Update README.md

README.md

@@ -55,5 +55,29 @@ for file in glob.glob(importDir + "*.ply"):
     bpy.ops.outliner.orphans_purge()
     bpy.ops.outliner.orphans_purge()
 ```
-Importing the PLY without normals causes Blender to automatically generate them.
+_Importing the PLY without normals causes Blender to automatically generate them._
 
+At this point the PLY files now need to be converted to training data, for this I wrote a C program [DatasetGen_2_6.7z](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/DatasetGen_2_6.7z?download=true) using [RPLY](https://w3.impa.br/~diego/software/rply/) to load the PLY files and convert them to binary data which I have provided here [HeadsNet-2-6.7z](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/HeadsNet-2-6.7z?download=true).
+
+It's always good to NAN check your training data after generating it so I have provided a simple Python script for that here [nan_check.py](https://huggingface.co/datasets/tfnn/HeadsNet/resolve/main/nan_check.py?download=true).
+
+This binary training data can be loaded into Python using:
+```
+load_x = []
+  with open("train_x.dat", 'rb') as f:
+    load_x = np.fromfile(f, dtype=np.float32)
+
+  load_y = []
+  with open("train_y.dat", 'rb') as f:
+    load_y = np.fromfile(f, dtype=np.float32)
+```
+
+The data can then be reshaped and saved back out as a numpy array which makes for faster loading:
+```
+inputsize = 2
+outputsize = 6
+train_x = np.reshape(load_x, [tss, inputsize])
+train_y = np.reshape(load_y, [tss, outputsize])
+np.save("train_x.npy", train_x)
+np.save("train_y.npy", train_y)
+```