Caffe Net Classifier using mean image, pixel wise (.mean(1).mean(1))

app.py

import os
import time
import cPickle
import datetime
import logging
import flask
import werkzeug
import optparse
import tornado.wsgi
import tornado.httpserver
import numpy as np
import pandas as pd
from PIL import Image as PILImage
import cStringIO as StringIO
import urllib
import caffe
import exifutil

REPO_DIRNAME = os.path.abspath(os.path.dirname(__file__) + '/../..')
UPLOAD_FOLDER = '/tmp/caffe_demos_uploads'
ALLOWED_IMAGE_EXTENSIONS = set(['png', 'bmp', 'jpg', 'jpe', 'jpeg', 'gif'])

# Obtain the flask app object
app = flask.Flask(__name__)


@app.route('/')
def index():
    return flask.render_template('index.html', has_result=False)


@app.route('/classify_url', methods=['GET'])
def classify_url():
    imageurl = flask.request.args.get('imageurl', '')
    try:
        string_buffer = StringIO.StringIO(
            urllib.urlopen(imageurl).read())
        image = caffe.io.load_image(string_buffer)


    except Exception as err:
        # For any exception we encounter in reading the image, we will just
        # not continue.
        logging.info('URL Image open error: %s', err)
        return flask.render_template(
            'index.html', has_result=True,
            result=(False, 'Cannot open image from URL.')
        )

    logging.info('Image: %s', imageurl)
    result = app.clf.classify_image(image)
    return flask.render_template(
        'index.html', has_result=True, result=result, imagesrc=imageurl)


@app.route('/classify_upload', methods=['POST'])
def classify_upload():
    try:
        # We will save the file to disk for possible data collection.
        imagefile = flask.request.files['imagefile']
        filename_ = str(datetime.datetime.now()).replace(' ', '_') + \
            werkzeug.secure_filename(imagefile.filename)
        filename = os.path.join(UPLOAD_FOLDER, filename_)
        imagefile.save(filename)
        logging.info('Saving to %s.', filename)
        image = exifutil.open_oriented_im(filename)

    except Exception as err:
        logging.info('Uploaded image open error: %s', err)
        return flask.render_template(
            'index.html', has_result=True,
            result=(False, 'Cannot open uploaded image.')
        )

    result = app.clf.classify_image(image)
    return flask.render_template(
        'index.html', has_result=True, result=result,
        imagesrc=embed_image_html(image)
    )


def embed_image_html(image):
    """Creates an image embedded in HTML base64 format."""
    image_pil = PILImage.fromarray((255 * image).astype('uint8'))
    image_pil = image_pil.resize((256, 256))
    string_buf = StringIO.StringIO()
    image_pil.save(string_buf, format='png')
    data = string_buf.getvalue().encode('base64').replace('\n', '')
    return 'data:image/png;base64,' + data


def allowed_file(filename):
    return (
        '.' in filename and
        filename.rsplit('.', 1)[1] in ALLOWED_IMAGE_EXTENSIONS
    )


class ImagenetClassifier(object):
    default_args = {
        'model_def_file': (
            '{}/models/bvlc_reference_caffenet/deploy.prototxt'.format(REPO_DIRNAME)),
        'pretrained_model_file': (
            '{}/models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'.format(REPO_DIRNAME)),
        'mean_file': (
            '{}/python/caffe/imagenet/ilsvrc_2012_mean.npy'.format(REPO_DIRNAME)),
        'class_labels_file': (
            '{}/data/ilsvrc12/synset_words.txt'.format(REPO_DIRNAME)),
        'bet_file': (
            '{}/data/ilsvrc12/imagenet.bet.pickle'.format(REPO_DIRNAME)),
    }
    for key, val in default_args.iteritems():
        if not os.path.exists(val):
            raise Exception(
                "File for {} is missing. Should be at: {}".format(key, val))
    default_args['image_dim'] = 256
    default_args['raw_scale'] = 255.
    default_args['gpu_mode'] = False

    def __init__(self, model_def_file, pretrained_model_file, mean_file,
                 raw_scale, class_labels_file, bet_file, image_dim, gpu_mode):
        logging.info('Loading net and associated files...')
        # in order to support any size images, please add .mean(1).mean(1) on the mean file, so we gonna use pixel wise image recognition

        if gpu_mode:
            caffe.set_mode_gpu()
        self.net = caffe.Classifier(
            model_def_file, pretrained_model_file,
            image_dims=(image_dim, image_dim), raw_scale=raw_scale,
            mean=np.load(mean_file).mean(1).mean(1), channel_swap=(2, 1, 0)
        )

        with open(class_labels_file) as f:
            labels_df = pd.DataFrame([
                {
                    'synset_id': l.strip().split(' ')[0],
                    'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]
                }
                for l in f.readlines()
            ])
        self.labels = labels_df.sort('synset_id')['name'].values

        self.bet = cPickle.load(open(bet_file))
        # A bias to prefer children nodes in single-chain paths
        # I am setting the value to 0.1 as a quick, simple model.
        # We could use better psychological models here...
        self.bet['infogain'] -= np.array(self.bet['preferences']) * 0.1

    def classify_image(self, image):
        try:
            starttime = time.time()
            scores = self.net.predict([image], oversample=True).flatten()
            endtime = time.time()

            indices = (-scores).argsort()[:5]
            predictions = self.labels[indices]

            # In addition to the prediction text, we will also produce
            # the length for the progress bar visualization.
            meta = [
                (p, '%.5f' % scores[i])
                for i, p in zip(indices, predictions)
            ]
            logging.info('result: %s', str(meta))

            # Compute expected information gain
            expected_infogain = np.dot(
                self.bet['probmat'], scores[self.bet['idmapping']])
            expected_infogain *= self.bet['infogain']

            # sort the scores
            infogain_sort = expected_infogain.argsort()[::-1]
            bet_result = [(self.bet['words'][v], '%.5f' % expected_infogain[v])
                          for v in infogain_sort[:5]]
            logging.info('bet result: %s', str(bet_result))

            return (True, meta, bet_result, '%.3f' % (endtime - starttime))

        except Exception as err:
            logging.info('Classification error: %s', err)
            return (False, 'Something went wrong when classifying the '
                           'image. Maybe try another one?')


def start_tornado(app, port=5000):
    http_server = tornado.httpserver.HTTPServer(
        tornado.wsgi.WSGIContainer(app))
    http_server.listen(port)
    print("Tornado server starting on port {}".format(port))
    tornado.ioloop.IOLoop.instance().start()


def start_from_terminal(app):
    """
    Parse command line options and start the server.
    """
    parser = optparse.OptionParser()
    parser.add_option(
        '-d', '--debug',
        help="enable debug mode",
        action="store_true", default=False)
    parser.add_option(
        '-p', '--port',
        help="which port to serve content on",
        type='int', default=5000)
    parser.add_option(
        '-g', '--gpu',
        help="use gpu mode",
        action='store_true', default=False)

    opts, args = parser.parse_args()
    ImagenetClassifier.default_args.update({'gpu_mode': opts.gpu})

    # Initialize classifier
    app.clf = ImagenetClassifier(**ImagenetClassifier.default_args)

    if opts.debug:
        app.run(debug=True, host='0.0.0.0', port=opts.port)
    else:
        start_tornado(app, opts.port)


if __name__ == '__main__':
    logging.getLogger().setLevel(logging.INFO)
    if not os.path.exists(UPLOAD_FOLDER):
        os.makedirs(UPLOAD_FOLDER)
    start_from_terminal(app)

Seet the complete example here (https://github.com/BVLC/caffe/tree/master/examples/web_demo).

You only need this GIST in case you get the 'operands could not be broadcast together with shapes (3,227,227) (3,256,256) (3,227,227)' error. In that case, you should use the pixel wise mean image, by applying .mean(1).mean(1) on your mean image, exactly as I did on line 130.