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gen_anchors.py
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gen_anchors.py
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import random
import argparse
import numpy as np
from preprocessing import parse_annotation
import json
argparser = argparse.ArgumentParser()
argparser.add_argument(
'-c',
'--conf',
default='config.json',
help='path to configuration file')
argparser.add_argument(
'-a',
'--anchors',
default=5,
help='number of anchors to use')
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,d = anns.shape
sum = 0.
for i in range(anns.shape[0]):
sum+= max(IOU(anns[i], centroids))
return sum/n
def print_anchors(centroids):
anchors = centroids.copy()
widths = anchors[:, 0]
sorted_indices = np.argsort(widths)
r = "anchors: ["
for i in sorted_indices[:-1]:
r += '%0.2f,%0.2f, ' % (anchors[i,0], anchors[i,1])
#there should not be comma after last anchor, that's why
r += '%0.2f,%0.2f' % (anchors[sorted_indices[-1:],0], anchors[sorted_indices[-1:],1])
r += "]"
print(r)
def run_kmeans(ann_dims, anchor_num):
ann_num = ann_dims.shape[0]
iterations = 0
prev_assignments = np.ones(ann_num)*(-1)
iteration = 0
old_distances = np.zeros((ann_num, anchor_num))
indices = [random.randrange(ann_dims.shape[0]) for i 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) # distances.shape = (ann_num, anchor_num)
print("iteration {}: dists = {}".format(iteration, np.sum(np.abs(old_distances-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()
old_distances = distances.copy()
def main(argv):
config_path = args.conf
num_anchors = args.anchors
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
train_imgs, train_labels = parse_annotation(config['train']['train_annot_folder'],
config['train']['train_image_folder'],
config['model']['labels'])
grid_w = config['model']['input_size']/32
grid_h = config['model']['input_size']/32
# run k_mean to find the anchors
annotation_dims = []
for image in train_imgs:
cell_w = image['width']/grid_w
cell_h = image['height']/grid_h
for obj in image['object']:
relative_w = (float(obj['xmax']) - float(obj['xmin']))/cell_w
relatice_h = (float(obj["ymax"]) - float(obj['ymin']))/cell_h
annotation_dims.append(tuple(map(float, (relative_w,relatice_h))))
annotation_dims = np.array(annotation_dims)
centroids = run_kmeans(annotation_dims, num_anchors)
# write anchors to file
print('\naverage IOU for', num_anchors, 'anchors:', '%0.2f' % avg_IOU(annotation_dims, centroids))
print_anchors(centroids)
if __name__ == '__main__':
args = argparser.parse_args()
main(args)