tools: Add initial basic texture analysis script

In order to calibrate the texture compression settings better, we need a
way to analyze a few representative files and plot the dependency
between quality/size and compression parameters. This version focuses on
UASTC and UASTC RDO lambda specifically as it's the most sensitive
parameter.

tools/gltfbasis.py

@@ -0,0 +1,61 @@
+#!/usr/bin/python3
+
+import argparse
+import matplotlib.pyplot as plt
+import os
+import os.path
+import re
+import subprocess
+
+argp = argparse.ArgumentParser()
+argp.add_argument('--basisu', type=str, required=True)
+argp.add_argument('files', type=str, nargs='+')
+args = argp.parse_args()
+
+def compress(path, flags):
+    temp_path = "/dev/null" if os.name == "posix" else "NUL"
+    output = subprocess.check_output([args.basisu, "-file", path, "-output_file", temp_path, "-stats", "-ktx2"] + flags)
+    for line in output.splitlines():
+        if m := re.match(r".*source image.*?(\d+)x(\d+)", line.decode()):
+            pixels = int(m.group(1)) * int(m.group(2))
+        elif m := re.match(r".*Compression succeeded.*size (\d+) bytes", line.decode()):
+            bytes = int(m.group(1))
+        elif m := re.match(r"\.basis RGB Avg:.*RMS: (\d+\.\d+) PSNR: (\d+\.\d+)", line.decode()):
+            rms = float(m.group(1))
+            psnr = float(m.group(2))
+
+    return {'path': path, 'bpp': bytes * 8 / pixels, 'rms': rms, 'psnr': psnr}
+
+def uastc_stats(path):
+    results = []
+    for rdo_l in range(0, 20):
+        flags = ["-uastc", "-uastc_level", "1", "-uastc_rdo_l", str(rdo_l / 5), "-uastc_rdo_d", "1024"]
+        res = compress(path, flags)
+        res['rdo_l'] = rdo_l / 5
+        results.append(res)
+    return results
+
+plt.figure(figsize=(15, 5))
+
+for path in args.files:
+    print('Processing', path)
+    results = uastc_stats(path)
+    name = os.path.basename(path)
+    plt.subplot(1, 3, 1)
+    plt.plot([r['rdo_l'] for r in results], [r['bpp'] for r in results], label=name)
+    plt.subplot(1, 3, 2)
+    plt.plot([r['rdo_l'] for r in results], [r['rms'] for r in results], label=name)
+    plt.subplot(1, 3, 3)
+    plt.plot([r['rdo_l'] for r in results], [r['psnr'] for r in results], label=name)
+
+plt.subplot(1, 3, 1)
+plt.title('bpp')
+plt.legend()
+plt.subplot(1, 3, 2)
+plt.title('rms')
+plt.legend()
+plt.subplot(1, 3, 3)
+plt.title('psnr')
+plt.legend()
+
+plt.savefig('basisu.png')