Merge pull request #1023 from luxonis/release_2.26.0.0

Release 2.26.0.0

.github/workflows/main.yml

@@ -72,7 +72,9 @@ jobs:
     steps:
       - name: Print home directory
         run: echo Home directory inside container $HOME
-
+      - name: Setup cmake
+        if: matrix.os == 'macos-latest'
+        uses: jwlawson/[email protected]
       - name: Cache .hunter folder
         if: matrix.os != 'windows-latest'
         uses: actions/cache@v3
@@ -230,13 +232,13 @@ jobs:
           ARTIFACTORY_PASS: ${{ secrets.ARTIFACTORY_PASS }}
 
   # This job builds wheels for macOS x86_64 arch
-  build-macos-x86_64:
+  build-macos:
     needs: build-docstrings
     runs-on: macos-latest
     strategy:
       matrix:
-        python-version: [3.6, 3.7, 3.8, 3.9, '3.10', '3.11', '3.12']
-      fail-fast: false
+        python-version: [3.8, 3.9, '3.10', '3.11', '3.12']
+        os: [macos-13, macos-14] # macos-13 is x64, macos-14 is arm64
     steps:
       - name: Cache .hunter folder
         uses: actions/cache@v3
@@ -290,63 +292,6 @@ jobs:
           ARTIFACTORY_USER: ${{ secrets.ARTIFACTORY_USER }}
           ARTIFACTORY_PASS: ${{ secrets.ARTIFACTORY_PASS }}
 
-  # This job builds wheels for macOS arm64 arch
-  build-macos-arm64:
-    needs: build-docstrings
-    runs-on: [self-hosted, macOS, ARM64]
-    steps:
-      # Cached locally on runner
-      # - name: Cache .hunter folder
-      #   uses: actions/cache@v3
-      #   with:
-      #     path: ~/.hunter
-      #     key: hunter-macos-latest
-      - name: List .hunter cache directory
-        run: |
-          ls -a -l ~/.hunter/_Base/ || true
-          echo "PATH=$PATH"
-
-      - uses: actions/checkout@v3
-        with:
-          submodules: 'recursive'
-
-      - uses: actions/download-artifact@v3
-        with:
-          name: 'docstrings'
-          path: docstrings
-      - name: Specify docstring to use while building the wheel
-        run: echo "DEPTHAI_PYTHON_DOCSTRINGS_INPUT=$PWD/docstrings/depthai_python_docstring.hpp" >> $GITHUB_ENV
-
-      - name: Append build hash if not a tagged commit
-        if: startsWith(github.ref, 'refs/tags/v') != true
-        run: echo "BUILD_COMMIT_HASH=${{github.sha}}" >> $GITHUB_ENV
-
-      # - name: Build and install depthai-core
-      #   run: |
-      #     echo "MACOSX_DEPLOYMENT_TARGET=11.0" >> $GITHUB_ENV
-      #     cmake -S depthai-core/ -B build_core -D CMAKE_BUILD_TYPE=Release -D CMAKE_TOOLCHAIN_FILE=$PWD/cmake/toolchain/pic.cmake
-      #     cmake --build build_core --target install --parallel 4
-      #     echo "DEPTHAI_INSTALLATION_DIR=$PWD/build_core/install/" >> $GITHUB_ENV
-
-      - name: Build wheels
-        run: for PYBIN in {9..12}; do "python3.${PYBIN}" -m pip wheel . -w wheelhouse/ --verbose; done
-
-      - name: Auditing wheels
-        run: delocate-wheel -v -w wheelhouse/audited wheelhouse/*.whl
-
-      - name: Archive wheel artifacts
-        uses: actions/upload-artifact@v3
-        with:
-          name: audited-wheels
-          path: wheelhouse/audited/
-      - name: Deploy wheels to artifactory (if not a release)
-        if: startsWith(github.ref, 'refs/tags/v') != true
-        run: bash ./ci/upload-artifactory.sh
-        env:
-          ARTIFACTORY_URL: ${{ secrets.ARTIFACTORY_URL }}
-          ARTIFACTORY_USER: ${{ secrets.ARTIFACTORY_USER }}
-          ARTIFACTORY_PASS: ${{ secrets.ARTIFACTORY_PASS }}
-
   # This job builds wheels for x86_64 arch
   build-linux-x86_64:
     needs: build-docstrings
@@ -470,7 +415,7 @@ jobs:
 
   release:
     if: startsWith(github.ref, 'refs/tags/v')
-    needs: [pytest, build-linux-armhf, build-windows-x86_64, build-macos-x86_64, build-macos-arm64, build-linux-x86_64, build-linux-arm64]
+    needs: [pytest, build-linux-armhf, build-windows-x86_64, build-macos, build-linux-x86_64, build-linux-arm64]
     runs-on: ubuntu-latest
 
     steps:

CMakeLists.txt

@@ -132,7 +132,8 @@ pybind11_add_module(${TARGET_NAME}
     src/pipeline/node/PointCloudBindings.cpp
     src/pipeline/node/SyncBindings.cpp
     src/pipeline/node/MessageDemuxBindings.cpp
-
+    src/pipeline/node/CastBindings.cpp
+    src/pipeline/node/ImageAlignBindings.cpp
     src/pipeline/datatype/ADatatypeBindings.cpp
     src/pipeline/datatype/AprilTagConfigBindings.cpp
     src/pipeline/datatype/AprilTagsBindings.cpp
@@ -157,6 +158,7 @@ pybind11_add_module(${TARGET_NAME}
     src/pipeline/datatype/TrackletsBindings.cpp
     src/pipeline/datatype/PointCloudConfigBindings.cpp
     src/pipeline/datatype/PointCloudDataBindings.cpp
+    src/pipeline/datatype/ImageAlignConfigBindings.cpp
 )
 
 if(WIN32)

examples/Camera/thermal_cam.py

@@ -16,6 +16,7 @@ def onMouse(event, x, y, *args):
 
 # Thermal camera
 thermalCam = pipeline.create(dai.node.Camera)
+thermalCam.setFps(25) # Limit to 25 to match what the sensor can do, capped even if left at default, but warns.
 width, height = -1, -1
 thermalFound = False
 for features in device.getConnectedCameraFeatures():

examples/Cast/blur.py

@@ -0,0 +1,49 @@
+import depthai as dai
+import cv2
+from pathlib import Path
+
+SHAPE = 300
+
+p = dai.Pipeline()
+
+camRgb = p.create(dai.node.ColorCamera)
+nn = p.create(dai.node.NeuralNetwork)
+rgbOut = p.create(dai.node.XLinkOut)
+cast = p.create(dai.node.Cast)
+castXout = p.create(dai.node.XLinkOut)
+
+camRgb.setPreviewSize(SHAPE, SHAPE)
+camRgb.setInterleaved(False)
+
+nnBlobPath = (Path(__file__).parent / Path('../models/blur_simplified_openvino_2021.4_6shave.blob')).resolve().absolute()
+
+nn.setBlobPath(nnBlobPath)
+
+rgbOut.setStreamName("rgb")
+
+castXout.setStreamName("cast")
+
+cast.setOutputFrameType(dai.RawImgFrame.Type.BGR888p)
+
+# Linking
+camRgb.preview.link(nn.input)
+camRgb.preview.link(rgbOut.input)
+nn.out.link(cast.input)
+cast.output.link(castXout.input)
+
+with dai.Device(p) as device:
+    qCam = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
+    qCast = device.getOutputQueue(name="cast", maxSize=4, blocking=False)
+
+
+    while True:
+        inCast = qCast.get()
+        assert isinstance(inCast, dai.ImgFrame)
+        inRgb = qCam.get()
+        assert isinstance(inRgb, dai.ImgFrame)
+        cv2.imshow("Blur", inCast.getCvFrame())
+        cv2.imshow("Original", inRgb.getCvFrame())
+
+
+        if cv2.waitKey(1) == ord('q'):
+            break

examples/Cast/concat.py

@@ -0,0 +1,59 @@
+import numpy as np
+import cv2
+import depthai as dai
+from pathlib import Path
+
+SHAPE = 300
+
+p = dai.Pipeline()
+
+camRgb = p.create(dai.node.ColorCamera)
+left = p.create(dai.node.MonoCamera)
+right = p.create(dai.node.MonoCamera)
+manipLeft = p.create(dai.node.ImageManip)
+manipRight = p.create(dai.node.ImageManip)
+nn = p.create(dai.node.NeuralNetwork)
+cast = p.create(dai.node.Cast)
+castXout = p.create(dai.node.XLinkOut)
+
+camRgb.setPreviewSize(SHAPE, SHAPE)
+camRgb.setInterleaved(False)
+camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
+
+left.setCamera("left")
+left.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
+manipLeft.initialConfig.setResize(SHAPE, SHAPE)
+manipLeft.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p)
+
+right.setCamera("right")
+right.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
+manipRight.initialConfig.setResize(SHAPE, SHAPE)
+manipRight.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p)
+
+nnBlobPath = (Path(__file__).parent / Path('../models/concat_openvino_2021.4_6shave.blob')).resolve().absolute()
+nn.setBlobPath(nnBlobPath)
+nn.setNumInferenceThreads(2)
+
+castXout.setStreamName("cast")
+cast.setOutputFrameType(dai.ImgFrame.Type.BGR888p)
+
+# Linking
+left.out.link(manipLeft.inputImage)
+right.out.link(manipRight.inputImage)
+manipLeft.out.link(nn.inputs['img1'])
+camRgb.preview.link(nn.inputs['img2'])
+manipRight.out.link(nn.inputs['img3'])
+nn.out.link(cast.input)
+cast.output.link(castXout.input)
+
+# Pipeline is defined, now we can connect to the device
+with dai.Device(p) as device:
+    qCast = device.getOutputQueue(name="cast", maxSize=4, blocking=False)
+
+    while True:
+        inCast = qCast.get()
+        assert isinstance(inCast, dai.ImgFrame)
+        cv2.imshow("Concated frames", inCast.getCvFrame())
+
+        if cv2.waitKey(1) == ord('q'):
+            break

examples/Cast/diff.py

@@ -0,0 +1,60 @@
+import cv2
+import depthai as dai
+from pathlib import Path
+
+SHAPE = 720
+
+p = dai.Pipeline()
+
+camRgb = p.create(dai.node.ColorCamera)
+nn = p.create(dai.node.NeuralNetwork)
+script = p.create(dai.node.Script)
+rgbXout = p.create(dai.node.XLinkOut)
+cast = p.create(dai.node.Cast)
+castXout = p.create(dai.node.XLinkOut)
+
+camRgb.setVideoSize(SHAPE, SHAPE)
+camRgb.setPreviewSize(SHAPE, SHAPE)
+camRgb.setInterleaved(False)
+
+nnBlobPath = (Path(__file__).parent / Path('../models/diff_openvino_2022.1_6shave.blob')).resolve().absolute()
+nn.setBlobPath(nnBlobPath)
+
+script.setScript("""
+old = node.io['in'].get()
+while True:
+    frame = node.io['in'].get()
+    node.io['img1'].send(old)
+    node.io['img2'].send(frame)
+    old = frame
+""")
+
+rgbXout.setStreamName("rgb")
+castXout.setStreamName("cast")
+cast.setOutputFrameType(dai.RawImgFrame.Type.GRAY8)
+
+# Linking
+camRgb.preview.link(script.inputs['in'])
+script.outputs['img1'].link(nn.inputs['img1'])
+script.outputs['img2'].link(nn.inputs['img2'])
+camRgb.video.link(rgbXout.input)
+nn.out.link(cast.input)
+cast.output.link(castXout.input)
+
+# Pipeline is defined, now we can connect to the device
+with dai.Device(p) as device:
+    qCam = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
+    qCast = device.getOutputQueue(name="cast", maxSize=4, blocking=False)
+
+
+    while True:
+        colorFrame = qCam.get()
+        assert isinstance(colorFrame, dai.ImgFrame)
+        cv2.imshow("Color", colorFrame.getCvFrame())
+
+        inCast = qCast.get()
+        assert isinstance(inCast, dai.ImgFrame)
+        cv2.imshow("Diff", inCast.getCvFrame())
+
+        if cv2.waitKey(1) == ord('q'):
+            break