# ROI 自动曝光 本示例展示了如何在应用运行时，根据边界框的位置动态调整 RGB 摄像头的自动曝光（AE）。 默认情况下，自动曝光区域会根据神经网络输出进行调整。如有需要，也可以手动设置该区域。按下以下任意按键即可： * w - 向上移动 AE 区域 * s - 向下移动 AE 区域 * a - 向左移动 AE 区域 * d - 向右移动 AE 区域 * n - 停用手动区域（切换回基于神经网络的 ROI） ## 演示 ## 设置 请运行[安装脚本](https://github.com/luxonis/depthai-python/blob/main/examples/install_requirements.py)以下载所有必需的依赖项。请注意，此脚本必须在 git 上下文中运行，因此您需要先下载 [depthai-python](https://github.com/luxonis/depthai-python) 仓库，然后运行该脚本。 ```bash git clone https://github.com/luxonis/depthai-python.git cd depthai-python/examples python3 install_requirements.py ``` 更多信息，请参考[安装指南](https://docs.luxonis.com/software/depthai/manual-install.md)。 ## 源代码 #### 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 ``` ## 管道 ### 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" } } ] ] } } ``` ### 需要帮助？ 请前往 [OAKChina 官网](https://www.oakchina.cn/) 获取技术支持或解答您的任何疑问。