Forked from WillieMaddox/k_means_anchor_points.py
Created
January 23, 2018 15:42
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create darknet anchor points using k-means.
Uses fast numpy array ops:
pascal ~= 1.0 s
coco ~= 16 s
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# -*- coding: utf-8 -*- | |
import numpy as np | |
import xml.etree.ElementTree as ET | |
from pycocotools.coco import COCO | |
def convert_coco_bbox(size, box): | |
dw = 1. / size[0] | |
dh = 1. / size[1] | |
x = box[0] + box[2] / 2.0 | |
y = box[1] + box[3] / 2.0 | |
w = box[2] | |
h = box[3] | |
x = x * dw | |
w = w * dw | |
y = y * dh | |
h = h * dh | |
return x, y, w, h | |
def convert_bbox(size, box): | |
dw = 1. / size[0] | |
dh = 1. / size[1] | |
x = (box[0] + box[1]) / 2.0 | |
y = (box[2] + box[3]) / 2.0 | |
w = box[1] - box[0] | |
h = box[3] - box[2] | |
x = x * dw | |
w = w * dw | |
y = y * dh | |
h = h * dh | |
return x, y, w, h | |
def area(x): | |
if len(x.shape) == 1: | |
return x[0] * x[1] | |
else: | |
return x[:, 0] * x[:, 1] | |
def kmeans_iou(k, centroids, points, iter_count=0, iteration_cutoff=25, feature_size=13): | |
best_clusters = [] | |
best_avg_iou = 0 | |
best_avg_iou_iteration = 0 | |
npoi = points.shape[0] | |
area_p = area(points) # (npoi, 2) -> (npoi,) | |
while True: | |
cen2 = centroids.repeat(npoi, axis=0).reshape(k, npoi, 2) | |
cdiff = points - cen2 | |
cidx = np.where(cdiff < 0) | |
cen2[cidx] = points[cidx[1], cidx[2]] | |
wh = cen2.prod(axis=2).T # (k, npoi, 2) -> (npoi, k) | |
dist = 1. - (wh / (area_p[:, np.newaxis] + area(centroids) - wh)) # -> (npoi, k) | |
belongs_to_cluster = np.argmin(dist, axis=1) # (npoi, k) -> (npoi,) | |
clusters_niou = np.min(dist, axis=1) # (npoi, k) -> (npoi,) | |
clusters = [points[belongs_to_cluster == i] for i in range(k)] | |
avg_iou = np.mean(1. - clusters_niou) | |
if avg_iou > best_avg_iou: | |
best_avg_iou = avg_iou | |
best_clusters = clusters | |
best_avg_iou_iteration = iter_count | |
print("\nIteration {}".format(iter_count)) | |
print("Average iou to closest centroid = {}".format(avg_iou)) | |
print("Sum of all distances (cost) = {}".format(np.sum(clusters_niou))) | |
new_centroids = np.array([np.mean(c, axis=0) for c in clusters]) | |
isect = np.prod(np.min(np.asarray([centroids, new_centroids]), axis=0), axis=1) | |
aa1 = np.prod(centroids, axis=1) | |
aa2 = np.prod(new_centroids, axis=1) | |
shifts = 1 - isect / (aa1 + aa2 - isect) | |
# for i, s in enumerate(shifts): | |
# print("{}: Cluster size: {}, Centroid distance shift: {}".format(i, len(clusters[i]), s)) | |
if sum(shifts) == 0 or iter_count >= best_avg_iou_iteration + iteration_cutoff: | |
break | |
centroids = new_centroids | |
iter_count += 1 | |
# Get anchor boxes from best clusters | |
anchors = np.asarray([np.mean(cluster, axis=0) for cluster in best_clusters]) | |
anchors = anchors[anchors[:, 0].argsort()] | |
print("k-means clustering pascal anchor points (original coordinates) \ | |
\nFound at iteration {} with best average IoU: {} \ | |
\n{}".format(best_avg_iou_iteration, best_avg_iou, anchors*feature_size)) | |
return anchors | |
def load_pascal_dataset(datasets): | |
name = 'pascal' | |
data = [] | |
for year, image_set in datasets: | |
img_ids_filename = '%s/%s/VOCdevkit/VOC%s/ImageSets/Main/%s.txt' % (source_dir, name, year, image_set) | |
ifs_img_ids = open(img_ids_filename) | |
img_ids = ifs_img_ids.read().strip().split() | |
for image_id in img_ids: | |
anno_filename = '%s/%s/VOCdevkit/VOC%s/Annotations/%s.xml' % (source_dir, name, year, image_id) | |
ifs_anno = open(anno_filename) | |
tree = ET.parse(ifs_anno) | |
root = tree.getroot() | |
size = root.find('size') | |
w = int(size.find('width').text) | |
h = int(size.find('height').text) | |
for obj in root.iter('object'): | |
xmlbox = obj.find('bndbox') | |
b = (float(xmlbox.find('xmin').text), | |
float(xmlbox.find('xmax').text), | |
float(xmlbox.find('ymin').text), | |
float(xmlbox.find('ymax').text)) | |
bb = convert_bbox((w, h), b) | |
data.append(bb[2:]) | |
ifs_anno.close() | |
ifs_img_ids.close() | |
return np.array(data) | |
def load_coco_dataset(datasets): | |
name = 'coco' | |
data = [] | |
for dataset in datasets: | |
annfile = '%s/%s/annotations/instances_%s.json' % (source_dir, name, dataset) | |
coco = COCO(annfile) | |
cats = coco.loadCats(coco.getCatIds()) | |
base_classes = {cat['id']: cat['name'] for cat in cats} | |
img_id_set = set() | |
for cat_ids in base_classes.iterkeys(): | |
img_ids = coco.getImgIds(catIds=cat_ids) | |
img_id_set = img_id_set.union(set(img_ids)) | |
image_ids = list(img_id_set) | |
for image_id in image_ids: | |
annIds = coco.getAnnIds(imgIds=image_id) | |
anns = coco.loadAnns(annIds) | |
img = coco.loadImgs(image_id)[0] | |
w = img['width'] | |
h = img['height'] | |
for ann in anns: | |
b = ann['bbox'] | |
bb = convert_coco_bbox((w, h), b) | |
data.append(bb[2:]) | |
return np.array(data) | |
if __name__ == "__main__": | |
# examples | |
# k, pascal, coco | |
# 1, 0.30933335617, 0.252004954777 | |
# 2, 0.45787906725, 0.365835079771 | |
# 3, 0.53198291772, 0.453180358467 | |
# 4, 0.57562962803, 0.500282182136 | |
# 5, 0.58694643198, 0.522010174068 | |
# 6, 0.61789602056, 0.549904351137 | |
# 7, 0.63443906479, 0.569485509501 | |
# 8, 0.65114747974, 0.585718648162 | |
# 9, 0.66393113546, 0.601564171461 | |
# k-means picking the first k points as centroids | |
img_size = 416 | |
k = 5 | |
# change this line to match your system. | |
source_dir = "/media/RED6/DATA" | |
random_data = np.random.random((1000, 2)) | |
centroids = np.random.random((k, 2)) | |
random_anchors = kmeans_iou(k, centroids, random_data) | |
subsets = (('2007', 'train'), ('2007', 'val'), ('2012', 'train'), ('2012', 'val')) | |
pascal_data = load_pascal_dataset(subsets) | |
centroids = pascal_data[np.random.choice(np.arange(len(pascal_data)), k, replace=False)] | |
# centroids = pascal_data[:k] | |
pascal_anchors = kmeans_iou(k, centroids, pascal_data, feature_size=img_size / 32) | |
subsets = ('train2014', 'val2014') | |
# subsets = ('test2014', 'test2015') | |
coco_data = load_coco_dataset(subsets) | |
centroids = coco_data[np.random.choice(np.arange(len(coco_data)), k, replace=False)] | |
# centroids = coco_data[:k] | |
coco_anchors = kmeans_iou(k, centroids, coco_data, feature_size=img_size / 32) | |
print 'done' |
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