#!/usr/bin/env python3

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

# 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"')

# 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"]

# Create pipeline
pipeline = dai.Pipeline()

# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
videoEncoder = pipeline.create(dai.node.VideoEncoder)
monoRight = pipeline.create(dai.node.MonoCamera)
monoLeft = pipeline.create(dai.node.MonoCamera)
depth = pipeline.create(dai.node.StereoDepth)
manip = pipeline.create(dai.node.ImageManip)
nn = pipeline.create(dai.node.MobileNetDetectionNetwork)

videoOut = pipeline.create(dai.node.XLinkOut)
xoutRight = pipeline.create(dai.node.XLinkOut)
disparityOut = pipeline.create(dai.node.XLinkOut)
manipOut = pipeline.create(dai.node.XLinkOut)
nnOut = pipeline.create(dai.node.XLinkOut)

videoOut.setStreamName('h265')
xoutRight.setStreamName('right')
disparityOut.setStreamName('disparity')
manipOut.setStreamName('manip')
nnOut.setStreamName('nn')

# Properties
camRgb.setBoardSocket(dai.CameraBoardSocket.RGB)
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)
monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)
monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
videoEncoder.setDefaultProfilePreset(30, dai.VideoEncoderProperties.Profile.H265_MAIN)

depth.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
depth.setRectifyEdgeFillColor(0) # Black, to better see the cutout

nn.setConfidenceThreshold(0.5)
nn.setBlobPath(nnPath)
nn.setNumInferenceThreads(2)
nn.input.setBlocking(False)

# The NN model expects BGR input. By default ImageManip output type would be same as input (gray in this case)
manip.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p)
manip.initialConfig.setResize(300, 300)

# Linking
camRgb.video.link(videoEncoder.input)
videoEncoder.bitstream.link(videoOut.input)
monoRight.out.link(xoutRight.input)
monoRight.out.link(depth.right)
monoLeft.out.link(depth.left)
depth.disparity.link(disparityOut.input)
depth.rectifiedRight.link(manip.inputImage)
manip.out.link(nn.input)
manip.out.link(manipOut.input)
nn.out.link(nnOut.input)

# Disparity range is used for normalization
disparityMultiplier = 255 / depth.initialConfig.getMaxDisparity()

# Connect to device and start pipeline
with dai.Device(pipeline) as device:

    queueSize = 8
    qRight = device.getOutputQueue("right", queueSize)
    qDisparity = device.getOutputQueue("disparity", queueSize)
    qManip = device.getOutputQueue("manip", queueSize)
    qDet = device.getOutputQueue("nn", queueSize)
    qRgbEnc = device.getOutputQueue('h265', maxSize=30, blocking=True)

    frame = None
    frameManip = None
    frameDisparity = None
    detections = []
    offsetX = (monoRight.getResolutionWidth() - monoRight.getResolutionHeight()) // 2
    color = (255, 0, 0)
    croppedFrame = np.zeros((monoRight.getResolutionHeight(), monoRight.getResolutionHeight()))

    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)

    videoFile = open('video.h265', 'wb')
    cv2.namedWindow("right", cv2.WINDOW_NORMAL)
    cv2.namedWindow("manip", cv2.WINDOW_NORMAL)

    while True:
        inRight = qRight.tryGet()
        inManip = qManip.tryGet()
        inDet = qDet.tryGet()
        inDisparity = qDisparity.tryGet()

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

        if inRight is not None:
            frame = inRight.getCvFrame()

        if inManip is not None:
            frameManip = inManip.getCvFrame()

        if inDisparity is not None:
            # Apply color map for better visualization
            frameDisparity = inDisparity.getCvFrame()
            frameDisparity = (frameDisparity*disparityMultiplier).astype(np.uint8)
            frameDisparity = cv2.applyColorMap(frameDisparity, cv2.COLORMAP_JET)

        if inDet is not None:
            detections = inDet.detections

        if frame is not None:
            for detection in detections:
                bbox = frameNorm(croppedFrame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
                bbox[::2] += offsetX
                cv2.putText(frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
                cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
                cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
            # Show the right cam frame
            cv2.imshow("right", frame)

        if frameDisparity is not None:
            for detection in detections:
                bbox = frameNorm(croppedFrame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
                bbox[::2] += offsetX
                cv2.rectangle(frameDisparity, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
                cv2.putText(frameDisparity, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
                cv2.putText(frameDisparity, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
            # Show the disparity frame
            cv2.imshow("disparity", frameDisparity)

        if frameManip is not None:
            for detection in detections:
                bbox = frameNorm(frameManip, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
                cv2.rectangle(frameManip, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
                cv2.putText(frameManip, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
                cv2.putText(frameManip, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, color)
            # Show the manip frame
            cv2.imshow("manip", frameManip)

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

    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")
