📝 Adding instructions

FORK.md

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+# Why this fork
+
+This is a fork from the original NeRF++ implementation. The original code has
+some isses I needed to fix in order to get the algorithm working and to be able
+to reproduce the authors work.
+Hopefully this version should be better explained, will the necessary scripts
+and without bugs to enable someone to train a dataset from start to finish
+without having delve into the code if do not want to.
+
+## How to install
+
+Create a virtual env if you want to
+```
+pip3 -m venv env
+source env/bin/activate
+```
+Install the needed dependencies
+```
+pip3 install -r requirements.txt
+```
+You will also need [COLMAP](colmap.github.io/)
+
+## How to run
+
+First you need to create or find a dataset. A large set of images (at least 30,
+more if you want a 360 degree reconstruction).
+In order to maximise the quality of the reconstuction it is recommended to take
+pictures with the same illumination, from differents angles from the same
+subject (take a step between each picture, do not only rotate).
+Remember that higher quality pictures can always be resized later if needed
+with tools like ImageMagick (mogrify or convert)
+
+```
+mogrify -resize 800 *.jpg
+```
+
+Then use the wrapper colmap `colmap_runner/run_colmap.py`
+
+First change the two lines corresponding to input and output at the end of the
+script `img_dir` is your dataset and `output_dir` is the ouput.  If your COLMAP
+binary is not located in `/usr/local/bin/colmap` also change it.
+
+```
+cd colmap_runner
+python3 run_colmap.py
+```
+
+Then you will need to use the `format_dataset.py` script to transform the
+wrapper COLMAP binary format data into the datastructure requirements by
+NeRF++.
+
+You again need to change the `input_path` and `output_path`.
+
+```
+python3 format_dataset.py
+```
+
+You then need to create the configuration file, copying the example in
+`configs` and tweaking the values to your need is recommended. Refer to the
+help inside `ddp_train_nerf.py` if you need to understand a parameter.
+
+```
+python3 ddp_train_nerf.py --config configs/lupo/training_lupo.txt
+```
+
+Your training should be running, if you want to visualise the training in real
+time using tensorboard you can use:
+
+```
+tensorboard --logdir logs --host localhost
+```
+And opening the given socket (`ip:port`) in your browser.
+It should be 0.0.0.0:6006