97 lines
3.2 KiB
Python
Executable File
97 lines
3.2 KiB
Python
Executable File
#!/usr/bin/env python2
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#
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# Copyright 2015-2016 Carnegie Mellon University
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import errno
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import os
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import random
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import shutil
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def mkdirP(path):
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try:
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os.makedirs(path)
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except OSError as exc: # Python >2.5
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if exc.errno == errno.EEXIST and os.path.isdir(path):
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pass
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else:
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raise
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def getImgs(imageDir):
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exts = ["jpg", "png"]
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# All images with one image from each class put into the validation set.
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allImgsM = []
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classes = set()
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valImgs = []
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for subdir, dirs, files in os.walk(imageDir):
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for fName in files:
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(imageClass, imageName) = (os.path.basename(subdir), fName)
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if any(imageName.lower().endswith("." + ext) for ext in exts):
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if imageClass not in classes:
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classes.add(imageClass)
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valImgs.append((imageClass, imageName))
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else:
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allImgsM.append((imageClass, imageName))
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print("+ Number of Classes: '{}'.".format(len(classes)))
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return (allImgsM, valImgs)
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def createTrainValSplit(imageDir, valRatio):
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print("+ Val ratio: '{}'.".format(valRatio))
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(allImgsM, valImgs) = getImgs(imageDir)
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trainValIdx = int((len(allImgsM) + len(valImgs)) * valRatio) - len(valImgs)
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assert(trainValIdx > 0) # Otherwise, valRatio is too small.
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random.shuffle(allImgsM)
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valImgs += allImgsM[0:trainValIdx]
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trainImgs = allImgsM[trainValIdx:]
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print("+ Training set size: '{}'.".format(len(trainImgs)))
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print("+ Validation set size: '{}'.".format(len(valImgs)))
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for person, img in trainImgs:
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origPath = os.path.join(imageDir, person, img)
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newDir = os.path.join(imageDir, 'train', person)
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newPath = os.path.join(imageDir, 'train', person, img)
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mkdirP(newDir)
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shutil.move(origPath, newPath)
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for person, img in valImgs:
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origPath = os.path.join(imageDir, person, img)
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newDir = os.path.join(imageDir, 'val', person)
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newPath = os.path.join(imageDir, 'val', person, img)
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mkdirP(newDir)
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shutil.move(origPath, newPath)
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for person, img in valImgs:
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d = os.path.join(imageDir, person)
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if os.path.isdir(d):
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os.rmdir(d)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'imageDir', type=str, help="Directory of images to partition in-place to 'train' and 'val' directories.")
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parser.add_argument('--valRatio', type=float, default=0.10,
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help="Validation to training ratio.")
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args = parser.parse_args()
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createTrainValSplit(args.imageDir, args.valRatio)
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