# Edge Detector This example performs edge detection on 3 different inputs: left, right and RGB camera. HW accelerated sobel filter 3x3 is used. Sobel filter parameters can be changed by keys 1 and 2. ## Demo This example requires the DepthAI v3 API, see [installation instructions](https://docs.luxonis.com/software-v3/depthai.md). ## Source code #### Python ```python #!/usr/bin/env python3 import cv2 import depthai as dai # Create pipeline pipeline = dai.Pipeline() # Define cameras camRgb = pipeline.create(dai.node.Camera).build(dai.CameraBoardSocket.CAM_A) monoLeft = pipeline.create(dai.node.Camera).build(dai.CameraBoardSocket.CAM_B) monoRight = pipeline.create(dai.node.Camera).build(dai.CameraBoardSocket.CAM_C) # Request outputs frameWidth, frameHeight = 1920, 1080 rgbOut = camRgb.requestOutput((frameWidth, frameHeight), type=dai.ImgFrame.Type.GRAY8) leftOut = monoLeft.requestOutput((640, 400), type=dai.ImgFrame.Type.GRAY8) rightOut = monoRight.requestOutput((640, 400), type=dai.ImgFrame.Type.GRAY8) # Define edge detectors edgeDetectorLeft = pipeline.create(dai.node.EdgeDetector) edgeDetectorRight = pipeline.create(dai.node.EdgeDetector) edgeDetectorRgb = pipeline.create(dai.node.EdgeDetector) edgeDetectorRgb.setMaxOutputFrameSize(frameWidth * frameHeight) # Create input queues edgeCfgLeftQueue = edgeDetectorLeft.inputConfig.createInputQueue() edgeCfgRightQueue = edgeDetectorRight.inputConfig.createInputQueue() edgeCfgRgbQueue = edgeDetectorRgb.inputConfig.createInputQueue() # Link camera outputs to edge detectors leftOut.link(edgeDetectorLeft.inputImage) rightOut.link(edgeDetectorRight.inputImage) rgbOut.link(edgeDetectorRgb.inputImage) # Create output queues edgeLeftQueue = edgeDetectorLeft.outputImage.createOutputQueue() edgeRightQueue = edgeDetectorRight.outputImage.createOutputQueue() edgeRgbQueue = edgeDetectorRgb.outputImage.createOutputQueue() # Start pipeline pipeline.start() print("Switch between sobel filter kernels using keys '1' and '2'") while pipeline.isRunning(): edgeLeft = edgeLeftQueue.get() edgeRight = edgeRightQueue.get() edgeRgb = edgeRgbQueue.get() # Convert to OpenCV format cv2.imshow("edge left", edgeLeft.getCvFrame()) cv2.imshow("edge right", edgeRight.getCvFrame()) cv2.imshow("edge rgb", edgeRgb.getCvFrame()) key = cv2.waitKey(1) if key == ord('q'): break if key == ord('1'): print("Switching sobel filter kernel.") cfg = dai.EdgeDetectorConfig() sobelHorizontalKernel = [[1, 0, -1], [2, 0, -2], [1, 0, -1]] sobelVerticalKernel = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]] cfg.setSobelFilterKernels(sobelHorizontalKernel, sobelVerticalKernel) edgeCfgLeftQueue.send(cfg) edgeCfgRightQueue.send(cfg) edgeCfgRgbQueue.send(cfg) if key == ord('2'): print("Switching sobel filter kernel.") cfg = dai.EdgeDetectorConfig() sobelHorizontalKernel = [[3, 0, -3], [10, 0, -10], [3, 0, -3]] sobelVerticalKernel = [[3, 10, 3], [0, 0, 0], [-3, -10, -3]] cfg.setSobelFilterKernels(sobelHorizontalKernel, sobelVerticalKernel) edgeCfgLeftQueue.send(cfg) edgeCfgRightQueue.send(cfg) edgeCfgRgbQueue.send(cfg) ``` ## Pipeline ### examples/edge_detector.pipeline.json ```json { "pipeline": { "connections": [ { "node1Id": 1, "node1Output": "out", "node1OutputGroup": "", "node2Id": 3, "node2Input": "inputImage", "node2InputGroup": "" }, { "node1Id": 9, "node1Output": "out", "node1OutputGroup": "", "node2Id": 3, "node2Input": "inputConfig", "node2InputGroup": "" }, { "node1Id": 9, "node1Output": "out", "node1OutputGroup": "", "node2Id": 4, "node2Input": "inputConfig", "node2InputGroup": "" }, { "node1Id": 2, "node1Output": "out", "node1OutputGroup": "", "node2Id": 4, "node2Input": "inputImage", "node2InputGroup": "" }, { "node1Id": 0, "node1Output": "video", "node1OutputGroup": "", "node2Id": 5, "node2Input": "inputImage", "node2InputGroup": "" }, { "node1Id": 9, "node1Output": "out", "node1OutputGroup": "", "node2Id": 5, "node2Input": "inputConfig", "node2InputGroup": "" }, { "node1Id": 3, "node1Output": "outputImage", "node1OutputGroup": "", "node2Id": 6, "node2Input": "in", "node2InputGroup": "" }, { "node1Id": 4, "node1Output": "outputImage", "node1OutputGroup": "", "node2Id": 7, "node2Input": "in", "node2InputGroup": "" }, { "node1Id": 5, "node1Output": "outputImage", "node1OutputGroup": "", "node2Id": 8, "node2Input": "in", "node2InputGroup": "" } ], "globalProperties": { "calibData": null, "cameraTuningBlobSize": null, "cameraTuningBlobUri": "", "leonCssFrequencyHz": 700000000.0, "leonMssFrequencyHz": 700000000.0, "pipelineName": null, "pipelineVersion": null, "sippBufferSize": 18432, "sippDmaBufferSize": 16384, "xlinkChunkSize": -1 }, "nodes": [ [ 0, { "id": 0, "ioInfo": [ [ [ "", "inputConfig" ], { "blocking": false, "group": "", "id": 1, "name": "inputConfig", "queueSize": 8, "type": 3, "waitForMessage": false } ], [ [ "", "raw" ], { "blocking": false, "group": "", "id": 6, "name": "raw", "queueSize": 8, "type": 0, "waitForMessage": false } ], [ [ "", "still" ], { "blocking": false, "group": "", "id": 7, "name": "still", "queueSize": 8, "type": 0, "waitForMessage": false } ], [ [ "", "inputControl" ], { "blocking": 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"waitForMessage": false } ], [ [ "", "inputImage" ], { "blocking": false, "group": "", "id": 18, "name": "inputImage", "queueSize": 4, "type": 3, "waitForMessage": true } ], [ [ "", "passthroughInputImage" ], { "blocking": false, "group": "", "id": 20, "name": "passthroughInputImage", "queueSize": 8, "type": 0, "waitForMessage": false } ] ], "name": "EdgeDetector", "properties": { "initialConfig": { "config": { "sobelFilterHorizontalKernel": [], "sobelFilterVerticalKernel": [] } }, "numFramesPool": 4, "outputFrameSize": 1048576 } } ], [ 4, { "id": 4, "ioInfo": [ [ [ "", "inputConfig" ], { "blocking": false, "group": "", "id": 21, "name": "inputConfig", "queueSize": 4, "type": 3, "waitForMessage": false } ], [ [ "", "outputImage" ], { "blocking": false, "group": "", "id": 23, "name": "outputImage", "queueSize": 8, "type": 0, "waitForMessage": false } ], [ [ "", "inputImage" ], { "blocking": false, "group": "", "id": 22, "name": "inputImage", "queueSize": 4, "type": 3, "waitForMessage": 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"waitForMessage": false } ] ], "name": "EdgeDetector", "properties": { "initialConfig": { "config": { "sobelFilterHorizontalKernel": [], "sobelFilterVerticalKernel": [] } }, "numFramesPool": 4, "outputFrameSize": 2073600 } } ], [ 6, { "id": 6, "ioInfo": [ [ [ "", "in" ], { "blocking": true, "group": "", "id": 29, "name": "in", "queueSize": 8, "type": 3, "waitForMessage": true } ] ], "name": "XLinkOut", "properties": { "maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "edge left" } } ], [ 7, { "id": 7, "ioInfo": [ [ [ "", "in" ], { "blocking": true, "group": "", "id": 30, "name": "in", "queueSize": 8, "type": 3, "waitForMessage": true } ] ], "name": "XLinkOut", "properties": { "maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "edge right" } } ], [ 8, { "id": 8, "ioInfo": [ [ [ "", "in" ], { "blocking": true, "group": "", "id": 31, "name": "in", "queueSize": 8, "type": 3, "waitForMessage": true } ] ], "name": "XLinkOut", "properties": { "maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "edge rgb" } } ], [ 9, { "id": 9, "ioInfo": [ [ [ "", "out" ], { "blocking": false, "group": "", "id": 32, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false } ] ], "name": "XLinkIn", "properties": { "maxDataSize": 5242880, "numFrames": 8, "streamName": "edge cfg" } } ] ] } } ``` ### Need assistance? 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