Miscellaneous cleanup

- Remove old dither code from dither.py
- Commit an old color-distance experiment

.gitignore

@@ -0,0 +1 @@
+__pycache__

color-distance.py

@@ -0,0 +1,50 @@
+# Playing with measuring color distances
+# Cleaned-up transcript from an ipython session, mostly experimental.
+# The actual compression algorithm probably won't use this.
+
+from dither import *
+
+def distance(x, y):
+    return 2.5*((x[0]-y[0])/255)**2 + 4*((x[1]-y[1])/255)**2 + 2.5*((x[2]-y[2])/255)**2
+
+distances = {(i,j): distance(PICO_RGB[i],PICO_RGB[j]) for i in range(len(PICO_RGB)) for j in range(len(PICO_RGB))}
+
+def rect_distance_sum(img1, img2, xy1, xy2):
+    """Returns summed color distance of two images, using a rectangular mask"""
+    result = 0.0
+    x1, y1 = xy1
+    x2, y2 = xy2
+    for y in range(y1, y2):
+        for x in range(x1, x2):
+            c1 = img1.getpixel((x, y))
+            c2 = img2.getpixel((x, y))
+            result += distances[(c1, c2)]
+    return result
+
+# d = dithered version of resized (peacock image, RGB color)
+d = pattern_dither(resized, PICO_RGB[:16], amount=1)
+
+# s = nearest-color image
+s = Image.new(size=d.size, mode='P', color=1)
+s.putpalette([c for rgb in PICO_RGB[:16] for c in rgb])
+
+def solid_cost(img, xy1, xy2, color):
+    """Returns cost of approximating a rectangular region with a solid color"""
+    x1, y1 = xy1
+    x2, y2 = xy2
+    result = 0.0
+    for y in range(y1, y2):
+        for x in range(x1, x2):
+            p = img.getpixel((x, y))
+            result += distances[(color, p)]
+    return result
+
+# Downsample the dithered image with a given size of rectangles
+skip = 2
+for y in range(0, 128, skip):
+    for x in range(0, 128, skip):
+        _, i = min(  (solid_cost(d, (x,y), (x+skip,y+skip), c), c) for c in range(16)  )
+        for x2 in range(x, x + skip):
+            for y2 in range(y, y + skip):
+                s.putpixel((x2,y2), i)
+# s.show()

dither.py

@@ -108,38 +108,3 @@ def pattern_dither(img, palette, *, pat_size=4, amount=0.75):
     result.putpalette([c for rgb in old_palette for c in rgb])
     result.putdata([old_palette_indexes[i] for i in output_indexes])
     return result
-
-def dither_pixel(img, xy, pal):
-    global bayer
-    pal2 = [np.array(rgb_to_linear(c)) for c in pal]
-    counts = [0]*len(pal)
-    x, y = xy
-    def diff(c1, c2):
-        return sum((c1-c2)**2)
-    def nearest(rgb):
-        cost, index = min((diff(rgb,c), i) for i,c in enumerate(pal2))
-        return index
-    color = np.array(rgb_to_linear(img.getpixel(xy)))
-    err = np.array([0.0,0.0,0.0])
-    for _ in range(bayer.size):
-        err += color
-        i = nearest(err)
-        counts[i] += 1
-        err -= pal2[i]
-    thresh = bayer[y % bayer.shape[0]][x % bayer.shape[1]]
-    for i, count in enumerate(counts):
-        thresh -= count
-        if thresh < 0:
-            return i
-
-dithered = Image.new('P', resized.size)
-dithered.putpalette([x for rgb in PICO_RGB[:16] for x in rgb])
-def do_it():
-    global dithered, resized
-    for x in tqdm(range(128)):
-        for y in range(128):
-            i = dither_pixel(resized, (x,y), PICO_RGB[:16])
-            dithered.putpixel((x,y), i)
-# import cProfile
-# cProfile.run('do_it()')
-#do_it()