andijakl

// Retrofit
implementation 'com.squareup.retrofit2:retrofit:2.5.0'
implementation "com.squareup.retrofit2:converter-moshi:2.5.0"
implementation "com.jakewharton.retrofit:retrofit2-kotlin-coroutines-adapter:0.9.2"

// Moshi
implementation "com.squareup.moshi:moshi:1.8.0"
kapt "com.squareup.moshi:moshi-kotlin-codegen:1.8.0"

// Kotlin Coroutines

andijakl

    // Source: https://github.com/Unity-Technologies/arfoundation-samples/blob/6296272a416925b56ce85470e0c7bef5c913ec0c/Assets/Scripts/CpuImageSample.cs
    private static void UpdateRawImage(RawImage rawImage, XRCpuImage cpuImage)
    {
        // Get the texture associated with the UI.RawImage that we wish to display on screen.
        var texture = rawImage.texture as Texture2D;

        // If the texture hasn't yet been created, or if its dimensions have changed, (re)create the texture.
        // Note: Although texture dimensions do not normally change frame-to-frame, they can change in response to
        //    a change in the camera resolution (for camera images) or changes to the quality of the human depth
        //    and human stencil buffers.

andijakl

# ------------------------------------------------------------
# CALCULATE DISPARITY (DEPTH MAP)
# Adapted from: https://github.com/opencv/opencv/blob/master/samples/python/stereo_match.py
# and: https://docs.opencv.org/master/dd/d53/tutorial_py_depthmap.html

# StereoSGBM Parameter explanations:
# https://docs.opencv.org/4.5.0/d2/d85/classcv_1_1StereoSGBM.html

# Matched block size. It must be an odd number >=1 . Normally, it should be somewhere in the 3..11 range.
block_size = 11

andijakl

plt.imshow(disparity_SGBM, cmap='plasma')
plt.colorbar()
plt.show()

andijakl

# Draw the rectified images
fig, axes = plt.subplots(1, 2, figsize=(15, 10))
axes[0].imshow(img1_rectified, cmap="gray")
axes[1].imshow(img2_rectified, cmap="gray")
axes[0].axhline(250)
axes[1].axhline(250)
axes[0].axhline(450)
axes[1].axhline(450)
plt.suptitle("Rectified images")
plt.savefig("rectified_images.png")

andijakl

# Undistort (rectify) the images and save them
# Adapted from: https://stackoverflow.com/a/62607343
img1_rectified = cv.warpPerspective(img1, H1, (w1, h1))
img2_rectified = cv.warpPerspective(img2, H2, (w2, h2))
cv.imwrite("rectified_1.png", img1_rectified)
cv.imwrite("rectified_2.png", img2_rectified)

andijakl

# Stereo rectification (uncalibrated variant)
# Adapted from: https://stackoverflow.com/a/62607343
h1, w1 = img1.shape
h2, w2 = img2.shape
_, H1, H2 = cv.stereoRectifyUncalibrated(
    np.float32(pts1), np.float32(pts2), fundamental_matrix, imgSize=(w1, h1)
)

andijakl

# Visualize epilines
# Adapted from: https://docs.opencv.org/master/da/de9/tutorial_py_epipolar_geometry.html
def drawlines(img1src, img2src, lines, pts1src, pts2src):
    ''' img1 - image on which we draw the epilines for the points in img2
        lines - corresponding epilines '''
    r, c = img1src.shape
    img1color = cv.cvtColor(img1src, cv.COLOR_GRAY2BGR)
    img2color = cv.cvtColor(img2src, cv.COLOR_GRAY2BGR)
    # Edit: use the same random seed so that two images are comparable!
    np.random.seed(0)

andijakl

app.get('/', async (req, res) => {
  // Import database node module
  const CosmosClientInterface = require("@azure/cosmos").CosmosClient;

  // Database and container IDs
  const databaseId = "ToDoList";
  const containerId = "Items";
  
  // Configure database access URI and primary key
  const endpoint = "https://<...>.documents.azure.com:443/";

andijakl

package com.andresjakl.partslist

import android.util.Log
import com.jakewharton.retrofit2.adapter.kotlin.coroutines.CoroutineCallAdapterFactory
import retrofit2.Retrofit
import retrofit2.converter.moshi.MoshiConverterFactory

// Singleton pattern in Kotlin: https://kotlinlang.org/docs/reference/object-declarations.html#object-declarations
object WebAccess {
    val partsApi : PartsApiClient by lazy {