def new_fun_use(self):
print 'new_fun_use'
cl = CaffeLoader(prototxt_path=prototxt_19, caffemodel_path=model_file_19)
model = cl.load()
im = cv2.resize(cv2.imread('Cats.jpg'), (224, 224))
im = im.transpose((2, 0, 1))
im = np.expand_dims(im, axis=0)
# Test pretrained model
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy')
out = model.predict(im)
top5 = np.argsort(out)[0][::-1][:5]
probs = np.sort(out)[0][::-1][:5]
print 'yes'
words = open('synset_words.txt').readlines()
words = [(w[0], ' '.join(w[1:])) for w in [w.split() for w in words]]
words = np.asarray(words)
for w, p in zip(words[top5], probs):
print('{}\tprobability:{}'.format(w, p))
def new_fun_use(self):
print 'new_fun_use'
cl = CaffeLoader(prototxt_path=prototxt_19,
caffemodel_path=model_file_19)
model = cl.load()
im = cv2.resize(cv2.imread('Cats.jpg'), (224, 224))
im = im.transpose((2, 0, 1))
im = np.expand_dims(im, axis=0)
# Test pretrained model
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy')
out = model.predict(im)
top5 = np.argsort(out)[0][::-1][:5]
probs = np.sort(out)[0][::-1][:5]
print 'yes'
words = open('synset_words.txt').readlines()
words = [(w[0], ' '.join(w[1:])) for w in [w.split() for w in words]]
words = np.asarray(words)
for w, p in zip(words[top5], probs):
print('{}\tprobability:{}'.format(w, p))
import tornado.wsgi
import tornado.httpserver
import numpy as np
import pandas as pd
from PIL import Image
import cStringIO as StringIO
import urllib
import exifutil
from caffe2keras.caffeloader import CaffeLoader
prototxt = '/mnt/share/projects/keras_test/chainer-imagenet-vgg-master/VGG_ILSVRC_16_layers_deploy.prototxt'
model_file = '/mnt/share/projects/keras_test/chainer-imagenet-vgg-master/VGG_ILSVRC_16_layers.caffemodel'
cl = CaffeLoader(prototxt_path=prototxt, caffemodel_path=model_file)
model = cl.load()
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy')
words = open('synset_words.txt').readlines()
words = [(w[0], ' '.join(w[1:])) for w in [w.split() for w in words]]
words = np.asarray(words)
UPLOAD_FOLDER = '/tmp/keras_demos_uploads'
ALLOWED_IMAGE_EXTENSIONS = set(['png', 'bmp', 'jpg', 'jpe', 'jpeg', 'gif'])
# Obtain the flask app object
app = flask.Flask(__name__)
@app.route('/')
