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import numpy as np
LARGE_MESH_THRESHOLD = 1000
MINIMUM_SAMPLE_COUNT = 100
MIN_AREA_WEIGHT = 1
MAX_AREA_WEIGHT = 10
def sample_colors(face_colors, sampling_ratio=0.1, face_areas=None):
total_faces = len(face_colors)
if total_faces > LARGE_MESH_THRESHOLD:
number_to_sample = max(int(total_faces * sampling_ratio), MINIMUM_SAMPLE_COUNT)
randomly_selected_indices = np.random.choice(
total_faces, size=number_to_sample, replace=False
)
selected_face_colors = face_colors[randomly_selected_indices]
print(
f"Sampled {number_to_sample} of {total_faces} faces ({sampling_ratio*100:.1f}%)"
)
if face_areas is not None:
selected_areas = face_areas[randomly_selected_indices]
area_based_repetitions = compute_area_weights(selected_areas)
return np.repeat(selected_face_colors, area_based_repetitions, axis=0)
return selected_face_colors
else:
print(f"Using all {total_faces} faces (below sampling threshold)")
if face_areas is not None:
area_based_repetitions = compute_area_weights(face_areas)
return np.repeat(face_colors, area_based_repetitions, axis=0)
return face_colors
def compute_area_weights(areas):
mean_area = areas.mean()
normalized_weights = areas / mean_area
integer_weights = normalized_weights.astype(int)
return integer_weights.clip(MIN_AREA_WEIGHT, MAX_AREA_WEIGHT)
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