app.py

from interactive_pipe import interactive_pipeline, interactive
from interactive_pipe import CircularControl
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
from color_conversions import rgb_to_hsv, hsv_to_rgb
from synthetic_charts import generate_color_wheel
import argparse
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], linestyle="-")
        )
    hist_curve = Curve(
        hist_curves,
        xlabel="Intensity",
        ylabel="Frequency",
        # title="Histogram"
    )
    return hist_curve


@interactive()
def set_tone_mapping_params(
    # shadow_boost: float = (0., [-1., 1.]),
    shadow_boost: float = CircularControl(0., [-1., 1.]),
    highlight_boost: float = (0., [-1., 1.]),
    exposure: float = (0., [-1., 1.]),
    contrast: float = (0., [-1., 1.]),
    global_params: dict = {}
) -> None:
    global_params["shadow_boost"] = shadow_boost
    global_params["highlight_boost"] = highlight_boost
    global_params["contrast"] = contrast
    global_params["exposure"] = exposure


@interactive(
    apply_tone_curve_on_luma=(True,)
)
def tone_map(
    hsv: np.ndarray,
    apply_tone_curve_on_luma: bool = True,
    global_params={}
) -> np.ndarray:
    shadow_boost = global_params.get("shadow_boost", 0.)
    highlight_boost = global_params.get("highlight_boost", 0.)
    contrast = global_params.get("contrast", 0.)
    exposure = global_params.get("exposure", 0.)
    if not apply_tone_curve_on_luma:
        return rgb_to_hsv(
            apply_s_curve_tone_mapping(
                hsv_to_rgb(hsv),
                shadow_boost,
                highlight_boost,
                contrast,
                exposure
            )
        )
    luma_tone_mapped = apply_s_curve_tone_mapping(
        hsv[..., -1],
        shadow_boost,
        highlight_boost,
        contrast,
        exposure
    )
    hsv[..., -1] = luma_tone_mapped
    return hsv


@interactive(
    vibrance=(0, [0, 100]),
    # vibrance_mask_smoothness=(3., [-0.9, 4.])
)
def modify_vibrance(
    hsv_in: np.ndarray,
    vibrance: float = 0.,
    vibrance_mask_smoothness: float = 3.
) -> np.ndarray:
    gain_luma = np.interp(
        hsv_in[..., 2],
        [0., 0.5, 1.],
        [0., 0., 1.]
    )
    gain_sat = np.interp(hsv_in[..., 1],
                         [0., 0.25, 1.],
                         [0., 0., 1.]
                         )**(1+vibrance_mask_smoothness)
    red_thresh = 55.
    pink_thresh = 10.
    overlap = 10.

    gain_hue = np.interp(
        hsv_in[..., 0],
        [0., red_thresh, red_thresh+overlap,
         360-pink_thresh-overlap, 360-pink_thresh, 360],
        [0., 0., 1., 1., 0., 0.]
    )**(1+vibrance_mask_smoothness)
    gain = 1. + gain_luma * (gain_sat + gain_hue).clip(0., 1.)*(vibrance / 100)
    hsv_in[..., 1] = np.clip(gain*hsv_in[..., 1], 0, 1)
    return hsv_in


def s_curve_visualization(global_params={}) -> Curve:
    shadow_boost = global_params.get("shadow_boost", 0.)
    highlight_boost = global_params.get("highlight_boost", 0.)
    contrast = global_params.get("contrast", 0.)
    exposure = global_params.get("exposure", 0.)
    x = np.linspace(0, 1, 256)
    y = apply_s_curve_tone_mapping(x, shadow_boost,
                                   highlight_boost,
                                   contrast,
                                   exposure)
    return Curve(
        [
            SingleCurve(x, y, style='m', linestyle='-'),
            SingleCurve(x, x, style='k', linestyle='--'),
        ],
        xlabel="Input", ylabel="Output",
        title=f"Tone Curve {shadow_boost:.2f}, {highlight_boost:.2f}, {contrast:.2f}, {exposure:.2f}"
    )


@interactive(
    input_image=("sample image", ["sample image",
                 "alternative image", "color wheel"]),
)
def pick_image(
    default_image: np.ndarray,
    second_image: np.ndarray,
    input_image: str = "sample image",
    global_params={}
) -> np.ndarray:
    if input_image == "sample image":
        return default_image
    elif input_image == "alternative image":
        return second_image
    elif input_image == "color wheel":
        color_wheel = global_params.get("color wheel", None)
        if color_wheel is None:
            color_wheel = generate_color_wheel(resolution=496)
            global_params["color wheel"] = color_wheel
        return color_wheel
    else:
        return default_image


def image_editing_pipeline(sample_image, second_image):
    input_image = pick_image(sample_image, second_image)
    set_tone_mapping_params()
    hsv_in = rgb_to_hsv(input_image)
    hsv_tc = tone_map(hsv_in)
    hsv_out = modify_vibrance(hsv_tc)
    tc_image = hsv_to_rgb(hsv_out)
    tone_curve = s_curve_visualization()
    histogram_curve = histogram(tc_image)
    return tc_image, histogram_curve, tone_curve


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("-b", "--backend", type=str,
                        choices=["gradio", "qt", "mpl"], default="gradio")
    args = parser.parse_args()
    img = Image.load_image("image_sample_2.jpg")
    img2 = Image.load_image("image_sample.jpg")
    markdown_description = r"# Tone mapping: S-curve" + "\n" \
        r"$$\text{shadow boost} \in [-1, 1]$$  $$\text{highlight boost} \in [-1, 1]$$  $$\text{contrast} \in [-1, 1]$$  $$\text{exposure} \in [-1, 1]$$" + "\n" \
        "If Tone curves are applied to RGB colors separately, you'll get serious color shifts. It is much better when tone curve is applied to the luminance channel." + "\n"
    interactive_pipeline(
        gui=args.backend,
        markdown_description=markdown_description,
        sliders_layout=["compact", "group",
                        "vertical", "collapsible", "smart"][2],
        sliders_per_row_layout=1
    )(image_editing_pipeline)(img, img2)