Gen2 主机上的双目深度 ================================= 此示例显示了使用立体图像从主机获取的深度图。您可以在代码内选择3种深度模式：左右检查，扩展（用于更近距离），子像素（用于更长距离）。否则，将激活kernel_7x7的中值。 演示 ***************** 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 -U pip python3 -m pip install opencv-python python3 -m pip install -U --force-reinstall depthai 有关更多信息，请参阅 :ref:`Python API 安装指南 ` 此示例还需要数据集文件夹才能工作——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 import cv2 import numpy as np import depthai as dai from time import sleep import datetime import argparse from pathlib import Path datasetDefault = str((Path(__file__).parent / Path('models/dataset')).resolve().absolute()) parser = argparse.ArgumentParser() parser.add_argument('-dataset', nargs='?', help="Path to recorded frames", default=datasetDefault) args = parser.parse_args() if not Path(datasetDefault).exists(): import sys raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"') # StereoDepth配置选项。 out_depth = False # 预期情况下的差异 out_rectified = True # 输出和显示整流后的流 lrcheck = True # 更好地处理咬合 extended = False # 接近最小深度，视差范围加倍 subpixel = True # 更好的精度，可实现更长的距离，分数差异为32级 median = dai.StereoDepthProperties.MedianFilter.KERNEL_7x7 # 清理一些不兼容的选项 if lrcheck or extended or subpixel: median = dai.StereoDepthProperties.MedianFilter.MEDIAN_OFF print("StereoDepth config options: ") print("Left-Right check: ", lrcheck) print("Extended disparity: ", extended) print("Subpixel: ", subpixel) print("Median filtering: ", median) right_intrinsic = [[860.0, 0.0, 640.0], [0.0, 860.0, 360.0], [0.0, 0.0, 1.0]] def create_stereo_depth_pipeline(): print("Creating Stereo Depth pipeline: ", end='') print("XLINK IN -> STEREO -> XLINK OUT") pipeline = dai.Pipeline() camLeft = pipeline.createXLinkIn() camRight = pipeline.createXLinkIn() stereo = pipeline.createStereoDepth() xoutLeft = pipeline.createXLinkOut() xoutRight = pipeline.createXLinkOut() xoutDepth = pipeline.createXLinkOut() xoutDisparity = pipeline.createXLinkOut() xoutRectifLeft = pipeline.createXLinkOut() xoutRectifRight = pipeline.createXLinkOut() camLeft.setStreamName('in_left') camRight.setStreamName('in_right') stereo.setConfidenceThreshold(200) stereo.setRectifyEdgeFillColor(0) # 黑色，以更好地看到切口 stereo.setMedianFilter(median) # 默认KERNEL_7x7 stereo.setLeftRightCheck(lrcheck) stereo.setExtendedDisparity(extended) stereo.setSubpixel(subpixel) stereo.setEmptyCalibration() # 设置输入帧是否已纠正 stereo.setInputResolution(1280, 720) xoutLeft.setStreamName('left') xoutRight.setStreamName('right') xoutDepth.setStreamName('depth') xoutDisparity.setStreamName('disparity') xoutRectifLeft.setStreamName('rectified_left') xoutRectifRight.setStreamName('rectified_right') camLeft.out.link(stereo.left) camRight.out.link(stereo.right) stereo.syncedLeft.link(xoutLeft.input) stereo.syncedRight.link(xoutRight.input) if out_depth: stereo.depth.link(xoutDepth.input) stereo.disparity.link(xoutDisparity.input) if out_rectified: stereo.rectifiedLeft.link(xoutRectifLeft.input) stereo.rectifiedRight.link(xoutRectifRight.input) streams = ['left', 'right'] if out_rectified: streams.extend(['rectified_left', 'rectified_right']) streams.extend(['disparity', 'depth']) return pipeline, streams def convert_to_cv2_frame(name, image): baseline = 75 #mm focal = right_intrinsic[0][0] max_disp = 96 disp_type = np.uint8 disp_levels = 1 if (extended): max_disp *= 2 if (subpixel): max_disp *= 32 disp_type = np.uint16 disp_levels = 32 data, w, h = image.getData(), image.getWidth(), image.getHeight() if name == 'depth': # 包含带有（lrcheck或Extended或subpixel）的FP16 frame = np.array(data).astype(np.uint8).view(np.uint16).reshape((h, w)) elif name == 'disparity': disp = np.array(data).astype(np.uint8).view(disp_type).reshape((h, w)) # 根据差异计算深度 with np.errstate(divide='ignore'): depth = (disp_levels * baseline * focal / disp).astype(np.uint16) if 1: # （可选）扩展视差范围以更好地可视化它 frame = (disp * 255. / max_disp).astype(np.uint8) if 1: # （可选）应用颜色图 frame = cv2.applyColorMap(frame, cv2.COLORMAP_HOT) else: # 单流/单通道 frame = np.array(data).reshape((h, w)).astype(np.uint8) if name.startswith('rectified_'): frame = cv2.flip(frame, 1) if name == 'rectified_right': last_rectif_right = frame return frame pipeline, streams = create_stereo_depth_pipeline() print("Connecting and starting the pipeline") # 连接并启动管道 with dai.Device(pipeline) as device: inStreams = ['in_right', 'in_left'] inStreamsCameraID = [dai.CameraBoardSocket.RIGHT, dai.CameraBoardSocket.LEFT] in_q_list = [] for s in inStreams: q = device.getInputQueue(s) in_q_list.append(q) # 为每个流创建一个接收队列 q_list = [] for s in streams: q = device.getOutputQueue(s, 8, blocking=False) q_list.append(q) # 需要为“立体声”节点中的同步阶段设置输入帧的时间戳 timestamp_ms = 0 timestamp_ms = 0 index = 0 while True: # 处理输入流（如果有） if in_q_list: dataset_size = 2 # 图像对数 frame_interval_ms = 500 for i, q in enumerate(in_q_list): path = args.dataset + '/' + str(index) + '/' + q.getName() + '.png' data = cv2.imread(path, cv2.IMREAD_GRAYSCALE).reshape(720*1280) tstamp = datetime.timedelta(seconds = timestamp_ms // 1000, milliseconds = timestamp_ms % 1000) img = dai.ImgFrame() img.setData(data) img.setTimestamp(tstamp) img.setInstanceNum(inStreamsCameraID[i]) img.setType(dai.ImgFrame.Type.RAW8) img.setWidth(1280) img.setHeight(720) q.send(img) if timestamp_ms == 0: # 发送两次以进行第一次迭代 q.send(img) print("Sent frame: {:25s}".format(path), 'timestamp_ms:', timestamp_ms) timestamp_ms += frame_interval_ms index = (index + 1) % dataset_size sleep(frame_interval_ms / 1000) # 处理输出流 for q in q_list: if q.getName() in ['left', 'right', 'depth']: continue frame = convert_to_cv2_frame(q.getName(), q.get()) cv2.imshow(q.getName(), frame) if cv2.waitKey(1) == ord('q'): break .. include:: /pages/includes/footer-short.rst