Summary

keras-yolo3-master/gen_anchors.py

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+import random
+import argparse
+import numpy as np
+
+from voc import parse_voc_annotation
+import json
+
+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):
+    out_string = ''
+
+    anchors = centroids.copy()
+
+    widths = anchors[:, 0]
+    sorted_indices = np.argsort(widths)
+
+    r = "anchors: ["
+    for i in sorted_indices:
+        out_string += str(int(anchors[i,0]*416)) + ',' + str(int(anchors[i,1]*416)) + ', '
+            
+    print(out_string[:-2])
+
+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_voc_annotation(
+        config['train']['train_annot_folder'],
+        config['train']['train_image_folder'],
+        config['train']['cache_name'],
+        config['model']['labels']
+    )
+
+    # run k_mean to find the anchors
+    annotation_dims = []
+    for image in train_imgs:
+        print(image['filename'])
+        for obj in image['object']:
+            relative_w = (float(obj['xmax']) - float(obj['xmin']))/image['width']
+            relatice_h = (float(obj["ymax"]) - float(obj['ymin']))/image['height']
+            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__':
+    argparser = argparse.ArgumentParser()
+
+    argparser.add_argument(
+        '-c',
+        '--conf',
+        default='config.json',
+        help='path to configuration file')
+    argparser.add_argument(
+        '-a',
+        '--anchors',
+        default=9,
+        help='number of anchors to use')
+
+    args = argparser.parse_args()
+    _main_(args)