First camera feed success
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@ -1,85 +1,51 @@
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import sys
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import threading
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import math
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import gi
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import logging
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from flask import Flask, Response, render_template_string
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from pypylon import pylon
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# GStreamer dependencies
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gi.require_version('Gst', '1.0')
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from gi.repository import Gst, GLib
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# --- Configuration ---
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CAMERA_1_SERIAL = "40650847"
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CAMERA_2_SERIAL = "40653314"
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STREAM_WIDTH = 1920
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STREAM_HEIGHT = 1080
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# The final output resolution of the tiled web stream
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WEB_OUTPUT_WIDTH = 1920
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WEB_OUTPUT_HEIGHT = 1080
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TILED_WIDTH = 1920
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TILED_HEIGHT = 1080
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# --- Flask Setup ---
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app = Flask(__name__)
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frame_buffer = None
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buffer_lock = threading.Lock()
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def discover_cameras():
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"""
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Uses pypylon to find all connected Basler cameras.
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Returns a list of Serial Numbers.
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"""
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tl_factory = pylon.TlFactory.GetInstance()
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devices = tl_factory.EnumerateDevices()
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serials = []
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for dev in devices:
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serials.append(dev.GetSerialNumber())
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if not serials:
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print("CRITICAL ERROR: No Basler cameras detected via Pylon.")
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sys.exit(1)
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print(f"Discovered {len(serials)} cameras: {serials}")
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return serials
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class GStreamerPipeline(threading.Thread):
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def __init__(self, camera_serials):
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def __init__(self):
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super().__init__()
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self.camera_serials = camera_serials
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self.loop = GLib.MainLoop()
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self.pipeline = None
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def calculate_grid(self, num_cams):
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"""
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Calculates the most square-like grid (rows, cols) for N cameras.
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"""
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rows = int(math.ceil(math.sqrt(num_cams)))
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cols = int(math.ceil(num_cams / rows))
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return rows, cols
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def run(self):
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Gst.init(None)
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self.build_dynamic_pipeline()
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self.build_pipeline()
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self.pipeline.set_state(Gst.State.PLAYING)
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try:
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self.loop.run()
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except Exception as e:
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print(f"Error in GStreamer loop: {e}")
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print(f"Error: {e}")
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finally:
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self.pipeline.set_state(Gst.State.NULL)
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def on_new_sample(self, sink):
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"""
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Callback: grabs the already-encoded JPEG from the pipeline.
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"""
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sample = sink.emit("pull-sample")
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if not sample:
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return Gst.FlowReturn.ERROR
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if not sample: return Gst.FlowReturn.ERROR
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buffer = sample.get_buffer()
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success, map_info = buffer.map(Gst.MapFlags.READ)
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if not success:
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return Gst.FlowReturn.ERROR
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if not success: return Gst.FlowReturn.ERROR
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global frame_buffer
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with buffer_lock:
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@ -88,65 +54,56 @@ class GStreamerPipeline(threading.Thread):
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buffer.unmap(map_info)
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return Gst.FlowReturn.OK
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def build_dynamic_pipeline(self):
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num_cams = len(self.camera_serials)
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rows, cols = self.calculate_grid(num_cams)
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def build_pipeline(self):
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# FIX: Added 'compute-hw=1' to nvvideoconvert.
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# This forces the conversion to happen on the GPU (CUDA) instead of the VIC,
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# which fixes the "RGB/BGR not supported" error.
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print(f"Building pipeline for {num_cams} cameras (Grid: {cols}x{rows})")
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# 1. Construct Sources
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# We need to build N pylonsrc elements, each linking to a specific pad on the muxer.
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sources_str = ""
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for i, serial in enumerate(self.camera_serials):
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# We explicitly link to muxer sink pad: m.sink_0, m.sink_1, etc.
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sources_str += (
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f"pylonsrc camera-device-serial-number={serial} ! "
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f"videoconvert ! nvvideoconvert ! m.sink_{i} "
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# Source 1
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src1 = (
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f"pylonsrc device-serial-number={CAMERA_1_SERIAL} "
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"cam::TriggerMode=Off cam::AcquisitionFrameRateEnable=true cam::AcquisitionFrameRate=30.0 ! "
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"videoconvert ! "
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"nvvideoconvert compute-hw=1 ! "
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"m.sink_0 "
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)
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# 2. Configure Muxer
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# batch-size must match number of cameras
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muxer_str = (
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f"nvstreammux name=m batch-size={num_cams} "
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f"width={STREAM_WIDTH} height={STREAM_HEIGHT} live-source=1 "
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# Source 2
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src2 = (
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f"pylonsrc device-serial-number={CAMERA_2_SERIAL} "
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"cam::TriggerMode=Off cam::AcquisitionFrameRateEnable=true cam::AcquisitionFrameRate=30.0 ! "
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"videoconvert ! "
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"nvvideoconvert compute-hw=1 ! "
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"m.sink_1 "
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)
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# 3. Configure Tiler
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# This combines the batch into one 2D image
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tiler_str = (
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f"nvmultistreamtiler width={WEB_OUTPUT_WIDTH} height={WEB_OUTPUT_HEIGHT} "
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f"rows={rows} columns={cols} "
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)
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# 4. Final Processing (Convert -> JPEG -> AppSink)
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output_str = (
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"nvvideoconvert ! video/x-raw, format=I420 ! "
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# Muxer -> Tiler -> Output
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processing = (
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f"nvstreammux name=m batch-size=2 width={STREAM_WIDTH} height={STREAM_HEIGHT} live-source=1 ! "
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f"nvmultistreamtiler width={TILED_WIDTH} height={TILED_HEIGHT} rows=2 columns=1 ! "
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"nvvideoconvert ! "
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"video/x-raw, format=I420 ! "
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"jpegenc quality=85 ! "
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"appsink name=sink emit-signals=True sync=False max-buffers=1 drop=True"
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)
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# Combine all parts
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full_pipeline_str = f"{sources_str} {muxer_str} ! {tiler_str} ! {output_str}"
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pipeline_str = f"{src1} {src2} {processing}"
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print(f"Pipeline String:\n{full_pipeline_str}")
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print(f"Launching Pipeline (GPU Mode)...")
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self.pipeline = Gst.parse_launch(pipeline_str)
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self.pipeline = Gst.parse_launch(full_pipeline_str)
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# Link callback
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appsink = self.pipeline.get_by_name("sink")
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appsink.connect("new-sample", self.on_new_sample)
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# --- Flask Routes ---
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# --- Flask ---
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@app.route('/')
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def index():
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return render_template_string('''
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<html>
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<body style="background:#111; color:white; text-align:center;">
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<h1>Basler Auto-Discovery Feed</h1>
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<img src="{{ url_for('video_feed') }}" style="border: 2px solid green; max-width:95%;">
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</body>
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</html>
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<html><body style="background:#111; color:white; text-align:center;">
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<h1>Basler Feed</h1>
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<img src="{{ url_for('video_feed') }}" style="border: 2px solid green; max-width:90%;">
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</body></html>
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''')
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@app.route('/video_feed')
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@ -157,21 +114,12 @@ def video_feed():
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if frame_buffer:
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yield (b'--frame\r\n'
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b'Content-Type: image/jpeg\r\n\r\n' + frame_buffer + b'\r\n')
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GLib.usleep(15000) # ~60fps poll cap
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GLib.usleep(33000)
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return Response(generate(), mimetype='multipart/x-mixed-replace; boundary=frame')
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# --- Main ---
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if __name__ == "__main__":
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# 1. Discover Cameras
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found_serials = discover_cameras()
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# 2. Start Pipeline Thread
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gst_thread = GStreamerPipeline(found_serials)
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gst_thread = GStreamerPipeline()
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gst_thread.daemon = True
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gst_thread.start()
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# 3. Start Web Server
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print(f"Stream available at http://0.0.0.0:5000")
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app.run(host='0.0.0.0', port=5000, debug=False, threaded=True)
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