> For the complete documentation index, see [llms.txt](https://docs.kby-ai.com/help/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.kby-ai.com/help/product/computer-vision-solutions/fire-smoke-detection-server.md). # Fire/Smoke Detection-Server Fires often begin in quiet, low-traffic areas—electrical rooms, loading docks, storage areas—where nobody’s watching. `KBY-AI`'s `fire/smoke detection SDK` turns your existing CCTV into smart fire sentries, instantly detecting smoke or flames before they spiral into a crisis. Perfect for warehouses, industrial sites, and logistics hubs. ## Features * [x] Fire Detection * [x] Smoke Detection * [x] Available on Docker * [x] Free Trial License * [x] Free Update & Technical Support * [x] On-Premise Solution ## License We offer `lifetime license(perpetual license)` based on `machine ID` for every server(`Windows`, `Linux`). The license is available for `one-time payment`. In other words, once you purchase license from me, you can use our `SDK` permanently. To request a license, please contact us: > **Email:** {% embed url="" %} {% embed url="" %} {% embed url="" %} {% embed url="" %} ## Recommended Spec. ### 1. Linux * **CPU:** 2 cores or more (Recommended: 2 cores) * **RAM:** 4 GB or more (Recommended: 8 GB) * HDD: 4 GB or more (Recommended: 8 GB) * **OS:** Windows 7 or later * **Architecture:** x64 ### 2. Windows * **CPU:** 2 cores or more (Recommended: 2 cores) * **RAM:** 4 GB or more (Recommended: 8 GB) * HDD: 4 GB or more (Recommended: 8 GB) * **OS:** Ubuntu 20.04 or later * **Architecture:** x64 ## Setting up SDK & Test * Pull `Docker`image. ```bash sudo docker pull kbyai/fire-smoke-detection:latest ``` * Read `machine code` 
sudo docker run -e LICENSE="xxxxx" kbyai/fire-smoke-detection:latest
* Send us `machine code` obtained.
* Create a `license.txt` file and write the license key that you received from `KBY-AI` team. * Run `Docker` container ``` sudo docker run -v ./license.txt:/home/openvino/kby-ai-fire/license.txt -p 8081:8080 -p 9001:9000 kbyai/fire-smoke-detection:latest ``` * Here are the endpoints to test the `API` through `Postman`: * To test with an image file, send a `POST` request to `http://{xx.xx.xx.xx}:8081/fire`.\ To test with a `base64-encoded` image, send a `POST` request to `http://{xx.xx.xx.xx}:8081/fire_base64`.
## APIs ### 1. Getting Machine Code The `SDK` provides a single API to get `machine code` ```python machineCode = getMachineCode() print("\nmachineCode: ", machineCode.decode('utf-8')) ``` ### 2. Activate `SDK` Read `license.txt` file and activate `SDK` with `setActivation()` function as follows. ```python try: with open(licensePath, 'r') as file: license = file.read().strip() except IOError as exc: print("failed to open license.txt: ", exc.errno) print("\nlicense: ", license) ret = setActivation(license.encode('utf-8')) ``` ### 3. Initialize SDK ```python ret = initSDK() print("init: ", ret) ``` ### 3. Get Detection Result ```python cnt = getFireDetection(img_byte, len(img_byte), label_array, box_array, score_array) rectangles = [ (box_array[i * 4], box_array[i * 4 + 1], box_array[i * 4 + 2], box_array[i * 4 + 3]) for i in range(cnt)] scores = [score_array[i] for i in range(cnt)] labels = [label_array[i] for i in range(cnt)] ``` --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.kby-ai.com/help/product/computer-vision-solutions/fire-smoke-detection-server.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.