feat: Porting host nodes to depthai-nodes

apps/default-app/main.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 import cv2
 import depthai as dai
-from depthai_nodes.node import DepthColorTransform
+from depthai_nodes.node import ApplyColormap
 from typing import Optional
 from utils.arguments import initialize_argparser
 
@@ -73,6 +73,7 @@
                 cam_mono_2 = feature.socket
                 break
     if cam_mono_1 and cam_mono_2:
+        device.setIrLaserDotProjectorIntensity(1)
         left_cam = pipeline.create(dai.node.Camera).build(cam_mono_1)
         right_cam = pipeline.create(dai.node.Camera).build(cam_mono_2)
         stereo = pipeline.create(dai.node.StereoDepth).build(
@@ -84,7 +85,7 @@
         if platform == dai.Platform.RVC2:
             stereo.setOutputSize(*STEREO_RESOLUTION)
 
-        coloredDepth = pipeline.create(DepthColorTransform).build(stereo.disparity)
+        coloredDepth = pipeline.create(ApplyColormap).build(stereo.disparity)
         coloredDepth.setColormap(cv2.COLORMAP_JET)
         visualizer.addTopic("Depth", coloredDepth.out)
 

camera-controls/depth-driven-focus/main.py

@@ -1,7 +1,6 @@
 import depthai as dai
-from depthai_nodes.node import ParsingNeuralNetwork
+from depthai_nodes.node import ParsingNeuralNetwork, ApplyColormap
 from util.arguments import initialize_argparser
-from util.depth_color_transform import DepthColorTransform
 from util.depth_driven_focus import DepthDrivenFocus
 from util.depth_merger import DepthMerger
 
@@ -44,8 +43,8 @@
     depth_merger = pipeline.create(DepthMerger).build(
         face_det_nn.out, stereo.depth, device.readCalibration2()
     )
-    depth_color_transform = pipeline.create(DepthColorTransform).build(stereo.disparity)
-    depth_color_transform.setMaxDisparity(stereo.initialConfig.getMaxDisparity())
+    depth_color_transform = pipeline.create(ApplyColormap).build(stereo.disparity)
+    depth_color_transform.setMaxValue(int(stereo.initialConfig.getMaxDisparity()))
 
     depth_driven_focus = pipeline.create(DepthDrivenFocus).build(
         control_queue=cam.inputControl.createInputQueue(),
@@ -54,7 +53,7 @@
 
     visualizer.addTopic("Video", color_out, "images")
     visualizer.addTopic("Visualizations", face_det_nn.out, "images")
-    visualizer.addTopic("Depth", depth_color_transform.output, "images")
+    visualizer.addTopic("Depth", depth_color_transform.out, "images")
     visualizer.addTopic("Focus distance", depth_driven_focus.output, "images")
 
     print("Pipeline created.")

depth-measurement/calc-spatial-on-host/main.py

@@ -3,8 +3,8 @@
 import cv2
 import depthai as dai
 from calc import HostSpatialsCalc
+from depthai_nodes.node import ApplyColormap
 from host_node.measure_distance import MeasureDistance
-from host_node.host_depth_color_transform import DepthColorTransform
 from host_node.host_display import Display
 from host_node.keyboard_reader import KeyboardReader
 
@@ -23,23 +23,21 @@
     )
 
     stereo = pipeline.create(dai.node.StereoDepth).build(
-        monoLeft, monoRight, presetMode=dai.node.StereoDepth.PresetMode.HIGH_ACCURACY
+        monoLeft, monoRight, presetMode=dai.node.StereoDepth.PresetMode.DEFAULT
     )
 
-    depth_color_transform = pipeline.create(DepthColorTransform).build(stereo.disparity)
+    depth_color_transform = pipeline.create(ApplyColormap).build(stereo.disparity)
     depth_color_transform.setColormap(cv2.COLORMAP_JET)
 
-    keyboard_reader = pipeline.create(KeyboardReader).build(
-        depth_color_transform.output
-    )
+    keyboard_reader = pipeline.create(KeyboardReader).build(depth_color_transform.out)
 
     measure_distance = pipeline.create(MeasureDistance).build(
         stereo.depth, device.readCalibration(), HostSpatialsCalc.INITIAL_ROI
     )
 
     calibdata = device.readCalibration()
     spatials = pipeline.create(HostSpatialsCalc).build(
-        disparity_frames=depth_color_transform.output,
+        disparity_frames=depth_color_transform.out,
         measured_depth=measure_distance.output,
         keyboard_input=keyboard_reader.output,
     )

depth-measurement/calc-spatial-on-host/requirements.txt

@@ -1,4 +1,3 @@
---extra-index-url https://artifacts.luxonis.com/artifactory/luxonis-python-release-local/
-depthai~=3.0.0a4
-opencv-python==4.10.0.84
-numpy>=1.22
+opencv-python
+numpy~=2.0
+depthai==3.0.0a12

neural-networks/counting/crowdcounting/main.py

@@ -1,11 +1,11 @@
 from pathlib import Path
 import depthai as dai
 from depthai_nodes.node import ParsingNeuralNetwork
+from depthai_nodes.node import ImgFrameOverlay
+from depthai_nodes.node import ApplyColormap
 
 from utils.arguments import initialize_argparser
 from utils.counter import CrowdCounter
-from utils.density_map_transform import DensityMapToFrame
-from utils.overlay import OverlayFrames
 
 _, args = initialize_argparser()
 
@@ -48,21 +48,16 @@
     # Counter Node
     crowd_counter_node = pipeline.create(CrowdCounter).build(nn.out)
 
-    # Density Map Transform and Resize Nodes
-    density_map_transform_node = pipeline.create(DensityMapToFrame).build(nn.out)
-    density_map_resize_node = pipeline.create(dai.node.ImageManipV2)
-    density_map_resize_node.setMaxOutputFrameSize(INPUT_WIDTH * INPUT_HEIGHT * 3)
-    density_map_resize_node.initialConfig.setOutputSize(INPUT_WIDTH, INPUT_HEIGHT)
-    density_map_resize_node.initialConfig.setFrameType(frame_type)
-    density_map_transform_node.output.link(density_map_resize_node.inputImage)
+    density_map_transform_node = pipeline.create(ApplyColormap).build(nn.out)
 
     # Overlay Frames Node
-    overlay_frames = pipeline.create(OverlayFrames).build(
-        nn.passthrough, density_map_resize_node.out
+    overlay_frames = pipeline.create(ImgFrameOverlay).build(
+        nn.passthrough,
+        density_map_transform_node.out,
     )
 
     # Visualizer
-    visualizer.addTopic("VideoOverlay", overlay_frames.output)
+    visualizer.addTopic("VideoOverlay", overlay_frames.out)
     visualizer.addTopic("Count", crowd_counter_node.output)
 
     print("Pipeline created.")

neural-networks/depth-estimation/crestereo-stereo-matching/main.py

@@ -1,8 +1,7 @@
 import argparse
 import cv2
 import depthai as dai
-from depthai_nodes.node import ParsingNeuralNetwork
-from utils.host_depth_color_transform import DepthColorTransform
+from depthai_nodes.node import ParsingNeuralNetwork, ApplyColormap
 
 device = dai.Device()
 platform = device.getPlatform()
@@ -86,10 +85,10 @@
     demux.outputs["left"].link(nn.inputs["left"])
     demux.outputs["right"].link(nn.inputs["right"])
 
-    disparity_coloring = pipeline.create(DepthColorTransform).build(stereo.disparity)
+    disparity_coloring = pipeline.create(ApplyColormap).build(stereo.disparity)
     disparity_coloring.setColormap(cv2.COLORMAP_PLASMA)
 
-    visualizer.addTopic("Stereo Disparity", disparity_coloring.output)
+    visualizer.addTopic("Stereo Disparity", disparity_coloring.out)
     visualizer.addTopic("NN", nn.out)
 
     print("Pipeline created.")

neural-networks/facial-detection/face-mask-detection/main.py

@@ -1,8 +1,7 @@
 from pathlib import Path
 import depthai as dai
-from depthai_nodes.node import ParsingNeuralNetwork
+from depthai_nodes.node import ParsingNeuralNetwork, ImgDetectionsFilter
 from utils.arguments import initialize_argparser
-from utils.filter import FilterDetections
 
 _, args = initialize_argparser()
 
@@ -46,11 +45,11 @@
         input_node, nn_archive, fps=args.fps_limit
     )
 
-    det_process_node = pipeline.create(FilterDetections).build(nn_with_parser.out)
-    det_process_node.set_label_encoding(LABEL_ENCODING)
+    det_process_node = pipeline.create(ImgDetectionsFilter).build(nn_with_parser.out)
+    det_process_node.setLabels(list(LABEL_ENCODING.keys()), keep=True)
 
     visualizer.addTopic("Video", nn_with_parser.passthrough, "images")
-    visualizer.addTopic("Detections", det_process_node.output, "detections")
+    visualizer.addTopic("Detections", det_process_node.out, "detections")
 
     print("Pipeline created.")