def main(imagedir, sim=0.5):
"""Example main app using this library.
Parameters
----------
imagedir : str
path to directory with images
sim : float (0..1)
similarity index (see imagecluster.cluster())
"""
dbfn = pj(imagedir, ic_base_dir, 'fingerprints.pk')
if not os.path.exists(dbfn):
if not os.path.exists(os.path.dirname(dbfn)):
os.makedirs(os.path.dirname(dbfn))
print("no fingerprints database {} found".format(dbfn))
files = co.get_files(imagedir)
model = get_model()
print("running all images through NN model ...".format(dbfn))
fps = fingerprints(files, model, size=(224, 224))
co.write_pk(fps, dbfn)
print("clustering ...")
make_links(cluster(fps, sim), pj(imagedir, ic_base_dir, 'clusters'))
else:
print("loading fingerprints database {} ...".format(dbfn))
fps = co.read_pk(dbfn)
print("clustering ...")
make_links(cluster(fps, sim), pj(imagedir, ic_base_dir, 'clusters'))
def link_parts( ic_base_dir = ic_base_dir, sim = 0.55 ) :
for dirpath, dirnames, filenames in os.walk(ic_base_dir):
dircs = dirnames
break
for dirc in dircs:
shutil.rmtree(ic_base_dir+'/'+dirc)
fpdict = co.read_pk(sampled_fp_path)
print("Dict has %d is clusting [sim=%f]" % (fpdict.__len__(), sim))
ic.make_links(ic.cluster(dict(fpdict), sim, method='average')
, ic_base_dir + '/cluster')
# resultFilter.filter_folder(ic_base_dir,total_num=fpdict.__len__(),img_folder=img_folder_path)
del fpdict
def get_fp():
fp = dict(co.read_pk(full_fp_path))
new_dic = {}
print fp.keys()[0:5]
result_list = pd.read_csv(converted_txt_path,
header=None)[0]
i = 0
num = result_list.__len__()
for res in result_list:
i += 1
new_dic[res] = fp[res]
sys.stdout.write('\r[%.2f%%]' % (i * 100.0 / num))
sys.stdout.flush()
print ('\n')
co.write_pk(new_dic,sampled_fp_path)
def get_iter_fp(txt_path, save_path, formerFP_path):
txt_path += '/nones.txt'
formerFP_path += '/fingerprints.pk'
files = pd.read_csv(txt_path, header=None, index_col=None)
files = list(files[0])
random.shuffle(files)
random.shuffle(files)
print('nones.txt contain %d ' % files.__len__())
fpdict = co.read_pk(formerFP_path)
print('former fingerprints contain %d ' % len(fpdict))
newdict = {}
for fname in files:
tkey = imagedir + '/' + fname
newdict[tkey] = fpdict[tkey]
co.write_pk(newdict, save_path + '/fingerprints.pk')
def main(imagedir, sim=0.5, layer='fc2', size=(224,224), links=True, vis=False,
max_csize=None, pca=False, pca_params=dict(n_components=0.9)):
"""Example main app using this library.
Upon first invocation, the image and fingerprint databases are built and
written to disk. Each new invocation loads those and only repeats
* clustering
* creation of links to files in clusters
* visualization (if `vis=True`)
This is good for playing around with the `sim` parameter, for
instance, which only influences clustering.
Parameters
----------
imagedir : str
path to directory with images
sim : float (0..1)
similarity index (see :func:`calc.cluster`)
layer : str
which layer to use as feature vector (see
:func:`calc.get_model`)
size : tuple
input image size (width, height), must match `model`, e.g. (224,224)
links : bool
create dirs with links
vis : bool
plot images in clusters
max_csize : max number of images per cluster for visualization (see
:mod:`~postproc`)
pca : bool
Perform PCA on fingerprints before clustering, using `pca_params`.
pca_params : dict
kwargs to sklearn's PCA
Notes
-----
imagedir : To select only a subset of the images, create an `imagedir` and
symlink your selected images there. In the future, we may add support
for passing a list of files, should the need arise. But then again,
this function is only an example front-end.
"""
fps_fn = pj(imagedir, ic_base_dir, 'fingerprints.pk')
ias_fn = pj(imagedir, ic_base_dir, 'images.pk')
ias = None
if not os.path.exists(fps_fn):
print(f"no fingerprints database {fps_fn} found")
os.makedirs(os.path.dirname(fps_fn), exist_ok=True)
model = ic.get_model(layer=layer)
if not os.path.exists(ias_fn):
print(f"create image array database {ias_fn}")
ias = ic.image_arrays(imagedir, size=size)
co.write_pk(ias, ias_fn)
else:
ias = co.read_pk(ias_fn)
print("running all images through NN model ...")
fps = ic.fingerprints(ias, model)
co.write_pk(fps, fps_fn)
else:
print(f"loading fingerprints database {fps_fn} ...")
fps = co.read_pk(fps_fn)
if pca:
fps = ic.pca(fps, **pca_params)
print("pca dims:", list(fps.values())[0].shape[0])
print("clustering ...")
clusters = ic.cluster(fps, sim)
if links:
pp.make_links(clusters, pj(imagedir, ic_base_dir, 'clusters'))
if vis:
if ias is None:
ias = co.read_pk(ias_fn)
pp.visualize(clusters, ias, max_csize=max_csize)
def main_kmeans(imagedir,
n_clusters=5,
layer='fc2',
size=(224, 224),
links=True,
pca=False,
pca_params=dict(n_components=0.9)):
"""Example main app using this library.
Upon first invocation, the image and fingerprint databases are built and
written to disk. Each new invocation loads those and only repeats
* clustering
* creation of links to files in clusters
* visualization (if `vis=True`)
This is good for playing around with the `sim` parameter, for
instance, which only influences clustering.
Parameters
----------
imagedir : str
path to directory with images
n_cluster : int (1...999)
num of kmeans cluster (see :func:`calc.cluster_kmeans`)
layer : str
which layer to use as feature vector (see
:func:`calc.get_model`)
size : tuple
input image size (width, height), must match `model`, e.g. (224,224)
links : bool
create dirs with links
pca : bool
Perform PCA on fingerprints before clustering, using `pca_params`.
pca_params : dict
kwargs to sklearn's PCA
Notes
-----
imagedir : To select only a subset of the images, create an `imagedir` and
symlink your selected images there. In the future, we may add support
for passing a list of files, should the need arise. But then again,
this function is only an example front-end.
"""
fps_fn = pj(imagedir, ic_base_dir, 'fingerprints.pk')
ias_fn = pj(imagedir, ic_base_dir, 'images.pk')
ias = None
logger_kmeans = log(logger_name='kmeans').logger
try:
if not os.path.exists(fps_fn):
print("no fingerprints database {} found".format(fps_fn))
logger_kmeans.info(
"no fingerprints database {} found".format(fps_fn))
os.makedirs(os.path.dirname(fps_fn), exist_ok=True)
try:
model = ic.get_model(layer=layer)
except Exception as e:
logger_kmeans.error(e)
if not os.path.exists(ias_fn):
logger_kmeans.info(
"create image array database {}".format(ias_fn))
print("create image array database {}".format(ias_fn))
ias = ic.image_arrays(imagedir, size=size)
co.write_pk(ias, ias_fn)
else:
ias = co.read_pk(ias_fn)
print("running all images through NN model ...")
fps = ic.fingerprints(ias, model)
co.write_pk(fps, fps_fn)
else:
print("loading fingerprints database {} ...".format(fps_fn))
fps = co.read_pk(fps_fn)
if pca:
fps = ic.pca(fps, **pca_params)
print("pca dims:", list(fps.values())[0].shape[0])
logger_kmeans.info("pca dims: " +
str(list(fps.values())[0].shape[0]))
#将每张图片转换成向量
#进行聚类
print("clustering ...")
logger_kmeans.info("clustering ...")
clusters = ic.cluster_kmeans(fps, n_clusters=n_clusters)
if links:
pp.make_links_v2(clusters, pj(imagedir, ic_base_dir, 'clusters'))
except Exception as e:
logger_kmeans.error(e)