add in residual network

Author: chen0040

README.md

@@ -43,6 +43,7 @@ trained classifiers:
 * multi-class DCNN classifier trained with CIFAR-10 data
 * multi-class VGG16 classifier trained with ImageNet data
 * multi-class VGG19 classifier trained with ImageNet data
+* multi-class Residual Network classifier trained with ImageNet data
 
 
 

keras_image_classifier_web/flaskr.py

@@ -3,6 +3,7 @@
 from keras_image_classifier_web.cifar10_classifier import Cifar10Classifier
 from keras_image_classifier_web.vgg16_classifier import VGG16Classifier
 from keras_image_classifier_web.vgg19_classifier import VGG19Classifier
+from keras_image_classifier_web.resnet50_classifier import ResNet50Classifier
 
 from flask import Flask, request, session, g, redirect, url_for, abort, \
     render_template, flash
@@ -31,6 +32,10 @@
 vgg19_classifier = VGG19Classifier()
 vgg19_classifier.run_test()
 
+resnet50_classifier = ResNet50Classifier()
+resnet50_classifier.run_test()
+
+
 @app.route('/')
 def classifiers():
     return render_template('classifiers.html')
@@ -92,6 +97,13 @@ def vgg19():
     return render_template('vgg19.html')
 
 
+@app.route('/resnet50', methods=['GET', 'POST'])
+def resnet50():
+    if request.method == 'POST':
+        return store_uploaded_image('resnet50_result')
+    return render_template('resnet50.html')
+
+
 @app.route('/cats_vs_dogs_result/<filename>')
 def cats_vs_dogs_result(filename):
     filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
@@ -124,6 +136,14 @@ def vgg19_result(filename):
                            top3=top3)
 
 
+@app.route('/resnet50_result/<filename>')
+def resnet50_result(filename):
+    filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+    top3 = resnet50_classifier.predict(filepath)
+    return render_template('resnet50_result.html', filename=filename,
+                           top3=top3)
+
+
 @app.route('/images/<filename>')
 def get_image(filename):
     return send_from_directory(app.config['UPLOAD_FOLDER'],

keras_image_classifier_web/resnet50_classifier.py

@@ -0,0 +1,31 @@
+from keras.models import Model
+from keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions
+from keras.optimizers import SGD
+from PIL import Image
+from keras.preprocessing.image import img_to_array
+import numpy as np
+
+
+class ResNet50Classifier:
+    model = None
+
+    def __init__(self):
+        self.model = ResNet50(include_top=True, weights='imagenet')
+        self.model.compile(optimizer=SGD(), loss='categorical_crossentropy', metrics=['accuracy'])
+
+    def predict(self, filename):
+        img = Image.open(filename)
+        img = img.resize((224, 224), Image.ANTIALIAS)
+        input = img_to_array(img)
+        input = np.expand_dims(input, axis=0)
+        input = preprocess_input(input)
+        output = decode_predictions(self.model.predict(input), top=3)
+        return output[0]
+
+    def run_test(self):
+        print(self.predict('../keras_image_classifier/bi_classifier_data/training/cat/cat.3.jpg'))
+
+
+if __name__ == '__main__':
+    classifier = ResNet50Classifier()
+    classifier.run_test()

keras_image_classifier_web/templates/classifiers.html

@@ -5,5 +5,6 @@
     <li><a href="{{ url_for('cifar10') }}">CIFAR-10</a></li>
     <li><a href="{{ url_for('vgg16') }}">VGG-16</a></li>
     <li><a href="{{ url_for('vgg19') }}">VGG-19</a></li>
+    <li><a href="{{ url_for('resnet50') }}">ResNet-50</a></li>
   </ul>
 {% endblock %}

keras_image_classifier_web/templates/resnet50.html

@@ -0,0 +1,8 @@
+{% extends "layout.html" %}
+{% block body %}
+    <p>Upload your picture and we will try to what it is using ResNet50 classifier</p>
+    <form method=post enctype=multipart/form-data>
+      <p><input type=file name=file>
+         <input type=submit value=Upload>
+    </form>
+{% endblock %}

keras_image_classifier_web/templates/resnet50_result.html

@@ -0,0 +1,14 @@
+{% extends "layout.html" %}
+{% block body %}
+    <img src="/images/{{ filename }}" />
+    <br />
+    <hr />
+    Below is my top 3 guesses of what is inside your picture:
+    <ol>
+    {% for pred in top3 %}
+        <li>{{ pred[1] }} ({{ pred[2] }})</li>
+    {% endfor %}
+    </ol>
+    <hr />
+    <a href="{{ url_for('resnet50') }}">Try another picture</a>
+{% endblock %}

keras_image_classifier_web/vgg16_classifier.py

@@ -24,3 +24,7 @@ def predict(self, filename):
 
     def run_test(self):
         print(self.predict('../keras_image_classifier/bi_classifier_data/training/cat/cat.3.jpg'))
+
+if __name__ == '__main__':
+    classifier = VGG16Classifier()
+    classifier.run_test()

keras_image_classifier_web/vgg19_classifier.py

@@ -24,3 +24,8 @@ def predict(self, filename):
 
     def run_test(self):
         print(self.predict('../keras_image_classifier/bi_classifier_data/training/cat/cat.3.jpg'))
+
+
+if __name__ == '__main__':
+    classifier = VGG19Classifier()
+    classifier.run_test()