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Dataset_Scraper.7z

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4daaf5218628fd767985030c6e6cadac425ed5ace3fc6f9557fbe135eea8e4e7
+size 52184

HeadsNet-2-6.7z

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9ab339e7b10dd320c0eb9f917654cf0a19bb453e5f34f02197981c2e989de067
+size 5074033744

fix_ply.py

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+# https://github.com/mrbid
+import bpy
+import glob
+import pathlib
+if not isdir(outputDir): mkdir(outputDir)
+importDir = "ply/"
+outputDir = "ply_norm/"
+if not isdir(outputDir): mkdir(outputDir)
+
+for file in glob.glob(importDir + "*.ply"):
+    model_name = pathlib.Path(file).stem
+    if pathlib.Path(outputDir+model_name+'.ply').is_file() == True: continue
+    bpy.ops.wm.ply_import(filepath=file)
+    bpy.ops.wm.ply_export(
+                            filepath=outputDir+model_name+'.ply',
+                            filter_glob='*.ply',
+                            check_existing=False,
+                            ascii_format=False,
+                            export_selected_objects=False,
+                            apply_modifiers=True,
+                            export_triangulated_mesh=True,
+                            export_normals=True,
+                            export_uv=False,
+                            export_colors='SRGB',
+                            global_scale=1.0,
+                            forward_axis='Y',
+                            up_axis='Z'
+                        )
+    bpy.ops.object.select_all(action='SELECT')
+    bpy.ops.object.delete(use_global=False)
+    bpy.ops.outliner.orphans_purge()
+    bpy.ops.outliner.orphans_purge()
+    bpy.ops.outliner.orphans_purge()

nan_check.py

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+# https://github.com/mrbid
+import sys
+import numpy as np
+from time import time_ns
+
+# print everything / no truncations
+np.set_printoptions(threshold=sys.maxsize)
+
+# load training data
+st = time_ns()
+train_x = []
+train_y = []
+print("Loading...")
+with open("train_x.dat", 'rb') as f: data = np.fromfile(f, dtype=np.float32)
+with open("train_y.dat", 'rb') as f: data = np.fromfile(f, dtype=np.float32)
+print("NaN's detected:", np.count_nonzero(np.isnan(data)))
+timetaken = (time_ns()-st)/1e+9
+print("Time Taken:", "{:.2f}".format(timetaken), "seconds")