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# MIT License
#
# Copyright (c) 2017 Ngoc Anh Huynh
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"""
This module provides a method to generate YOLO anchors from dataset annotations.
"""
import random
import numpy as np
def _iou(ann, centroids):
w, h = ann
similarities = []
for centroid in centroids:
c_w, c_h = centroid
if c_w >= w and c_h >= h:
similarity = w * h / (c_w * c_h)
elif c_w >= w and c_h <= h:
similarity = w * c_h / (w * h + (c_w - w) * c_h)
elif c_w <= w and c_h >= h:
similarity = c_w * h / (w * h + c_w * (c_h - h))
else: # means both w,h are bigger than c_w and c_h respectively
similarity = (c_w * c_h) / (w * h)
similarities.append(similarity) # will become (k,) shape
return np.array(similarities)
def _avg_iou(anns, centroids):
n, _ = anns.shape
s = 0.
for i in range(anns.shape[0]):
s += max(_iou(anns[i], centroids))
return s / n
def _run_kmeans(ann_dims, anchor_num):
ann_num = ann_dims.shape[0]
prev_assignments = np.ones(ann_num) * (-1)
iteration = 0
indices = [random.randrange(ann_dims.shape[0]) for _ in range(anchor_num)]
centroids = ann_dims[indices]
anchor_dim = ann_dims.shape[1]
while True:
distances = []
iteration += 1
for i in range(ann_num):
d = 1 - _iou(ann_dims[i], centroids)
distances.append(d)
distances = np.array(distances)
# assign samples to centroids
assignments = np.argmin(distances, axis=1)
if (assignments == prev_assignments).all():
return centroids
# calculate new centroids
centroid_sums = np.zeros((anchor_num, anchor_dim), np.float)
for i in range(ann_num):
centroid_sums[assignments[i]] += ann_dims[i]
for j in range(anchor_num):
centroids[j] = centroid_sums[j] / (np.sum(assignments == j) + 1e-6)
prev_assignments = assignments.copy()
[docs]def generate_anchors(annotations_data, num_anchors=5, grid_size=(7, 7)):
""" Creates anchors by clustering dimensions of the ground truth boxes
from the training dataset.
Args:
annotations_data (dict): dictionnary of preprocessed VOC data
num_anchors (int, optional): number of anchors
grid_size (tuple, optional): size of the YOLO grid
Returns:
list: the computed anchors
"""
annotation_dims = []
# run k_mean to find the anchors
for item in annotations_data:
cell_w = item['image_shape'][0] / grid_size[0]
cell_h = item['image_shape'][1] / grid_size[1]
for box in item['boxes']:
relative_w = float(box['x2'] - box['x1']) / cell_w
relative_h = float(box['y2'] - box['y1']) / cell_h
annotation_dims.append(tuple(map(float, (relative_w, relative_h))))
annotation_dims = np.array(annotation_dims)
centroids = _run_kmeans(annotation_dims, num_anchors)
print('\nAverage IOU for', num_anchors, 'anchors:',
'%0.2f' % _avg_iou(annotation_dims, centroids))
anchors = np.sort(np.round(centroids, 5), 0).tolist()
print('Anchors: ', anchors)
return anchors