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gi.require_version('Gst', '1.0')
gi.require_version('GstRtspServer', '1.0')
from gi.repository import Gst, GstRtspServer, GLib
import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator
class RtspMediaFactory(GstRtspServer.RTSPMediaFactory):
def __init__(self):
super(RtspMediaFactory, self).__init__()
# YOLOv8 modelini yükle
self.model = YOLO('yolov8n.pt')
def do_create_element(self, url):
# Kameradan gelen görüntüyü yakalayan pipeline
pipeline_str = (
"nvarguscamerasrc sensor-id=0 ! "
"video/x-raw(memory:NVMM), width=(int)1280, height=(int)720, format=(string)NV12, framerate=(fraction)30/1 ! "
"nvvidconv ! "
"video/x-raw, format=(string)BGR ! " # YOLO için BGR formatında yakalama
"appsink name=sink emit-signals=true"
)
return Gst.parse_launch(pipeline_str)
def on_new_sample(self, sink):
# appsink'ten gelen görüntü yakalanıyor
sample = sink.emit("pull-sample")
buf = sample.get_buffer()
caps = sample.get_caps()
# Veri boyutlarını alalım
width = caps.get_structure(0).get_value('width')
height = caps.get_structure(0).get_value('height')
# Görüntü verisini numpy array olarak al
success, map_info = buf.map(Gst.MapFlags.READ)
if success:
frame = np.ndarray(
(height, width, 3),
buffer=map_info.data,
dtype=np.uint8
)
buf.unmap(map_info)
# YOLO ile nesne tespiti yap
results = self.model(frame)
annotator = Annotator(frame)
for result in results:
for box in result.boxes:
# Tespit edilen nesne kutularını ekrana çiz
annotator.box_label(box.xyxy, box.cls)
# İşlenmiş kareyi geri döndür
return frame
return None
class RtspServer:
def __init__(self):
self.server = GstRtspServer.RTSPServer()
self.factory = RtspMediaFactory()
self.factory.set_shared(True)
# Mount point ekleme
mount_points = self.server.get_mount_points()
mount_points.add_factory("/test", self.factory)
self.server.attach(None)
def run(self):
loop = GLib.MainLoop()
loop.run()
if __name__ == "__main__":
Gst.init(None)
server = RtspServer()
print("RTSP sunucusu başlatıldı: rtsp://127.0.0.1:8554/test")
server.run()
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