зеркало из https://github.com/microsoft/caffe.git
Merge pull request #679 from sergeyk/master
[example] image classification web demo
This commit is contained in:
Sergey Karayev
Коммит
dd292da248
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@ -4,6 +4,7 @@
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# This script downloads the imagenet example auxiliary files including:
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# - the ilsvrc12 image mean, binaryproto
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# - synset ids and words
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# - Python pickle-format data of ImageNet graph structure and relative infogain
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# - the training splits with labels
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DIR="$( cd "$(dirname "$0")" ; pwd -P )"
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@ -8,7 +8,8 @@ layout: default
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Note that unlike Caffe itself, these models are licensed for **academic research / non-commercial use only**.
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If you have any questions, please get in touch with us.
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This page will be updated as more models become available.
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*UPDATE* July 2014: we are actively working on a service for hosting user-uploaded model definition and trained weight files.
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Soon, the community will be able to easily contribute different architectures!
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### ImageNet
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@ -26,4 +27,6 @@ This page will be updated as more models become available.
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validation accuracy 57.258% and loss 1.83948.
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- This model obtains a top-1 accuracy 57.1% and a top-5 accuracy 80.2% on the validation set, using just the center crop. (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy)
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### Auxiliary Data
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Additionally, you will probably eventually need some auxiliary data (mean image, synset list, etc.): run `data/ilsvrc12/get_ilsvrc_aux.sh` from the root directory to obtain it.
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@ -0,0 +1,215 @@
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import os
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import time
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import cPickle
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import datetime
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import logging
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import flask
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import werkzeug
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import optparse
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import tornado.wsgi
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import tornado.httpserver
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import numpy as np
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import pandas as pd
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from PIL import Image as PILImage
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import cStringIO as StringIO
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import urllib
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import caffe
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import exifutil
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REPO_DIRNAME = os.path.abspath(os.path.dirname(__file__) + '/../..')
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UPLOAD_FOLDER = '/tmp/caffe_demos_uploads'
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ALLOWED_IMAGE_EXTENSIONS = set(['png', 'bmp', 'jpg', 'jpe', 'jpeg', 'gif'])
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# Obtain the flask app object
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app = flask.Flask(__name__)
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@app.route('/')
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def index():
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return flask.render_template('index.html', has_result=False)
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@app.route('/classify_url', methods=['GET'])
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def classify_url():
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imageurl = flask.request.args.get('imageurl', '')
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try:
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string_buffer = StringIO.StringIO(
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urllib.urlopen(imageurl).read())
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image = caffe.io.load_image(string_buffer)
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except Exception as err:
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# For any exception we encounter in reading the image, we will just
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# not continue.
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logging.info('URL Image open error: %s', err)
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return flask.render_template(
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'index.html', has_result=True,
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result=(False, 'Cannot open image from URL.')
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)
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logging.info('Image: %s', imageurl)
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result = app.clf.classify_image(image)
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return flask.render_template(
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'index.html', has_result=True, result=result, imagesrc=imageurl)
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@app.route('/classify_upload', methods=['POST'])
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def classify_upload():
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try:
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# We will save the file to disk for possible data collection.
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imagefile = flask.request.files['imagefile']
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filename_ = str(datetime.datetime.now()).replace(' ', '_') + \
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werkzeug.secure_filename(imagefile.filename)
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filename = os.path.join(UPLOAD_FOLDER, filename_)
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imagefile.save(filename)
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logging.info('Saving to %s.', filename)
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image = exifutil.open_oriented_im(filename)
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except Exception as err:
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logging.info('Uploaded image open error: %s', err)
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return flask.render_template(
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'index.html', has_result=True,
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result=(False, 'Cannot open uploaded image.')
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)
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result = app.clf.classify_image(image)
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return flask.render_template(
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'index.html', has_result=True, result=result,
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imagesrc=embed_image_html(image)
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)
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def embed_image_html(image):
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"""Creates an image embedded in HTML base64 format."""
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image_pil = PILImage.fromarray((255 * image).astype('uint8'))
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image_pil = image_pil.resize((256, 256))
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string_buf = StringIO.StringIO()
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image_pil.save(string_buf, format='png')
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data = string_buf.getvalue().encode('base64').replace('\n', '')
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return 'data:image/png;base64,' + data
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def allowed_file(filename):
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return (
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'.' in filename and
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filename.rsplit('.', 1)[1] in ALLOWED_IMAGE_EXTENSIONS
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)
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class ImagenetClassifier(object):
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default_args = {
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'model_def_file': (
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'{}/examples/imagenet/imagenet_deploy.prototxt'.format(REPO_DIRNAME)),
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'pretrained_model_file': (
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'{}/examples/imagenet/caffe_reference_imagenet_model'.format(REPO_DIRNAME)),
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'mean_file': (
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'{}/python/caffe/imagenet/ilsvrc_2012_mean.npy'.format(REPO_DIRNAME)),
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'class_labels_file': (
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'{}/data/ilsvrc12/synset_words.txt'.format(REPO_DIRNAME)),
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'bet_file': (
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'{}/data/ilsvrc12/imagenet.bet.pickle'.format(REPO_DIRNAME)),
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}
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for key, val in default_args.iteritems():
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if not os.path.exists(val):
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raise Exception(
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"File for {} is missing. Should be at: {}".format(key, val))
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default_args['image_dim'] = 227
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default_args['gpu_mode'] = True
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def __init__(self, model_def_file, pretrained_model_file, mean_file,
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class_labels_file, bet_file, image_dim, gpu_mode=False):
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logging.info('Loading net and associated files...')
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self.net = caffe.Classifier(
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model_def_file, pretrained_model_file, input_scale=255,
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image_dims=(image_dim, image_dim), gpu=gpu_mode,
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mean_file=mean_file, channel_swap=(2, 1, 0)
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)
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with open(class_labels_file) as f:
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labels_df = pd.DataFrame([
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{
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'synset_id': l.strip().split(' ')[0],
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'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]
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}
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for l in f.readlines()
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])
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self.labels = labels_df.sort('synset_id')['name'].values
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self.bet = cPickle.load(open(bet_file))
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# A bias to prefer children nodes in single-chain paths
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# I am setting the value to 0.1 as a quick, simple model.
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# We could use better psychological models here...
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self.bet['infogain'] -= np.array(self.bet['preferences']) * 0.1
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def classify_image(self, image):
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try:
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starttime = time.time()
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scores = self.net.predict([image], oversample=True).flatten()
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endtime = time.time()
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indices = (-scores).argsort()[:5]
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predictions = self.labels[indices]
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# In addition to the prediction text, we will also produce
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# the length for the progress bar visualization.
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meta = [
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(p, '%.5f' % scores[i])
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for i, p in zip(indices, predictions)
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]
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logging.info('result: %s', str(meta))
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# Compute expected information gain
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expected_infogain = np.dot(
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self.bet['probmat'], scores[self.bet['idmapping']])
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expected_infogain *= self.bet['infogain']
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# sort the scores
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infogain_sort = expected_infogain.argsort()[::-1]
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bet_result = [(self.bet['words'][v], '%.5f' % expected_infogain[v])
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for v in infogain_sort[:5]]
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logging.info('bet result: %s', str(bet_result))
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return (True, meta, bet_result, '%.3f' % (endtime - starttime))
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except Exception as err:
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logging.info('Classification error: %s', err)
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return (False, 'Something went wrong when classifying the '
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'image. Maybe try another one?')
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def start_tornado(app, port=5000):
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http_server = tornado.httpserver.HTTPServer(
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tornado.wsgi.WSGIContainer(app))
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http_server.listen(port)
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print("Tornado server starting on port {}".format(port))
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tornado.ioloop.IOLoop.instance().start()
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def start_from_terminal(app):
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"""
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Parse command line options and start the server.
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"""
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parser = optparse.OptionParser()
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parser.add_option(
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'-d', '--debug',
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help="enable debug mode",
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action="store_true", default=False)
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parser.add_option(
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'-p', '--port',
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help="which port to serve content on",
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type='int', default=5000)
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opts, args = parser.parse_args()
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# Initialize classifier
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app.clf = ImagenetClassifier(**ImagenetClassifier.default_args)
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if opts.debug:
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app.run(debug=True, host='0.0.0.0', port=opts.port)
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else:
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start_tornado(app, opts.port)
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if __name__ == '__main__':
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logging.getLogger().setLevel(logging.INFO)
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if not os.path.exists(UPLOAD_FOLDER):
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os.makedirs(UPLOAD_FOLDER)
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start_from_terminal(app)
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"""
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This script handles the skimage exif problem.
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"""
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from PIL import Image
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import numpy as np
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ORIENTATIONS = { # used in apply_orientation
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2: (Image.FLIP_LEFT_RIGHT,),
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3: (Image.ROTATE_180,),
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4: (Image.FLIP_TOP_BOTTOM,),
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5: (Image.FLIP_LEFT_RIGHT, Image.ROTATE_90),
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6: (Image.ROTATE_270,),
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7: (Image.FLIP_LEFT_RIGHT, Image.ROTATE_270),
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8: (Image.ROTATE_90,)
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}
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def open_oriented_im(im_path):
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im = Image.open(im_path)
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if hasattr(im, '_getexif'):
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exif = im._getexif()
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if exif is not None and 274 in exif:
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orientation = exif[274]
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im = apply_orientation(im, orientation)
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return np.asarray(im).astype(np.float32) / 255.
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def apply_orientation(im, orientation):
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if orientation in ORIENTATIONS:
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for method in ORIENTATIONS[orientation]:
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im = im.transpose(method)
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return im
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---
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title: Web demo
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description: Image classification demo running as a Flask web server.
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category: example
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layout: default
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include_in_docs: true
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---
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# Web Demo
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## Requirements
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The demo server requires Python with some dependencies.
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To make sure you have the dependencies, please run `pip install -r examples/web_demo/requirements.txt`, and also make sure that you've compiled the Python Caffe interface and that it is on your `PYTHONPATH` (see [installation instructions](/installation.html)).
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Make sure that you have obtained the Caffe Reference ImageNet Model and the ImageNet Auxiliary Data ([instructions](/getting_pretrained_models.html)).
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NOTE: if you run into trouble, try re-downloading the auxiliary files.
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## Run
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Running `python examples/web_demo/app.py` will bring up the demo server, accessible at `http://0.0.0.0:5000`.
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You can enable debug mode of the web server, or switch to a different port:
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% python examples/web_demo/app.py -h
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Usage: app.py [options]
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Options:
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-h, --help show this help message and exit
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-d, --debug enable debug mode
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-p PORT, --port=PORT which port to serve content on
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@ -0,0 +1,138 @@
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<meta name="description" content="Caffe demos">
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<meta name="author" content="BVLC (http://bvlc.eecs.berkeley.edu/)">
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<title>Caffe Demos</title>
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<link href="//netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
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<script type="text/javascript" src="//code.jquery.com/jquery-2.1.1.js"></script>
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<script src="//netdna.bootstrapcdn.com/bootstrap/3.1.1/js/bootstrap.min.js"></script>
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<!-- Script to instantly classify an image once it is uploaded. -->
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<script type="text/javascript">
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$(document).ready(
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function(){
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$('#classifyfile').attr('disabled',true);
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$('#imagefile').change(
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function(){
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if ($(this).val()){
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$('#formupload').submit();
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}
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}
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);
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}
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);
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</script>
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<style>
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body {
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font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
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line-height:1.5em;
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color: #232323;
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-webkit-font-smoothing: antialiased;
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}
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h1, h2, h3 {
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font-family: Times, serif;
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line-height:1.5em;
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border-bottom: 1px solid #ccc;
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}
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</style>
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</head>
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<body>
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<!-- Begin page content -->
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<div class="container">
|
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<div class="page-header">
|
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<h1><a href="/">Caffe Demos</a></h1>
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<p>
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The <a href="http://caffe.berkeleyvision.org">Caffe</a> neural network library makes implementing state-of-the-art computer vision systems easy.
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</p>
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</div>
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<div>
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<h2>Classification</h2>
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<a href="/classify_url?imageurl=http%3A%2F%2Fi.telegraph.co.uk%2Fmultimedia%2Farchive%2F02351%2Fcross-eyed-cat_2351472k.jpg">Click for a Quick Example</a>
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</div>
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{% if has_result %}
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{% if not result[0] %}
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<!-- we have error in the result. -->
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<div class="alert alert-danger">{{ result[1] }} Did you provide a valid URL or a valid image file? </div>
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{% else %}
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<div class="media">
|
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<a class="pull-left" href="#"><img class="media-object" width="192" height="192" src={{ imagesrc }}></a>
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<div class="media-body">
|
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<div class="bs-example bs-example-tabs">
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<ul id="myTab" class="nav nav-tabs">
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<li class="active"><a href="#infopred" data-toggle="tab">Maximally accurate</a></li>
|
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<li><a href="#flatpred" data-toggle="tab">Maximally specific</a></li>
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</ul>
|
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<div id="myTabContent" class="tab-content">
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<div class="tab-pane fade in active" id="infopred">
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<ul class="list-group">
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{% for single_pred in result[2] %}
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<li class="list-group-item">
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<span class="badge">{{ single_pred[1] }}</span>
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<h4 class="list-group-item-heading">
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<a href="https://www.google.com/#q={{ single_pred[0] }}" target="_blank">{{ single_pred[0] }}</a>
|
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</h4>
|
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</li>
|
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{% endfor %}
|
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</ul>
|
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</div>
|
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<div class="tab-pane fade" id="flatpred">
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<ul class="list-group">
|
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{% for single_pred in result[1] %}
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<li class="list-group-item">
|
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<span class="badge">{{ single_pred[1] }}</span>
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<h4 class="list-group-item-heading">
|
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<a href="https://www.google.com/#q={{ single_pred[0] }}" target="_blank">{{ single_pred[0] }}</a>
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</h4>
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</li>
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{% endfor %}
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
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|
||||
</div>
|
||||
</div>
|
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<p> CNN took {{ result[3] }} seconds. </p>
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{% endif %}
|
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<hr>
|
||||
{% endif %}
|
||||
|
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<form role="form" action="classify_url" method="get">
|
||||
<div class="form-group">
|
||||
<div class="input-group">
|
||||
<input type="text" class="form-control" name="imageurl" id="imageurl" placeholder="Provide an image URL">
|
||||
<span class="input-group-btn">
|
||||
<input class="btn btn-primary" value="Classify URL" type="submit" id="classifyurl"></input>
|
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</span>
|
||||
</div><!-- /input-group -->
|
||||
</div>
|
||||
</form>
|
||||
|
||||
<form id="formupload" class="form-inline" role="form" action="classify_upload" method="post" enctype="multipart/form-data">
|
||||
<div class="form-group">
|
||||
<label for="imagefile">Or upload an image:</label>
|
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<input type="file" name="imagefile" id="imagefile">
|
||||
</div>
|
||||
<!--<input type="submit" class="btn btn-primary" value="Classify File" id="classifyfile"></input>-->
|
||||
</form>
|
||||
</div>
|
||||
|
||||
<hr>
|
||||
<div id="footer">
|
||||
<div class="container">
|
||||
<p>© BVLC 2014</p>
|
||||
</div>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
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