Initial commit: RE tooling and docs for The I of the Dragon localization/HD

tools/statictext.py

@@ -0,0 +1,73 @@
+from PIL import Image
+import glob, statistics
+
+files = sorted(glob.glob(r"C:\Users\29452\AppData\Local\Temp\opencode\seq*.png"))
+imgs = [Image.open(f).convert("L") for f in files]
+W, H = imgs[0].size
+n = len(imgs)
+pxs = [im.load() for im in imgs]
+
+# temporal std per pixel (sample every pixel but coarse for speed? do full)
+# spatial gradient on mean image
+# Build mean and std
+import array
+step = 1
+cand = Image.new("L", (W, H), 0)
+cp = cand.load()
+for y in range(0, H, 2):
+    for x in range(0, W, 2):
+        vals = [px[x, y] for px in pxs]
+        m = sum(vals) / n
+        var = sum((v - m) ** 2 for v in vals) / n
+        std = var ** 0.5
+        # spatial contrast from mean image
+        xm = x - 2 if x - 2 >= 0 else x
+        xp = x + 2 if x + 2 < W else x
+        gx = abs(pxs[0][xp, y] - pxs[0][xm, y])  # placeholder
+        # use mean image gradient
+        mean_px = [[sum(px[xx, yy] for px in pxs) / n for xx in (x,)] for yy in (y,)]
+        cp[x, y] = 255 - min(255, int(std))
+import os
+# Better: compute static mask = std < 6, then high contrast of mean
+mean = Image.new("L", (W, H), 0)
+mp = mean.load()
+for y in range(H):
+    for x in range(W):
+        mp[x, y] = int(sum(px[x, y] for px in pxs) / n)
+# gradient of mean
+ed = Image.new("L", (W, H), 0)
+ep = ed.load()
+for y in range(1, H - 1):
+    for x in range(1, W - 1):
+        g = abs(mp[x + 1, y] - mp[x - 1, y]) + abs(mp[x, y + 1] - mp[x, y - 1])
+        ep[x, y] = min(255, g)
+# static mask
+st = Image.new("L", (W, H), 0)
+sp = st.load()
+for y in range(H):
+    for x in range(W):
+        vals = [px[x, y] for px in pxs]
+        m = sum(vals) / n
+        var = sum((v - m) ** 2 for v in vals) / n
+        sp[x, y] = 255 if var ** 0.5 < 4 else 0
+# candidate = static AND edge
+cand = Image.new("L", (W, H), 0)
+cp = cand.load()
+for y in range(H):
+    for x in range(W):
+        cp[x, y] = ep[x, y] if sp[x, y] else 0
+cand.save(r"C:\Users\29452\AppData\Local\Temp\opencode\staticcand.png")
+
+# report bounding regions: coarse grid of activity
+gw, gh = 40, 24
+cellw, cellh = W // gw, H // gh
+print("grid activity (static-edge):")
+for gy in range(gh):
+    line = ""
+    for gx in range(gw):
+        s = 0
+        for y in range(gy * cellh, min(H, (gy + 1) * cellh), 3):
+            for x in range(gx * cellw, min(W, (gx + 1) * cellw), 3):
+                s += cp[x, y]
+        line += " .:-=+*#%@"[min(9, s // 300)]
+    print(line)