feat(api): add normal and uniform noise sources

api/onnx_web/image.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 
+
 def blend_mult(a):
     return float(a) / 256
 
@@ -13,14 +14,64 @@ def blend_imult(a):
 
 
 def blend_mask_inverse_source(source: Tuple[int, int, int], mask: Tuple[int, int, int], noise: Tuple[int, int, int]) -> Tuple[int, int, int]:
+    '''
+    Blend operation, linear interpolation from source to noise based on mask: `(s * (1 - m)) + (n * m)`
+    '''
     return (
-        int((source[0] * blend_imult(mask[0])) + (noise[0] * blend_mult(mask[0]))),
-        int((source[1] * blend_imult(mask[1])) + (noise[1] * blend_mult(mask[1]))),
-        int((source[2] * blend_imult(mask[2])) + (noise[2] * blend_mult(mask[2]))),
+        int((source[0] * blend_imult(mask[0])) +
+            (noise[0] * blend_mult(mask[0]))),
+        int((source[1] * blend_imult(mask[1])) +
+            (noise[1] * blend_mult(mask[1]))),
+        int((source[2] * blend_imult(mask[2])) +
+            (noise[2] * blend_mult(mask[2]))),
     )
 
 
-def blend_source_histogram(source_image: Image, dims: Tuple[int, int]) -> Tuple[float, float, float]:
+def noise_source_uniform(source_image: Image, dims: Tuple[int, int]) -> Tuple[float, float, float]:
+    width, height = dims
+    size = width * height
+
+    noise_r = random.uniform(0, 256, size=size)
+    noise_g = random.uniform(0, 256, size=size)
+    noise_b = random.uniform(0, 256, size=size)
+
+    noise = Image.new('RGB', (width, height))
+
+    for x in range(width):
+        for y in range(height):
+            i = x * y
+            noise.putpixel((x, y), (
+                int(noise_r[i]),
+                int(noise_g[i]),
+                int(noise_b[i])
+            ))
+
+    return noise
+
+
+def noise_source_gaussian(source_image: Image, dims: Tuple[int, int]) -> Tuple[float, float, float]:
+    width, height = dims
+    size = width * height
+
+    noise_r = random.normal(128, 32, size=size)
+    noise_g = random.normal(128, 32, size=size)
+    noise_b = random.normal(128, 32, size=size)
+
+    noise = Image.new('RGB', (width, height))
+
+    for x in range(width):
+        for y in range(height):
+            i = x * y
+            noise.putpixel((x, y), (
+                int(noise_r[i]),
+                int(noise_g[i]),
+                int(noise_b[i])
+            ))
+
+    return noise
+
+
+def noise_source_histogram(source_image: Image, dims: Tuple[int, int]) -> Tuple[float, float, float]:
     r, g, b = source_image.split()
     width, height = dims
     size = width * height
@@ -29,9 +80,12 @@ def blend_source_histogram(source_image: Image, dims: Tuple[int, int]) -> Tuple[
     hist_g = g.histogram()
     hist_b = b.histogram()
 
-    noise_r = random.choice(256, p=np.divide(np.copy(hist_r), np.sum(hist_r)), size=size)
-    noise_g = random.choice(256, p=np.divide(np.copy(hist_g), np.sum(hist_g)), size=size)
-    noise_b = random.choice(256, p=np.divide(np.copy(hist_b), np.sum(hist_b)), size=size)
+    noise_r = random.choice(256, p=np.divide(
+        np.copy(hist_r), np.sum(hist_r)), size=size)
+    noise_g = random.choice(256, p=np.divide(
+        np.copy(hist_g), np.sum(hist_g)), size=size)
+    noise_b = random.choice(256, p=np.divide(
+        np.copy(hist_b), np.sum(hist_b)), size=size)
 
     noise = Image.new('RGB', (width, height))
 
@@ -47,10 +101,16 @@ def blend_source_histogram(source_image: Image, dims: Tuple[int, int]) -> Tuple[
     return noise
 
 
-
 # based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/master/scripts/outpainting_mk_2.py#L175-L232
-def expand_image(source_image: Image, mask_image: Image, dims: Tuple[int, int, int, int], fill = 'white', blend_source=blend_source_histogram, blend_op=blend_mask_inverse_source):
-    left, right, top, bottom = dims
+def expand_image(
+        source_image: Image,
+        mask_image: Image,
+        expand_by: Tuple[int, int, int, int],
+        fill='white',
+        noise_source=noise_source_histogram,
+        blend_op=blend_mask_inverse_source
+):
+    left, right, top, bottom = expand_by
 
     full_width = left + source_image.width + right
     full_height = top + source_image.height + bottom
@@ -61,7 +121,7 @@ def expand_image(source_image: Image, mask_image: Image, dims: Tuple[int, int, i
     full_mask = Image.new('RGB', (full_width, full_height), fill)
     full_mask.paste(mask_image, (left, top))
 
-    full_noise = blend_source(source_image, (full_width, full_height))
+    full_noise = noise_source(source_image, (full_width, full_height))
 
     for x in range(full_source.width):
         for y in range(full_source.height):
@@ -70,6 +130,7 @@ def expand_image(source_image: Image, mask_image: Image, dims: Tuple[int, int, i
             source_color = full_source.getpixel((x, y))
 
             if mask_color[0] > 0:
-                full_source.putpixel((x, y), blend_op(source_color, mask_color, noise_color))
+                full_source.putpixel((x, y), blend_op(
+                    source_color, mask_color, noise_color))
 
     return (full_source, full_mask, full_noise, (full_width, full_height))