This is an update for previous post.
http://lengerrong.blogspot.com/2017/04/create-your-own-dataset-for-machine.html
It is hard to collect enough images for your machine traning.
So I update this post to append images once you find some to your existed dataset.
All you need to do is copy your images to a folder and run the python script.
Of cause, you have to label each image while running the python script.
Below python script just have two label, 0 means cat, 1 means dog.
You can add more labels by just modify blue codes.
The running screenshot :
For my machine traning study example, you can refer to :
https://github.com/lengerrong/tensorflow/tree/master/tensorflow/examples/clixsence
The dataset format is same to cifa10.
the first byte is the label of the first image, which is a number in the
range 0-9. The next 3072 bytes are the values of the pixels of the
image. The first 1024 bytes are the red channel values, the next 1024
the green, and the final 1024 the blue. The values are stored in
row-major order, so the first 32 bytes are the red channel values of the
first row of the image.
import os
import subprocess
import sys
import Image
IMAGE_GEN_OK = True
IMAGE_GEN_FAIL = False
def image_gen(filepath, label):
class ImageGen(object):
pass
result = ImageGen()
result.status = IMAGE_GEN_FAIL
try:
im = Image.open(filepath)
im = (np.array(im))
r = im[:,:,0].flatten()
g = im[:,:,1].flatten()
b = im[:,:,2].flatten()
labels = [label]
result.imagebytes = np.array(list(labels) + list(r) + list(g) + list(b), np.uint8)
result.status = IMAGE_GEN_OK
except Exception, e:
print (e)
return result
def main(argv):
folder = argv[0]
if (len(argv) > 1):
if os.path.exists(argv[1]):
folder = argv[1]
else:
print ('%s not existed' % argv[1])
return
else:
print ("Usage : ")
print ("python %s data_dir" % argv[0])
return
NUM_MAX_EXAMPLES_PER_DATA_BATCH = 3000
dataset = os.path.join(folder, 'data_batch_1.bin')
filenames = [os.path.join(folder, 'data_batch_%d.bin' % i)
for i in xrange(1, 6)]
dataset_f = open(dataset, "a+")
if not dataset_f:
print "unable to open " + dataset + " with w+ mode"
return
cc = 0
dc = 1
ii = 0
cat = 0
dog = 0
nor = 0
for f in os.listdir(folder):
filepath = os.path.join(folder, f)
try:
imf = open(filepath, "r")
im = Image.open(imf)
imf.close()
p = subprocess.Popen(["display", filepath])
ii = ii + 1
label = raw_input("please label the %d image:\n\t0 means cat:\n\t1 means dog:\n\t-1 not a cat or dog:\n" % ii)
p.kill()
if (label == '-1'):
os.remove(filepath)
nor = nor + 1
continue
elif (label == '1'):
dog = dog + 1
elif (label == '0'):
cat = cat + 1
print ("generate data for %s" % filepath)
result = image_gen(filepath, label)
os.remove(filepath)
if (result.status == IMAGE_GEN_OK):
if (dc < 5 and cc >= NUM_MAX_EXAMPLES_PER_DATA_BATCH):
cc = 0
dc = dc + 1
dataset_f.close()
dataset_f.close()
dataset = folder + '/data_batch_' + str(dc) + '.bin'
dataset_f = open(dataset, "a+")
cc = cc + 1
result.imagebytes.tofile(dataset_f)
except Exception, e:
print ('%s : %s' % (e, filepath))
dataset_f.close()
print ("%d cats found, %d dogs found, %d not a cat or dog" % (cat, dog, nor))
if __name__ == '__main__':
main(sys.argv)
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