def method_select(name):
if name == "color":
ret = Color()
elif name == "edge":
ret = Edge()
elif name == "gabor":
ret = Gabor()
elif name == "daisy":
ret = Daisy()
elif name == "HOG":
ret = HOG()
return ret
def _get_feat(self, db, f_class):
if f_class == 'color':
f_c = Color()
elif f_class == 'daisy':
f_c = Daisy()
elif f_class == 'edge':
f_c = Edge()
elif f_class == 'gabor':
f_c = Gabor()
elif f_class == 'hog':
f_c = HOG()
return f_c.make_samples(db, verbose=False)
# retrieve by daisy
method = Daisy()
samples = method.make_samples(db)
query = samples[query_idx]
_, result = infer(query, samples=samples, depth=depth, d_type=d_type)
print(result)
# retrieve by edge
method = Edge()
samples = method.make_samples(db)
query = samples[query_idx]
_, result = infer(query, samples=samples, depth=depth, d_type=d_type)
print(result)
# retrieve by gabor
method = Gabor()
samples = method.make_samples(db)
query = samples[query_idx]
_, result = infer(query, samples=samples, depth=depth, d_type=d_type)
print(result)
# retrieve by HOG
method = HOG()
samples = method.make_samples(db)
query = samples[query_idx]
_, result = infer(query, samples=samples, depth=depth, d_type=d_type)
print(result)
# retrieve by VGG
method = VGGNetFeat()
samples = method.make_samples(db)
name, weight = s.split(':')
weight = int(weight)
if name not in features.keys() or weight < 1:
raise Exception
return name, weight
except:
raise argparse.ArgumentTypeError(
f"\nFeature must be 'name:weight'\n\tname in {features.keys()}\n\tweight >= 1"
)
features = {
"color": Color(),
"daisy": Daisy(),
"edge": Edge(),
"gabor": Gabor(),
"hog": HOG(),
"vgg": VGGNetFeat(),
"res": ResNetFeat(),
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-n",
"--neighbor",
help="neighbor by class",
type=int,
default=3)
parser.add_argument(
"-c",
help="Copy images in a result path (src/CBIR/result/retrieval/)",
