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import numpy as np |
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import pandas as pd |
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import os |
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class YOLO_Kmeans: |
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def __init__(self, cluster_number, data_file, anchors_file): |
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self.cluster_number = cluster_number |
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self.data_file = data_file |
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self.anchors_file = anchors_file |
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def iou(self, boxes, clusters): |
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n = boxes.shape[0] |
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k = self.cluster_number |
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box_area = boxes[:, 0] * boxes[:, 1] |
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box_area = box_area.repeat(k) |
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box_area = np.reshape(box_area, (n, k)) |
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cluster_area = clusters[:, 0] * clusters[:, 1] |
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cluster_area = np.tile(cluster_area, [1, n]) |
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cluster_area = np.reshape(cluster_area, (n, k)) |
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box_w_matrix = np.reshape(boxes[:, 0].repeat(k), (n, k)) |
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cluster_w_matrix = np.reshape(np.tile(clusters[:, 0], (1, n)), (n, k)) |
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min_w_matrix = np.minimum(cluster_w_matrix, box_w_matrix) |
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box_h_matrix = np.reshape(boxes[:, 1].repeat(k), (n, k)) |
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cluster_h_matrix = np.reshape(np.tile(clusters[:, 1], (1, n)), (n, k)) |
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min_h_matrix = np.minimum(cluster_h_matrix, box_h_matrix) |
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inter_area = np.multiply(min_w_matrix, min_h_matrix) |
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result = inter_area / (box_area + cluster_area - inter_area) |
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return result |
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def avg_iou(self, boxes, clusters): |
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accuracy = np.mean([np.max(self.iou(boxes, clusters), axis=1)]) |
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return accuracy |
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def kmeans(self, boxes, k, dist=np.median): |
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box_number = boxes.shape[0] |
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distances = np.empty((box_number, k)) |
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last_nearest = np.zeros((box_number,)) |
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np.random.seed() |
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clusters = boxes[np.random.choice( |
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box_number, k, replace=False)] |
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while True: |
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distances = 1 - self.iou(boxes, clusters) |
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current_nearest = np.argmin(distances, axis=1) |
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if (last_nearest == current_nearest).all(): |
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break |
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for cluster in range(k): |
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clusters[cluster] = dist( |
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boxes[current_nearest == cluster], axis=0) |
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last_nearest = current_nearest |
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return clusters |
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def result2txt(self, data): |
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f = open(self.anchors_file, 'w') |
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row = np.shape(data)[0] |
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for i in range(row): |
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if i == 0: |
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x_y = "%d,%d" % (data[i][0], data[i][1]) |
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else: |
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x_y = ", %d,%d" % (data[i][0], data[i][1]) |
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f.write(x_y) |
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f.close() |
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def txt2boxes(self): |
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test = os.listdir(self.data_file + 'test/') |
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train = os.listdir(self.data_file + 'train/') |
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dataSet = [] |
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for i in train: |
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f = open(self.data_file + 'train/' + i, 'r') |
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for line in f: |
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infos = line.split(" ") |
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width = int(float(infos[3]) * 416) |
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height = int(float(infos[4]) * 416) |
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dataSet.append([width, height]) |
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f.close() |
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for i in test: |
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f = open(self.data_file + 'test/' + i, 'r') |
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for line in f: |
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infos = line.split(" ") |
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width = int(float(infos[3]) * 416) |
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height = int(float(infos[4]) * 416) |
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dataSet.append([width, height]) |
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f.close() |
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result = np.array(dataSet) |
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return result |
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def csv2boxes(self): |
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dataSet = [] |
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train_data = pd.read_csv(self.data_file, header=None) |
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for value_i in train_data.values: |
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end_num = len(value_i) |
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for i in range(1, end_num, 5): |
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width = value_i[i + 2] - value_i[i] |
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height = value_i[i + 3] - value_i[i + 1] |
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dataSet.append([width, height]) |
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result = np.array(dataSet) |
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return result |
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def txt2clusters(self): |
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all_boxes = self.txt2boxes() |
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result = self.kmeans(all_boxes, k=self.cluster_number) |
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result = result[np.lexsort(result.T[0, None])] |
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self.result2txt(result) |
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print("K anchors:\n {}".format(result)) |
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print("Accuracy: {:.2f}%".format( |
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self.avg_iou(all_boxes, result) * 100)) |
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if __name__ == "__main__": |
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cluster_number = 6 |
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data_file = "labels/" |
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anchors_file = "anchors_6.txt" |
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kmeans = YOLO_Kmeans(cluster_number=cluster_number, data_file=data_file, anchors_file=anchors_file) |
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kmeans.txt2clusters() |
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