def run_main():
parser = argparse.ArgumentParser(
description='Add a new image into database.')
parser.add_argument('--id',
dest='image_id',
type=int,
required=True,
help='ID of the image to add into EuclidesDB.')
parser.add_argument('--file',
dest='filename',
type=str,
required=True,
help='Image file name.')
args = parser.parse_args()
image = Image.open(args.filename)
image_id = int(args.image_id)
image.thumbnail((300, 300), Image.ANTIALIAS)
image = F.center_crop(image, 224)
with euclides.Channel("localhost", 50000) as channel:
db = euclides.EuclidesDB(channel)
ret_add = db.add_image(image_id, ["resnet18"], image)
# After finishing adding items, you need to tell
# the database to refresh the indexes to add newly
# indexed items.
db.refresh_index()
predictions = ret_add.vectors[0].predictions
print("Preds Len: ", len(predictions))
# Category should be 281: 'tabby, tabby cat' for cat.jpg
# Classes from https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a
print("Category : ", np.array(predictions).argmax())
def run_main():
parser = argparse.ArgumentParser(
description='Add all images from a directory into the database.')
parser.add_argument('--directory',
dest='directory',
type=str,
required=True,
help='Image file name (ex. ./images).')
parser.add_argument('--pattern',
dest='pattern',
type=str,
required=True,
help='Image file pattern (ex. *.jpg).')
parser.add_argument('--output',
dest='output',
type=str,
required=True,
help='Output filename with IDs (ex. output.json).')
args = parser.parse_args()
channel = euclides.Channel("localhost", 50000)
db = euclides.EuclidesDB(channel)
path = Path(args.directory)
items_dict = {}
for id_item, pfile in enumerate(path.glob(args.pattern)):
print("Adding file", pfile.name)
add_image(db, id_item, pfile.absolute())
items_dict[pfile.name] = id_item
with open(args.output, "w") as fhandle:
json.dump(items_dict, fhandle)
db.refresh_index()
channel.close()
def run_main():
parser = argparse.ArgumentParser(description='Find similar images in EuclidesDB.')
parser.add_argument('--topk', dest='topk', type=int, required=True,
help='Find top k results.')
parser.add_argument('--file', dest='filename', type=str, required=True,
help='Image file name.')
args = parser.parse_args()
image = Image.open(args.filename)
image.thumbnail((300, 300), Image.ANTIALIAS)
image = F.center_crop(image, 224)
with euclides.Channel("localhost", 50000) as channel:
db = euclides.EuclidesDB(channel)
ret_similar = db.find_similar(image, ["resnet18"], args.topk)
print(ret_similar)
def recommend():
image_url = request.args.get('url')
response = requests.get(image_url)
image = Image.open(BytesIO(response.content))
image_id = int(1)
image.thumbnail((300, 300), Image.ANTIALIAS)
image = f.center_crop(image, 224)
with euclides.Channel("localhost", 8000) as channel:
db = euclides.EuclidesDB(channel)
ret_add = db.add_image(image_id, ["resnet18"], image)
# After finishing adding items, you need to tell
# the database to refresh the indexes to add newly
# indexed items.
db.refresh_index()
predictions = ret_add.vectors[0].predictions
print("Preds Len: ", len(predictions))
labels = requests.get(
"https://gist.githubusercontent.com/ShamsUlAzeem/3ea5740b960d854f4bd43e7322b858e3/raw"
"/e8e3d428fb94af3c1c20beea9183de364ecbc3a5/imagenet-labels.json").json(
)
category = np.array(predictions).argmax()
output = labels[str(category)]
images = []
for name in output.split(","):
images.extend(find_images(name))
return render_template('show_images.html',
images=images,
output=output,
url=image_url)