fallthrough/build_leveldb.py Secret

## build_leveldb.py
#!/usr/bin/python
# Copyright 2014 SIG2D
# Licensed under the Apache License, Version 2.0.

import os
import shutil
import subprocess
import sys

from caffe.proto import caffe_pb2
import leveldb
import numpy as np
import PIL.Image
import random

THUMBNAIL_SIZE = 32


def make_thumbnail(image):
  image = image.convert('RGB')
  square_size = min(image.size)
  offset_x = (image.size[0] - square_size) / 2
  offset_y = (image.size[1] - square_size) / 2
  image = image.crop((offset_x, offset_y,
                      offset_x + square_size, offset_y + square_size))
  image.thumbnail((THUMBNAIL_SIZE, THUMBNAIL_SIZE), PIL.Image.ANTIALIAS)
  return image


def make_datum(thumbnail, label):
  return caffe_pb2.Datum(
      channels=3,
      width=THUMBNAIL_SIZE,
      height=THUMBNAIL_SIZE,
      label=label,
      data=np.rollaxis(np.asarray(thumbnail), 2).tostring())


def create_leveldb(name):
  path = os.path.join(os.environ['HOME'], 'caffe/examples/cifar10', name)
  try:
    shutil.rmtree(path)
  except OSError:
    pass
  print 'opening', path
  return leveldb.LevelDB(
      path, create_if_missing=True, error_if_exists=True, paranoid_checks=True)


def main():
  train_db = create_leveldb('cifar10_train_leveldb')
  test_db = create_leveldb('cifar10_test_leveldb')

  filepath_and_label = []
  for dirpath, _, filenames in os.walk('.'):
    try:
      label = int(dirpath.split('/')[1])
    except Exception:
      continue
    for filename in filenames:
      if filename.endswith(('.png', '.jpg')):
        filepath_and_label.append((os.path.join(dirpath, filename), label))

  random.shuffle(filepath_and_label)

  for seq, (filepath, label) in enumerate(filepath_and_label):
    print seq, label, filepath
    image = PIL.Image.open(filepath)
    thumbnail = make_thumbnail(image)
    datum = make_datum(thumbnail, label)
    db = test_db if seq % 10 == 0 else train_db
    db.Put('%08d' % seq, datum.SerializeToString())


if __name__ == '__main__':
  sys.exit(main())

## frontend.html
<!doctype html>
<html>
  <head>
    <meta charset="UTF-8">
    <!--
    Copyright 2014 SIG2D
    Licensed under the Apache License, Version 2.0.
    -->
    <meta http-equiv="X-UA-Compatible" content="IE=edge">
    <meta name="viewport" content="width=device-width, initial-scale=1">

    <title>Online Kin-patsu Image Classifier</title>

    <link href="//netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
    <!--[if lt IE 9]>
      <script src="https://oss.maxcdn.com/libs/html5shiv/3.7.0/html5shiv.js"></script>
      <script src="https://oss.maxcdn.com/libs/respond.js/1.4.2/respond.min.js"></script>
    <![endif]-->

    <style>
      .result {
        display: inline-block;
        margin: 2px;
        text-align: center;
      }
    </style>
  </head>
  <body>
    <div class="container-fluid">
      <div class="page-header">
        <h1>Online Kin-patsu Image Classifier</h1>
      </div>
      <div class="row">
        <div class="col-lg-12">
          <div class="well well-sm">
            Drag &amp; drop image files here to classify.<br>
            API is available: try <code>curl -F file=@image.jpg http://kinpatsu.api.sig2d.org/</code>
          </div>
        </div>
      </div>
      <div class="row">
        <div class="col-lg-12">
          <div id="results">
          </div>
        </div>
      </div>
    </div>

    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.0/jquery.min.js"></script>
    <script src="//netdna.bootstrapcdn.com/bootstrap/3.1.1/js/bootstrap.min.js"></script>
    <script>
      var queue = [];
      var processing = false;
      var queueFile = function(file) {
        queue.push(file);
        if (!processing) {
          processNext();
        }
      };
      var processNext = function() {
        if (queue.length == 0) {
          processing = false;
          return;
        }
        processing = true;
        var file = queue.shift();
        var formData = new FormData();
        formData.append('file', file);
        $.ajax({
          url: '',
          type: 'POST',
          data: formData,
          contentType: false,
          processData: false
        }).always(processNext).then(function(data) {
          var reader = new FileReader();
          reader.onload = function() {
            var img = $('<img>');
            img.on('load', function() {
              var width = img[0].width;
              var height = img[0].height;
              var zoom = 128 / Math.max(width, height);
              var displayWidth = Math.floor(width * zoom);
              var displayHeight = Math.floor(height * zoom);
              var label = data['result']['label'];
              var score = data['result']['score'];
              var percent = Math.floor(100 * score) + '%';
              img.attr('title', score)
                 .css('width', displayWidth + 'px')
                 .css('height', displayHeight + 'px')
                 .css('margin',
                      ((128 - displayHeight) / 2 + 8) + 'px ' +
                      ((128 - displayWidth) / 2 + 8) + 'px');
              var cell = $('<div class="result">');
              cell.append($('<div>').append(img));
              cell.append($('<div>').text(label));
              cell.append($('<div>').text(percent));
              if (label == 'blonde_hair') {
                cell.css('background-color', '#fe0');
              }
              if (label == 'silver_hair') {
                cell.css('background-color', '#ddd');
              }
              $('#results').append(cell);
            });
            img.attr('src', reader.result);
          };
          reader.readAsDataURL(file);
        });
      };
      $(document).on('dragover', function(e) {
        e.preventDefault();
        return false;
      }).on('dragend', function(e) {
        e.preventDefault();
        return false;
      }).on('drop', function(e) {
        e.preventDefault();
        var files = e.originalEvent.dataTransfer.files;
        for (var i = 0; i < files.length; ++i) {
          queueFile(files[i]);
        }
        return false;
      });
    </script>
  </body>
</html>

## frontend.py
#!/usr/bin/python
# Copyright 2014 SIG2D
# Licensed under the Apache License, Version 2.0.

import sys

import bottle
import caffe
from caffe.proto import caffe_pb2
import numpy as np


@bottle.get('/')
def index_handler():
  return bottle.template('frontend.html', {})


@bottle.post('/')
def classify_handler():
  upload = bottle.request.files.values()[0]
  image = caffe.io.load_image(upload.file)
  prediction = g_classifier.predict([image], oversample=False)[0]
  clazz, score = max(enumerate(prediction.tolist()), key=lambda (i, p): p)
  label = g_labels[clazz]
  return {'result': {'label': label, 'score': score}}


def main():
  mean_blob = caffe_pb2.BlobProto()
  with open('mean.binaryproto') as f:
    mean_blob.ParseFromString(f.read())
  mean_array = np.asarray(mean_blob.data, dtype=np.float32).reshape(
      (mean_blob.channels, mean_blob.height, mean_blob.width))

  global g_classifier
  g_classifier = caffe.Classifier(
      'cifar10_quick.prototxt',
      'cifar10_quick_iter_5000.caffemodel',
      mean=mean_array,
      raw_scale=255)

  global g_labels
  with open('labels.txt') as f:
    g_labels = f.read().decode('utf-8').splitlines()

  bottle.debug()
  bottle.run(host='0.0.0.0', port=8080)


if __name__ == '__main__':
  sys.exit(main())
	#!/usr/bin/python
	# Copyright 2014 SIG2D
	# Licensed under the Apache License, Version 2.0.

	import os
	import shutil
	import subprocess
	import sys

	from caffe.proto import caffe_pb2
	import leveldb
	import numpy as np
	import PIL.Image
	import random

	THUMBNAIL_SIZE = 32


	def make_thumbnail(image):
	image = image.convert('RGB')
	square_size = min(image.size)
	offset_x = (image.size[0] - square_size) / 2
	offset_y = (image.size[1] - square_size) / 2
	image = image.crop((offset_x, offset_y,
	offset_x + square_size, offset_y + square_size))
	image.thumbnail((THUMBNAIL_SIZE, THUMBNAIL_SIZE), PIL.Image.ANTIALIAS)
	return image


	def make_datum(thumbnail, label):
	return caffe_pb2.Datum(
	channels=3,
	width=THUMBNAIL_SIZE,
	height=THUMBNAIL_SIZE,
	label=label,
	data=np.rollaxis(np.asarray(thumbnail), 2).tostring())


	def create_leveldb(name):
	path = os.path.join(os.environ['HOME'], 'caffe/examples/cifar10', name)
	try:
	shutil.rmtree(path)
	except OSError:
	pass
	print 'opening', path
	return leveldb.LevelDB(
	path, create_if_missing=True, error_if_exists=True, paranoid_checks=True)


	def main():
	train_db = create_leveldb('cifar10_train_leveldb')
	test_db = create_leveldb('cifar10_test_leveldb')

	filepath_and_label = []
	for dirpath, _, filenames in os.walk('.'):
	try:
	label = int(dirpath.split('/')[1])
	except Exception:
	continue
	for filename in filenames:
	if filename.endswith(('.png', '.jpg')):
	filepath_and_label.append((os.path.join(dirpath, filename), label))

	random.shuffle(filepath_and_label)

	for seq, (filepath, label) in enumerate(filepath_and_label):
	print seq, label, filepath
	image = PIL.Image.open(filepath)
	thumbnail = make_thumbnail(image)
	datum = make_datum(thumbnail, label)
	db = test_db if seq % 10 == 0 else train_db
	db.Put('%08d' % seq, datum.SerializeToString())


	if __name__ == '__main__':
	sys.exit(main())
	<!doctype html>
	<html>
	<head>
	<meta charset="UTF-8">
	<!--
	Copyright 2014 SIG2D
	Licensed under the Apache License, Version 2.0.
	-->
	<meta http-equiv="X-UA-Compatible" content="IE=edge">
	<meta name="viewport" content="width=device-width, initial-scale=1">

	<title>Online Kin-patsu Image Classifier</title>

	<link href="//netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
	<!--[if lt IE 9]>
	<script src="https://oss.maxcdn.com/libs/html5shiv/3.7.0/html5shiv.js"></script>
	<script src="https://oss.maxcdn.com/libs/respond.js/1.4.2/respond.min.js"></script>
	<![endif]-->

	<style>
	.result {
	display: inline-block;
	margin: 2px;
	text-align: center;
	}
	</style>
	</head>
	<body>
	<div class="container-fluid">
	<div class="page-header">
	<h1>Online Kin-patsu Image Classifier</h1>
	</div>
	<div class="row">
	<div class="col-lg-12">
	<div class="well well-sm">
	Drag & drop image files here to classify.<br>
	API is available: try <code>curl -F file=@image.jpg http://kinpatsu.api.sig2d.org/</code>
	</div>
	</div>
	</div>
	<div class="row">
	<div class="col-lg-12">
	<div id="results">
	</div>
	</div>
	</div>
	</div>

	<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.0/jquery.min.js"></script>
	<script src="//netdna.bootstrapcdn.com/bootstrap/3.1.1/js/bootstrap.min.js"></script>
	<script>
	var queue = [];
	var processing = false;
	var queueFile = function(file) {
	queue.push(file);
	if (!processing) {
	processNext();
	}
	};
	var processNext = function() {
	if (queue.length == 0) {
	processing = false;
	return;
	}
	processing = true;
	var file = queue.shift();
	var formData = new FormData();
	formData.append('file', file);
	$.ajax({
	url: '',
	type: 'POST',
	data: formData,
	contentType: false,
	processData: false
	}).always(processNext).then(function(data) {
	var reader = new FileReader();
	reader.onload = function() {
	var img = $('<img>');
	img.on('load', function() {
	var width = img[0].width;
	var height = img[0].height;
	var zoom = 128 / Math.max(width, height);
	var displayWidth = Math.floor(width * zoom);
	var displayHeight = Math.floor(height * zoom);
	var label = data['result']['label'];
	var score = data['result']['score'];
	var percent = Math.floor(100 * score) + '%';
	img.attr('title', score)
	.css('width', displayWidth + 'px')
	.css('height', displayHeight + 'px')
	.css('margin',
	((128 - displayHeight) / 2 + 8) + 'px ' +
	((128 - displayWidth) / 2 + 8) + 'px');
	var cell = $('<div class="result">');
	cell.append($('<div>').append(img));
	cell.append($('<div>').text(label));
	cell.append($('<div>').text(percent));
	if (label == 'blonde_hair') {
	cell.css('background-color', '#fe0');
	}
	if (label == 'silver_hair') {
	cell.css('background-color', '#ddd');
	}
	$('#results').append(cell);
	});
	img.attr('src', reader.result);
	};
	reader.readAsDataURL(file);
	});
	};
	$(document).on('dragover', function(e) {
	e.preventDefault();
	return false;
	}).on('dragend', function(e) {
	e.preventDefault();
	return false;
	}).on('drop', function(e) {
	e.preventDefault();
	var files = e.originalEvent.dataTransfer.files;
	for (var i = 0; i < files.length; ++i) {
	queueFile(files[i]);
	}
	return false;
	});
	</script>
	</body>
	</html>