Gen2 自动曝光roi =============================== 此示例说明如何在应用程序运行时根据边界框位置动态设置RGB相机的自动曝光 (AE)。 默认情况下，自动曝光区域根据神经网络输出进行调整。如果需要，可以手动设置区域。您可以通过按以下按钮之一来执行此操作： - 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nn的roi） 演示 ********************** .. raw:: html
设置 ******************** .. warning:: 说明：此处安装的是第二代depthai库 请运行以下命令来安装所需的依赖项 .. code-block:: bash python3 -m pip install --extra-index-url https://artifacts.luxonis.com/artifactory/luxonis-python-snapshot-local/ depthai==0.0.2.1+c9a19df719cb668e438d6eafd193cdf60a0d9354 numpy==1.19.5 opencv-python==4.5.1.48 有关更多信息，请参阅 :ref:`Python API 安装指南 ` 这个示例还需要 mobileenetsdd blob ( :code:`mobilenet.blob` 文件 )才能工作——您可以从 `这里 `_ 下载它。 源代码 ********************* 可以在 `GitHub `_ 上找到。国内用户也可以在 `gitee `_ 上找到。 .. code-block:: 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 (nonblocking) 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 .. include:: /pages/includes/footer-short.rst