diff --git a/neural-networks/README.md b/neural-networks/README.md
index e37dd16ff..ee0fc1094 100644
--- a/neural-networks/README.md
+++ b/neural-networks/README.md
@@ -49,26 +49,27 @@ LEGEND: ✅: available; ❌: not available; 🚧: work in progress
| :---------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------: |
| 
| 
|
-| Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
-| -------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---- | ----------------- | ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------- |
-| [generic-example](generic-example/) | [mobilenet-ssd](https://models.luxonis.com/luxonis/mobilenet-ssd/2da6e0a5-4785-488d-8cf5-c35f7ec1a1ed) | ✅ | ❌ | ❌ | [gen2-efficientDet](https://github.com/luxonis/oak-examples/tree/master/gen2-efficientDet) | Model not available for RVC4. |
-| [generic-example](generic-example/) | [yolov6-nano](https://models.luxonis.com/luxonis/yolov6-nano/face58c4-45ab-42a0-bafc-19f9fee8a034) / [yolov6-large](https://models.luxonis.com/luxonis/yolov6-large/7937248a-c310-4765-97db-0850086f2dd9) / [yolov10-nano](https://models.luxonis.com/luxonis/yolov10-nano/03153a9a-06f7-4ce9-b655-3762d21d0a8a) | ✅ | ✅ | ✅ | [gen2-yolo](https://github.com/luxonis/oak-examples/tree/master/gen2-yolo) | |
-| [generic-example](generic-example/) | [qrdet](https://models.luxonis.com/luxonis/qrdet/d1183a0f-e9a0-4fa2-8437-f2f5b0181739) | ✅ | ✅ | ✅ | [gen2-qr-code-scanner](https://github.com/luxonis/oak-examples/tree/master/gen2-qr-code-scanner) | |
-| [generic-example](generic-example/) | [barcode-detection](https://models.luxonis.com/luxonis/barcode-detection/75edea0f-79c9-4091-a48c-f81424b3ccab) | ✅ | ✅ | ✅ | | |
-| [generic-example](generic-example/) | [scrfd-person-detection](https://models.luxonis.com/luxonis/scrfd-person-detection/c3830468-3178-4de6-bc09-0543bbe28b1c) | ✅ | ✅ | ✅ | | |
-| [generic-example](generic-example/) | [mediapipe-palm-detection](https://models.luxonis.com/luxonis/mediapipe-palm-detection/9531aba9-ef45-4ad3-ae03-808387d61bf3) | ✅ | ✅ | ✅ | [gen2-palm-detection](https://github.com/luxonis/oak-examples/tree/master/gen2-palm-detection) | |
-| [generic-example](generic-example/) | [ppe-detection](https://models.luxonis.com/luxonis/ppe-detection/fd8699bf-3819-4134-9374-3735b9660d3c) | ✅ | ✅ | ✅ | | |
-| [generic-example](generic-example/) | [paddle-text-detection](https://models.luxonis.com/luxonis/paddle-text-detection/131d855c-60b1-4634-a14d-1269bb35dcd2) | ✅ | ✅ | ✅ | | |
-| [human-machine-safety](object-detection/human-machine-safety/) | [yolov6-nano](https://models.luxonis.com/luxonis/yolov6-nano/face58c4-45ab-42a0-bafc-19f9fee8a034), [mediapipe-palm-detection](https://models.luxonis.com/luxonis/mediapipe-palm-detection/9531aba9-ef45-4ad3-ae03-808387d61bf3) | ✅ | ✅ | ✅ | [gen2-human-machine-safety](https://github.com/luxonis/oak-examples/tree/master/gen2-human-machine-safety) | Needs a device with color and stereo (left, right) cameras. |
-| [spatial-detections](object-detection/spatial-detections/) | [yolov6-nano](https://models.luxonis.com/luxonis/yolov6-nano/face58c4-45ab-42a0-bafc-19f9fee8a034) / [yolov6-large](https://models.luxonis.com/luxonis/yolov6-large/7937248a-c310-4765-97db-0850086f2dd9) / [yolov10-nano](https://models.luxonis.com/luxonis/yolov10-nano/03153a9a-06f7-4ce9-b655-3762d21d0a8a) | ✅ | ✅ | ✅ | | Needs a device with color and stereo (left, right) cameras. |
-| [social-distancing](object-detection/social-distancing/) | [scrfd-person-detection](https://models.luxonis.com/luxonis/scrfd-person-detection/c3830468-3178-4de6-bc09-0543bbe28b1c) | ✅ | ✅ | ✅ | [gen2-social-distancing](https://github.com/luxonis/oak-examples/tree/master/gen2-social-distancing) | Needs a device with color and stereo (left, right) cameras. |
-| [generic-example](generic-example/) | [fire-detection](https://models.luxonis.com/luxonis/fire-detection/899c54a8-9e62-4c99-843d-a51751949a7e) | ✅ | ✅ | ✅ | [gen2-fire-detection](https://github.com/luxonis/oak-examples/tree/master/gen2-fire-detection) | |
-| [generic-example](generic-example/) | [mobile-object-localizer](https://models.luxonis.com/luxonis/mobile-object-localizer/d7187509-c6c5-4979-9f59-af885011ad2e) | ✅ | ✅ | ✅ | [gen2-mobile-object-localizer](https://github.com/luxonis/oak-examples/tree/master/gen2-mobile-object-localizer) | |
-| [text-blur](object-detection/text-blur/) | [paddle-text-detection](https://models.luxonis.com/luxonis/paddle-text-detection/131d855c-60b1-4634-a14d-1269bb35dcd2) | ✅ | ✅ | ✅ | [gen2-text-blur](https://github.com/luxonis/oak-examples/tree/master/gen2-text-blur) | |
-| [thermal-detection](object-detection/thermal-detection/) | [thermal-person-detection](https://models.luxonis.com/luxonis/thermal-person-detection/b1d7a62f-7020-469c-8fa9-a6d1ff3499b2) | ✅ | ❌ | ❌ | | Only available for OAK Thermal(RVC2). |
-| [yolo-host-decoding](object-detection/yolo-host-decoding/) | [yolov6-nano](https://models.luxonis.com/luxonis/yolov6-nano/face58c4-45ab-42a0-bafc-19f9fee8a034) | ✅ | ✅ | ✅ | [gen2-yolo/host-decoding](https://github.com/luxonis/oak-examples/tree/master/gen2-yolo/host-decoding) | |
-| [yolo-p](object-detection/yolo-p/) | [yolo-p](https://models.luxonis.com/luxonis/yolo-p/0a22d194-d525-46e7-a785-a267b7958a39) | ✅ | ✅ | ✅ | [gen2-yolo/yolop](https://github.com/luxonis/oak-examples/tree/master/gen2-yolo/yolop), [gen2-road-segmentation](https://github.com/luxonis/oak-examples/tree/master/gen2-road-segmentation) | |
-| [yolo-world](object-detection/yolo-world/) | [yolo-world-l](https://models.luxonis.com/luxonis/yolo-world-l/6684e96f-11fc-4d92-8657-12a5fd8e532a) | ❌ | ✅ | ✅ | | Model is only available for RVC4. |
+| Name | HubAI Model | RVC2 | RVC4 (peripheral) | RVC4 (standalone) | DepthAIv2 | Notes |
+| ------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---- | ----------------- | ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------- |
+| [generic-example](generic-example/) | [mobilenet-ssd](https://models.luxonis.com/luxonis/mobilenet-ssd/2da6e0a5-4785-488d-8cf5-c35f7ec1a1ed) | ✅ | ❌ | ❌ | [gen2-efficientDet](https://github.com/luxonis/oak-examples/tree/master/gen2-efficientDet) | Model not available for RVC4. |
+| [generic-example](generic-example/) | [yolov6-nano](https://models.luxonis.com/luxonis/yolov6-nano/face58c4-45ab-42a0-bafc-19f9fee8a034) / [yolov6-large](https://models.luxonis.com/luxonis/yolov6-large/7937248a-c310-4765-97db-0850086f2dd9) / [yolov10-nano](https://models.luxonis.com/luxonis/yolov10-nano/03153a9a-06f7-4ce9-b655-3762d21d0a8a) | ✅ | ✅ | ✅ | [gen2-yolo](https://github.com/luxonis/oak-examples/tree/master/gen2-yolo) | |
+| [generic-example](generic-example/) | [qrdet](https://models.luxonis.com/luxonis/qrdet/d1183a0f-e9a0-4fa2-8437-f2f5b0181739) | ✅ | ✅ | ✅ | [gen2-qr-code-scanner](https://github.com/luxonis/oak-examples/tree/master/gen2-qr-code-scanner) | |
+| [generic-example](generic-example/) | [barcode-detection](https://models.luxonis.com/luxonis/barcode-detection/75edea0f-79c9-4091-a48c-f81424b3ccab) | ✅ | ✅ | ✅ | | |
+| [generic-example](generic-example/) | [scrfd-person-detection](https://models.luxonis.com/luxonis/scrfd-person-detection/c3830468-3178-4de6-bc09-0543bbe28b1c) | ✅ | ✅ | ✅ | | |
+| [generic-example](generic-example/) | [mediapipe-palm-detection](https://models.luxonis.com/luxonis/mediapipe-palm-detection/9531aba9-ef45-4ad3-ae03-808387d61bf3) | ✅ | ✅ | ✅ | [gen2-palm-detection](https://github.com/luxonis/oak-examples/tree/master/gen2-palm-detection) | |
+| [generic-example](generic-example/) | [ppe-detection](https://models.luxonis.com/luxonis/ppe-detection/fd8699bf-3819-4134-9374-3735b9660d3c) | ✅ | ✅ | ✅ | | |
+| [generic-example](generic-example/) | [paddle-text-detection](https://models.luxonis.com/luxonis/paddle-text-detection/131d855c-60b1-4634-a14d-1269bb35dcd2) | ✅ | ✅ | ✅ | | |
+| [human-machine-safety](object-detection/human-machine-safety/) | [yolov6-nano](https://models.luxonis.com/luxonis/yolov6-nano/face58c4-45ab-42a0-bafc-19f9fee8a034), [mediapipe-palm-detection](https://models.luxonis.com/luxonis/mediapipe-palm-detection/9531aba9-ef45-4ad3-ae03-808387d61bf3) | ✅ | ✅ | ✅ | [gen2-human-machine-safety](https://github.com/luxonis/oak-examples/tree/master/gen2-human-machine-safety) | Needs a device with color and stereo (left, right) cameras. |
+| [spatial-detections](object-detection/spatial-detections/) | [yolov6-nano](https://models.luxonis.com/luxonis/yolov6-nano/face58c4-45ab-42a0-bafc-19f9fee8a034) / [yolov6-large](https://models.luxonis.com/luxonis/yolov6-large/7937248a-c310-4765-97db-0850086f2dd9) / [yolov10-nano](https://models.luxonis.com/luxonis/yolov10-nano/03153a9a-06f7-4ce9-b655-3762d21d0a8a) | ✅ | ✅ | ✅ | | Needs a device with color and stereo (left, right) cameras. |
+| [social-distancing](object-detection/social-distancing/) | [scrfd-person-detection](https://models.luxonis.com/luxonis/scrfd-person-detection/c3830468-3178-4de6-bc09-0543bbe28b1c) | ✅ | ✅ | ✅ | [gen2-social-distancing](https://github.com/luxonis/oak-examples/tree/master/gen2-social-distancing) | Needs a device with color and stereo (left, right) cameras. |
+| [generic-example](generic-example/) | [fire-detection](https://models.luxonis.com/luxonis/fire-detection/899c54a8-9e62-4c99-843d-a51751949a7e) | ✅ | ✅ | ✅ | [gen2-fire-detection](https://github.com/luxonis/oak-examples/tree/master/gen2-fire-detection) | |
+| [generic-example](generic-example/) | [mobile-object-localizer](https://models.luxonis.com/luxonis/mobile-object-localizer/d7187509-c6c5-4979-9f59-af885011ad2e) | ✅ | ✅ | ✅ | [gen2-mobile-object-localizer](https://github.com/luxonis/oak-examples/tree/master/gen2-mobile-object-localizer) | |
+| [text-blur](object-detection/text-blur/) | [paddle-text-detection](https://models.luxonis.com/luxonis/paddle-text-detection/131d855c-60b1-4634-a14d-1269bb35dcd2) | ✅ | ✅ | ✅ | [gen2-text-blur](https://github.com/luxonis/oak-examples/tree/master/gen2-text-blur) | |
+| [thermal-detection](object-detection/thermal-detection/) | [thermal-person-detection](https://models.luxonis.com/luxonis/thermal-person-detection/b1d7a62f-7020-469c-8fa9-a6d1ff3499b2) | ✅ | ❌ | ❌ | | Only available for OAK Thermal(RVC2). |
+| [yolo-host-decoding](object-detection/yolo-host-decoding/) | [yolov6-nano](https://models.luxonis.com/luxonis/yolov6-nano/face58c4-45ab-42a0-bafc-19f9fee8a034) | ✅ | ✅ | ✅ | [gen2-yolo/host-decoding](https://github.com/luxonis/oak-examples/tree/master/gen2-yolo/host-decoding) | |
+| [yolo-p](object-detection/yolo-p/) | [yolo-p](https://models.luxonis.com/luxonis/yolo-p/0a22d194-d525-46e7-a785-a267b7958a39) | ✅ | ✅ | ✅ | [gen2-yolo/yolop](https://github.com/luxonis/oak-examples/tree/master/gen2-yolo/yolop), [gen2-road-segmentation](https://github.com/luxonis/oak-examples/tree/master/gen2-road-segmentation) | |
+| [yolo-world](object-detection/yolo-world/) | [yolo-world-l](https://models.luxonis.com/luxonis/yolo-world-l/6684e96f-11fc-4d92-8657-12a5fd8e532a) | ❌ | ✅ | ✅ | | Model is only available for RVC4. |
+| [barcode-detection-conveyor-belt](object-detection/barcode-detection-conveyor-belt/) | [barcode-detection](https://models.luxonis.com/luxonis/barcode-detection/75edea0f-79c9-4091-a48c-f81424b3ccab) | ✅ | ✅ | ✅ | | Most suited for OAK4-CS |
## Face Detection
diff --git a/neural-networks/object-detection/barcode-detection-conveyor-belt/README.md b/neural-networks/object-detection/barcode-detection-conveyor-belt/README.md
new file mode 100644
index 000000000..04bd1cd1b
--- /dev/null
+++ b/neural-networks/object-detection/barcode-detection-conveyor-belt/README.md
@@ -0,0 +1,111 @@
+# Barcode Detection on Conveyor Belt
+
+This example demonstrates how to detect and decode barcodes in real-time using computer vision. The application is designed for conveyor belt applications where barcodes need to be detected and decoded from video streams. It uses a [barcode detection model](https://models.luxonis.com/luxonis/barcode-detection/75edea0f-79c9-4091-a48c-f81424b3ccab) for detecting barcode regions and combines multiple decoding strategies (pyzbar and zxing-cpp) to ensure robust barcode recognition across various formats and conditions.
+
+The system processes high-resolution camera input, intelligently crops detected barcode regions, and applies multiple fallback decoding strategies including rotation and color inversion to maximize recognition success rates.
+
+## ⚠️ Important Notice
+
+**This application will work poorly on fixed focus devices.** The barcode detection and decoding algorithms require clear, focused images to function effectively. Fixed focus cameras may struggle to capture sharp barcode images at varying distances, leading to:
+
+- Reduced detection accuracy
+- Failed barcode decoding attempts
+- Inconsistent performance
+
+For optimal results, use devices with autofocus capabilities or ensure barcodes are positioned at the camera's fixed focal distance.
+
+## Recommended Devices
+
+We recommend using **OAK4-CS** for this example. Its **global-shutter** color sensor is best for fast-moving conveyor belts, reducing motion blur and rolling-shutter artifacts that can cause missed or incorrect decodes.
+
+The application will also run on **OAK4-S** and **OAK4-D** devices. For rolling-shutter or fixed-focus variants, keep barcodes near the best-focus distance and ensure good lighting to maximize decoding reliability. See the notice above regarding fixed-focus cameras.
+
+## Demo
+
+
+
+> **Note:** The stream may appear purplish because the OAK4-CS lacks an IR-cut filter.
+
+## Usage
+
+Running this example requires a **Luxonis device** connected to your computer. Refer to the [documentation](https://docs.luxonis.com/software-v3/) to setup your device if you haven't done it already.
+
+You can run the example fully on device ([`STANDALONE` mode](#standalone-mode-rvc4-only)) or using your computer as host ([`PERIPHERAL` mode](#peripheral-mode)).
+
+Here is a list of all available parameters:
+
+```
+-d DEVICE, --device DEVICE
+ Optional name, DeviceID or IP of the camera to connect to. (default: None)
+-fps FPS_LIMIT, --fps_limit FPS_LIMIT
+ FPS limit for the model runtime. (default: 10 for RVC2, 30 for RVC4)
+--media_path MEDIA_PATH
+ Optional path to video file for processing instead of live camera feed. (default: None)
+```
+
+## Peripheral Mode
+
+### Installation
+
+Install libraries:
+
+**Ubuntu:**
+
+```bash
+sudo apt-get update && apt-get install -y libzbar0 libzbar-dev
+```
+
+**macOS:**
+
+```bash
+brew install zbar
+```
+
+You need to first prepare a **Python 3.10** environment with the following packages installed:
+
+- [DepthAI](https://pypi.org/project/depthai/),
+- [DepthAI Nodes](https://pypi.org/project/depthai-nodes/).
+
+You can simply install them by running:
+
+```bash
+pip install -r requirements.txt
+```
+
+Running in peripheral mode requires a host computer and there will be communication between device and host which could affect the overall speed of the app. Below are some examples of how to run the example.
+
+### Examples
+
+Start the demo:
+
+```bash
+python3 main.py
+```
+
+This will run the example with default arguments.
+
+```bash
+python3 main.py --device 192.168.1.100 --fps_limit 15
+```
+
+This will connect to a specific device and set the FPS limit to 15.
+
+```bash
+python3 main.py --media_path test_video.mp4
+```
+
+This will process a video file instead of live camera feed.
+
+## Standalone Mode (RVC4 only)
+
+Running the example in the standalone mode, app runs entirely on the device.
+To run the example in this mode, first install the `oakctl` tool using the installation instructions [here](https://docs.luxonis.com/software-v3/oak-apps/oakctl).
+
+The app can then be run with:
+
+```bash
+oakctl connect
+oakctl app run .
+```
+
+This will run the example with default argument values. If you want to change these values you need to edit the `oakapp.toml` file (refer [here](https://docs.luxonis.com/software-v3/oak-apps/configuration/) for more information about this configuration file).
diff --git a/neural-networks/object-detection/barcode-detection-conveyor-belt/depthai_models/barcode-detection.RVC2.yaml b/neural-networks/object-detection/barcode-detection-conveyor-belt/depthai_models/barcode-detection.RVC2.yaml
new file mode 100644
index 000000000..416772f42
--- /dev/null
+++ b/neural-networks/object-detection/barcode-detection-conveyor-belt/depthai_models/barcode-detection.RVC2.yaml
@@ -0,0 +1,2 @@
+model: luxonis/barcode-detection:768x576
+platform: RVC2
\ No newline at end of file
diff --git a/neural-networks/object-detection/barcode-detection-conveyor-belt/depthai_models/barcode-detection.RVC4.yaml b/neural-networks/object-detection/barcode-detection-conveyor-belt/depthai_models/barcode-detection.RVC4.yaml
new file mode 100644
index 000000000..46a1e1dd0
--- /dev/null
+++ b/neural-networks/object-detection/barcode-detection-conveyor-belt/depthai_models/barcode-detection.RVC4.yaml
@@ -0,0 +1,2 @@
+model: luxonis/barcode-detection:768x576
+platform: RVC4
\ No newline at end of file
diff --git a/neural-networks/object-detection/barcode-detection-conveyor-belt/main.py b/neural-networks/object-detection/barcode-detection-conveyor-belt/main.py
new file mode 100644
index 000000000..91f80128e
--- /dev/null
+++ b/neural-networks/object-detection/barcode-detection-conveyor-belt/main.py
@@ -0,0 +1,95 @@
+from pathlib import Path
+import depthai as dai
+from depthai_nodes.node import ParsingNeuralNetwork
+
+from utils.arguments import initialize_argparser
+from utils.simple_barcode_overlay import SimpleBarcodeOverlay
+from utils.barcode_decoder import BarcodeDecoder
+from utils.host_crop_config_creator import CropConfigsCreator
+
+_, args = initialize_argparser()
+
+visualizer = dai.RemoteConnection(httpPort=8082)
+device = dai.Device(dai.DeviceInfo(args.device)) if args.device else dai.Device()
+platform = device.getPlatform().name
+print(f"Platform: {platform}")
+
+frame_type = (
+ dai.ImgFrame.Type.BGR888i if platform == "RVC4" else dai.ImgFrame.Type.BGR888p
+)
+
+if not args.fps_limit:
+ args.fps_limit = 10 if platform == "RVC2" else 30
+ print(
+ f"\nFPS limit set to {args.fps_limit} for {platform} platform. If you want to set a custom FPS limit, use the --fps_limit flag.\n"
+ )
+
+with dai.Pipeline(device) as pipeline:
+ print("Creating pipeline...")
+
+ model_description = dai.NNModelDescription.fromYamlFile(
+ f"barcode-detection.{platform}.yaml"
+ )
+ nn_archive = dai.NNArchive(
+ dai.getModelFromZoo(
+ model_description,
+ )
+ )
+
+ if args.media_path:
+ replay = pipeline.create(dai.node.ReplayVideo)
+ replay.setReplayVideoFile(Path(args.media_path))
+ replay.setOutFrameType(frame_type)
+ replay.setLoop(True)
+ if args.fps_limit:
+ replay.setFps(args.fps_limit)
+ else:
+ cam = pipeline.create(dai.node.Camera).build()
+
+ cam_out = cam.requestOutput((2592, 1944), frame_type, fps=args.fps_limit)
+ input_node = replay.out if args.media_path else cam_out
+
+ resize_node = pipeline.create(dai.node.ImageManip)
+ resize_node.setMaxOutputFrameSize(
+ nn_archive.getInputWidth() * nn_archive.getInputHeight() * 3
+ )
+ resize_node.initialConfig.setOutputSize(
+ nn_archive.getInputWidth(),
+ nn_archive.getInputHeight(),
+ mode=dai.ImageManipConfig.ResizeMode.STRETCH,
+ )
+ resize_node.initialConfig.setFrameType(frame_type)
+ input_node.link(resize_node.inputImage)
+
+ detection_nn: ParsingNeuralNetwork = pipeline.create(ParsingNeuralNetwork).build(
+ resize_node.out, nn_archive
+ )
+
+ crop_code = pipeline.create(CropConfigsCreator).build(
+ detection_nn.out,
+ source_size=(2592, 1944),
+ target_size=(640, 480),
+ resize_mode=dai.ImageManipConfig.ResizeMode.LETTERBOX,
+ )
+
+ crop_manip = pipeline.create(dai.node.ImageManip)
+ crop_manip.inputConfig.setReusePreviousMessage(False)
+ crop_manip.setMaxOutputFrameSize(640 * 480 * 5)
+ input_node.link(crop_manip.inputImage)
+ crop_code.config_output.link(crop_manip.inputConfig)
+
+ decoder = pipeline.create(BarcodeDecoder)
+ crop_manip.out.link(decoder.input)
+
+ barcode_overlay = pipeline.create(SimpleBarcodeOverlay).build(
+ decoder.output, resize_node.out, detection_nn.out
+ )
+
+ visualizer.addTopic("Barcode Overlay", barcode_overlay.output)
+
+ pipeline.run()
+
+ while True:
+ key = visualizer.waitKey(1)
+ if key == ord("q"):
+ break
diff --git a/neural-networks/object-detection/barcode-detection-conveyor-belt/media/barcode_demo.gif b/neural-networks/object-detection/barcode-detection-conveyor-belt/media/barcode_demo.gif
new file mode 100644
index 000000000..14a5a8f3b
Binary files /dev/null and b/neural-networks/object-detection/barcode-detection-conveyor-belt/media/barcode_demo.gif differ
diff --git a/neural-networks/object-detection/barcode-detection-conveyor-belt/oakapp.toml b/neural-networks/object-detection/barcode-detection-conveyor-belt/oakapp.toml
new file mode 100644
index 000000000..08a1c952c
--- /dev/null
+++ b/neural-networks/object-detection/barcode-detection-conveyor-belt/oakapp.toml
@@ -0,0 +1,17 @@
+identifier = "com.example.object-detection.conveyor-application-barcodes"
+app_version = "1.0.0"
+
+prepare_container = [
+ { type = "RUN", command = "apt-get update" },
+ { type = "RUN", command = "apt-get install -y python3 python3-pip libzbar0 libzbar-dev" },
+ { type = "COPY", source = "requirements.txt", target = "requirements.txt" },
+ { type = "RUN", command = "pip3 install -r /app/requirements.txt --break-system-packages"},
+]
+
+prepare_build_container = []
+
+build_steps = []
+
+depthai_models = { yaml_path = "./depthai_models" }
+
+entrypoint = ["bash", "-c", "python3 -u /app/main.py"]
\ No newline at end of file
diff --git a/neural-networks/object-detection/barcode-detection-conveyor-belt/requirements.txt b/neural-networks/object-detection/barcode-detection-conveyor-belt/requirements.txt
new file mode 100644
index 000000000..445462a22
--- /dev/null
+++ b/neural-networks/object-detection/barcode-detection-conveyor-belt/requirements.txt
@@ -0,0 +1,6 @@
+depthai>=3.0.0
+depthai-nodes==0.3.4
+numpy>=1.22
+opencv-python-headless~=4.10.0
+pyzbar==0.1.9
+Pillow==12.0.0
\ No newline at end of file
diff --git a/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/annotation_node.py b/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/annotation_node.py
new file mode 100644
index 000000000..b1a38851f
--- /dev/null
+++ b/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/annotation_node.py
@@ -0,0 +1,77 @@
+from typing import List
+import depthai as dai
+
+from depthai_nodes import ImgDetectionsExtended, SECONDARY_COLOR
+from depthai_nodes.utils import AnnotationHelper
+
+
+class AnnotationNode(dai.node.ThreadedHostNode):
+ def __init__(self) -> None:
+ super().__init__()
+
+ self.input = self.createInput()
+ self.input.setPossibleDatatypes([(dai.DatatypeEnum.Buffer, True)])
+
+ self.out = self.createOutput()
+ self.out.setPossibleDatatypes([(dai.DatatypeEnum.Buffer, True)])
+
+ def build(
+ self,
+ gather_data_msg: dai.Node.Output,
+ ) -> "AnnotationNode":
+ gather_data_msg.link(self.input)
+ return self
+
+ def run(self) -> None:
+ while self.isRunning():
+ gather_data_msg: dai.Buffer = self.input.get()
+
+ img_detections_extended_msg: ImgDetectionsExtended = (
+ gather_data_msg.reference_data
+ )
+
+ msg_group_list: List[dai.MessageGroup] = gather_data_msg.gathered
+
+ annotations = AnnotationHelper()
+
+ for img_detection_extended_msg, msg_group in zip(
+ img_detections_extended_msg.detections, msg_group_list
+ ):
+ xmin, ymin, xmax, ymax = (
+ img_detection_extended_msg.rotated_rect.getOuterRect()
+ )
+
+ try:
+ xmin = float(xmin)
+ ymin = float(ymin)
+ xmax = float(xmax)
+ ymax = float(ymax)
+ except Exception:
+ pass
+
+ xmin = max(0.0, min(1.0, xmin))
+ ymin = max(0.0, min(1.0, ymin))
+ xmax = max(0.0, min(1.0, xmax))
+ ymax = max(0.0, min(1.0, ymax))
+
+ annotations.draw_rectangle((xmin, ymin), (xmax, ymax))
+
+ barcode_text = ""
+ if "0" in msg_group:
+ buf_msg: dai.Buffer = msg_group["0"]
+ barcode_text = buf_msg.getData().decode("utf-8", errors="ignore")
+
+ if barcode_text:
+ annotations.draw_text(
+ text=barcode_text,
+ position=(xmin + 0.01, ymin + 0.03),
+ size=20,
+ color=SECONDARY_COLOR,
+ )
+
+ annotations_msg = annotations.build(
+ timestamp=img_detections_extended_msg.getTimestamp(),
+ sequence_num=img_detections_extended_msg.getSequenceNum(),
+ )
+
+ self.out.send(annotations_msg)
diff --git a/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/arguments.py b/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/arguments.py
new file mode 100644
index 000000000..4ef0b9862
--- /dev/null
+++ b/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/arguments.py
@@ -0,0 +1,38 @@
+import argparse
+
+
+def initialize_argparser():
+ """Initialize the argument parser for the script."""
+ parser = argparse.ArgumentParser(
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter
+ )
+
+ parser.add_argument(
+ "-d",
+ "--device",
+ help="Optional name, DeviceID or IP of the camera to connect to.",
+ required=False,
+ default=None,
+ type=str,
+ )
+
+ parser.add_argument(
+ "-fps",
+ "--fps_limit",
+ help="FPS limit for the model runtime.",
+ required=False,
+ default=None,
+ type=int,
+ )
+
+ parser.add_argument(
+ "-media",
+ "--media_path",
+ help="Path to the media file you aim to run the model on. If not set, the model will run on the camera input.",
+ required=False,
+ default=None,
+ type=str,
+ )
+ args = parser.parse_args()
+
+ return parser, args
diff --git a/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/barcode_decoder.py b/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/barcode_decoder.py
new file mode 100644
index 000000000..00a2b2946
--- /dev/null
+++ b/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/barcode_decoder.py
@@ -0,0 +1,54 @@
+import time
+import depthai as dai
+from PIL import Image
+from pyzbar.pyzbar import decode
+import cv2
+
+
+class BarcodeDecoder(dai.node.ThreadedHostNode):
+ """
+ Custom host node that receives ImgFrame messages,
+ runs pyzbar (plus optional fallbacks), and emits raw bytes
+ in dai.Buffer messages.
+ """
+
+ def __init__(self):
+ super().__init__()
+
+ self.input = self.createInput()
+ self.input.setPossibleDatatypes([(dai.DatatypeEnum.ImgFrame, True)])
+
+ self.output = self.createOutput()
+ self.output.setPossibleDatatypes([(dai.DatatypeEnum.Buffer, True)])
+
+ def run(self):
+ while self.isRunning():
+ in_msg = self.input.tryGet()
+ if in_msg is None:
+ time.sleep(0.001)
+ continue
+
+ cv_frame = in_msg.getCvFrame()
+ pil_img = Image.fromarray(cv2.cvtColor(cv_frame, cv2.COLOR_BGR2RGB))
+
+ barcodes = decode(pil_img)
+
+ if not barcodes:
+ for angle in (90, 180, 270):
+ rotated = pil_img.rotate(angle, expand=True)
+ barcodes = decode(rotated)
+ if barcodes:
+ break
+
+ if not barcodes:
+ inv_frame = cv2.bitwise_not(cv_frame)
+ inv_pil = Image.fromarray(cv2.cvtColor(inv_frame, cv2.COLOR_BGR2RGB))
+ barcodes = decode(inv_pil)
+
+ for bc in barcodes:
+ buf = dai.Buffer()
+ buf.setData(bc.data)
+ self.output.send(buf)
+
+ if not barcodes:
+ time.sleep(0.001)
diff --git a/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/host_crop_config_creator.py b/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/host_crop_config_creator.py
new file mode 100644
index 000000000..bcee42f3b
--- /dev/null
+++ b/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/host_crop_config_creator.py
@@ -0,0 +1,275 @@
+from typing import Optional, Tuple
+import time
+
+import depthai as dai
+
+from depthai_nodes import ImgDetectionExtended, ImgDetectionsExtended
+
+
+class CropConfigsCreator(dai.node.HostNode):
+ """A node to create and send a dai.ImageManipConfigV2 crop configuration for each
+ detection in a list of detections. An optional target size and resize mode can be
+ set to ensure uniform crop sizes.
+
+ To ensure correct synchronization between the crop configurations and the image,
+ ensure "inputConfig.setReusePreviousMessage" is set to False in the dai.ImageManipV2 node.
+
+ Attributes
+ ----------
+ detections_input : dai.Input
+ The input link for the ImageDetectionsExtended | dai.ImgDetections message.
+ config_output : dai.Output
+ The output link for the ImageManipConfigV2 messages.
+ detections_output : dai.Output
+ The output link for the ImgDetectionsExtended message.
+ source_size : Tuple[int, int]
+ The size of the source image (width, height).
+ target_size : Optional[Tuple[int, int]] = None
+ The size of the target image (width, height). If None, crop sizes will not be uniform.
+ resize_mode : dai.ImageManipConfigV2.ResizeMode = dai.ImageManipConfigV2.ResizeMode.STRETCH
+ The resize mode to use when target size is set. Options are: CENTER_CROP, LETTERBOX, NONE, STRETCH.
+ """
+
+ def __init__(self) -> None:
+ """Initializes the node."""
+ super().__init__()
+ self.config_output = self.createOutput(
+ possibleDatatypes=[
+ dai.Node.DatatypeHierarchy(dai.DatatypeEnum.ImageManipConfig, True)
+ ]
+ )
+
+ self.detections_output = self.createOutput(
+ possibleDatatypes=[
+ dai.Node.DatatypeHierarchy(dai.DatatypeEnum.Buffer, True)
+ ]
+ )
+ self._w: int = None
+ self._h: int = None
+ self._target_w: int = None
+ self._target_h: int = None
+ self.resize_mode: dai.ImageManipConfig.ResizeMode = None
+
+ @property
+ def w(self) -> int:
+ """Returns the width of the source image.
+
+ @return: Width of the source image.
+ @rtype: int
+ """
+ return self._w
+
+ @property
+ def h(self) -> int:
+ """Returns the height of the source image.
+
+ @return: Height of the source image.
+ @rtype: int
+ """
+ return self._h
+
+ @property
+ def target_w(self) -> int:
+ """Returns the width of the target image.
+
+ @return: Width of the target image.
+ @rtype: int
+ """
+ return self._target_w
+
+ @property
+ def target_h(self) -> int:
+ """Returns the height of the target image.
+
+ @return: Height of the target image.
+ @rtype: int
+ """
+ return self._target_h
+
+ @w.setter
+ def w(self, w: int):
+ """Sets the width of the source image.
+
+ @param w: Width of the source image.
+ @type w: int
+ @raise TypeError: If w is not an integer.
+ @raise ValueError: If w is less than 1.
+ """
+ self._validate_positive_integer(w)
+ self._w = w
+
+ @h.setter
+ def h(self, h: int):
+ """Sets the height of the source image.
+
+ @param h: Height of the source image.
+ @type h: int
+ @raise TypeError: If h is not an integer.
+ @raise ValueError: If h is less than 1.
+ """
+ self._validate_positive_integer(h)
+ self._h = h
+
+ @target_w.setter
+ def target_w(self, target_w: int):
+ """Sets the width of the target image.
+
+ @param target_w: Width of the target image.
+ @type target_w: int
+ @raise TypeError: If target_w is not an integer.
+ @raise ValueError: If target_w is less than 1.
+ """
+ self._validate_positive_integer(target_w)
+ self._target_w = target_w
+
+ @target_h.setter
+ def target_h(self, target_h: int):
+ """Sets the height of the target image.
+
+ @param target_h: Height of the target image.
+ @type target_h: int
+ @raise TypeError: If target_h is not an integer.
+ @raise ValueError: If target_h is less than 1.
+ """
+ self._validate_positive_integer(target_h)
+ self._target_h = target_h
+
+ def build(
+ self,
+ detections_input: dai.Node.Output,
+ source_size: Tuple[int, int],
+ target_size: Optional[Tuple[int, int]] = None,
+ resize_mode: dai.ImageManipConfig.ResizeMode = dai.ImageManipConfig.ResizeMode.STRETCH,
+ ) -> "CropConfigsCreator":
+ """Link the node input and set the correct source and target image sizes.
+
+ Parameters
+ ----------
+ detections_input : dai.Node.Output
+ The input link for the ImgDetectionsExtended message
+ source_size : Tuple[int, int]
+ The size of the source image (width, height).
+ target_size : Optional[Tuple[int, int]]
+ The size of the target image (width, height). If None, crop sizes will not be uniform.
+ resize_mode : dai.ImageManipConfigV2.ResizeMode = dai.ImageManipConfigV2.ResizeMode.STRETCH
+ The resize mode to use when target size is set. Options are: CENTER_CROP, LETTERBOX, NONE, STRETCH.
+ """
+
+ self.w = source_size[0]
+ self.h = source_size[1]
+
+ if target_size is not None:
+ self.target_w = target_size[0]
+ self.target_h = target_size[1]
+
+ self.resize_mode = resize_mode
+
+ self.link_args(detections_input)
+
+ return self
+
+ def process(self, detections_input: dai.Buffer) -> None:
+ """Process the input detections and create crop configurations. This function is
+ ran every time a new ImgDetectionsExtended or dai.ImgDetections message is
+ received.
+
+ Sends len(detections) number of crop configurations to the config_output link.
+ In addition sends an ImgDetectionsExtended object containing the corresponding
+ detections to the detections_output link.
+ """
+
+ assert isinstance(detections_input, (ImgDetectionsExtended, dai.ImgDetections))
+ sequence_num = detections_input.getSequenceNum()
+ timestamp = detections_input.getTimestamp()
+
+ if isinstance(detections_input, dai.ImgDetections):
+ detections_msg = self._convert_to_extended(detections_input)
+ else:
+ detections_msg = detections_input
+
+ detections = detections_msg.detections
+
+ # Skip the current frame / load new frame
+ cfg = dai.ImageManipConfig()
+ cfg.setSkipCurrentImage(True)
+ cfg.setTimestamp(timestamp)
+ cfg.setSequenceNum(sequence_num)
+ send_status = False
+ attempts = 0
+ while (
+ not send_status and attempts < 100
+ ): # Limit attempts to prevent infinite loop
+ send_status = self.config_output.trySend(cfg)
+ if not send_status:
+ attempts += 1
+ time.sleep(0.001) # Small delay to prevent busy waiting
+
+ for i in range(len(detections)):
+ cfg = dai.ImageManipConfig()
+ detection: ImgDetectionExtended = detections[i]
+ rect = detection.rotated_rect
+ rect = rect.denormalize(self.w, self.h)
+
+ cfg.addCropRotatedRect(rect, normalizedCoords=False)
+
+ if self.target_w is not None and self.target_h is not None:
+ cfg.setOutputSize(self.target_w, self.target_h, self.resize_mode)
+
+ cfg.setReusePreviousImage(True)
+ cfg.setTimestamp(timestamp)
+ cfg.setSequenceNum(sequence_num)
+
+ send_status = False
+ attempts = 0
+ while (
+ not send_status and attempts < 100
+ ): # Limit attempts to prevent infinite loop
+ send_status = self.config_output.trySend(cfg)
+ if not send_status:
+ attempts += 1
+ time.sleep(0.001) # Small delay to prevent busy waiting
+
+ self.detections_output.send(detections_msg)
+
+ def _convert_to_extended(
+ self, detections: dai.ImgDetections
+ ) -> ImgDetectionsExtended:
+ rotated_rectangle_detections = []
+ for det in detections.detections:
+ img_detection = ImgDetectionExtended()
+ img_detection.label = det.label
+ img_detection.confidence = det.confidence
+
+ x_center = (det.xmin + det.xmax) / 2
+ y_center = (det.ymin + det.ymax) / 2
+ width = det.xmax - det.xmin
+ height = det.ymax - det.ymin
+ width = width * 1.15
+ height = height * 1.15
+
+ img_detection.rotated_rect = (x_center, y_center, width, height, 0.0)
+
+ rotated_rectangle_detections.append(img_detection)
+
+ img_detections_extended = ImgDetectionsExtended()
+ img_detections_extended.setSequenceNum(detections.getSequenceNum())
+ img_detections_extended.setTimestamp(detections.getTimestamp())
+ img_detections_extended.detections = rotated_rectangle_detections
+ transformation = detections.getTransformation()
+ if transformation is not None:
+ img_detections_extended.setTransformation(transformation)
+
+ return img_detections_extended
+
+ def _validate_positive_integer(self, value: int):
+ """Validates that the set size is a positive integer.
+
+ @param value: The value to validate.
+ @type value: int
+ @raise TypeError: If value is not an integer.
+ @raise ValueError: If value is less than 1.
+ """
+ if not isinstance(value, int):
+ raise TypeError("Value must be an integer.")
+ if value < 1:
+ raise ValueError("Value must be greater than 1.")
diff --git a/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/simple_barcode_overlay.py b/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/simple_barcode_overlay.py
new file mode 100644
index 000000000..3a99ee463
--- /dev/null
+++ b/neural-networks/object-detection/barcode-detection-conveyor-belt/utils/simple_barcode_overlay.py
@@ -0,0 +1,271 @@
+import cv2
+import depthai as dai
+from threading import Lock
+import time
+
+
+class SimpleBarcodeOverlay(dai.node.HostNode):
+ """
+ Simple overlay that shows the most recently decoded barcode on the video stream and detection boxes.
+ """
+
+ def __init__(self):
+ super().__init__()
+
+ self.barcodes = {}
+ self.latest_detections = None
+ self.lock = Lock()
+ self.max_display_time = 3.0
+ self.max_barcodes = 5
+ self.input_barcode = self.createInput()
+ self.output = self.createOutput()
+ self.output.setPossibleDatatypes([(dai.DatatypeEnum.ImgFrame, True)])
+
+ def build(
+ self,
+ barcode_source: dai.Node.Output,
+ video_source: dai.Node.Output,
+ detection_source: dai.Node.Output = None,
+ ) -> "SimpleBarcodeOverlay":
+ self.link_args(video_source, detection_source)
+ barcode_source.link(self.input_barcode)
+
+ self.sendProcessingToPipeline(True)
+ return self
+
+ def process(
+ self, video_msg: dai.ImgFrame, detection_msg: dai.Buffer = None
+ ) -> dai.ImgFrame:
+ try:
+ with self.lock:
+ current_time = time.time()
+ while self.input_barcode.has():
+ barcode_msg = self.input_barcode.get()
+ data = barcode_msg.getData()
+
+ if isinstance(data, bytes):
+ barcode_text = data.decode("utf-8", errors="ignore")
+ elif hasattr(data, "tobytes"):
+ barcode_text = data.tobytes().decode("utf-8", errors="ignore")
+ else:
+ barcode_text = str(data)
+
+ self.barcodes[barcode_text] = current_time
+
+ self.barcodes = {
+ code: timestamp
+ for code, timestamp in self.barcodes.items()
+ if current_time - timestamp < self.max_display_time
+ }
+
+ if len(self.barcodes) > self.max_barcodes:
+ sorted_barcodes = sorted(
+ self.barcodes.items(), key=lambda x: x[1], reverse=True
+ )
+ self.barcodes = dict(sorted_barcodes[: self.max_barcodes])
+ except Exception as e:
+ pass
+
+ try:
+ with self.lock:
+ self.latest_detections = detection_msg
+ except Exception as e:
+ pass
+
+ try:
+ frame = video_msg.getCvFrame()
+
+ with self.lock:
+ current_time = time.time()
+
+ if self.latest_detections:
+ self._draw_detection_boxes(frame, self.latest_detections)
+
+ active_barcodes = {
+ code: timestamp
+ for code, timestamp in self.barcodes.items()
+ if current_time - timestamp < self.max_display_time
+ }
+ self.barcodes = active_barcodes
+
+ if active_barcodes:
+ self._draw_multiple_barcodes(frame, active_barcodes, current_time)
+
+ output_msg = dai.ImgFrame()
+ output_msg.setData(frame)
+ output_msg.setTimestamp(video_msg.getTimestamp())
+ output_msg.setSequenceNum(video_msg.getSequenceNum())
+ output_msg.setWidth(frame.shape[1])
+ output_msg.setHeight(frame.shape[0])
+ output_msg.setType(video_msg.getType())
+
+ # Send the frame to the node's output so it appears in the DepthAI Visualizer
+ self.output.send(output_msg)
+
+ except Exception as e:
+ pass
+
+ def _draw_multiple_barcodes(self, frame, active_barcodes, current_time):
+ """Draw multiple barcode texts with prettier styling"""
+ try:
+ h, w = frame.shape[:2]
+
+ sorted_barcodes = sorted(
+ active_barcodes.items(), key=lambda x: x[1], reverse=True
+ )
+
+ panel_width = int(w * 0.35)
+ panel_x = w - panel_width - 20
+ panel_y_start = 20
+
+ panel_height = min(len(sorted_barcodes) * 80 + 40, h - 40)
+ cv2.rectangle(
+ frame,
+ (panel_x - 15, panel_y_start),
+ (panel_x + panel_width + 15, panel_y_start + panel_height),
+ (0, 0, 0),
+ -1,
+ )
+
+ cv2.rectangle(
+ frame,
+ (panel_x - 15, panel_y_start),
+ (panel_x + panel_width + 15, panel_y_start + panel_height),
+ (100, 200, 255),
+ 3,
+ )
+
+ header_text = f"Detected Barcodes ({len(sorted_barcodes)})"
+ cv2.putText(
+ frame,
+ header_text,
+ (panel_x, panel_y_start + 25),
+ cv2.FONT_HERSHEY_SIMPLEX,
+ 0.7,
+ (100, 200, 255),
+ 2,
+ )
+
+ cv2.line(
+ frame,
+ (panel_x, panel_y_start + 35),
+ (panel_x + panel_width, panel_y_start + 35),
+ (100, 200, 255),
+ 2,
+ )
+
+ for i, (barcode_text, timestamp) in enumerate(sorted_barcodes):
+ if i >= self.max_barcodes:
+ break
+
+ y_pos = panel_y_start + 60 + (i * 70)
+ if y_pos > h - 50:
+ break
+
+ age = current_time - timestamp
+ alpha = max(0.3, 1.0 - (age / self.max_display_time))
+
+ if age < 1.0:
+ color = (0, 255, 0)
+ bg_color = (0, 50, 0)
+ elif age < 2.0:
+ color = (0, 255, 255)
+ bg_color = (0, 50, 50)
+ else:
+ color = (0, 150, 255)
+ bg_color = (0, 25, 50)
+
+ color = tuple(int(c * alpha) for c in color)
+
+ cv2.rectangle(
+ frame,
+ (panel_x, y_pos - 25),
+ (panel_x + panel_width, y_pos + 15),
+ bg_color,
+ -1,
+ )
+
+ display_text = barcode_text
+ if len(display_text) > 18:
+ display_text = display_text[:15] + "..."
+
+ cv2.putText(
+ frame,
+ display_text,
+ (panel_x + 10, y_pos),
+ cv2.FONT_HERSHEY_SIMPLEX,
+ 0.8,
+ color,
+ 2,
+ )
+
+ age_text = f"{age:.1f}s"
+ cv2.putText(
+ frame,
+ age_text,
+ (panel_x + panel_width - 50, y_pos - 8),
+ cv2.FONT_HERSHEY_SIMPLEX,
+ 0.5,
+ (150, 150, 150),
+ 1,
+ )
+
+ dot_color = (
+ (0, 255, 0)
+ if age < 1.0
+ else (0, 255, 255)
+ if age < 2.0
+ else (0, 150, 255)
+ )
+ cv2.circle(frame, (panel_x + 5, y_pos - 5), 4, dot_color, -1)
+
+ except Exception as e:
+ pass
+
+ def _draw_detection_boxes(self, frame, detections):
+ """Draw detection bounding boxes on the frame"""
+ try:
+ h, w = frame.shape[:2]
+
+ for detection in detections.detections:
+ xmin, ymin, xmax, ymax = detection.rotated_rect.getOuterRect()
+
+ x1 = int(xmin * w)
+ y1 = int(ymin * h)
+ x2 = int(xmax * w)
+ y2 = int(ymax * h)
+
+ x1 = max(0, min(w - 1, x1))
+ y1 = max(0, min(h - 1, y1))
+ x2 = max(0, min(w - 1, x2))
+ y2 = max(0, min(h - 1, y2))
+
+ cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+
+ confidence = detection.confidence
+ label = f"Barcode: {confidence:.2f}"
+
+ (text_width, text_height), baseline = cv2.getTextSize(
+ label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2
+ )
+
+ cv2.rectangle(
+ frame,
+ (x1, y1 - text_height - 10),
+ (x1 + text_width, y1),
+ (0, 255, 0),
+ -1,
+ )
+
+ cv2.putText(
+ frame,
+ label,
+ (x1, y1 - 5),
+ cv2.FONT_HERSHEY_SIMPLEX,
+ 0.6,
+ (0, 0, 0),
+ 2,
+ )
+
+ except Exception as e:
+ pass