added line fitting module

app.py

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+# Example showing how to fit a 2d line with kornia / pytorch
+import matplotlib.pyplot as plt
+import torch
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import gradio as gr
+from kornia.geometry.line import ParametrizedLine, fit_line
+
+
+def inference(point1, point2, point3, point4):
+    std = 1.2  # standard deviation for the points
+    num_points = 50  # total number of points
+    
+    # create a baseline
+    p0 = torch.tensor([point1, point2], dtype=torch.float32)
+    p1 = torch.tensor([point3, point4], dtype=torch.float32)
+
+    l1 = ParametrizedLine.through(p0, p1)
+    
+    # sample some points and weights
+    pts, w = [], []
+    for t in torch.linspace(-10, 10, num_points):
+        p2 = l1.point_at(t)
+        p2_noise = torch.rand_like(p2) * std
+        p2 += p2_noise
+        pts.append(p2)
+        w.append(1 - p2_noise.mean())
+    pts = torch.stack(pts)
+    w = torch.stack(w)
+
+    l2 = fit_line(pts, w)
+    
+    # project some points along the estimated line
+    p3 = l2.point_at(-10)
+    p4 = l2.point_at(10)
+
+    X = torch.stack((p3, p4)).detach().numpy()
+    X_pts = pts.detach().numpy()
+
+    fig = plt.figure()
+    plt.plot(X_pts[:, 0], X_pts[:, 1], 'ro')
+    plt.plot(X[:, 0], X[:, 1])
+    return fig
+
+inputs = [
+    gr.inputs.Slider(0.0, 10.0, default=0.0, label="Point 1"),
+    gr.inputs.Slider(0.0, 10.0, default=0.0, label="Point 2"),
+    gr.inputs.Slider(0.0, 10.0, default=0.0, label="Point 3"),
+    gr.inputs.Slider(0.0, 10.0, default=0.0, label="Point 4"),
+]
+outputs = gr.Plot()
+
+examples = [
+    [[0.0, 0.0, 1.0, 1.0]],
+    [[0.0, 0.0, 1.0, 2.0]],
+]
+
+title = 'Line Fitting'
+
+demo = gr.Interface(
+    fn=inference,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    cache_examples=True,
+    theme='huggingface',
+    live=True,
+
+)
+demo.launch()

requirements.txt

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+kornia
+kornia_rs
+matplotlib
+torch