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Merge pull request #1023 from luxonis/release_2.26.0.0
Release 2.26.0.0
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Submodule depthai-core
updated
37 files
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,49 @@ | ||
| import depthai as dai | ||
| import cv2 | ||
| from pathlib import Path | ||
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| SHAPE = 300 | ||
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| p = dai.Pipeline() | ||
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| 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) | ||
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| camRgb.setPreviewSize(SHAPE, SHAPE) | ||
| camRgb.setInterleaved(False) | ||
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| nnBlobPath = (Path(__file__).parent / Path('../models/blur_simplified_openvino_2021.4_6shave.blob')).resolve().absolute() | ||
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| nn.setBlobPath(nnBlobPath) | ||
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| rgbOut.setStreamName("rgb") | ||
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| castXout.setStreamName("cast") | ||
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| cast.setOutputFrameType(dai.RawImgFrame.Type.BGR888p) | ||
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| # Linking | ||
| camRgb.preview.link(nn.input) | ||
| camRgb.preview.link(rgbOut.input) | ||
| nn.out.link(cast.input) | ||
| cast.output.link(castXout.input) | ||
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| with dai.Device(p) as device: | ||
| qCam = device.getOutputQueue(name="rgb", maxSize=4, blocking=False) | ||
| qCast = device.getOutputQueue(name="cast", maxSize=4, blocking=False) | ||
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| 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()) | ||
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| if cv2.waitKey(1) == ord('q'): | ||
| break |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,59 @@ | ||
| import numpy as np | ||
| import cv2 | ||
| import depthai as dai | ||
| from pathlib import Path | ||
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| SHAPE = 300 | ||
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| p = dai.Pipeline() | ||
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| 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) | ||
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| camRgb.setPreviewSize(SHAPE, SHAPE) | ||
| camRgb.setInterleaved(False) | ||
| camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR) | ||
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| left.setCamera("left") | ||
| left.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P) | ||
| manipLeft.initialConfig.setResize(SHAPE, SHAPE) | ||
| manipLeft.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p) | ||
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| right.setCamera("right") | ||
| right.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P) | ||
| manipRight.initialConfig.setResize(SHAPE, SHAPE) | ||
| manipRight.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p) | ||
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| nnBlobPath = (Path(__file__).parent / Path('../models/concat_openvino_2021.4_6shave.blob')).resolve().absolute() | ||
| nn.setBlobPath(nnBlobPath) | ||
| nn.setNumInferenceThreads(2) | ||
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| castXout.setStreamName("cast") | ||
| cast.setOutputFrameType(dai.ImgFrame.Type.BGR888p) | ||
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| # 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) | ||
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| # 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) | ||
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| while True: | ||
| inCast = qCast.get() | ||
| assert isinstance(inCast, dai.ImgFrame) | ||
| cv2.imshow("Concated frames", inCast.getCvFrame()) | ||
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| if cv2.waitKey(1) == ord('q'): | ||
| break |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,60 @@ | ||
| import cv2 | ||
| import depthai as dai | ||
| from pathlib import Path | ||
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| SHAPE = 720 | ||
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| p = dai.Pipeline() | ||
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| 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) | ||
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| camRgb.setVideoSize(SHAPE, SHAPE) | ||
| camRgb.setPreviewSize(SHAPE, SHAPE) | ||
| camRgb.setInterleaved(False) | ||
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| nnBlobPath = (Path(__file__).parent / Path('../models/diff_openvino_2022.1_6shave.blob')).resolve().absolute() | ||
| nn.setBlobPath(nnBlobPath) | ||
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| 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 | ||
| """) | ||
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| rgbXout.setStreamName("rgb") | ||
| castXout.setStreamName("cast") | ||
| cast.setOutputFrameType(dai.RawImgFrame.Type.GRAY8) | ||
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| # 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) | ||
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| # 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) | ||
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| while True: | ||
| colorFrame = qCam.get() | ||
| assert isinstance(colorFrame, dai.ImgFrame) | ||
| cv2.imshow("Color", colorFrame.getCvFrame()) | ||
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| inCast = qCast.get() | ||
| assert isinstance(inCast, dai.ImgFrame) | ||
| cv2.imshow("Diff", inCast.getCvFrame()) | ||
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| if cv2.waitKey(1) == ord('q'): | ||
| break |
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