add basic image editing pipeline with tone mapping

app.py

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+from interactive_pipe import interactive_pipeline, interactive
+from interactive_pipe.data_objects.curves import SingleCurve, Curve
+from interactive_pipe.data_objects.image import Image
+from global_tone_mapping import apply_s_curve_tone_mapping
+import numpy as np
+
+
+def histogram(img: np.ndarray) -> Curve:
+    hist_curves = []
+    for ch in range(img.shape[-1]):
+        hist, bins = np.histogram(
+            img[..., ch].flatten(), bins=128, range=(0, 1))
+        hist_curves.append(SingleCurve(bins[:-1], hist, style='rgb'[ch]+"-"))
+    hist_curve = Curve(hist_curves,
+                       xlabel="Intensity", ylabel="Frequency")
+    return hist_curve
+
+
+@interactive()
+def set_tone_mapping_params(
+    shadow_boost: float = (0., [-1., 1.]),
+    highlight_boost: float = (0., [-1., 1.]),
+    exposure: float = (0., [-1., 1.]),
+    global_params: dict = {}
+) -> None:
+    global_params["shadow_boost"] = shadow_boost
+    global_params["highlight_boost"] = highlight_boost
+    global_params["exposure"] = exposure
+
+
+def s_curve_tone_mapping(img: np.ndarray, global_params={}) -> np.ndarray:
+    exposure = global_params.get("exposure", 0.)
+    shadow_boost = global_params.get("shadow_boost", 0.)
+    highlight_boost = global_params.get("highlight_boost", 0.)
+    return apply_s_curve_tone_mapping(
+        img,
+        shadow_boost,
+        highlight_boost,
+        exposure
+    )
+
+
+def s_curve_visualization(global_params={}) -> Curve:
+    exposure = global_params.get("exposure", 0.)
+    shadow_boost = global_params.get("shadow_boost", 0.)
+    highlight_boost = global_params.get("highlight_boost", 0.)
+    x = np.linspace(0, 1, 256)
+    y = apply_s_curve_tone_mapping(x, shadow_boost,
+                                   highlight_boost,
+                                   exposure)
+    return Curve(
+        [
+            SingleCurve(x, y, style='m-'),
+            SingleCurve(x, x, style='k--'),
+        ],
+        xlabel="Input", ylabel="Output"
+    )
+
+
+@ interactive_pipeline(gui="gradio")
+def image_editing_pipeline(input_image):
+    set_tone_mapping_params()
+    tc_image = s_curve_tone_mapping(input_image)
+    tone_curve = s_curve_visualization()
+    histogram_curve = histogram(tc_image)
+    return tc_image, histogram_curve, tone_curve
+
+
+if __name__ == "__main__":
+    img = Image.load_image("image_sample.png")
+    image_editing_pipeline(img)

global_tone_mapping.py

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+import numpy as np
+
+
+def apply_s_curve_tone_mapping(
+    x: np.ndarray,
+    shadow_boost: float = 0.,
+    highlight_boost: float = 0.,
+    exposure: float = 0.,
+) -> np.ndarray:
+    """
+    Apply an S-curve tone mapping to input x with given parameters.
+
+    Parameters:
+    x : array-like
+        Input values in the range [0, 1].
+    shadow_boost : float
+        Shadow boost parameter (tangent).
+        Positive values decreases the steepness in shadows.
+    highlight_boost : float
+        Highlight boost parameter (tangent). 
+        Positive values increase the steepness in highlights.
+    exposure : float
+        Exposure adjustment parameter. When exposure=0, 0.5 maps to 0.5.
+
+    Returns:
+    y : array-like
+        Tone-mapped output values in the range [0, 1].
+    """
+    x_expo = np.clip(x * 2 ** exposure, 0, 1)
+
+    # Adjust s and h parameters
+    s = 1 - shadow_boost
+    h = 1 + highlight_boost
+
+    # Compute numerator and denominator for the S-curve formula
+    numerator = x_expo ** s
+    denominator = numerator + (1 - x_expo) ** h
+
+    # Avoid division by zero
+    with np.errstate(divide='ignore', invalid='ignore'):
+        y = numerator / denominator
+        y = np.nan_to_num(y)
+
+    return y

requirements.txt

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+interactive-pipe