Create Yolov8n train.pt

Yolov8n train.pt

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+pip install inference
+
+# Import the InferencePipeline object
+from inference import InferencePipeline
+import cv2
+
+def my_sink(result, video_frame):
+    if result.get("output_image"): # Display an image from the workflow response
+        cv2.imshow("Workflow Image", result["output_image"].numpy_image)
+        cv2.waitKey(1)
+    print(result) # do something with the predictions of each frame
+
+
+# initialize a pipeline object
+pipeline = InferencePipeline.init_with_workflow(
+    api_key="dxkgGGHSZ3DI8XzVn29U",
+    workspace_name="naveen-kumar-hnmil",
+    workflow_id="detect-count-and-visualize-5",
+    video_reference=0, # Path to video, device id (int, usually 0 for built in webcams), or RTSP stream url
+    max_fps=30,
+    on_prediction=my_sink
+)
+pipeline.start() #start the pipeline
+pipeline.join() #wait for the pipeline thread to finish