update

app.py

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+import gradio as gr
+import torch
+import kornia as K
+
+import cv2
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+matplotlib.use('Agg')
+from scipy.cluster.vq import kmeans,vq,whiten
+
+def get_coordinates_from_mask(mask_in):
+    x_y = np.where(mask_in != [0,0,0,255])[:2]
+    x_y = np.column_stack((x_y[1], x_y[0]))
+    x_y = np.float32(x_y)
+    centroids,_ = kmeans(x_y,4)
+    centroids = np.int64(centroids)
+
+    return centroids
+
+def get_top_bottom_coordinates(coords: list[list[int,int]]) -> (list[int,int],list[int,int]):
+    top_coord = min(coords, key=lambda x : x[1])
+    bottom_coord = max(coords, key=lambda x : x[1])
+    
+    return top_coord, bottom_coord
+
+
+def sort_centroids_clockwise(centroids: np.ndarray):    
+    c_list = centroids.tolist()
+    c_list.sort(key = lambda y : y[0])
+    
+    left_coords = c_list[:2]
+    right_coords = c_list[-2:]
+    
+    top_left, bottom_left = get_top_bottom_coordinates(left_coords)
+    top_right, bottom_right = get_top_bottom_coordinates(right_coords)
+    
+    return top_left, top_right, bottom_right, bottom_left
+
+
+def infer(image_input, dst_height:str, dst_width:str):
+    image_in = image_input["image"]
+    mask_in = image_input["mask"]
+    torch_img = K.image_to_tensor(image_in)
+    
+    centroids = get_coordinates_from_mask(mask_in)
+    ordered_src_coords = sort_centroids_clockwise(centroids)
+    # the source points are the region to crop corners
+    points_src = torch.tensor([list(ordered_src_coords)], dtype=torch.float32)
+
+    # the destination points are the image vertexes
+    h, w = dst_height, dst_width  # destination size
+    points_dst = torch.tensor([[
+        [0., 0.], [w - 1., 0.], [w - 1., h - 1.], [0., h - 1.],
+    ]], dtype=torch.float32)
+
+    # compute perspective transform
+    M: torch.tensor = K.geometry.get_perspective_transform(points_src, points_dst)
+
+    # warp the original image by the found transform
+    torch_img = torch.stack([torch_img],)
+    img_warp: torch.tensor = K.geometry.warp_perspective(torch_img.float(), M, dsize=(h, w))
+
+    
+    # convert back to numpy
+    img_np = K.tensor_to_image(torch_img.byte())
+    img_warp_np: np.ndarray = K.tensor_to_image(img_warp.byte())
+
+    # draw points into original image
+    for i in range(4):
+        center = tuple(points_src[0, i].long().numpy())
+        img_np = cv2.circle(img_np.copy(), center, 5, (0, 255, 0), -1)
+
+    # create the plot
+    fig, axs = plt.subplots(1, 2, figsize=(16, 10))
+    axs = axs.ravel()
+
+    axs[0].axis('off')
+    axs[0].set_title('image source')
+    axs[0].imshow(img_np)
+
+    axs[1].axis('off')
+    axs[1].set_title('image destination')
+    axs[1].imshow(img_warp_np)
+
+    return fig
+
+
+description = """Homography Warping"""
+
+example_mask = np.empty((327,600,4))
+example_mask[:] = [0,0,0,255]
+example_image_dict = {"image": "bruce.png", "mask": example_mask}
+
+Iface = gr.Interface(
+    fn=infer,
+    inputs=[gr.components.Image(tool="sketch"),
+            gr.components.Textbox(label="Destination Height"),
+            gr.components.Textbox(label="Destination Width"),
+            ],
+    outputs=gr.components.Plot(),
+    examples=[["bruce.png", example_mask], "64", "128"],
+    title="Homography Warping",
+    description=description,
+).launch()

requirements.txt

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+kornia
+opencv-python
+matplotlib