def extract_batch(filelist, savedir, descobj, verbose=False):
""" Extract features/descriptors from a batch of images. Single-threaded.
This function calls an image descripor object on a batch of images in order
to extract the images descripor. If a feature/descriptor file already exists
for the image, it is skipped. This is a single-threaded pipeline.
Arguments:
filelist: A list of files of image names including their paths of images
to read and extract descriptors from
savedir: A directory in which to save all of the image features. They
are pickled objects (protocol 2) with the same name as the
image file. The object that is pickled is the return from
descobj.extract().
decobj: An image descriptor object which does the actual extraction
work. the method called is descobj.extract(image). See
descriptors.Descriptor for an abstract base class.
verbose: bool, display progress?
Returns:
True if there we any errors extracting image features. False otherwise.
If there is a problem extracting any image descriptors, a file
"errors.log" is created in the savedir directory with a list of file
names, error number and messages.
"""
# Try to make the save path
if not os.path.exists(savedir):
os.mkdir(savedir)
errflag = False
# Set up progess updates
nfiles = len(filelist)
progbar = Progress(nfiles, title="Extracting descriptors", verbose=verbose)
# Iterate through all of the images in filelist and extract features
for i, impath in enumerate(filelist):
errflag |= extract(impath, savedir, descobj)
progbar.update(i)
progbar.finished()
if errflag == True:
print('Done with errors. See the "errors.log" file in ' + savedir)
def extract_batch(filelist, savedir, descobj, verbose=False):
""" Extract features/descriptors from a batch of images. Single-threaded.
This function calls an image descripor object on a batch of images in order
to extract the images descripor. If a feature/descriptor file already exists
for the image, it is skipped. This is a single-threaded pipeline.
Arguments:
filelist: A list of files of image names including their paths of images
to read and extract descriptors from
savedir: A directory in which to save all of the image features. They
are pickled objects (protocol 2) with the same name as the
image file. The object that is pickled is the return from
descobj.extract().
decobj: An image descriptor object which does the actual extraction
work. the method called is descobj.extract(image). See
descriptors.Descriptor for an abstract base class.
verbose: bool, display progress?
Returns:
True if there we any errors extracting image features. False otherwise.
If there is a problem extracting any image descriptors, a file
"errors.log" is created in the savedir directory with a list of file
names, error number and messages.
"""
# Try to make the save path
if not os.path.exists(savedir):
os.mkdir(savedir)
errflag = False
# Set up progess updates
nfiles = len(filelist)
progbar = Progress(nfiles, title='Extracting descriptors', verbose=verbose)
# Iterate through all of the images in filelist and extract features
for i, impath in enumerate(filelist):
errflag |= extract(impath, savedir, descobj)
progbar.update(i)
progbar.finished()
if errflag == True:
print('Done with errors. See the "errors.log" file in ' + savedir)
def extract_smp(filelist, savedir, descobj, njobs=None, verbose=False):
""" Extract features/descriptors from a batch of images. Multi-threaded.
This function calls an image descripor object on a batch of images in order
to extract the images descripor. If a feature/descriptor file already exists
for the image, it is skipped. This is a multi-threaded (SMP) pipeline
suitable for running on a single computer.
Arguments:
filelist: A list of files of image names including their paths of images
to read and extract descriptors from
savedir: A directory in which to save all of the image features. They
are pickled objects (protocol 2) with the same name as the
image file. The object that is pickled is the return from
descobj.extract().
decobj: An image descriptor object which does the actual extraction
work. the method called is descobj.extract(image). See
descriptors.Descriptor for an abstract base class.
njobs: int, Number of threads to use. If None, then the number of
threads is chosen to be the same as the number of cores.
verbose: bool, display progress?
Returns:
True if there we any errors extracting image features. False otherwise.
If there is a problem extracting any image descriptors, a file
"errors.log" is created in the savedir directory with a list of file
names, error number and messages.
"""
# Try to make the save path
if not os.path.exists(savedir):
os.mkdir(savedir)
# Set up parallel job
pool = mp.Pool(processes=njobs)
# Iterate through all of the images in filelist and extract features
result = pool.map_async(
__extract_star, itertools.izip(filelist, itertools.repeat(savedir), itertools.repeat(descobj))
)
# Set up progess updates
nfiles = len(filelist)
progbar = Progress(nfiles, title="Extracting descriptors", verbose=verbose)
# Get the status
while (result.ready() is not True) and (verbose == True):
approx_rem = nfiles - result._number_left * result._chunksize
progbar.update(max(0, approx_rem))
time.sleep(5)
progbar.finished()
# Get notification of errors
errflag = any(result.get())
pool.close()
pool.join()
if errflag == True:
print('Done, with errors. See the "errors.log" file in ' + savedir)
def extract_smp(filelist, savedir, descobj, njobs=None, verbose=False):
""" Extract features/descriptors from a batch of images. Multi-threaded.
This function calls an image descripor object on a batch of images in order
to extract the images descripor. If a feature/descriptor file already exists
for the image, it is skipped. This is a multi-threaded (SMP) pipeline
suitable for running on a single computer.
Arguments:
filelist: A list of files of image names including their paths of images
to read and extract descriptors from
savedir: A directory in which to save all of the image features. They
are pickled objects (protocol 2) with the same name as the
image file. The object that is pickled is the return from
descobj.extract().
decobj: An image descriptor object which does the actual extraction
work. the method called is descobj.extract(image). See
descriptors.Descriptor for an abstract base class.
njobs: int, Number of threads to use. If None, then the number of
threads is chosen to be the same as the number of cores.
verbose: bool, display progress?
Returns:
True if there we any errors extracting image features. False otherwise.
If there is a problem extracting any image descriptors, a file
"errors.log" is created in the savedir directory with a list of file
names, error number and messages.
"""
# Try to make the save path
if not os.path.exists(savedir):
os.mkdir(savedir)
# Set up parallel job
pool = mp.Pool(processes=njobs)
# Iterate through all of the images in filelist and extract features
result = pool.map_async(
__extract_star,
itertools.izip(filelist, itertools.repeat(savedir),
itertools.repeat(descobj)))
# Set up progess updates
nfiles = len(filelist)
progbar = Progress(nfiles, title='Extracting descriptors', verbose=verbose)
# Get the status
while ((result.ready() is not True) and (verbose == True)):
approx_rem = nfiles - result._number_left * result._chunksize
progbar.update(max(0, approx_rem))
time.sleep(5)
progbar.finished()
# Get notification of errors
errflag = any(result.get())
pool.close()
pool.join()
if errflag == True:
print('Done, with errors. See the "errors.log" file in ' + savedir)
