Merge branch 'feature/random-colors'

TODO

@@ -0,0 +1,6 @@
+Todo:
+  Gradio:
+    ☐ Upload button
+    ☐ Color group
+  CLI:
+    ☐ Buffer context

myapp/__main__.py

@@ -0,0 +1,4 @@
+from myapp.cli import cli
+
+if __name__ == "__main__":
+    cli()

myapp/app.py

@@ -2,7 +2,7 @@ import re
 from functools import partial
 from io import BytesIO
 from pathlib import Path
-from typing import Any
+from typing import Any, cast
 
 import gradio as gr
 import segno
@@ -10,6 +10,10 @@ from gradio.components import Component
 from huggingface_hub import InferenceClient
 from PIL import Image
 from qrcode_artistic import write_artistic
+from segno.consts import ERROR_MAPPING
+
+from myapp.colorutils import array_to_hex
+from myapp.palette import extract_color_clusters, sort_color_clusters
 
 try:
     import dotenv
@@ -19,6 +23,7 @@ except ImportError:
     pass
 
 client = InferenceClient(model="black-forest-labs/FLUX.1-schnell")
+static_path = Path(__file__).parent.relative_to(Path.cwd()) / "static"
 
 MODELS = [
     "stabilityai/stable-diffusion-3.5-large",
@@ -39,13 +44,20 @@ with gr.Blocks() as demo:
 
         with gr.Column():
             output = gr.Image()
-            background = gr.Image("static/example.webp", visible=False, type="filepath")
+            background = gr.Image(
+                str(static_path / "example.webp"), visible=False, type="filepath"
+            )
             scale = gr.Slider(3, 15, 9, step=1, label="Scale")
+            error = gr.Radio(list(ERROR_MAPPING), value="H", label="Error")
 
             with gr.Row():
                 color_dark = gr.ColorPicker("#000000", label="Dark")
                 color_light = gr.ColorPicker("#FFFFFF", label="Light")
 
+            with gr.Row():
+                extract_colors = gr.Button("Extract")
+                gr.ClearButton([color_dark, color_light], value="Reset")
+
     def generate_background(data: dict[Component, Any]):
         if not data.get(prompt):
             return gr.skip(), gr.skip()
@@ -57,6 +69,9 @@ with gr.Blocks() as demo:
             return None
 
         def to_hex_format(value: str):
+            if value is None:
+                return None
+
             if value.startswith("#"):
                 return value
 
@@ -66,7 +81,7 @@ with gr.Blocks() as demo:
             return f"#{r:02X}{g:02X}{b:02X}"
 
         image = Image.open(data[background])
-        qr_code = segno.make(data[text], error="h")
+        qr_code = segno.make(data[text], error=data[error])
 
         with BytesIO() as buffer:
             write_artistic(
@@ -77,12 +92,23 @@ with gr.Blocks() as demo:
                 scale=data[scale],
                 light=to_hex_format(data[color_light]),
                 dark=to_hex_format(data[color_dark]),
+                quiet_zone=cast(Any, "#FFFFFF"),
             )
 
             return Image.open(buffer)
 
+    def generate_palette(data: dict[Component, Any]):
+        if data[background] is None:
+            return None, None
+
+        image = Image.open(data[background])
+        k_means = extract_color_clusters(image, n_clusters=2)
+        primary, secondary = map(array_to_hex, sort_color_clusters(k_means))
+
+        return primary, secondary
+
     gr.on(
-        [button.click, prompt.submit],
+        [button.click, prompt.submit, demo.load],
         partial(gr.update, interactive=False),
         outputs=button,
     ).then(
@@ -100,6 +126,7 @@ with gr.Blocks() as demo:
             text.submit,
             background.change,
             scale.change,
+            error.change,
             color_light.change,
             color_dark.change,
         ],
@@ -108,12 +135,20 @@ with gr.Blocks() as demo:
             text,
             background,
             scale,
+            error,
             color_light,
             color_dark,
         },
         outputs=output,
     )
 
+    gr.on(
+        [extract_colors.click],
+        generate_palette,
+        inputs={background},
+        outputs=[color_dark, color_light],
+    )
+
 
 if __name__ == "__main__":
     demo.launch()

myapp/cli.py

@@ -0,0 +1,60 @@
+import click
+import dotenv
+import segno
+from huggingface_hub import InferenceClient
+from qrcode_artistic import write_artistic
+from segno.consts import ERROR_MAPPING
+
+from myapp.palette import extract_color_clusters, generate_palette_image
+from myapp.params import ImageParamType
+
+dotenv.load_dotenv()
+client = InferenceClient()
+
+
+@click.group()
+def cli():
+    pass
+
+
+@cli.command()
+@click.option("--prompt", required=True)
+@click.option("--target", type=click.Path(dir_okay=False), required=True)
+@click.option("--model", default="black-forest-labs/FLUX.1-schnell")
+@click.option("--width", default=400)
+@click.option("--height", default=400)
+def generate_image(prompt, target, model, width, height):
+    image = client.text_to_image(
+        prompt=prompt,
+        model=model,
+        width=width,
+        height=height,
+    )
+
+    image.save(target)
+
+
+@cli.command()
+@click.option("--image", type=ImageParamType(), required=True)
+@click.option("--target", type=click.Path(dir_okay=False), required=True)
+@click.option("--n-colors", default=4)
+@click.option("--shade", "shades", default=(0.0,), multiple=True)
+def generate_palette(image, target, n_colors, shades):
+    k_means = extract_color_clusters(image, n_colors)
+    palette = generate_palette_image(k_means, shades=shades)
+    palette.save(target)
+
+
+@cli.command()
+@click.option("--text", required=True)
+@click.option("--background", type=ImageParamType(), required=True)
+@click.option("--target", type=click.Path(dir_okay=False), required=True)
+@click.option("--scale", type=click.IntRange(min=3, max=15), default=9)
+@click.option("--error", type=click.Choice(list(ERROR_MAPPING)))
+def generate_qr_code(text, background, target, scale, error):
+    write_artistic(
+        segno.make(text, error=error),
+        background.filename,
+        target,
+        scale=scale,
+    )

myapp/cli/main.py

@@ -0,0 +1,42 @@
+import click
+import dotenv
+from huggingface_hub import InferenceClient
+
+from myapp.palette import extract_color_clusters, generate_palette_image
+from myapp.params import ImageParamType
+
+dotenv.load_dotenv()
+client = InferenceClient()
+
+
+@click.group()
+def cli():
+    pass
+
+
+@cli.command()
+@click.option("--prompt", required=True)
+@click.option("--target", type=click.Path(dir_okay=False), required=True)
+@click.option("--model", default="black-forest-labs/FLUX.1-schnell")
+@click.option("--width", default=400)
+@click.option("--height", default=400)
+def generate_image(prompt, target, model, width, height):
+    image = client.text_to_image(
+        prompt=prompt,
+        model=model,
+        width=width,
+        height=height,
+    )
+
+    image.save(target)
+
+
+@cli.command()
+@click.option("--image", type=ImageParamType(), required=True)
+@click.option("--target", type=click.Path(dir_okay=False), required=True)
+@click.option("--n-colors", default=4)
+@click.option("--shade", "shades", default=(0.0,), multiple=True)
+def generate_palette(image, target, n_colors, shades):
+    k_means = extract_color_clusters(image, n_colors)
+    palette = generate_palette_image(k_means, shades=shades)
+    palette.save(target)

myapp/colorutils.py

@@ -0,0 +1,33 @@
+import colorsys
+from typing import NamedTuple
+
+import numpy as np
+
+
+class HSVUpdate(NamedTuple):
+    h: int = 0
+    s: int = 0
+    v: int = 0
+
+    def apply(self, color: np.ndarray):
+        hsv = colorsys.rgb_to_hsv(*color / 255)
+        tmp = np.add(hsv, self).clip(0, 1)
+        rgb = colorsys.hsv_to_rgb(*tmp)
+
+        return np.array(rgb) * 255
+
+
+def get_hsv_value(cluster: np.ndarray):
+    return colorsys.rgb_to_hsv(*cluster / 255)[2]
+
+
+def add_hsv_saturation(cluster: np.ndarray, delta: float):
+    h, s, v = colorsys.rgb_to_hsv(*cluster / 255)
+    s = max(0, min(1, s + delta))
+
+    return np.array(colorsys.hsv_to_rgb(h, s, v)) * 255
+
+
+def array_to_hex(values: np.ndarray):
+    values = np.round(values).astype(int)
+    return "#" + ("{:02X}" * len(values)).format(*values)

myapp/palette.py

@@ -0,0 +1,41 @@
+import itertools
+
+import numpy as np
+from PIL import Image
+from sklearn.cluster import KMeans
+
+from myapp.colorutils import add_hsv_saturation, get_hsv_value
+
+
+def extract_color_clusters(image_array: np.ndarray | Image.Image, n_clusters=2):
+    if not isinstance(image_array, np.ndarray):
+        image_array = np.array(image_array)
+
+    w, h, d = image_array.shape
+    pixels = image_array.reshape(w * h, d)
+
+    return KMeans(n_clusters=n_clusters).fit(pixels)
+
+
+def sort_color_clusters(k_means: KMeans):
+    return sorted(k_means.cluster_centers_, key=get_hsv_value)
+
+
+def iter_color_shades(k_means: KMeans, shades: tuple[float, ...]):
+    cluster_centers = sort_color_clusters(k_means)
+
+    for delta, cluster_center in itertools.product(shades, cluster_centers):
+        yield add_hsv_saturation(cluster_center, delta)
+
+
+def generate_palette_image(k_means: KMeans, size=40, shades=(0.0,)):
+    num_cluster_centers = len(k_means.cluster_centers_)
+    image = Image.new("RGB", (num_cluster_centers * size, len(shades) * size))
+
+    for i, color in enumerate(iter_color_shades(k_means, shades)):
+        color = tuple(map(int, color))
+        part = Image.new("RGB", (size, size), color)
+        position = (i % num_cluster_centers * size, i // num_cluster_centers * size)
+        image.paste(part, position)
+
+    return image

myapp/palette_demo.py

@@ -0,0 +1,30 @@
+from contextlib import ExitStack
+
+import gradio as gr
+import numpy as np
+
+from myapp.colorutils import array_to_hex
+from myapp.palette import extract_color_clusters, iter_color_shades
+
+with gr.Blocks() as demo:
+    image = gr.Image("static/vulture.webp")
+    n_colors = gr.Slider(1, 16, 4, step=1)
+    button = gr.Button()
+
+    @gr.render(inputs=[image, n_colors])
+    def render_palette(image_array: np.ndarray, n_clusers: int):
+        model = extract_color_clusters(image_array, n_clusers)
+        cluster_shades = iter_color_shades(model, (0, 0.2, 0.4, 0.6))
+
+        with ExitStack() as stack:
+            for i, cluster in enumerate(cluster_shades):
+                if i % n_clusers == 0:
+                    stack.pop_all().close()
+                    stack.enter_context(gr.Group())
+                    stack.enter_context(gr.Row(variant="compact"))
+
+                gr.ColorPicker(array_to_hex(cluster), container=False)
+
+
+if __name__ == "__main__":
+    demo.launch()

myapp/params.py

@@ -0,0 +1,19 @@
+from typing import Any
+
+import click
+from PIL import Image
+
+
+class ImageParamType(click.ParamType):
+    name = "image"
+
+    def convert(
+        self, value: Any, param: click.Parameter | None, ctx: click.Context | None
+    ) -> Any:
+        if value is None or isinstance(value, Image.Image):
+            return value
+
+        try:
+            return Image.open(value)
+        except OSError as e:
+            self.fail(str(e))

pyproject.toml

@@ -8,6 +8,7 @@ dependencies = [
     "gradio>=5.14.0",
     "huggingface-hub>=0.27.1",
     "qrcode-artistic>=3.0.2",
+    "scikit-learn>=1.6.1",
     "segno>=1.6.1",
 ]
 
@@ -19,3 +20,6 @@ develop = [
 
 [tool.setuptools.packages.find]
 include = ["myapp"]
+
+[project.scripts]
+myapp = "myapp.cli:cli"

requirements.txt

@@ -57,6 +57,8 @@ idna==3.10
     #   requests
 jinja2==3.1.5
     # via gradio
+joblib==1.4.2
+    # via scikit-learn
 markdown-it-py==3.0.0
     # via rich
 markupsafe==2.1.5
@@ -69,6 +71,8 @@ numpy==2.2.2
     # via
     #   gradio
     #   pandas
+    #   scikit-learn
+    #   scipy
 orjson==3.10.15
     # via gradio
 packaging==24.2
@@ -112,6 +116,10 @@ ruff==0.9.4
     # via gradio
 safehttpx==0.1.6
     # via gradio
+scikit-learn==1.6.1
+    # via myapp (pyproject.toml)
+scipy==1.15.1
+    # via scikit-learn
 segno==1.6.1
     # via
     #   myapp (pyproject.toml)
@@ -128,6 +136,8 @@ starlette==0.41.3
     # via
     #   fastapi
     #   gradio
+threadpoolctl==3.5.0
+    # via scikit-learn
 tomlkit==0.13.2
     # via gradio
 tqdm==4.67.1

uv.lock

@@ -386,6 +386,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/bd/0f/2ba5fbcd631e3e88689309dbe978c5769e883e4b84ebfe7da30b43275c5a/jinja2-3.1.5-py3-none-any.whl", hash = "sha256:aba0f4dc9ed8013c424088f68a5c226f7d6097ed89b246d7749c2ec4175c6adb", size = 134596 },
 ]
 
+[[package]]
+name = "joblib"
+version = "1.4.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/64/33/60135848598c076ce4b231e1b1895170f45fbcaeaa2c9d5e38b04db70c35/joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e", size = 2116621 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6", size = 301817 },
+]
+
 [[package]]
 name = "markdown-it-py"
 version = "3.0.0"
@@ -445,6 +454,7 @@ dependencies = [
     { name = "gradio" },
     { name = "huggingface-hub" },
     { name = "qrcode-artistic" },
+    { name = "scikit-learn" },
     { name = "segno" },
 ]
 
@@ -461,6 +471,7 @@ requires-dist = [
     { name = "ipdb", marker = "extra == 'develop'", specifier = ">=0.13.13" },
     { name = "python-dotenv", marker = "extra == 'develop'", specifier = ">=1.0.1" },
     { name = "qrcode-artistic", specifier = ">=3.0.2" },
+    { name = "scikit-learn", specifier = ">=1.6.1" },
     { name = "segno", specifier = ">=1.6.1" },
 ]
 
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 ]
 
+[[package]]
+name = "scikit-learn"
+version = "1.6.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "joblib" },
+    { name = "numpy" },
+    { name = "scipy" },
+    { name = "threadpoolctl" },
+]
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