David vision streaming

ai/ai-vision/video-stream-analysis/LICENSE

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+Copyright (c) 2025 Oracle and/or its affiliates.
+
+The Universal Permissive License (UPL), Version 1.0
+
+Subject to the condition set forth below, permission is hereby granted to any
+person obtaining a copy of this software, associated documentation and/or data
+(collectively the "Software"), free of charge and under any and all copyright
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+licensable by each licensor hereunder covering either (i) the unmodified
+Software as contributed to or provided by such licensor, or (ii) the Larger
+Works (as defined below), to deal in both
+
+(a) the Software, and
+(b) any piece of software and/or hardware listed in the lrgrwrks.txt file if
+one is included with the Software (each a "Larger Work" to which the Software
+is contributed by such licensors),
+
+without restriction, including without limitation the rights to copy, create
+derivative works of, display, perform, and distribute the Software and make,
+use, sell, offer for sale, import, export, have made, and have sold the
+Software and the Larger Work(s), and to sublicense the foregoing rights on
+either these or other terms.
+
+This license is subject to the following condition:
+The above copyright notice and either this complete permission notice or at
+a minimum a reference to the UPL must be included in all copies or
+substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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+SOFTWARE.

ai/ai-vision/video-stream-analysis/README.md

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+# OCI Vision Streaming Setup and Consumption
+
+## Quick Start Guide
+
+- The code files in this demo are 
+  - OCIVisionStreamSetup.ipynb - Notebook containing cells to run to start the stream job(streaming frames from the RTSP server to the OCI Vision).
+  - Stream-analysis.py - Streamlit application that can be used to consume the generated outputs of stream video analysis
+
+## 1. Region Selection
+
+OCI Vision Streaming is available in multiple regions.
+
+- For the **simplest setup**, run your pipeline within the **Ashburn** region.  
+  No subnet or private endpoint creation is required in Ashburn.
+
+- To run in a region other than Ashburn, **[follow these steps](https://docs.oracle.com/en-us/iaas/Content/vision/using/video-stream-processing-top.htm)**.
+  > _Running in other regions may require additional VCN/Subnet/Endpoint configuration._
+
+---
+
+## 2. Prerequisites
+
+- **An active RTSP stream URL** (e.g., from an IP camera or RTSP streaming source)
+- Access to a **Jupyter environment**
+- Required permissions in your OCI compartment to use Vision, Stream Jobs, and Object Storage buckets
+
+---
+
+## 3. Setting up the Vision Streaming Job
+
+1. **Open the** `OCIVisionStreamSetup.ipynb` **notebook in Jupyter**.
+
+2. **Fill out the required fields at the top of the notebook:**
+
+    - `CONFIG_PROFILE` — OCI CLI profile to use (e.g., `"DEFAULT"`)
+    - `COMPARTMENT_ID` — your OCI compartment's OCID
+    - `STREAM_JOB_DISPLAY_NAME` — a display name for your stream job
+    - `CAMERA_URL` — your RTSP stream URL
+    - `NAMESPACE` — your Object Storage namespace
+    - `BUCKET` — target Object Storage bucket
+    - `PREFIX` — (Optional) Prefix for output files in Object Storage
+    - `FEATURES` — Detection features (`face`, `object`, etc.)
+
+3. **Run the following notebook cells in order:**
+    - Create Stream Source
+    - Create Stream Job
+    - Run Stream Job
+
+    _This will start streaming frames from your camera to OCI Vision for detection (e.g., face or object recognition). The results (bounding box coordinates, frame encodings, etc.) will be stored as JSON files in the specified Object Storage bucket._
+
+---
+
+## 4. Consuming Streams with Streamlit
+
+  Install the required dependencies by running 
+
+      pip install -r requirements.txt
+
+
+While the stream job is active:
+
+1. **Start the Streamlit app (in terminal):**
+   ```bash
+   streamlit run streamlit_app.py 
+2. In the Streamlit UI, enter the following:
+    - Stream Job OCID
+    - Bucket Name
+    - Prefix (Object Storage path, if applicable)
+    - Object Storage Namespace
+    - Detection Type: choose between "Object Detection" or "Face Detection"
+3. Click on Consume Stream The video with detected objects/faces will appear in your browser as annotated frames.
+Note: Frames may appear slow; OCI Vision Streaming currently supports a maximum of 2 frames per second.
+- Depending on whether you selected object detection or face detection in the features during the creation of the stream job, select the appropriate radio button to get the relevant analytics.
+![UI Screenshot](./media/appUI.png)
+
+
+## 5. Resource Management — Important!
+Stop the Stream Job when finished!
+Leaving it running will keep streaming data into your bucket, leading to large storage consumption.
+Clean up resources:
+Use the notebook cells to delete the Stream Job and Stream Source.
+Optionally, store the OCIDs of created resources and reuse them to avoid recreating each time.
+
+
+## 6. References
+- [OCI Vision Stream Analysis Documentation](https://docs.oracle.com/en-us/iaas/Content/vision/using/video-stream-processing-top.htm)  
+- [OCI Python SDK API Reference](https://docs.oracle.com/en-us/iaas/tools/python/2.162.0/api/landing.html)
+
+## 7. Troubleshooting
+**Common Errors: 
+If you encounter a 405 – Request Not Allowed error while running the stream job, it may be because the job is currently transitioning between states (for example, from CREATED to RUNNING). During this UPDATING phase, the job is temporarily locked, and you won’t be able to perform any actions on it. If it remains stuck in this state, try deleting the stream job and creating a new one.
+
+## 8. License
+Copyright (c) 2025 Oracle and/or its affiliates.
+Licensed under the Universal Permissive License (UPL), Version 1.0.
+See [LICENSE](LICENSE) for more details.