Spaces:
Sleeping
Sleeping
add gifs for backend speeds
Browse files- .gitattributes +1 -0
- library.py +18 -5
- tutorial.md +8 -1
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*gif* filter=lfs diff=lfs merge=lfs -text
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library.py
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@@ -7,26 +7,39 @@ from interactive_pipe import Curve, SingleCurve
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# -----------------
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def gen_color(
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lin_coord = np.linspace(0, 1., 256)
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X, Y = np.meshgrid(lin_coord, isotropy*lin_coord)
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radius = 0.5+0.5*np.cos(frequency*np.sqrt(X**2 + Y**2))
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return np.stack([np.abs(X), np.abs(Y), radius], axis=-1).clip(0, 1)
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def modify_geometry(
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img = img[::-1] if "flip" in effect else img
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img = img[:, ::-1] if "mirror" in effect else img
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return img
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def change_color(
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if bnw:
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return np.mean(img, axis=-1, keepdims=True).repeat(3, axis=-1)
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return img
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def compare_by_splitting(
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out = np.zeros_like(img_1)
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split = int(ratio*img_1.shape[1])
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out[:, :split] = img_2[:, :split]
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@@ -34,7 +47,7 @@ def compare_by_splitting(img_1, img_2, ratio=0.5):
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return out
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def extract_profile(img):
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luma = img.mean(axis=-1)
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h_profile = SingleCurve(y=luma[0, :], label="H profile")
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v_profile = SingleCurve(y=luma[:, 0], label="V profile")
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# -----------------
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def gen_color(
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frequency: int = 0, # discrete slider (int)
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isotropy: float = 0., # continuous slider (float)
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) -> np.ndarray:
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lin_coord = np.linspace(0, 1., 256)
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X, Y = np.meshgrid(lin_coord, isotropy*lin_coord)
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radius = 0.5+0.5*np.cos(frequency*np.sqrt(X**2 + Y**2))
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return np.stack([np.abs(X), np.abs(Y), radius], axis=-1).clip(0, 1)
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def modify_geometry(
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img: np.ndarray,
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effect: str = "flip" # dropdown menus (str)
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) -> np.ndarray:
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img = img[::-1] if "flip" in effect else img
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img = img[:, ::-1] if "mirror" in effect else img
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return img
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def change_color(
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img: np.ndarray,
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bnw: bool = True # checkboxes (bool)
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):
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if bnw:
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return np.mean(img, axis=-1, keepdims=True).repeat(3, axis=-1)
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return img
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def compare_by_splitting(
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img_1: np.ndarray,
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img_2: np.ndarray,
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ratio: float = 0.5, # continuous slider (float)
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) -> np.ndarray:
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out = np.zeros_like(img_1)
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split = int(ratio*img_1.shape[1])
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out[:, :split] = img_2[:, :split]
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return out
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def extract_profile(img: np.ndarray,) -> Curve:
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luma = img.mean(axis=-1)
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h_profile = SingleCurve(y=luma[0, :], label="H profile")
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v_profile = SingleCurve(y=luma[:, 0], label="V profile")
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tutorial.md
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# Tutorial
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## Keep separation between "production code" (library / not interactive) & interactivity
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- π‘ One of the strength of interactive pipe is to avoid adding hundreds of lines of code dedicated to GUI (graphical user interfaces).
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- With interactive pipe, you have the choice to keep this design choice. Each processing block can be defined as a regular function, without even importing the `interactive` decorator. In a separate file, you'll add sliders.
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```
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- β Shortest code
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- β The decorated function can't be re-used somewhere else
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# Tutorial
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## Keep separation between "production code" (library / not interactive) & interactivity
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- π‘ One of the strength of interactive pipe is to avoid adding hundreds of lines of code dedicated to GUI (graphical user interfaces).
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- With interactive pipe, you have the choice to keep this design choice. Each processing block can be defined as a regular function, without even importing the `interactive` decorator. In a separate file, you'll add sliders.
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```
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- β Shortest code
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- β The decorated function can't be re-used somewhere else
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## Backend choice
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| π Qt | π’ Gradio |
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|:---:|:---:|
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