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()
elif f_class == 'vgg':
f_c = VGGNetFeat()
elif f_class == 'res':
f_c = ResNetFeat()
return f_c.make_samples(db, verbose=False)
def __init__(self, db, f_class=None, d_type='L1'):
self.NGT_dir = 'NGT_{}_{}'.format(f_class,d_type)
self.NGT_path = b''
self.fearure = f_class
self.SQLdb = SQLite()
if f_class == 'daisy':
self.f_c = Daisy()
self.NGT_path = b'NGT/NGT_daisy_'+d_type.encode()
elif f_class == 'edge':
self.f_c = Edge()
self.NGT_path = b'NGT/NGT_edge_'+d_type.encode()
elif f_class == 'hog':
self.f_c = HOG()
self.NGT_path = b'NGT/NGT_hog_'+d_type.encode()
elif f_class == 'vgg':
self.f_c = VGGNetFeat()
self.NGT_path = b'NGT/NGT_vgg_'+d_type.encode()
elif f_class == 'res':
self.f_c = ResNetFeat()
self.NGT_path = b'NGT/NGT_res_'+d_type.encode()
if not os.path.exists(os.path.join(NGT_dir,self.NGT_dir)):
samples = self.f_c.make_samples(db, verbose=False)
dim = 0
try:
dim = samples[0]['hist'].shape[0]
except:
pass
images= []
objects = []
for i, row in enumerate(samples):
vector = row['hist']
link = row['img']
lable = row['cls']
data = {'index':i,'link':link,'lable':lable}
images.append(data)
objects.append(vector)
self.SQLdb.updateMuti(f_class,images)
# cPickle.dump(images, open(os.path.join(NGT_dir, sample_cache), "wb", True))
ngtpy.create(path=self.NGT_path, dimension=dim, distance_type=d_type)
self.index = ngtpy.Index(self.NGT_path)
self.index.batch_insert(objects)
self.index.save()
self.index = ngtpy.Index(self.NGT_path)
_, 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)
query = samples[query_idx]
_, result = infer(query, samples=samples, depth=depth, d_type=d_type)
print(result)
# retrieve by resnet
method = ResNetFeat()
samples = method.make_samples(db)
query = samples[query_idx]
_, result = infer(query, samples=samples, depth=depth, d_type=d_type)
print(result)
def test(db, query_idx):
results = {}
# retrieve by color
method = Color()
samples = method.make_samples(db)
query = samples[query_idx]
# print(samples)
img = scipy.misc.imread(query['img'])
# print(query)
_, result = infer(query, samples=samples, depth=depth, d_type=d_type)
# results.append(result[0]['cls'])
inc(results, result[0]['cls'])
# # 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)
# # results.append(result[0]['cls'])
# inc(results, result[0]['cls'])
# # # retrieve by edge
method = Edge()
samples = method.make_samples(db)
query = samples[query_idx]
# print(samples)
query = samples[query_idx]
img = scipy.misc.imread(query['img'])
_, result = infer(query, samples=samples, depth=depth, d_type=d_type)
# results.append(result[0]['cls'])
inc(results, result[0]['cls'])
# # # 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)
# # inc(results, result[0]['cls'])
# # 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)
# results.append(result[0]['cls'])
inc(results, result[0]['cls'])
# # retrieve by VGG
method = VGGNetFeat()
samples = method.make_samples(db)
query = samples[query_idx]
_, result = infer(query, samples=samples, depth=depth, d_type=d_type)
# results.append(result[0]['cls'])
inc(results, result[0]['cls'])
# # retrieve by resnet
method = ResNetFeat()
samples = method.make_samples(db)
query = samples[query_idx]
_, result = infer(query, samples=samples, depth=depth, d_type=d_type)
# results.append(result[0]['cls'])
inc(results, result[0]['cls'])
import os
from PIL import Image
print(results)
finalresult = max(results.items(), key=operator.itemgetter(1))[0]
#string=".../database/"+finalresult+"/"
string = "./database/" + finalresult + "/"
print(string)
a = 1
for file in os.listdir(string):
a += 1
tempimg = Image.open(string + file)
tempimg.show()
print(string + file)
if (a == 10):
break
print("Final result is: ", finalresult)
scipy.misc.imshow(img)
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/)",
action="store_true")
egroup = parser.add_mutually_exclusive_group(required=True)