File size: 3,191 Bytes
abd2a81 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
import numpy as np
import matplotlib.pyplot as plt
import shapely.geometry
import torch_lydorn.torchvision
from numpy.core._multiarray_umath import ndarray
from scipy import ndimage
import vectorization_ambiguities
def create_polygons():
polygons = [
np.array([
[25, 25],
[75, 25],
[75, 50],
[50, 50],
[50, 75],
[25, 75],
[25, 25],
])
]
return polygons
def displace_polygons(polygons, max_global, max_polygon, max_vertex, max_rot_deg):
new_polygons = []
global_disp = np.random.uniform(-1, 1, 2) * max_global
for polygon in polygons:
polygon_disp = np.random.uniform(-1, 1, 2) * max_polygon
vertex_disp = np.random.uniform(-1, 1, polygon.shape) * max_vertex
new_polygon = polygon + global_disp + polygon_disp + vertex_disp
# Rotation
geom = shapely.geometry.Polygon(new_polygon)
angle = np.random.uniform(-1, 1, 1) * max_rot_deg
geom = shapely.affinity.rotate(geom, angle, origin='center', use_radians=False)
new_polygon = geom.exterior.coords[:]
new_polygons.append(new_polygon)
return new_polygons
def rasterize(image, polygons):
polygons = [shapely.geometry.Polygon(polygon) for polygon in polygons]
raster = torch_lydorn.torchvision.transforms.Rasterize(fill=True, edges=False, vertices=False, line_width=4, antialiasing=True)(image, polygons)
raster = raster[:, :, 0]
raster = ndimage.gaussian_filter(raster, sigma=1) # Simulates blurriness of overhead image, which leads to blurriness of segmentation
return raster
def plot(image, out_filepath, dpi=300):
height = image.shape[0]
width = image.shape[1]
f, axis = plt.subplots(1, 1, figsize=(width, height), dpi=dpi)
# Plot image
axis.imshow(image, cmap="gray")
axis.autoscale(False)
axis.axis('equal')
axis.axis('off')
plt.subplots_adjust(left=0, right=1, top=1, bottom=0) # Plot without margins
plt.savefig(out_filepath, transparent=True, dpi=dpi)
plt.close()
def main():
shape = (100, 100)
samples = 10
all_rasters: ndarray = np.empty((*shape, samples))
methods = [
"marching_squares",
"border_following",
"rasterio"
]
for s in range(samples):
polygons = create_polygons()
polygons = displace_polygons(polygons, max_global=0, max_polygon=3, max_vertex=0.5, max_rot_deg=1) # Simulates imperfect ground truth annotations
raster = rasterize(np.zeros(shape), polygons) / 255
all_rasters[:, :, s] = raster
plot(raster, f"rounded_corners_sample_{s:02d}.png", dpi=1)
mean_raster_s = np.mean(all_rasters[:, :, :(s+1)], axis=-1) # Simulates training to reduce average loss over all (noisy) ground truth
plot(mean_raster_s, f"rounded_corners_avg_{s:02d}.png", dpi=1)
for m in methods:
contours = vectorization_ambiguities.detect_contours(mean_raster_s, method=m)
vectorization_ambiguities.plot(mean_raster_s, contours, f"rounded_corners_avg_contour_{m}.pdf", linewidth=60, dpi=1, grid=False)
if __name__ == "__main__":
main()
|