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Porting apps to hub #775

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0d9538e
Porting apps to hub
jflidr Nov 13, 2025
23b27e4
added encoders to lossless zoom
klemen1999 Nov 13, 2025
d150589
added encodings to triangulation
klemen1999 Nov 13, 2025
2ddc1ec
added encoding to age-gender
klemen1999 Nov 13, 2025
7a0eca6
added encoding on OCR example
klemen1999 Nov 13, 2025
5ece7a6
small fix on default app
klemen1999 Nov 13, 2025
446ebe2
increased face detection threshold on age-gender example
klemen1999 Nov 13, 2025
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7 changes: 6 additions & 1 deletion .gitignore
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Expand Up @@ -168,4 +168,9 @@ cython_debug/
# depthai models
.depthai_cached_models

.python-version
.python-version

# npm modules
node_modules/
dist/
styled-system/
7 changes: 2 additions & 5 deletions apps/default-app/main.py
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Expand Up @@ -42,12 +42,9 @@

outputToEncode = cameraNode.requestOutput((1440, 1080), type=dai.ImgFrame.Type.NV12)
h264Encoder = pipeline.create(dai.node.VideoEncoder)
encoding = (
dai.VideoEncoderProperties.Profile.MJPEG
if platform == dai.Platform.RVC2
else dai.VideoEncoderProperties.Profile.H264_MAIN
h264Encoder.setDefaultProfilePreset(
30, dai.VideoEncoderProperties.Profile.H264_MAIN
)
h264Encoder.setDefaultProfilePreset(30, encoding)
outputToEncode.link(h264Encoder.input)

# Add the remote connector topics
Expand Down
34 changes: 21 additions & 13 deletions camera-controls/lossless-zooming/main.py
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Expand Up @@ -69,21 +69,29 @@
crop_face.config_output.link(crop_manip.inputConfig)
cam_out.link(crop_manip.inputImage)

cropped_output = crop_manip.out

if platform == dai.Platform.RVC4:
crop_encoder = pipeline.create(dai.node.VideoEncoder)
crop_encoder.setMaxOutputFrameSize(1920 * 1088 * 3)
crop_encoder.setDefaultProfilePreset(
fps, dai.VideoEncoderProperties.Profile.H264_MAIN
)
crop_manip.out.link(crop_encoder.input)
cropped_output = crop_encoder.out
crop_encoder = pipeline.create(dai.node.VideoEncoder)
crop_encoder.setMaxOutputFrameSize(1920 * 1088 * 3)
crop_encoder.setDefaultProfilePreset(
fps, dai.VideoEncoderProperties.Profile.H264_MAIN
)
crop_manip.out.link(crop_encoder.input)

video_encode_manip = pipeline.create(dai.node.ImageManip)
video_encode_manip.setMaxOutputFrameSize(model_width * model_height * 3)
video_encode_manip.initialConfig.setOutputSize(model_width, model_height)
video_encode_manip.initialConfig.setFrameType(dai.ImgFrame.Type.NV12)
nn_with_parser.passthrough.link(video_encode_manip.inputImage)

video_encoder = pipeline.create(dai.node.VideoEncoder)
video_encoder.setMaxOutputFrameSize(model_width * model_height * 3)
video_encoder.setDefaultProfilePreset(
fps, dai.VideoEncoderProperties.Profile.H264_MAIN
)
video_encode_manip.out.link(video_encoder.input)

visualizer.addTopic("Video", image_manip.out, "images")
visualizer.addTopic("Video", video_encoder.out, "images")
visualizer.addTopic("Visualizations", nn_with_parser.out, "images")

visualizer.addTopic("Cropped Face", cropped_output, "crop")
visualizer.addTopic("Cropped Face", crop_encoder.out, "crop")

print("Pipeline created.")

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6 changes: 3 additions & 3 deletions camera-controls/lossless-zooming/oakapp.toml
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@@ -1,6 +1,6 @@
identifier = "com.example.camere-controls.lossless-zooming"
identifier = "com.luxonis.camere-controls.lossless-zooming"
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@klemen1999 klemen1999 Nov 13, 2025

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By default I think we should still keep the identifiers to com.example and change to com.luxonis just locally (or in CI/CD in the future) when app is being built for upload

entrypoint = ["bash", "-c", "/usr/bin/runsvdir -P /etc/service"]
app_version = "1.0.0"
app_version = "1.0.1"
assign_frontend_port = true

prepare_container = [
Expand All @@ -23,4 +23,4 @@ oauth_url = "https://auth.docker.io/token"
auth_type = "repository"
auth_name = "luxonis/oakapp-base"
image_name = "luxonis/oakapp-base"
image_tag = "1.2.5"
image_tag = "1.2.6"
3 changes: 3 additions & 0 deletions depth-measurement/triangulation/backend-run.sh
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@@ -0,0 +1,3 @@
#!/bin/sh
echo "Starting Backend"
exec python3.12 /app/main.py
34 changes: 31 additions & 3 deletions depth-measurement/triangulation/main.py
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Expand Up @@ -24,6 +24,16 @@ def populate_pipeline(
return cam_output, face_nn.out


def create_video_encoder(p: dai.Pipeline, input_node: dai.Node, fps: float, size: int):
video_encoder = p.create(dai.node.VideoEncoder)
video_encoder.setMaxOutputFrameSize(size)
video_encoder.setDefaultProfilePreset(
fps, dai.VideoEncoderProperties.Profile.H264_MAIN
)
input_node.link(video_encoder.input)
return video_encoder.out


visualizer = dai.RemoteConnection(httpPort=8082)
device = dai.Device(dai.DeviceInfo(args.device)) if args.device else dai.Device()
with dai.Pipeline(device) as pipeline:
Expand Down Expand Up @@ -52,15 +62,33 @@ def populate_pipeline(
resolution_number=model_dimension,
)

visualizer.addTopic("Face Left", face_left, "left")
face_left_encoded = create_video_encoder(
p=pipeline,
input_node=face_left,
fps=30,
size=model_dimension[0] * model_dimension[1],
)
visualizer.addTopic("Face Left", face_left_encoded, "left")
visualizer.addTopic("Left Detections", triangulation.bbox_left, "left")
visualizer.addTopic("Left Keypoints", triangulation.keypoints_left, "left")

visualizer.addTopic("Face Right", face_right, "right")
face_right_encoded = create_video_encoder(
p=pipeline,
input_node=face_right,
fps=30,
size=model_dimension[0] * model_dimension[1],
)
visualizer.addTopic("Face Right", face_right_encoded, "right")
visualizer.addTopic("Right Detections", triangulation.bbox_right, "right")
visualizer.addTopic("Right Keypoints", triangulation.keypoints_right, "right")

visualizer.addTopic("Combined", triangulation.combined_frame, "combined")
combined_encoded = create_video_encoder(
p=pipeline,
input_node=triangulation.combined_frame,
fps=30,
size=model_dimension[0] * model_dimension[1],
)
visualizer.addTopic("Combined", combined_encoded, "combined")
visualizer.addTopic("Left Face Detections", triangulation.bbox_left, "combined")
visualizer.addTopic("Right Face Detections", triangulation.bbox_right, "combined")
visualizer.addTopic(
Expand Down
29 changes: 19 additions & 10 deletions depth-measurement/triangulation/oakapp.toml
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@@ -1,17 +1,26 @@
identifier = "com.example.depth-measurement.triangulation"
app_version = "1.0.0"
identifier = "com.luxonis.depth-measurement.triangulation"
entrypoint = ["bash", "-c", "/usr/bin/runsvdir -P /etc/service"]
app_version = "1.0.1"
assign_frontend_port = true

prepare_container = [
{ type = "RUN", command = "apt-get update" },
{ type = "RUN", command = "apt-get install -y python3-pip" },
{ type = "COPY", source = "requirements.txt", target = "requirements.txt" },
{ type = "RUN", command = "pip3 install -r /app/requirements.txt --break-system-packages" },
{ type = "COPY", source = "./requirements.txt", target = "./requirements.txt" },
{ type = "RUN", command = "python3.12 -m pip install -r /app/requirements.txt --break-system-packages"}
]

prepare_build_container = []

build_steps = []
build_steps = [
"mkdir -p /etc/service/backend",
"cp /app/backend-run.sh /etc/service/backend/run",
"chmod +x /etc/service/backend/run",
]

depthai_models = { yaml_path = "./depthai_models" }

entrypoint = ["bash", "-c", "python3 -u /app/main.py"]
[base_image]
api_url = "https://registry-1.docker.io"
service = "registry.docker.io"
oauth_url = "https://auth.docker.io/token"
auth_type = "repository"
auth_name = "luxonis/oakapp-base"
image_name = "luxonis/oakapp-base"
image_tag = "1.2.6"
2 changes: 1 addition & 1 deletion depth-measurement/triangulation/utils/host_triangulation.py
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Expand Up @@ -233,6 +233,6 @@ def _create_output_frame(
self, msg: dai.ImgFrame, frame: np.ndarray
) -> dai.ImgFrame:
output_frame = dai.ImgFrame()
output_frame.setCvFrame(frame, dai.ImgFrame.Type.BGR888i)
output_frame.setCvFrame(frame, dai.ImgFrame.Type.NV12)
output_frame.setTimestamp(msg.getTimestamp())
return output_frame
3 changes: 3 additions & 0 deletions neural-networks/face-detection/age-gender/backend-run.sh
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@@ -0,0 +1,3 @@
#!/bin/sh
echo "Starting Backend"
exec python3.12 /app/main.py
17 changes: 16 additions & 1 deletion neural-networks/face-detection/age-gender/main.py
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Expand Up @@ -71,6 +71,7 @@
det_nn: ParsingNeuralNetwork = pipeline.create(ParsingNeuralNetwork).build(
resize_node.out, det_model_nn_archive
)
det_nn.getParser(0).conf_threshold = 0.9 # for more stable detections

# detection processing
det_bridge = pipeline.create(ImgDetectionsBridge).build(
Expand Down Expand Up @@ -106,8 +107,22 @@
# annotation
annotation_node = pipeline.create(AnnotationNode).build(gather_data_node.out)

# video encoding
video_encode_manip = pipeline.create(dai.node.ImageManip)
video_encode_manip.setMaxOutputFrameSize(REQ_WIDTH * REQ_HEIGHT * 3)
video_encode_manip.initialConfig.setOutputSize(REQ_WIDTH, REQ_HEIGHT)
video_encode_manip.initialConfig.setFrameType(dai.ImgFrame.Type.NV12)
input_node_out.link(video_encode_manip.inputImage)

video_encoder = pipeline.create(dai.node.VideoEncoder)
video_encoder.setMaxOutputFrameSize(REQ_WIDTH * REQ_HEIGHT * 3)
video_encoder.setDefaultProfilePreset(
args.fps_limit, dai.VideoEncoderProperties.Profile.H264_MAIN
)
video_encode_manip.out.link(video_encoder.input)

# visualization
visualizer.addTopic("Video", input_node_out, "images")
visualizer.addTopic("Video", video_encoder.out, "images")
visualizer.addTopic("AgeGender", annotation_node.out, "images")

print("Pipeline created.")
Expand Down
29 changes: 19 additions & 10 deletions neural-networks/face-detection/age-gender/oakapp.toml
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@@ -1,17 +1,26 @@
identifier = "com.example.face-detection.age-gender"
app_version = "1.0.0"
identifier = "com.luxonis.face-detection.age-gender"
entrypoint = ["bash", "-c", "/usr/bin/runsvdir -P /etc/service"]
app_version = "1.0.1"
assign_frontend_port = true

prepare_container = [
{ type = "RUN", command = "apt-get update" },
{ type = "RUN", command = "apt-get install -y python3-pip" },
{ type = "COPY", source = "requirements.txt", target = "requirements.txt" },
{ type = "RUN", command = "pip3 install -r /app/requirements.txt --break-system-packages" },
{ type = "COPY", source = "./requirements.txt", target = "./requirements.txt" },
{ type = "RUN", command = "python3.12 -m pip install -r /app/requirements.txt --break-system-packages"}
]

prepare_build_container = []

build_steps = []
build_steps = [
"mkdir -p /etc/service/backend",
"cp /app/backend-run.sh /etc/service/backend/run",
"chmod +x /etc/service/backend/run",
]

depthai_models = { yaml_path = "./depthai_models" }

entrypoint = ["bash", "-c", "python3 -u /app/main.py"]
[base_image]
api_url = "https://registry-1.docker.io"
service = "registry.docker.io"
oauth_url = "https://auth.docker.io/token"
auth_type = "repository"
auth_name = "luxonis/oakapp-base"
image_name = "luxonis/oakapp-base"
image_tag = "1.2.6"
3 changes: 3 additions & 0 deletions neural-networks/ocr/general-ocr/backend-run.sh
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@@ -0,0 +1,3 @@
#!/bin/sh
echo "Starting Backend"
exec python3.12 /app/main.py
16 changes: 15 additions & 1 deletion neural-networks/ocr/general-ocr/main.py
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Expand Up @@ -105,8 +105,22 @@
gather_data_node.out.link(annotation_node.input)
det_nn.passthrough.link(annotation_node.passthrough)

# video encoding
video_encode_manip = pipeline.create(dai.node.ImageManip)
video_encode_manip.setMaxOutputFrameSize(REQ_WIDTH * REQ_HEIGHT * 3)
video_encode_manip.initialConfig.setOutputSize(REQ_WIDTH, REQ_HEIGHT)
video_encode_manip.initialConfig.setFrameType(dai.ImgFrame.Type.NV12)
annotation_node.frame_output.link(video_encode_manip.inputImage)

video_encoder = pipeline.create(dai.node.VideoEncoder)
video_encoder.setMaxOutputFrameSize(REQ_WIDTH * REQ_HEIGHT * 3)
video_encoder.setDefaultProfilePreset(
args.fps_limit, dai.VideoEncoderProperties.Profile.H264_MAIN
)
video_encode_manip.out.link(video_encoder.input)

# visualization
visualizer.addTopic("Video", annotation_node.frame_output)
visualizer.addTopic("Video", video_encoder.out)
visualizer.addTopic("Text", annotation_node.text_annotations_output)

print("Pipeline created.")
Expand Down
29 changes: 19 additions & 10 deletions neural-networks/ocr/general-ocr/oakapp.toml
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@@ -1,17 +1,26 @@
identifier = "com.example.ocr.general-ocr"
app_version = "1.0.0"
identifier = "com.luxonis.ocr.general-ocr"
entrypoint = ["bash", "-c", "/usr/bin/runsvdir -P /etc/service"]
app_version = "1.0.1"
assign_frontend_port = true

prepare_container = [
{ type = "RUN", command = "apt-get update" },
{ type = "RUN", command = "apt-get install -y python3-pip" },
{ type = "COPY", source = "requirements.txt", target = "requirements.txt" },
{ type = "RUN", command = "pip3 install -r /app/requirements.txt --break-system-packages" },
{ type = "COPY", source = "./requirements.txt", target = "./requirements.txt" },
{ type = "RUN", command = "python3.12 -m pip install -r /app/requirements.txt --break-system-packages"}
]

prepare_build_container = []

build_steps = []
build_steps = [
"mkdir -p /etc/service/backend",
"cp /app/backend-run.sh /etc/service/backend/run",
"chmod +x /etc/service/backend/run",
]

depthai_models = { yaml_path = "./depthai_models" }

entrypoint = ["bash", "-c", "python3 -u /app/main.py"]
[base_image]
api_url = "https://registry-1.docker.io"
service = "registry.docker.io"
oauth_url = "https://auth.docker.io/token"
auth_type = "repository"
auth_name = "luxonis/oakapp-base"
image_name = "luxonis/oakapp-base"
image_tag = "1.2.6"
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