added demo

Demo_PP1/app.py

+from __future__ import division, print_function
+# coding=utf-8
+import sys
+import os
+import glob
+import re
+import numpy as np
+
+# Keras
+from keras.applications.imagenet_utils import preprocess_input, decode_predictions
+from keras.models import load_model
+from keras.preprocessing import image
+
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+from keras.preprocessing import image
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+from keras.applications.vgg16 import VGG16
+import cv2
+import math
+import os
+from glob import glob
+from scipy import stats as s
+
+# Flask utils
+from flask import Flask, redirect, url_for, request, render_template
+from werkzeug.utils import secure_filename
+from gevent.pywsgi import WSGIServer
+
+# Define a flask app
+app = Flask(__name__)
+
+# Model saved with Keras model.save()
+MODEL_PATH = 'model/weight.hdf5'
+
+base_model = VGG16(weights='imagenet', include_top=False)
+#defining the model architecture
+model = Sequential()
+model.add(Dense(1024, activation='relu', input_shape=(25088,)))
+model.add(Dropout(0.5))
+model.add(Dense(512, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(256, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(128, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(97, activation='softmax'))
+model.load_weights(MODEL_PATH)
+model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
+
+train = pd.read_csv('model/train_new6.csv')
+y = train['class']
+y = pd.get_dummies(y)
+#print('Model loaded. Check http://127.0.0.1:5000/')
+
+
+def model_predict(img_path, model):
+    predict = []
+    actual = []
+    count = 0
+    cap = cv2.VideoCapture(img_path)
+    frameRate = cap.get(5) #frame rate
+    x=1
+    # removing all other files from the temp folder
+    files = glob('temp/*')
+    for f in files:
+        os.remove(f)
+    while(cap.isOpened()):
+        frameId = cap.get(1) #current frame number
+        ret, frame = cap.read()
+        if (ret != True):
+            break
+        if (frameId % math.floor(frameRate) == 0):
+            # storing the frames of this particular video in temp folder
+            filename ='temp/' + "_frame%d.jpg" % count;count+=1
+            cv2.imwrite(filename, frame)
+    cap.release()
+
+    # reading all the frames from temp folder
+    images = glob("temp/*.jpg")
+
+    prediction_images = []
+    for i in range(len(images)):
+        img = image.load_img(images[i], target_size=(224,224,3))
+        img = image.img_to_array(img)
+        img = img/255
+        prediction_images.append(img)
+        
+    # converting all the frames for a test video into numpy array
+    prediction_images = np.array(prediction_images)
+    # extracting features using pre-trained model
+    prediction_images = base_model.predict(prediction_images)
+    # converting features in one dimensional array
+    prediction_images = prediction_images.reshape(prediction_images.shape[0], 7*7*512)
+    # predicting tags for each array
+    prediction = np.argmax(model.predict(prediction_images), axis=1)
+    # appending the mode of predictions in predict list to assign the tag to the video
+    predict.append(y.columns.values[s.mode(prediction)[0][0]])
+    # appending the actual tag of the video
+    print(y.columns[prediction])
+    return y.columns[prediction[0]]  
+
+
+@app.route('/', methods=['GET'])
+def index():
+    # Main page
+    return render_template('index.html')
+
+
+@app.route('/predict', methods=['GET', 'POST'])
+def upload():
+    if request.method == 'POST':
+        # Get the file from post request
+        f = request.files['file']
+
+        # Save the file to ./uploads
+        basepath = os.path.dirname(__file__)
+        file_path = os.path.join(
+            basepath, 'uploads', secure_filename(f.filename))
+        f.save(file_path)
+        # Make prediction
+        result = model_predict(file_path, model)
+        #remove uploaed video
+        os.remove(file_path)
+        return result
+    return None
+
+
+if __name__ == '__main__':
+    app.run(debug=True)
+

Demo_PP1/static/css/main.css

+.img-preview {
+    width: 256px;
+    height: 256px;
+    position: relative;
+    border: 5px solid #F8F8F8;
+    box-shadow: 0px 2px 4px 0px rgba(0, 0, 0, 0.1);
+    margin-top: 1em;
+    margin-bottom: 1em;    
+}
+
+.img-preview>div {
+    width: 100%;
+    height: 100%;
+    background-size: 256px 256px;
+    background-repeat: no-repeat;
+    background-position: center;
+}
+
+input[type="file"] {
+    display: none;
+}
+
+.upload-label{
+    display: inline-block;
+    padding: 12px 30px;
+    background: #39D2B4;
+    color: #fff;
+    font-size: 1em;
+    transition: all .4s;
+    cursor: pointer;
+}
+
+.upload-label:hover{
+    background: #34495E;
+    color: #39D2B4;
+}
+
+.loader {
+    border: 8px solid #f3f3f3; /* Light grey */
+    border-top: 8px solid #3498db; /* Blue */
+    border-radius: 50%;
+    width: 50px;
+    height: 50px;
+    animation: spin 1s linear infinite;
+}
+
+@keyframes spin {
+    0% { transform: rotate(0deg); }
+    100% { transform: rotate(360deg); }
+}
\ No newline at end of file

Demo_PP1/static/js/main.js

+$(document).ready(function () {
+    // Init
+    $('.image-section').hide();
+    $('.loader').hide();
+    $('#result').hide();
+
+    // Upload Preview
+    function readURL(input) {
+        if (input.files && input.files[0]) {
+            var reader = new FileReader();
+            reader.onload = function (e) {
+                $('#imagePreview').css('background-image', 'url(' + e.target.result + ')');
+                $('#imagePreview').hide();
+                $('#imagePreview').fadeIn(650);
+            }
+            reader.readAsDataURL(input.files[0]);
+        }
+    }
+    $("#imageUpload").change(function () {
+        $('.image-section').show();
+        $('#btn-predict').show();
+        $('#result').text('');
+        $('#result').hide();
+        readURL(this);
+    });
+
+    // Predict
+    $('#btn-predict').click(function () {
+        var form_data = new FormData($('#upload-file')[0]);
+
+        // Show loading animation
+        $(this).hide();
+        $('.loader').show();
+
+        // Make prediction by calling api /predict
+        $.ajax({
+            type: 'POST',
+            url: '/predict',
+            data: form_data,
+            contentType: false,
+            cache: false,
+            processData: false,
+            async: true,
+            success: function (data) {
+                // Get and display the result
+                $('.loader').hide();
+                $('#result').fadeIn(600);
+                $('#result').text(' Result:  ' + data);
+                console.log('Success!');
+            },
+        });
+    });
+
+});

Demo_PP1/templates/base.html

+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <meta http-equiv="X-UA-Compatible" content="ie=edge">
+    <title>Demo</title>
+    <link href="https://cdn.bootcss.com/bootstrap/4.0.0/css/bootstrap.min.css" rel="stylesheet">
+    <script src="https://cdn.bootcss.com/popper.js/1.12.9/umd/popper.min.js"></script>
+    <script src="https://cdn.bootcss.com/jquery/3.3.1/jquery.min.js"></script>
+    <script src="https://cdn.bootcss.com/bootstrap/4.0.0/js/bootstrap.min.js"></script>
+    <link href="{{ url_for('static', filename='css/main.css') }}" rel="stylesheet">      
+</head>
+
+<body>
+    <nav class="navbar navbar-dark bg-dark">
+        <div class="container">
+            <a class="navbar-brand" href="#">Dynamic Sign Detection Demo</a>
+            <button class="btn btn-outline-secondary my-2 my-sm-0" type="submit">Help</button>
+        </div>
+    </nav>
+    <div class="container">
+        <div id="content" style="margin-top:2em">{% block content %}{% endblock %}</div>
+    </div>
+</body>
+
+<footer>
+    <script src="{{ url_for('static', filename='js/main.js') }}" type="text/javascript"></script>    
+</footer>
+
+</html>
\ No newline at end of file

Demo_PP1/templates/index.html

+{% extends "base.html" %} {% block content %}
+
+<h2>Video Classifier</h2>
+
+<div>
+    <form id="upload-file" method="post" enctype="multipart/form-data">
+        <label for="imageUpload" class="upload-label">
+            Choose...
+        </label>
+        <input type="file" name="file" id="imageUpload" accept=".mp4, .avi, .mpg">
+    </form>
+
+    <div class="image-section" style="display:none;">
+        <div class="img-preview">
+            <div id="imagePreview">
+            </div>
+        </div>
+        <div>
+            <button type="button" class="btn btn-primary btn-lg " id="btn-predict">Predict!</button>
+        </div>
+    </div>
+
+    <div class="loader" style="display:none;"></div>
+
+    <h3 id="result">
+        <span> </span>
+    </h3>
+
+</div>
+
+{% endblock %}
\ No newline at end of file