routes ---
@app.route('/')
def index():
return render_template_string('''
<html><body style="background:#111; color:white; text-align:center;">
<h1>Basler Feed</h1>
<img src="{{ url_for('video_feed') }}" style="border: 2px solid green; max-width:90%;">
<h1>Basler CUDA Feed</h1>
<img src="{{ url_for('video_feed') }}" style="border: 2px solid #555; max-width:95%;">
</body></html>
''')
@ -114,7 +161,7 @@ def video_feed():
if frame_buffer:
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame_buffer + b'\r\n')
GLib.usleep(33000)
GLib.usleep(25000) # ~40 FPS cap for web
return Response(generate(), mimetype='multipart/x-mixed-replace; boundary=frame')