first commit with deletions

LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2018 qqwweee
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

coco_annotation.py

@@ -1,52 +0,0 @@
-import json
-from collections import defaultdict
-
-name_box_id = defaultdict(list)
-id_name = dict()
-f = open(
-    "mscoco2017/annotations/instances_train2017.json",
-    encoding='utf-8')
-data = json.load(f)
-
-annotations = data['annotations']
-for ant in annotations:
-    id = ant['image_id']
-    name = 'mscoco2017/train2017/%012d.jpg' % id
-    cat = ant['category_id']
-
-    if cat >= 1 and cat <= 11:
-        cat = cat - 1
-    elif cat >= 13 and cat <= 25:
-        cat = cat - 2
-    elif cat >= 27 and cat <= 28:
-        cat = cat - 3
-    elif cat >= 31 and cat <= 44:
-        cat = cat - 5
-    elif cat >= 46 and cat <= 65:
-        cat = cat - 6
-    elif cat == 67:
-        cat = cat - 7
-    elif cat == 70:
-        cat = cat - 9
-    elif cat >= 72 and cat <= 82:
-        cat = cat - 10
-    elif cat >= 84 and cat <= 90:
-        cat = cat - 11
-
-    name_box_id[name].append([ant['bbox'], cat])
-
-f = open('train.txt', 'w')
-for key in name_box_id.keys():
-    f.write(key)
-    box_infos = name_box_id[key]
-    for info in box_infos:
-        x_min = int(info[0][0])
-        y_min = int(info[0][1])
-        x_max = x_min + int(info[0][2])
-        y_max = y_min + int(info[0][3])
-
-        box_info = " %d,%d,%d,%d,%d" % (
-            x_min, y_min, x_max, y_max, int(info[1]))
-        f.write(box_info)
-    f.write('\n')
-f.close()

kmeans.py

@@ -1,101 +0,0 @@
-import numpy as np
-
-
-class YOLO_Kmeans:
-
-    def __init__(self, cluster_number, filename):
-        self.cluster_number = cluster_number
-        self.filename = "2012_train.txt"
-
-    def iou(self, boxes, clusters):  # 1 box -> k clusters
-        n = boxes.shape[0]
-        k = self.cluster_number
-
-        box_area = boxes[:, 0] * boxes[:, 1]
-        box_area = box_area.repeat(k)
-        box_area = np.reshape(box_area, (n, k))
-
-        cluster_area = clusters[:, 0] * clusters[:, 1]
-        cluster_area = np.tile(cluster_area, [1, n])
-        cluster_area = np.reshape(cluster_area, (n, k))
-
-        box_w_matrix = np.reshape(boxes[:, 0].repeat(k), (n, k))
-        cluster_w_matrix = np.reshape(np.tile(clusters[:, 0], (1, n)), (n, k))
-        min_w_matrix = np.minimum(cluster_w_matrix, box_w_matrix)
-
-        box_h_matrix = np.reshape(boxes[:, 1].repeat(k), (n, k))
-        cluster_h_matrix = np.reshape(np.tile(clusters[:, 1], (1, n)), (n, k))
-        min_h_matrix = np.minimum(cluster_h_matrix, box_h_matrix)
-        inter_area = np.multiply(min_w_matrix, min_h_matrix)
-
-        result = inter_area / (box_area + cluster_area - inter_area)
-        return result
-
-    def avg_iou(self, boxes, clusters):
-        accuracy = np.mean([np.max(self.iou(boxes, clusters), axis=1)])
-        return accuracy
-
-    def kmeans(self, boxes, k, dist=np.median):
-        box_number = boxes.shape[0]
-        distances = np.empty((box_number, k))
-        last_nearest = np.zeros((box_number,))
-        np.random.seed()
-        clusters = boxes[np.random.choice(
-            box_number, k, replace=False)]  # init k clusters
-        while True:
-
-            distances = 1 - self.iou(boxes, clusters)
-
-            current_nearest = np.argmin(distances, axis=1)
-            if (last_nearest == current_nearest).all():
-                break  # clusters won't change
-            for cluster in range(k):
-                clusters[cluster] = dist(  # update clusters
-                    boxes[current_nearest == cluster], axis=0)
-
-            last_nearest = current_nearest
-
-        return clusters
-
-    def result2txt(self, data):
-        f = open("yolo_anchors.txt", 'w')
-        row = np.shape(data)[0]
-        for i in range(row):
-            if i == 0:
-                x_y = "%d,%d" % (data[i][0], data[i][1])
-            else:
-                x_y = ", %d,%d" % (data[i][0], data[i][1])
-            f.write(x_y)
-        f.close()
-
-    def txt2boxes(self):
-        f = open(self.filename, 'r')
-        dataSet = []
-        for line in f:
-            infos = line.split(" ")
-            length = len(infos)
-            for i in range(1, length):
-                width = int(infos[i].split(",")[2]) - \
-                    int(infos[i].split(",")[0])
-                height = int(infos[i].split(",")[3]) - \
-                    int(infos[i].split(",")[1])
-                dataSet.append([width, height])
-        result = np.array(dataSet)
-        f.close()
-        return result
-
-    def txt2clusters(self):
-        all_boxes = self.txt2boxes()
-        result = self.kmeans(all_boxes, k=self.cluster_number)
-        result = result[np.lexsort(result.T[0, None])]
-        self.result2txt(result)
-        print("K anchors:\n {}".format(result))
-        print("Accuracy: {:.2f}%".format(
-            self.avg_iou(all_boxes, result) * 100))
-
-
-if __name__ == "__main__":
-    cluster_number = 9
-    filename = "2012_train.txt"
-    kmeans = YOLO_Kmeans(cluster_number, filename)
-    kmeans.txt2clusters()