for i in range(N):
image = cvd.convertimage(imagelist[i], args.gray,
args.resize_imageW,
args.resize_imageH,
args.normalize_image)
imagemean += image
if((i + 1) % 100 == 0):
logger_root.info('Processed %d files.' %(i + 1))
if((i + 1) % 100 != 0):
logger_root.info('Processed %d files.' %(i + 1))
imagemean /= (N + 1)
# range [0.0 255.0] means image is uint8
if(args.normalize_image[0] == 0 and args.normalize_image[1] == 255):
return imagemean.astype(np.uint8)
return imagemean.astype(np.float32)
def computerandsavemean(args):
imagemean = computerimagemean(args)
meanshape = tuple([1]) + imagemean.shape
# write to h5py dataset
with h5py.File('datasetmean.hdf5') as f:
mset = f.create_dataset(name = 'mean', shape = meanshape,
dtype = np.dtype(np.float32),
compression = "gzip", compression_opts = 4)
mset[0] = imagemean
if __name__ == '__main__':
cvd.setuplogging()
# parse argv
args = cvd.parseArgument()
computerandsavemean(args)
import convertdata
import numpy as np
import skimage.io, skimage.color, skimage.transform
scenesaliency_root = "/home/humt/SoftwareProgram/SceneSaliency/Sources/SceneSaliency/"
caffe_root = scenesaliency_root + "caffe/"
sys.path.insert(0, caffe_root + "python")
import caffe
if __name__ == "__main__":
convertdata.setuplogging()
# parse argv
args = convertdata.parseArgument()
if args.resize_imageW <= 0:
logger_root = logging.getLogger()
logger_root.error("resize_imageW should be greater than 0")
sys.exit(1)
if args.resize_imageH <= 0:
logger_root = logging.getLogger()
logger_root.error("resize_imageH should be greater than 0")
sys.exit(1)
if args.resize_saliencyW <= 0:
logger_root = logging.getLogger()
logger_root.error("resize_saliencyW should be greater than 0")
sys.exit(1)
if args.resize_saliencyH <= 0:
