3.17. Gen2 彩色相机编码&灰度相机深度流&运行MobilenetSSD模型

这个例子显示了如何配置 depthai 视频编码器在 h. 265格式编码 RGB 摄像机输入在30 FPS 的全高清分辨率，并传输编码的视频通过 XLINK 到主机，保存到磁盘作为视频文件。同时，一个 mobileetv2ssd 网络在右灰度摄像机的帧上运行，同时该应用程序还显示两个灰度摄像机产生的深度图。请注意，在这种情况下使用了差异，因为它以更直观的方式着色。

按 Ctrl + c 将停止程序，然后使用 ffmpeg 将其转换为 mp4，使其可播放。请注意，ffmpeg 需要安装并运行才能成功转换为 mp4。

注意，此示例将编码视频保存到主机存储中。因此，如果您让它们一直运行，您可以填满您的主机上的存储空间。

3.17.1. 演示

3.17.2. 设置

请运行以下命令来安装所需的依赖项

Warning

说明：此处安装的是第二代depthai库

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 Python API 安装指南

这个示例还需要 mobileenetsdd blob ( mobilenet.blob 文件 )才能工作——您可以从这里下载它。

3.17.3. 源代码

可以在 GitHub 上找到。国内用户也可以在 gitee 上找到。

from pathlib import Path
import sys
import cv2
import depthai as dai
import numpy as np


# 首先获取模型
mobilenet_path = str((Path(__file__).parent / Path('models/mobilenet.blob')).resolve().absolute())
if len(sys.argv) > 1:
    mobilenet_path = sys.argv[1]

pipeline = dai.Pipeline()

cam = pipeline.createColorCamera()
cam.setBoardSocket(dai.CameraBoardSocket.RGB)
cam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)

# 创建视频编码流
videoEncoder = pipeline.createVideoEncoder()
videoEncoder.setDefaultProfilePreset(1920, 1080, 30, dai.VideoEncoderProperties.Profile.H265_MAIN)
cam.video.link(videoEncoder.input)

videoOut = pipeline.createXLinkOut()
videoOut.setStreamName('h265')
videoEncoder.bitstream.link(videoOut.input)

left = pipeline.createMonoCamera()
left.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
left.setBoardSocket(dai.CameraBoardSocket.LEFT)

right = pipeline.createMonoCamera()
right.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
right.setBoardSocket(dai.CameraBoardSocket.RIGHT)

depth = pipeline.createStereoDepth()
depth.setConfidenceThreshold(200)
# 注意：默认情况下，校准后的流是水平镜像的
depth.setOutputRectified(True)
depth.setRectifyEdgeFillColor(0) # 黑色，以更好地看到切口
left.out.link(depth.left)
right.out.link(depth.right)

detection_nn = pipeline.createNeuralNetwork()
detection_nn.setBlobPath(mobilenet_path)

xout_depth = pipeline.createXLinkOut()
xout_depth.setStreamName("depth")
depth.disparity.link(xout_depth.input)

xout_right = pipeline.createXLinkOut()
xout_right.setStreamName("rect_right")
depth.rectifiedRight.link(xout_right.input)

manip = pipeline.createImageManip()
manip.initialConfig.setResize(300, 300)
# NN模型需要BGR输入。默认情况下，ImageManip输出类型将与输入相同（在这种情况下为灰色）
manip.initialConfig.setFrameType(dai.RawImgFrame.Type.BGR888p)
depth.rectifiedRight.link(manip.inputImage)
manip.out.link(detection_nn.input)

xout_manip = pipeline.createXLinkOut()
xout_manip.setStreamName("manip")
manip.out.link(xout_manip.input)

xout_nn = pipeline.createXLinkOut()
xout_nn.setStreamName("nn")
detection_nn.out.link(xout_nn.input)

# MobilenetSSD标签文本
texts = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
         "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]


# 管道已定义，现在设备已连接到管道
with dai.Device(pipeline) as device:
    # 启动管道
    device.startPipeline()

    q_right = device.getOutputQueue(name="rect_right", maxSize=8, blocking=False)
    q_manip = device.getOutputQueue(name="manip", maxSize=8, blocking=False)
    q_depth = device.getOutputQueue(name="depth", maxSize=8, blocking=False)
    q_nn = device.getOutputQueue(name="nn", maxSize=8, blocking=False)
    q_rgb_enc = device.getOutputQueue(name="h265", maxSize=30, blocking=True)

    frame_right = None
    frame_manip = None
    frame_depth = None
    bboxes = []
    labels = []


    def frame_norm(frame, bbox):
        norm_vals = np.full(len(bbox), frame.shape[0])
        norm_vals[::2] = frame.shape[1]
        return (np.clip(np.array(bbox), 0, 1) * norm_vals).astype(int)

    videoFile = open('video.h265','wb')

    while True:
        in_right = q_right.tryGet()
        in_manip = q_manip.tryGet()
        in_nn = q_nn.tryGet()
        in_depth = q_depth.tryGet()

        while q_rgb_enc.has():
            q_rgb_enc.get().getData().tofile(videoFile)

        if in_right is not None:
            shape = (in_right.getHeight(), in_right.getWidth())
            frame_right = in_right.getData().reshape(shape).astype(np.uint8)
            frame_right = np.ascontiguousarray(frame_right)

        if in_manip is not None:
            shape = (3, in_manip.getHeight(), in_manip.getWidth())
            frame_manip = in_manip.getData().reshape(shape).transpose(1, 2, 0).astype(np.uint8)
            frame_manip = np.ascontiguousarray(frame_manip)

        if in_nn is not None:
            bboxes = np.array(in_nn.getFirstLayerFp16())
            bboxes = bboxes.reshape((bboxes.size // 7, 7))
            bboxes = bboxes[bboxes[:, 2] > 0.5]
            # 剪切bbox和标签
            labels = bboxes[:, 1].astype(int)
            bboxes = bboxes[:, 3:7]

        if in_depth is not None:
            frame_depth = in_depth.getData().reshape((in_depth.getHeight(), in_depth.getWidth())).astype(np.uint8)
            frame_depth = np.ascontiguousarray(frame_depth)
            frame_depth = cv2.applyColorMap(frame_depth, cv2.COLORMAP_JET)

        if frame_right is not None:
            for raw_bbox, label in zip(bboxes, labels):
                bbox = frame_norm(frame_right, raw_bbox)
                cv2.rectangle(frame_right, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2)
                cv2.putText(frame_right, texts[label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.imshow("rectif_right", frame_right)

        if frame_manip is not None:
            for raw_bbox, label in zip(bboxes, labels):
                bbox = frame_norm(frame_manip, raw_bbox)
                cv2.rectangle(frame_manip, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2)
                cv2.putText(frame_manip, texts[label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
            cv2.imshow("manip", frame_manip)

        if frame_depth is not None:
            for raw_bbox, label in zip(bboxes, labels):
                bbox = frame_norm(frame_depth, raw_bbox)
                cv2.rectangle(frame_depth, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 0, 255), 2)
                cv2.putText(frame_depth, texts[label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, (0, 0, 255))
            cv2.imshow("depth", frame_depth)

        if cv2.waitKey(1) == ord('q'):
            break

    videoFile.close()

    print("To view the encoded data, convert the stream file (.h265) into a video file (.mp4) using a command below:")
    print("ffmpeg -framerate 30 -i video.h265 -c copy video.mp4")