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import sys
import os
import cv2
import numpy as np
import tensorflow as tf

sys.path.append("..")

from object_detection.utils import label_map_util

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VisualSFM by Changchang Wu

Probably the most straight forward way to start generating Point Clouds from a set of pictures.

VisualSFM is a GUI application for 3D reconstruction using structure from motion (SFM). The reconstruction system integrates several of my previous projects: SIFT on GPU(SiftGPU), Multicore Bundle Adjustment, and Towards Linear-time Incremental Structure from Motion. VisualSFM runs fast by exploiting multicore parallelism for feature detection, feature matching, and bundle adjustment.

For dense reconstruction, this program supports Yasutaka Furukawa's PMVS/CMVS tool chain, and can prepare data for Michal Jancosek's CMP-MVS. In addition, the output of VisualSFM is natively supported by Mathias Rothermel and Konrad Wenzel's [SURE]

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In a dedicated directory:

1. save custom-plotly.js and package.json
2. specify the traces you want inside custom-plotly.js
3. run `git clone git@github.com:plotly/plotly.js.git`
4. run `cd plotly.js && npm install && cd ..` to install plotly's dependencies
5. run `npm install && npm run build` to create custom-plotly.min.js
6. move custom-plotly.min.js to your project and add <script src="custom-plotly.min.js" charset="utf-8"></script>
7. profit? global `Plotly` available.

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The introduction to Reactive Programming you've been missing

(by @andrestaltz)

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This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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