#!/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 nnLabels
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
monoRight = pipeline.create(dai.node.MonoCamera)
monoLeft = pipeline.create(dai.node.MonoCamera)
stereo = pipeline.create(dai.node.StereoDepth)
manip = pipeline.create(dai.node.ImageManip)
nn = pipeline.create(dai.node.MobileNetDetectionNetwork)

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

disparityOut.setStreamName("disparity")
xoutRight.setStreamName("rectifiedRight")
nnOut.setStreamName("nn")

# Properties
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)

# Produce the depth map (using disparity output as it's easier to visualize depth this way)
stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
stereo.setRectifyEdgeFillColor(0)  # Black, to better see the cutout from rectification (black stripe on the edges)
# Convert the grayscale frame into the nn-acceptable form
manip.initialConfig.setResize(300, 300)
# 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)

# Define a neural network that will make predictions based on the source frames
nn.setConfidenceThreshold(0.5)
nn.setBlobPath(nnPath)
nn.setNumInferenceThreads(2)
nn.input.setBlocking(False)

# Linking
monoRight.out.link(stereo.right)
monoLeft.out.link(stereo.left)
stereo.rectifiedRight.link(manip.inputImage)
stereo.disparity.link(disparityOut.input)
manip.out.link(nn.input)
manip.out.link(xoutRight.input)
nn.out.link(nnOut.input)

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

    # Output queues will be used to get the grayscale / depth frames and nn data from the outputs defined above
    qRight = device.getOutputQueue("rectifiedRight", maxSize=4, blocking=False)
    qDisparity = device.getOutputQueue("disparity", maxSize=4, blocking=False)
    qDet = device.getOutputQueue("nn", maxSize=4, blocking=False)

    rightFrame = None
    disparityFrame = None
    detections = []

    # nn data, being the 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)

    # Add bounding boxes and text to the frame and show it to the user
    def show(name, frame):
        color = (255, 0, 0)
        for detection in detections:
            bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
            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 frame
        cv2.imshow(name, frame)

    disparityMultiplier = 255 / stereo.initialConfig.getMaxDisparity()

    while True:
        # Instead of get (blocking), we use tryGet (non-blocking) which will return the available data or None otherwise
        if qDet.has():
            detections = qDet.get().detections

        if qRight.has():
            rightFrame = qRight.get().getCvFrame()

        if qDisparity.has():
            # Frame is transformed, normalized, and color map will be applied to highlight the depth info
            disparityFrame = qDisparity.get().getFrame()
            disparityFrame = (disparityFrame*disparityMultiplier).astype(np.uint8)
            # Available color maps: https://docs.opencv.org/3.4/d3/d50/group__imgproc__colormap.html
            disparityFrame = cv2.applyColorMap(disparityFrame, cv2.COLORMAP_JET)
            show("disparity", disparityFrame)

        if rightFrame is not None:
            show("rectified right", rightFrame)

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