initial commit - project upload

j-zarp · Mar 3, 2018 · 03033b9 · 03033b9
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diff --git a/.gitignore b/.gitignore
diff --git a/db.sqlite3 b/db.sqlite3
diff --git a/deepemotion/__init__.py b/deepemotion/__init__.py
diff --git a/deepemotion/__init__.pyc b/deepemotion/__init__.pyc
diff --git a/deepemotion/__pycache__/__init__.cpython-35.pyc b/deepemotion/__pycache__/__init__.cpython-35.pyc
diff --git a/deepemotion/__pycache__/admin.cpython-35.pyc b/deepemotion/__pycache__/admin.cpython-35.pyc
diff --git a/deepemotion/__pycache__/models.cpython-35.pyc b/deepemotion/__pycache__/models.cpython-35.pyc
diff --git a/deepemotion/__pycache__/views.cpython-35.pyc b/deepemotion/__pycache__/views.cpython-35.pyc
diff --git a/deepemotion/admin.py b/deepemotion/admin.py
@@ -0,0 +1,3 @@
+from django.contrib import admin
+
+# Register your models here.
diff --git a/deepemotion/admin.pyc b/deepemotion/admin.pyc
diff --git a/deepemotion/apps.py b/deepemotion/apps.py
@@ -0,0 +1,5 @@
+from django.apps import AppConfig
+
+
+class DeepemotionConfig(AppConfig):
+    name = 'deepemotion'
diff --git a/deepemotion/core/__init__.py b/deepemotion/core/__init__.py
diff --git a/deepemotion/core/__init__.pyc b/deepemotion/core/__init__.pyc
diff --git a/deepemotion/core/effect.py b/deepemotion/core/effect.py
@@ -0,0 +1,94 @@
+# -*- coding: utf-8 -*-
+######## REFs face detection ########
+# https://www.superdatascience.com/opencv-face-detection/
+#####################################
+
+from keras.models import load_model
+from keras import backend as K
+import tensorflow as tf
+
+import os
+import numpy as np
+import cv2
+
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "" # do not use the GPU
+
+img_rows, img_cols = 48, 48
+
+labels_txt = np.array(['Colère', 'Peur', 'Joie', 'Tristesse', 'Surprise', 'Neutre'])
+
+num_classes = labels_txt.size
+
+graph = tf.get_default_graph()
+model = load_model(r'/home/jeremie/jeremie/topten/static/model/trained_model')
+IMP = np.load(r'/home/jeremie/jeremie/topten/static/model/IMP.npy')
+print "model loaded"
+
+face_cascade = cv2.CascadeClassifier()
+face_cascade_name = "/home/jeremie/miniconda2/envs/keras/lib/python2.7/site-packages/cv2/data/haarcascade_frontalface_default.xml"
+face_cascade.load(face_cascade_name)
+print "face detector loaded"
+
+def transform_image(im):
+    im2 = im.astype(np.float32)*1
+    im2 -= 127.817
+    im2 /= 74.3659
+    return im2
+
+def cut_face(im, cascade_classifier):
+    faces = cascade_classifier.detectMultiScale(im)
+    print(len(faces), "face(s) detected")
+    if len(faces) != 0:
+        x, y, w, h = [ v for v in faces[0] ]
+        return im[y:y+h, x:x+w]
+    else:
+        return im*0
+    return im
+
+def reconstruct_image(im):
+    im2 = im.astype(np.float32)*1
+    im2 *= 74.3659
+    im2 += 127.817
+    return np.squeeze(im2.astype(np.uint8))
+
+def transform(fname):
+    image_path = fname
+    custom_image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+    custom_image = cut_face(custom_image, face_cascade)
+
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
+    custom_input = clahe.apply(custom_image)
+
+    sh = custom_input.shape
+    custom_input = cv2.resize(custom_input, (img_rows, img_cols))
+    custom_input = transform_image(custom_input)
+    custom_input = np.expand_dims(custom_input, axis=0)
+    custom_input = np.expand_dims(custom_input, axis=0)
+    with graph.as_default():
+        res1 = model.predict(custom_input, verbose=0)[0]
+        res2 = model.predict(np.fliplr(custom_input), verbose=0)[0]
+        res = (res1+res2)/2.
+        k = np.argmax(res)
+
+        input_img = model.layers[0].input
+        for l in model.layers:
+            if(l.name == 'last_conv'):
+                layer_output = l.output
+        get_acti = K.function([input_img, K.learning_phase()], [layer_output[:,:,:,:]])
+
+        fig = plt.figure()
+        img = np.zeros((sh[0], sh[1]))
+        acti = np.squeeze(get_acti([custom_input,0])[0])
+        for i in range(IMP[k].size):
+            img += IMP[k][i]*cv2.resize(np.abs(acti[i]), (sh[1], sh[0]))
+        plt.imshow(custom_image, cmap='gray')
+        plt.imshow(img, alpha=0.4, cmap='bwr')
+        plt.axis('off')
+        fig.tight_layout()
+        fig.savefig("static/temp2.jpeg")
+    return res[0], labels_txt[k]
+
diff --git a/deepemotion/core/effect.pyc b/deepemotion/core/effect.pyc
diff --git a/deepemotion/migrations/__init__.py b/deepemotion/migrations/__init__.py
diff --git a/deepemotion/migrations/__init__.pyc b/deepemotion/migrations/__init__.pyc
diff --git a/deepemotion/migrations/__pycache__/__init__.cpython-35.pyc b/deepemotion/migrations/__pycache__/__init__.cpython-35.pyc
diff --git a/deepemotion/models.py b/deepemotion/models.py
@@ -0,0 +1,3 @@
+from django.db import models
+
+# Create your models here.
diff --git a/deepemotion/models.pyc b/deepemotion/models.pyc
diff --git a/deepemotion/templates/deepemotion/index.html b/deepemotion/templates/deepemotion/index.html
@@ -0,0 +1,80 @@
+<!doctype html>
+{% load static %}
+<html lang="fr">
+<head>
+	<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+	<title>DeepEmotion</title>
+	<link rel="stylesheet" type="text/css" href="{% static 'bootstrap-4.0.0-dist/css/bootstrap.min.css' %}">
+	<link rel="stylesheet" type="text/css" href="{% static 'css/styles.css' %}">
+</head>
+
+<body id="body">
+    <div class="container-fluid">
+        <div class="row justify-content-between">
+        	<div class="col-4">
+        		<img src="{% static 'logos/CI4CB_logo.png' %}" class="float-left" alt="Responsive image" width="50%">
+        	</div>
+        	<div class="col-4">
+        		<img src="{% static 'logos/HEIG-VD_Logo-83x25_CMJN-ROUGE.jpg' %}" class="float-right" alt="Responsive image" width="75%">
+        	</div>
+        </div>
+    </div>
+	<div class="jumbotron text-center">
+	    <div class="container">
+	        <h1>DeepEmotion</h1>
+			<h3>Un réseau de neurones capable de lire les émotions sur votre visage</h3>
+			<form id="theForm" method="POST" enctype="multipart/form-data">{% csrf_token %}
+				<input id="id_pic" name="url" type=text value="" autofocus="" hidden>
+				<input id="id_image_data" name="img" type="text" value="" autofocus="" hidden>
+				<input type='submit' value='Take Snapshot' hidden/>
+				<button id="button" onCLick='switchForm();' hidden>Dynamic mode</button>         
+			</form>
+		</div>
+	</div>
+
+    <div class="container-fluid">
+    	<div class="row align-items-center">
+	    	<div class="col text-center">
+	            <h4>Entrée</h4>
+			    <div id="my_camera" class="d-inline"></div>
+	    	</div>
+	        <div class="col text-center">
+	            <h4>Votre visage laisse transparaître l'émotion</h4>
+				<h2 id="id_emotion">...Cliquez pour le découvrir...</h2>
+				<h5> Certitude de la prédiction:</h5>
+                {% for key, value in emotions.items%}
+                <div class="progress-bar-alt vertical rounded">
+					<div class="progress-track">
+						<div class="progress-fill" style="background: linear-gradient(to bottom, {{value.1}} 0%, {{value.2}} 100%);">
+							<span id="{{value.0}}">0.1%</span>
+						</div>
+					</div>
+					<p class="histlabel">{{key}}</p>
+				</div>
+                {% endfor %}
+	        </div>
+
+	        <div class="col text-center" id="results">
+	            <h4>Zones déterminantes *</h4>
+				<img id="t_pic2" src="{% static 'temp2.jpeg' %}" class="float-center" alt="Responsive image" width="75%">
+	        </div>
+	    </div>
+	    <div class="row justify-content-end">
+	    	<p id="heatmaplegend">* plus une zone est rouge, plus celle-ci a été déterminante dans la décision du réseau.</p>
+	    </div>
+    </div>
+
+    <!-- javascript imports -->
+    <script src="{% static 'js/jquery-3.1.0.min.js' %}"></script>
+	<script type="text/javascript" src="{% static 'js/html2canvas.min.js' %}"></script>
+	<script type="text/javascript" src="{% static 'js/jquery_script.js' %}"></script>
+	<!-- bootstrap specifics -->
+	<script type="text/javascript" src="{% static 'js/popper.min.js' %}"></script>
+	<script type="text/javascript" src="{% static 'bootstrap-4.0.0-dist/js/bootstrap.min.js' %}"></script>
+	<!-- Webcam specific scripts -->
+	<script type="text/javascript" src="{% static 'js/webcam.min.js' %}"></script>
+	<script type="text/javascript" src="{% static 'js/webcam_specifics.js' %}"></script>
+</body>
+</html>
+
diff --git a/deepemotion/tests.py b/deepemotion/tests.py
@@ -0,0 +1,3 @@
+from django.test import TestCase
+
+# Create your tests here.
diff --git a/deepemotion/views.py b/deepemotion/views.py
@@ -0,0 +1,40 @@
+# -*- coding: utf-8 -*-
+
+from __future__ import unicode_literals
+from django.shortcuts import render, redirect
+from django.templatetags.static import static
+from django.http import HttpResponse
+import json
+
+# Create your views here.
+
+from core import effect
+import urllib
+import re
+from collections import OrderedDict
+
+def IndexView(request):
+    prob=[0,0,0,0,0,0]
+    emotions = OrderedDict()
+    emotions['Joie']=['happy', '#ff9900', '#ffff99']
+    emotions['Colère']=['angry', '#ff0000', '#800000']
+    emotions['Tristesse']=['sad', '#0033cc', '#0099cc']
+    emotions['Peur']=['scared', '#006600', '#009900']
+    emotions['Surprise']=['surprised', '#3366cc', '#66ffff']
+    emotions['Neutre']=['neutral', '#669999', '#99ccff']
+    if request.is_ajax():
+        URL=request.POST.get("url")
+        screen=request.POST.get("img")
+        url=urllib.urlopen(URL)
+        with open('static/temp.jpeg', 'wb') as f:
+            f.write(url.read())
+        prob,label=effect.transform('static/temp.jpeg')
+        prob=[str(x) for x in prob]
+        screen=screen.replace("/static/","/home/jeremie/jeremie/topten/static/")
+        screen=re.sub('<script.*?</script>',"",screen)
+        screen=re.sub('<div id="camera".*?<\/div>.*?<\/div>.*?<\/div>','<div id="results" style="float:left;margin-top:125px;">	    <img id="t_pic2" src="/home/jeremie/jeremie/topten/static/temp.jpeg">	    </div>',screen)
+        f=open("static/out.html","w")
+        f.write(screen.encode('utf-8'))
+        f.close()
+        return HttpResponse(json.dumps({"prob":prob,"label":label}),content_type="application/json")
+    return render(request, 'deepemotion/index.html',{"emotions":emotions})
diff --git a/deepemotion/views.pyc b/deepemotion/views.pyc
diff --git a/manage.py b/manage.py
@@ -0,0 +1,22 @@
+#!/usr/bin/env python
+import os
+import sys
+
+if __name__ == "__main__":
+    os.environ.setdefault("DJANGO_SETTINGS_MODULE", "topten.settings")
+    try:
+        from django.core.management import execute_from_command_line
+    except ImportError:
+        # The above import may fail for some other reason. Ensure that the
+        # issue is really that Django is missing to avoid masking other
+        # exceptions on Python 2.
+        try:
+            import django
+        except ImportError:
+            raise ImportError(
+                "Couldn't import Django. Are you sure it's installed and "
+                "available on your PYTHONPATH environment variable? Did you "
+                "forget to activate a virtual environment?"
+            )
+        raise
+    execute_from_command_line(sys.argv)
diff --git a/run.sh b/run.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+source activate keras
+python manage.py runserver 0.0.0.0:8000
+
+
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		@@ -0,0 +1,3 @@
		from django.contrib import admin

		# Register your models here.
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		@@ -0,0 +1,3 @@
		from django.db import models

		# Create your models here.
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		@@ -0,0 +1,3 @@
		from django.test import TestCase

		# Create your tests here.