# Auto Exposure on ROI This example shows how to dynamically set the Auto Exposure (AE) of the RGB camera dynamically, during application runtime, based on bounding box position. By default, AutoExposure region is adjusted based on neural network output. If desired, the region can be set manually. You can do so by pressing one of the following buttons: * w - move AE region up * s - move AE region down * a - move AE region left * d - move AE region right * n - deactivate manual region (switch back to nn-based roi) ## Demo ## Setup Please run the [install script](https://github.com/luxonis/depthai-python/blob/main/examples/install_requirements.py) to download all required dependencies. Please note that this script must be ran from git context, so you have to download the [depthai-python](https://github.com/luxonis/depthai-python) repository first and then run the script ```bash git clone https://github.com/luxonis/depthai-python.git cd depthai-python/examples python3 install_requirements.py ``` For additional information, please follow the [installation guide](https://docs.luxonis.com/software/depthai/manual-install.md). ## Source code #### Python ```python #!/usr/bin/env python3 from pathlib import Path import sys import cv2 import depthai as dai import numpy as np # Press WASD to move a manual ROI window for auto-exposure control. # Press N to go back to the region controlled by the NN detections. # Get argument first nnPath = str((Path(__file__).parent / Path('../models/mobilenet-ssd_openvino_2021.4_6shave.blob')).resolve().absolute()) if len(sys.argv) > 1: nnPath = sys.argv[1] if not Path(nnPath).exists(): import sys raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"') previewSize = (300, 300) # Create pipeline pipeline = dai.Pipeline() # Define source and outputs camRgb = pipeline.create(dai.node.ColorCamera) camRgb.setPreviewSize(*previewSize) camRgb.setInterleaved(False) camControlIn = pipeline.create(dai.node.XLinkIn) camControlIn.setStreamName('camControl') camControlIn.out.link(camRgb.inputControl) # Define a neural network that will make predictions based on the source frames nn = pipeline.create(dai.node.MobileNetDetectionNetwork) nn.setConfidenceThreshold(0.5) nn.setBlobPath(nnPath) nn.setNumInferenceThreads(2) nn.input.setBlocking(False) camRgb.preview.link(nn.input) # Linking xoutRgb = pipeline.create(dai.node.XLinkOut) xoutRgb.setStreamName("rgb") camRgb.preview.link(xoutRgb.input) nnOut = pipeline.create(dai.node.XLinkOut) nnOut.setStreamName("nn") nn.out.link(nnOut.input) # MobilenetSSD label texts labelMap = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] def clamp(num, v0, v1): return max(v0, min(num, v1)) def asControl(roi): camControl = dai.CameraControl() camControl.setAutoExposureRegion(*roi) return camControl class AutoExposureRegion: step = 10 position = (0, 0) size = (100, 100) resolution = camRgb.getResolutionSize() maxDims = previewSize[0], previewSize[1] def grow(self, x=0, y=0): self.size = ( clamp(x + self.size[0], 1, self.maxDims[0]), clamp(y + self.size[1], 1, self.maxDims[1]) ) def move(self, x=0, y=0): self.position = ( clamp(x + self.position[0], 0, self.maxDims[0]), clamp(y + self.position[1], 0, self.maxDims[1]) ) def endPosition(self): return ( clamp(self.position[0] + self.size[0], 0, self.maxDims[0]), clamp(self.position[1] + self.size[1], 0, self.maxDims[1]), ) def toRoi(self): roi = np.array([*self.position, *self.size]) # Convert to absolute camera coordinates roi = roi * self.resolution[1] // 300 roi[0] += (self.resolution[0] - self.resolution[1]) // 2 # x offset for device crop return roi @staticmethod def bboxToRoi(bbox): startX, startY = bbox[:2] width, height = bbox[2] - startX, bbox[3] - startY roi = frameNorm(np.empty(camRgb.getResolutionSize()), (startX, startY, width, height)) return roi # Connect to device and start pipeline with dai.Device(pipeline) as device: # Output queues will be used to get the rgb frames and nn data from the outputs defined above qControl = device.getInputQueue(name="camControl") qRgb = device.getOutputQueue(name="rgb", maxSize=4, blocking=False) qDet = device.getOutputQueue(name="nn", maxSize=4, blocking=False) frame = None detections = [] nnRegion = True region = AutoExposureRegion() # nn data (bounding box locations) are in <0..1> range - they need to be normalized with frame width/height def frameNorm(frame, bbox): normVals = np.full(len(bbox), frame.shape[0]) normVals[::2] = frame.shape[1] return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int) def displayFrame(name, frame): for detection in detections: bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax)) cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2) cv2.putText(frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255) if not nnRegion: cv2.rectangle(frame, region.position, region.endPosition(), (0, 255, 0), 2) cv2.imshow(name, frame) while True: # Instead of get (blocking), we use tryGet (non-blocking) which will return the available data or None otherwise inRgb = qRgb.tryGet() inDet = qDet.tryGet() if inRgb is not None: frame = inRgb.getCvFrame() if inDet is not None: detections = inDet.detections if nnRegion and len(detections) > 0: bbox = (detections[0].xmin, detections[0].ymin, detections[0].xmax, detections[0].ymax) qControl.send(asControl(AutoExposureRegion.bboxToRoi(bbox))) if frame is not None: displayFrame("rgb", frame) key = cv2.waitKey(1) if key == ord('n'): print("AE ROI controlled by NN") nnRegion = True elif key in [ord('w'), ord('a'), ord('s'), ord('d'), ord('+'), ord('-')]: nnRegion = False if key == ord('a'): region.move(x=-region.step) if key == ord('d'): region.move(x=region.step) if key == ord('w'): region.move(y=-region.step) if key == ord('s'): region.move(y=region.step) if key == ord('+'): region.grow(x=10, y=10) region.step = region.step + 1 if key == ord('-'): region.grow(x=-10, y=-10) region.step = max(region.step - 1, 1) print(f"Setting static AE ROI: {region.toRoi()} (on frame: {[*region.position, *region.endPosition()]})") qControl.send(asControl(region.toRoi())) elif key == ord('q'): break ``` ## Pipeline ### examples/autoexposure_roi.pipeline.json ```json { "pipeline": { "connections": [ { "node1Id": 1, "node1Output": "out", "node1OutputGroup": "", "node2Id": 0, "node2Input": "inputControl", "node2InputGroup": "" }, { "node1Id": 0, "node1Output": "preview", "node1OutputGroup": "", "node2Id": 2, "node2Input": "in", "node2InputGroup": "" }, { "node1Id": 0, "node1Output": "preview", "node1OutputGroup": "", "node2Id": 3, "node2Input": "in", "node2InputGroup": "" }, { "node1Id": 2, "node1Output": "out", "node1OutputGroup": "", "node2Id": 4, "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": true, "group": "", "id": 2, "name": "inputControl", "queueSize": 8, "type": 3, "waitForMessage": false } ], [ [ "", "video" ], { "blocking": false, "group": "", "id": 3, "name": "video", "queueSize": 8, "type": 0, "waitForMessage": false } ], [ [ "", "isp" ], { "blocking": false, "group": "", "id": 4, "name": "isp", "queueSize": 8, "type": 0, "waitForMessage": false } ], [ [ "", "preview" ], { "blocking": false, "group": "", "id": 5, "name": "preview", "queueSize": 8, "type": 0, "waitForMessage": false } ], [ [ "", "frameEvent" ], { "blocking": false, "group": "", "id": 8, "name": "frameEvent", "queueSize": 8, "type": 0, "waitForMessage": false } ] ], "name": "ColorCamera", "properties": { "boardSocket": -1, "cameraName": "", "colorOrder": 0, "fp16": false, "fps": 30.0, "imageOrientation": -1, "initialControl": { "aeLockMode": false, "aeMaxExposureTimeUs": 0, "aeRegion": { "height": 0, "priority": 0, "width": 0, "x": 0, "y": 0 }, "afRegion": { "height": 0, "priority": 0, "width": 0, "x": 0, "y": 0 }, "antiBandingMode": 0, "autoFocusMode": 3, "awbLockMode": false, "awbMode": 0, "brightness": 0, "captureIntent": 0, "chromaDenoise": 0, "cmdMask": 0, "contrast": 0, "controlMode": 0, "effectMode": 0, "expCompensation": 0, "expManual": { "exposureTimeUs": 0, "frameDurationUs": 0, "sensitivityIso": 0 }, "frameSyncMode": 0, "lensPosAutoInfinity": 0, "lensPosAutoMacro": 0, "lensPosition": 0, "lensPositionRaw": 0.0, "lowPowerNumFramesBurst": 0, "lowPowerNumFramesDiscard": 0, "lumaDenoise": 0, "saturation": 0, "sceneMode": 0, "sharpness": 0, "strobeConfig": { "activeLevel": 0, "enable": 0, "gpioNumber": 0 }, "strobeTimings": { "durationUs": 0, "exposureBeginOffsetUs": 0, "exposureEndOffsetUs": 0 }, "wbColorTemp": 0 }, "interleaved": false, "isp3aFps": 0, "ispScale": { "horizDenominator": 0, "horizNumerator": 0, "vertDenominator": 0, "vertNumerator": 0 }, "numFramesPoolIsp": 3, "numFramesPoolPreview": 4, "numFramesPoolRaw": 3, "numFramesPoolStill": 4, "numFramesPoolVideo": 4, "previewHeight": 300, "previewKeepAspectRatio": true, "previewWidth": 300, "rawPacked": null, "resolution": 0, "sensorCropX": -1.0, "sensorCropY": -1.0, "stillHeight": -1, "stillWidth": -1, "videoHeight": -1, "videoWidth": -1 } } ], [ 1, { "id": 1, "ioInfo": [ [ [ "", "out" ], { "blocking": false, "group": "", "id": 9, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false } ] ], "name": "XLinkIn", "properties": { "maxDataSize": 5242880, "numFrames": 8, "streamName": "camControl" } } ], [ 2, { "id": 2, "ioInfo": [ [ [ "", "in" ], { "blocking": false, "group": "", "id": 10, "name": "in", "queueSize": 5, "type": 3, "waitForMessage": true } ], [ [ "", "out" ], { "blocking": false, "group": "", "id": 11, "name": "out", "queueSize": 8, "type": 0, "waitForMessage": false } ], [ [ "", "passthrough" ], { "blocking": false, "group": "", "id": 12, "name": "passthrough", "queueSize": 8, "type": 0, "waitForMessage": false } ] ], "name": "DetectionNetwork", "properties": { "blobSize": 14499200, "blobUri": "asset:__blob", "numFrames": 8, "numNCEPerThread": 0, "numThreads": 2, "parser": { "anchorMasks": {}, "anchors": [], "classes": 0, "confidenceThreshold": 0.5, "coordinates": 0, "iouThreshold": 0.0, "nnFamily": 1 } } } ], [ 3, { "id": 3, "ioInfo": [ [ [ "", "in" ], { "blocking": true, "group": "", "id": 13, "name": "in", "queueSize": 8, "type": 3, "waitForMessage": true } ] ], "name": "XLinkOut", "properties": { "maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "rgb" } } ], [ 4, { "id": 4, "ioInfo": [ [ [ "", "in" ], { "blocking": true, "group": "", "id": 14, "name": "in", "queueSize": 8, "type": 3, "waitForMessage": true } ] ], "name": "XLinkOut", "properties": { "maxFpsLimit": -1.0, "metadataOnly": false, "streamName": "nn" } } ] ] } } ``` ### Need assistance? 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