Create app.py

app.py

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+# app.py
+# Hugging Face Space (Gradio) for color correction:
+# Takes two images: (1) image to correct, (2) reference image.
+# Returns a single, color-corrected image.
+
+import gradio as gr
+from PIL import Image
+import numpy as np
+import cv2
+from skimage import exposure
+
+
+def _to_rgb_np(img: Image.Image) -> np.ndarray:
+    """PIL -> RGB numpy uint8"""
+    if img.mode != "RGB":
+        img = img.convert("RGB")
+    return np.array(img)
+
+
+def _lab_histogram_match(src_rgb: np.ndarray, ref_rgb: np.ndarray) -> np.ndarray:
+    """
+    Match histogram of src to ref in LAB space and return as RGB uint8.
+    """
+    src_lab = cv2.cvtColor(src_rgb, cv2.COLOR_RGB2LAB)
+    ref_lab = cv2.cvtColor(ref_rgb, cv2.COLOR_RGB2LAB)
+
+    matched_lab = exposure.match_histograms(src_lab, ref_lab, channel_axis=2)
+    matched_lab = np.clip(matched_lab, 0, 255).astype(np.uint8)
+
+    matched_rgb = cv2.cvtColor(matched_lab, cv2.COLOR_LAB2RGB)
+    return matched_rgb
+
+
+def _luminosity_blend(base_rgb: np.ndarray, blend_rgb: np.ndarray) -> np.ndarray:
+    """
+    Photoshop-like 'Luminosity' blend: keep hue/saturation from base,
+    take luminance from blend. Implemented via HLS space.
+    """
+    base_hls = cv2.cvtColor(base_rgb, cv2.COLOR_RGB2HLS)
+    blend_hls = cv2.cvtColor(blend_rgb, cv2.COLOR_RGB2HLS)
+
+    out_hls = base_hls.copy()
+    out_hls[..., 1] = blend_hls[..., 1]  # replace Lightness channel
+
+    out_rgb = cv2.cvtColor(out_hls, cv2.COLOR_HLS2RGB)
+    return out_rgb
+
+
+def color_correct(img_to_correct: Image.Image, reference_img: Image.Image) -> Image.Image:
+    """
+    Color-correct `img_to_correct` to match the look of `reference_img`.
+    Steps:
+    1) Histogram match in LAB space.
+    2) Luminosity blend to preserve original hue/saturation.
+    """
+    if img_to_correct is None or reference_img is None:
+        return None
+
+    src = _to_rgb_np(img_to_correct)
+    ref = _to_rgb_np(reference_img)
+
+    matched = _lab_histogram_match(src, ref)
+    result = _luminosity_blend(src, matched)
+
+    return Image.fromarray(result)
+
+
+title = "Color Correction (Histogram Match + Luminosity Blend)"
+description = (
+    "Upload the image you want to correct (left) and a reference image (right). "
+    "The output transfers the overall tonal feel of the reference while preserving "
+    "the original image's colors via a luminosity blend."
+)
+
+with gr.Blocks() as demo:
+    gr.Markdown(f"# {title}\n{description}")
+
+    with gr.Row():
+        with gr.Column():
+            img_a = gr.Image(type="pil", label="Image to Correct")
+        with gr.Column():
+            img_b = gr.Image(type="pil", label="Reference Image")
+
+    out = gr.Image(type="pil", label="Corrected Image")
+
+    btn = gr.Button("Run Color Correction")
+    btn.click(color_correct, inputs=[img_a, img_b], outputs=[out])
+
+if __name__ == "__main__":
+    demo.launch()