def test_api_get_image_data():
with ImagedirCtx() as ctx:
# run 1: create fingerprints database, run clustering
images, fingerprints, timestamps = icio.get_image_data(ctx.imagedir)
# run 2: only run clustering, should be much faster, this time use all
# kwds (test API)
images, fingerprints, timestamps = icio.get_image_data(
ctx.imagedir,
pca_kwds=dict(n_components=0.95),
model_kwds=dict(layer='fc2'),
img_kwds=dict(size=(224, 224)),
timestamps_kwds=dict(source='auto'))
assert len(fingerprints.keys()) == len(ctx.image_fns)
assert set(fingerprints.keys()) == set(ctx.image_fns)
def main():
clusters_path = os.path.join(IMAGE_PATH, icio.ic_base_dir, 'clusters')
# The bottleneck is calc.fingerprints() called in this function, all other
# operations are very fast. get_image_data() writes fingerprints to disk and
# loads them again instead of re-calculating them.
print('\nFingerprinting images...\n')
images, fingerprints, timestamps = icio.get_image_data(IMAGE_PATH)
print('\nImage fingerprinting done.\n')
# Run clustering on the fingerprints. Select clusters with similarity index
print('\nClustering images...\n')
clusters = calc.cluster(fingerprints, sim=SIMILARITY)
print('\nClustering done.\n')
# Re-format clusters into a simple 2D list
simple_clusters = list()
for i, (num_in_cluster, cluster_list) in enumerate(clusters.items()):
for cluster in cluster_list:
simple_clusters.append(cluster)
# Find unclustered images
unclustered_images = set(images.keys()) # Start set with all images
for cluster in simple_clusters:
for image in cluster:
unclustered_images = unclustered_images.difference(set([image]))
unclustered_images = list(unclustered_images) # Convert to list
if ACTION == 'copy':
print('\nCopying images to clusters...\n')
elif ACTION == 'move':
print('\nMoving images to clusters...\n')
# Remove existing clusters (if present)
if os.path.exists(clusters_path):
shutil.rmtree(clusters_path)
# Move images into cluster folders
cluster_dir_length = len(str(len(simple_clusters)))
for i, cluster in enumerate(simple_clusters):
cluster_name = str(i).zfill(cluster_dir_length)
cluster_dir = os.path.join(clusters_path, cluster_name)
os.makedirs(cluster_dir)
for image in cluster:
if ACTION == 'copy':
shutil.copy(os.path.abspath(image), cluster_dir)
elif ACTION == 'move':
shutil.move(os.path.abspath(image), cluster_dir)
# Move unclustered images too
for i, image in enumerate(unclustered_images):
if ACTION == 'copy':
shutil.copy(os.path.abspath(image), clusters_path)
elif ACTION == 'move':
shutil.move(os.path.abspath(image), clusters_path)
print('\nAll done!')
print('Clustered images can be found in ' + clusters_path + '\n')
#!/usr/bin/python3
# Minimal example. Use the convenience function io.get_image_data() without any
# extra arguments.
from imagecluster import calc, io as icio, postproc
# The bottleneck is calc.fingerprints() called in this function, all other
# operations are very fast. get_image_data() writes fingerprints to disk and
# loads them again instead of re-calculating them.
images, fingerprints, timestamps = icio.get_image_data('downloads/cart icon/')
# Run clustering on the fingerprints. Select clusters with similarity index
# sim=0.5.
clusters = calc.cluster(fingerprints, sim=0.5)
# Create dirs with links to images. Dirs represent the clusters the images
# belong to.
postproc.make_links(clusters, 'downloads/cart icon/imagecluster/clusters')
# Plot images arranged in clusters.
postproc.visualize(clusters, images)
#!/usr/bin/python3
# Minimal example. Use the convenience function io.get_image_data() without any
# extra arguments.
from imagecluster import calc, io as icio, postproc
# The bottleneck is calc.fingerprints() called in this function, all other
# operations are very fast. get_image_data() writes fingerprints to disk and
# loads them again instead of re-calculating them.
images, fingerprints, timestamps = icio.get_image_data('pics/')
# Run clustering on the fingerprints. Select clusters with similarity index
# sim=0.5.
clusters = calc.cluster(fingerprints, sim=0.5)
# Create dirs with links to images. Dirs represent the clusters the images
# belong to.
postproc.make_links(clusters, 'pics/imagecluster/clusters')
# Plot images arranged in clusters.
postproc.visualize(clusters, images)