#!/usr/bin/env python3

import cv2
import depthai as dai
import numpy as np

# Closer-in minimum depth, disparity range is doubled (from 95 to 190):
extended_disparity = False
# Better accuracy for longer distance, fractional disparity 32-levels:
subpixel = False
# Better handling for occlusions:
lr_check = True

# Create pipeline
pipeline = dai.Pipeline()

# Define sources and outputs
monoLeft = pipeline.create(dai.node.MonoCamera)
monoRight = pipeline.create(dai.node.MonoCamera)
depth = pipeline.create(dai.node.StereoDepth)
xout = pipeline.create(dai.node.XLinkOut)

xout.setStreamName("disparity")

# Properties
monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)
monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)

# Create a node that will produce the depth map (using disparity output as it's easier to visualize depth this way)
depth.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
# Options: MEDIAN_OFF, KERNEL_3x3, KERNEL_5x5, KERNEL_7x7 (default)
depth.initialConfig.setMedianFilter(dai.MedianFilter.KERNEL_7x7)
depth.setLeftRightCheck(lr_check)
depth.setExtendedDisparity(extended_disparity)
depth.setSubpixel(subpixel)

config = depth.initialConfig.get()
config.postProcessing.speckleFilter.enable = False
config.postProcessing.speckleFilter.speckleRange = 50
config.postProcessing.temporalFilter.enable = True
config.postProcessing.spatialFilter.enable = True
config.postProcessing.spatialFilter.holeFillingRadius = 2
config.postProcessing.spatialFilter.numIterations = 1
config.postProcessing.thresholdFilter.minRange = 400
config.postProcessing.thresholdFilter.maxRange = 15000
config.postProcessing.decimationFilter.decimationFactor = 1
depth.initialConfig.set(config)

# Linking
monoLeft.out.link(depth.left)
monoRight.out.link(depth.right)
depth.disparity.link(xout.input)

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

    # Output queue will be used to get the disparity frames from the outputs defined above
    q = device.getOutputQueue(name="disparity", maxSize=4, blocking=False)

    while True:
        inDisparity = q.get()  # blocking call, will wait until a new data has arrived
        frame = inDisparity.getFrame()
        # Normalization for better visualization
        frame = (frame * (255 / depth.initialConfig.getMaxDisparity())).astype(np.uint8)

        cv2.imshow("disparity", frame)

        # Available color maps: https://docs.opencv.org/3.4/d3/d50/group__imgproc__colormap.html
        frame = cv2.applyColorMap(frame, cv2.COLORMAP_JET)
        cv2.imshow("disparity_color", frame)

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