Add preferential optimization picture (#327)

* add prederential-optimization-picture

* add README

* add blank line

* fix import

* add CLI argument for image

* delete shell file

* add blankline

* formatted

* fix launch command

* fix small

Author: milkcoffeen

dashboard/preferential-optimization-picture/README.md

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+# How to Run Preferential Optimization Image
+
+First, ensure the necessary packages are installed by executing the following command in your terminal:
+
+```bash
+$ pip install "optuna>=3.3.0" "optuna-dashboard[preferential]>=0.13.0b1" pillow
+```
+
+Next, execute the Python script. 
+
+```bash
+$ python generator.py --image_path sample.png
+```
+
+Then, launch Optuna Dashboard in a separate process using the following command.
+
+```bash
+optuna-dashboard sqlite:///db.sqlite3 --artifact-dir ./artifact
+```

dashboard/preferential-optimization-picture/generator.py

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+from __future__ import annotations
+
+import argparse
+import os
+import tempfile
+import time
+from typing import NoReturn
+
+from optuna.artifacts import FileSystemArtifactStore
+from optuna.artifacts import upload_artifact
+from optuna_dashboard import register_preference_feedback_component
+from optuna_dashboard.preferential import create_study
+from optuna_dashboard.preferential.samplers.gp import PreferentialGPSampler
+from PIL import Image
+from PIL import ImageEnhance
+
+
+STORAGE_URL = "sqlite:///db.sqlite3"
+artifact_path = os.path.join(os.path.dirname(__file__), "artifact")
+artifact_store = FileSystemArtifactStore(base_path=artifact_path)
+os.makedirs(artifact_path, exist_ok=True)
+
+
+def main() -> NoReturn:
+    # Parse command-line arguments.
+    parser = argparse.ArgumentParser(description="Optimize image enhancement parameters.")
+    parser.add_argument(
+        "--image_path", type=str, required=True, help="Path to the input image file."
+    )
+    args = parser.parse_args()
+
+    # Validate the image path.
+    if not os.path.exists(args.image_path):
+        raise FileNotFoundError(f"The specified image file does not exist: {args.image_path}")
+
+    study = create_study(
+        n_generate=4,
+        study_name="Preferential Optimization Image Scene",
+        storage=STORAGE_URL,
+        sampler=PreferentialGPSampler(),
+        load_if_exists=True,
+    )
+    # Change the component, displayed on the human feedback pages.
+    # By default (component_type="note"), the Trial's Markdown note is displayed.
+    user_attr_key = "rgb_image"
+    register_preference_feedback_component(study, "artifact", user_attr_key)
+    image_sample = Image.open(args.image_path)  # Use the image path from command-line arguments.
+    with tempfile.TemporaryDirectory() as tmpdir:
+        while True:
+            # If study.should_generate() returns False,
+            # the generator waits for human evaluation.
+            if not study.should_generate():
+                time.sleep(0.1)  # Avoid busy-loop.
+                continue
+
+            trial = study.ask()
+            # 1. Ask new parameters.
+            contrast_factor = trial.suggest_float("contrast_factor", 0.0, 2.0)
+            brightness_factor = trial.suggest_float("brightness_factor", 0.0, 2.0)
+            color_factor = trial.suggest_float("color_factor", 0.0, 2.0)
+            sharpness_factor = trial.suggest_float("sharpness_factor", 0.0, 2.0)
+
+            # 2. Generate image.
+            image_path = os.path.join(tmpdir, f"sample-{trial.number}.png")
+            image = image_sample.copy()
+
+            image = ImageEnhance.Contrast(image).enhance(contrast_factor)
+            image = ImageEnhance.Brightness(image).enhance(brightness_factor)
+            image = ImageEnhance.Color(image).enhance(color_factor)
+            image = ImageEnhance.Sharpness(image).enhance(sharpness_factor)
+
+            image.save(image_path)
+
+            # 3. Upload Artifact and set artifact_id to trial.user_attrs["rgb_image"].
+            artifact_id = upload_artifact(
+                artifact_store=artifact_store,
+                file_path=image_path,
+                study_or_trial=trial,
+            )
+            trial.set_user_attr(user_attr_key, artifact_id)
+
+
+if __name__ == "__main__":
+    main()