def load_from_img_enhance(img_path):
loader = image_tool.ImageTool()
new_imgs = []
if 'jpeg' in img_path:
img_path = img_path.replace('dcm', 'jpeg')
elif 'png' in img_path:
img_path = img_path.replace('dcm', 'png')
else:
raise Exception('Unsupported image format: ', img_path)
return loader.load(img_path, grayscale=True).rotate_by_range(
(-5, 5)).enhance(0.1).get()
# limitations under the License.
import sys
import time
import numpy as np
import traceback
from argparse import ArgumentParser
from scipy.misc import imread
from singa import device
from singa import tensor
from singa import image_tool
from rafiki.agent import Agent, MsgType
import model
tool = image_tool.ImageTool()
num_augmentation = 1
crop_size = 224
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1] in \
["PNG", "png", "jpg", "JPG", "JPEG", "jpeg"]
def serve(net, label_map, dev, agent, topk=5):
'''Serve to predict image labels.
It prints the topk food names for each image.