File size: 4,450 Bytes
747451d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | # /*---------------------------------------------------------------------------------------------
# * Copyright (c) 2025 STMicroelectronics.
# * All rights reserved.
# *
# * This software is licensed under terms that can be found in the LICENSE file in
# * the root directory of this software component.
# * If no LICENSE file comes with this software, it is provided AS-IS.
# *--------------------------------------------------------------------------------------------*/
import glob
import sys
import os
import xml.etree.ElementTree as ET
from random import random
def main(filename):
# ratio to divide up the images
train = 0.7
val = 0.2
test = 0.1
if (train + test + val) != 1.0:
print("probabilities must equal 1")
exit()
# get the labels
labels = []
imgnames = []
annotations = {}
with open(filename, 'r') as labelfile:
label_string = ""
for line in labelfile:
label_string += line.rstrip()
labels = label_string.split(',')
labels = [elem.replace(" ", "") for elem in labels]
# get image names
for filename in os.listdir("./JPEGImages"):
if filename.endswith(".jpg"):
img = filename.rstrip('.jpg')
imgnames.append(img)
print("Labels:", labels, "imgcnt:", len(imgnames))
# initialise annotation list
for label in labels:
annotations[label] = []
# Scan the annotations for the labels
for img in imgnames:
annote = "Annotations/" + img + '.xml'
if os.path.isfile(annote):
tree = ET.parse(annote)
root = tree.getroot()
annote_labels = []
for labelname in root.findall('*/name'):
labelname = labelname.text
annote_labels.append(labelname)
if labelname in labels:
annotations[labelname].append(img)
annotations[img] = annote_labels
else:
print("Missing annotation for ", annote)
exit()
# divvy up the images to the different sets
sampler = imgnames.copy()
train_list = []
val_list = []
test_list = []
while len(sampler) > 0:
dice = random()
elem = sampler.pop()
if dice <= test:
test_list.append(elem)
elif dice <= (test + val):
val_list.append(elem)
else:
train_list.append(elem)
print("Training set:", len(train_list), "validation set:", len(val_list), "test set:", len(test_list))
# create the dataset files
create_folder("./ImageSets/Main/")
with open("./ImageSets/Main/train.txt", 'w') as outfile:
for name in train_list:
outfile.write(name + "\n")
with open("./ImageSets/Main/val.txt", 'w') as outfile:
for name in val_list:
outfile.write(name + "\n")
with open("./ImageSets/Main/trainval.txt", 'w') as outfile:
for name in train_list:
outfile.write(name + "\n")
for name in val_list:
outfile.write(name + "\n")
with open("./ImageSets/Main/test.txt", 'w') as outfile:
for name in test_list:
outfile.write(name + "\n")
# create the individiual files for each label
for label in labels:
with open("./ImageSets/Main/"+ label +"_train.txt", 'w') as outfile:
for name in train_list:
if label in annotations[name]:
outfile.write(name + " 1\n")
else:
outfile.write(name + " -1\n")
with open("./ImageSets/Main/"+ label +"_val.txt", 'w') as outfile:
for name in val_list:
if label in annotations[name]:
outfile.write(name + " 1\n")
else:
outfile.write(name + " -1\n")
with open("./ImageSets/Main/"+ label +"_test.txt", 'w') as outfile:
for name in test_list:
if label in annotations[name]:
outfile.write(name + " 1\n")
else:
outfile.write(name + " -1\n")
def create_folder(foldername):
if os.path.exists(foldername):
print('folder already exists:', foldername)
else:
os.makedirs(foldername)
if __name__=='__main__':
if len(sys.argv) < 2:
print("usage: python generate_vocdata.py <labelfile>")
exit()
main(sys.argv[1])
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