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app/Dockerfile

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+# syntax=docker/dockerfile:1.4
+
+FROM --platform=$BUILDPLATFORM python:3.10-alpine AS builder
+EXPOSE 8000
+WORKDIR /app 
+COPY requirements.txt /app
+RUN pip3 install -r requirements.txt --no-cache-dir
+COPY . /app 
+ENTRYPOINT ["python3"] 
+CMD ["manage.py", "runserver", "0.0.0.0:8000"]
+
+FROM builder as dev-envs
+RUN <<EOF
+apk update
+apk add git
+EOF
+
+RUN <<EOF
+addgroup -S docker
+adduser -S --shell /bin/bash --ingroup docker vscode
+EOF
+# install Docker tools (cli, buildx, compose)
+COPY --from=gloursdocker/docker / /
+CMD ["manage.py", "runserver", "0.0.0.0:8000"]

app/apps/emotion_recognition/admin.py

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+from django.contrib import admin
+from .models import UserImageRecognition
+# Register your models here.
+
+admin.site.register(UserImageRecognition)
+

app/apps/emotion_recognition/apps.py

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+from django.apps import AppConfig
+
+
+class EmotionRecognitionConfig(AppConfig):
+    default_auto_field = 'django.db.models.BigAutoField'
+    name = 'apps.emotion_recognition'

app/apps/emotion_recognition/emotion_recognition.py

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+
+from tensorflow import keras
+from keras.models import Sequential
+from keras.models import load_model
+from keras.models import model_from_json
+from keras.utils import img_to_array
+import keras.utils as image 
+
+import cv2
+import numpy as np
+import os
+
+from django_app.settings import BASE_DIR
+
+
+model = Sequential()
+
+model = model_from_json(open(
+    os.path.join(BASE_DIR,'model/model_4layer_2_2_pool.json'), "r").read())
+
+model.load_weights(os.path.join(
+			BASE_DIR,'model/model_4layer_2_2_pool.h5'))
+
+class_labels = {0: 'Angry', 1: 'Disgust', 2: 'Fear',
+                3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}
+classes = list(class_labels.values())
+
+face_classifier = cv2.CascadeClassifier(os.path.join(
+			BASE_DIR,'model/haarcascade_frontalface.xml'))
+
+camera = cv2.VideoCapture(0)
+
+
+def text_on_detected_boxes(text, text_x, text_y, image, font_scale=1,
+                           font=cv2.FONT_HERSHEY_SIMPLEX,
+                           FONT_COLOR=(0, 0, 0),
+                           FONT_THICKNESS=2,
+                           rectangle_bgr=(0, 255, 0)):
+    (text_width, text_height) = cv2.getTextSize(
+        text, font, fontScale=font_scale, thickness=2)[0]
+    box_coords = ((text_x-10, text_y+4), (text_x +
+                  text_width+10, text_y - text_height-5))
+    cv2.rectangle(image, box_coords[0],
+                  box_coords[1], rectangle_bgr, cv2.FILLED)
+    cv2.putText(image, text, (text_x, text_y), font,
+                fontScale=font_scale, color=FONT_COLOR, thickness=FONT_THICKNESS)
+
+
+def face_detector_image(img):
+    """
+    Обнаружение лиц на изображении.
+
+    Args:
+        img (numpy array): Исходное изображение.
+
+    Returns:
+        tuple: (rects, allfaces, img) - координаты лиц, обрезанные лица и изображение с рамками.
+    """
+    gray = cv2.cvtColor(img.copy(), cv2.COLOR_BGR2GRAY)
+    faces = face_classifier.detectMultiScale(gray, 1.3, 5)
+    if faces == ():
+        return (0, 0, 0, 0), np.zeros((48, 48), np.uint8), img
+    allfaces = []
+    rects = []
+    for (x, y, w, h) in faces:
+        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2) 
+        roi_gray = gray[y:y + h, x:x + w]
+        roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)
+        allfaces.append(roi_gray)
+        rects.append((x, w, y, h))
+    return rects, allfaces, img 
+
+
+def emotionImage(imgPath):
+    img = cv2.imread(BASE_DIR + '\\media\\' + imgPath)
+    rects, faces, image = face_detector_image(img)
+    i = 0
+    for face in faces:
+        roi = face.astype("float") / 255.0
+        roi = img_to_array(roi)
+        roi = np.expand_dims(roi, axis=0)
+        preds = model.predict(roi)[0]
+        label = class_labels[preds.argmax()]
+        label_position = (
+            rects[i][0] + int((rects[i][1] / 2)), abs(rects[i][2] - 10))
+        i += 1
+
+        # Отрисовка текста и рамок
+        text_on_detected_boxes(
+            label, label_position[0], label_position[1], image)
+
+        precentages = dict(zip(classes, preds*100))
+
+    return image, precentages, label
+
+
+def emotionImageFromArray(img_array):
+    """
+    Обрабатывает изображение и возвращает результат обработки.
+
+    Args:
+        img_array (numpy array): Исходное изображение (numpy array).
+
+    Returns:
+        tuple: (image, precentages, label)
+            - image: Изображение с рамками и текстом эмоций.
+            - precentages: Вероятности каждой эмоции.
+            - label: Определенная эмоция.
+    """
+    rects, faces, image = face_detector_image(img_array)
+    i = 0
+    for face in faces:
+        roi = face.astype("float") / 255.0
+        roi = img_to_array(roi)
+        roi = np.expand_dims(roi, axis=0)
+        preds = model.predict(roi)[0]
+        label = class_labels[preds.argmax()]
+        label_position = (
+            rects[i][0] + int((rects[i][1] / 2)), abs(rects[i][2] - 10))
+        i += 1
+
+        # Отрисовка текста и рамок
+        text_on_detected_boxes(
+            label, label_position[0], label_position[1], image)
+
+        precentages = dict(zip(classes, preds*100))
+
+    return image, precentages, label
+
+def face_detector_video(img):
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    faces = face_classifier.detectMultiScale(gray, 1.3, 5)
+    if faces is ():
+        return (0, 0, 0, 0), np.zeros((48, 48), np.uint8), img
+    for (x, y, w, h) in faces:
+        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), thickness=2)
+        roi_gray = gray[y:y + h, x:x + w]
+    roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)
+    return (x, w, y, h), roi_gray, img
+
+
+def emotionVideo():
+    while True:
+        ret, frame = camera.read()
+        rect, face, image = face_detector_video(frame)
+        if np.sum([face]) != 0.0:
+            roi = face.astype("float") / 255.0
+            roi = img_to_array(roi)
+            roi = np.expand_dims(roi, axis=0)
+            preds = model.predict(roi)[0]
+            label = class_labels[preds.argmax()]
+            label_position = (rect[0] + rect[1]//50, rect[2] + rect[3]//50)
+            text_on_detected_boxes(label, label_position[0], label_position[1], image) 
+            fps = camera.get(cv2.CAP_PROP_FPS)
+            cv2.putText(image, str(fps),(5, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        else:
+            cv2.putText(image, "No Face Found", (5, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
+
+        ret, buffer = cv2.imencode('.jpg', image)
+        
+        frame = buffer.tobytes()
+        yield (b'--frame\r\n'
+            b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')  
+
+
+
+def gen_frames():                                     
+    while True:
+        success, frame = camera.read()
+        if not success:
+            cv2.putText(image, "No Face Found", (5, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
+            break
+        else:
+            gray_img= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  
+        
+            faces_detected = face_classifier.detectMultiScale(gray_img, 1.32, 5)  
+            
+        
+            for (x,y,w,h) in faces_detected:
+                
+                cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),thickness=7)  
+                roi_gray=gray_img[y:y+w,x:x+h]          
+                roi_gray=cv2.resize(roi_gray,(48,48))  
+                img_pixels = image.img_to_array(roi_gray)  
+                img_pixels = np.expand_dims(img_pixels, axis = 0)  
+                img_pixels /= 255  
+        
+                predictions = model.predict(img_pixels)  
+        
+                max_index = np.argmax(predictions[0])   
+        
+                emotions = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']  
+                predicted_emotion = emotions[max_index]  
+                
+                cv2.putText(frame, predicted_emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)  
+        
+            resized_img = cv2.resize(frame, (600, 400))  
+            
+            ret, buffer = cv2.imencode('.jpg', frame)
+            
+            frame = buffer.tobytes()
+            yield (b'--frame\r\n'
+                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')  

app/apps/emotion_recognition/forms.py

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+from django import forms
+
+from .models import UserImageRecognition
+
+
+class RecognitionEditForm(forms.ModelForm):
+    class Meta:
+        model = UserImageRecognition
+        fields = ('__all__')

app/apps/emotion_recognition/models.py

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+from django.db import models
+from PIL import Image
+
+
+# Create your models here.
+class UserImageRecognition(models.Model):
+    uploaded_image = models.ImageField(null=False, blank=False)
+    final_image = models.ImageField(null=True, blank=True)
+    recognized_emotion = models.CharField(max_length=20,null=True, blank=True)
+    predicted_emotions = models.CharField(max_length=155,null=True,blank=True)
+    created_at = models.DateTimeField(
+        auto_now_add=True, null=False, blank=False)
+
+    STATUS_CHOICES = (
+        ('PEN', 'Pending'),
+        ('COM', 'Complete'),
+        ('ERR', 'Error'),
+    )
+    status = models.CharField(
+        max_length=3, choices=STATUS_CHOICES, null=False, blank=False, default='PEN')
+
+    class Meta:
+        ordering = ['-created_at']
+
+    def save(self, *args, **kwargs):
+        super().save(*args, **kwargs)
+        uploaded_img = Image.open(self.uploaded_image.path)
+        if uploaded_img.height > 400 or uploaded_img.width > 400:
+            output_size = (400, 400)
+            uploaded_img.thumbnail(output_size)
+            uploaded_img.save(self.uploaded_image.path)
+            
+    def __str__(self):
+        return f'{self.user} - {self.uploaded_image}'

app/apps/emotion_recognition/serializers.py

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+from rest_framework import serializers
+
+class ImageSerializer(serializers.Serializer):
+    image = serializers.ImageField(required=True)

app/apps/emotion_recognition/tasks.py

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+from django.shortcuts import get_object_or_404
+from django.core.files.base import ContentFile
+
+from .models import UserImageRecognition
+from .emotion_recognition import emotionImage, emotionImageFromArray
+
+from io import BytesIO
+import cv2
+import numpy as np
+from PIL import Image
+
+
+def proccess_uploaded_image(image_data_id):
+    image_data = None
+    image_data = get_object_or_404(UserImageRecognition, pk=image_data_id)
+
+    final_image, predicted_emotions, recognized_emotion = emotionImage(
+        image_data.uploaded_image.name)
+    final_image = converter_to_django_file(final_image)
+    
+    image_data.final_image = final_image
+    image_data.predicted_emotions = predicted_emotions
+    image_data.recognized_emotion = recognized_emotion
+    image_data.status = "COM"
+    image_data.save()
+
+def process_image_from_api(image_file):
+    """
+    Обрабатывает изображение, переданное через API, и возвращает финальное изображение и данные эмоций.
+
+    Args:
+        image_file (InMemoryUploadedFile): Исходное изображение, полученное через API.
+
+    Returns:
+        tuple: (final_image, predicted_emotions, recognized_emotion)
+            - final_image: обработанное изображение в формате numpy array.
+            - predicted_emotions: словарь с предсказанными эмоциями и их вероятностями.
+            - recognized_emotion: самая вероятная эмоция.
+    """
+    # Конвертируем загруженный файл в OpenCV-совместимый формат
+    file_bytes = np.frombuffer(image_file.read(), np.uint8)
+    image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+
+    # Обрабатываем изображение с помощью emotionImageFromArray
+    final_image, predicted_emotions, recognized_emotion = emotionImageFromArray(image)
+
+    return final_image, predicted_emotions, recognized_emotion
+
+def converter_to_django_file(image):
+    img_io = BytesIO()
+    image = Image.fromarray(image)
+    image.save(img_io, format='JPEG',  quality=100)
+    img_content = ContentFile(img_io.getvalue(), 'final_image.jpg')
+    
+    return img_content
+
+def convert_image_to_bytes(image):
+    # Конвертируем обработанное изображение в байты
+    _, buffer = cv2.imencode('.jpg', image)
+    output_image = BytesIO(buffer)
+    
+    return output_image

app/apps/emotion_recognition/templates/recognition/index.html

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+{%  extends 'base.html' %}
+{% load static %}
+{% load crispy_forms_tags %}
+{% load crispy_forms_tags humanize %}
+
+{% block content %}
+
+<div class="container d-flex flex-wrap flex-column justify-center">
+
+    <form action="" method="POST" class='col-4'enctype="multipart/form-data">
+        {% csrf_token %}
+            <label for="formFile" class="form-label">Выберите изображение для определения эмоции</label>
+            <input  id='formFile' name="uploaded_file" class="form-control" type="file" accept="image/*" placeholder="Выберите изображение" required multiple>
+            <button type="submit" class="btn btn-primary mt-3">Определить</button>
+    </form>
+    <div class="d-flex flex-column flex-wrap align-content-center">
+    </div>
+    <table class="table table-hover mt-5">
+        <thead>
+            <tr class="table-secondary">
+              <th scope="col">Оригинальное Изображение</th>
+              <th scope="col">Результат</th>
+              <th scope="col">Эмоция</th>
+              <th scope="col">Статуc</th>
+              <th scope="col">Дата</th>
+              <th scope="col"></th>
+            </tr>
+          </thead>
+          <tbody>
+            {% for sample in samples%}
+                <td>
+                    <img src="{{sample.uploaded_image.url}}" alt="" style="width: 250px">
+                </td>
+                <td>
+                    {% if sample.final_image %}
+                        <img src="{{sample.final_image.url}}" alt="" style="width: 250px">
+                    {% else %}
+                        <p>Произошла ошибка</p>
+                    {% endif %}
+                </td>
+                <td>
+                    {{sample.recognized_emotion}}
+                </td>
+                <td class="text-center">
+                    {% if sample.status == "COM" %}
+                    <svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" fill="green" class="bi bi-check-circle" viewBox="0 0 16 16">
+                        <path d="M8 15A7 7 0 1 1 8 1a7 7 0 0 1 0 14zm0 1A8 8 0 1 0 8 0a8 8 0 0 0 0 16z"/>
+                        <path d="M10.97 4.97a.235.235 0 0 0-.02.022L7.477 9.417 5.384 7.323a.75.75 0 0 0-1.06 1.06L6.97 11.03a.75.75 0 0 0 1.079-.02l3.992-4.99a.75.75 0 0 0-1.071-1.05z"/>
+                      </svg>
+                    {%elif sample.status == "PEN"%}
+                    <svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" fill="orange" class="bi bi-arrow-clockwise" viewBox="0 0 16 16">
+                        <path fill-rule="evenodd" d="M8 3a5 5 0 1 0 4.546 2.914.5.5 0 0 1 .908-.417A6 6 0 1 1 8 2v1z"/>
+                        <path d="M8 4.466V.534a.25.25 0 0 1 .41-.192l2.36 1.966c.12.1.12.284 0 .384L8.41 4.658A.25.25 0 0 1 8 4.466z"/>
+                      </svg>
+                    {%else%}
+                    <svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" fill="red" class="bi bi-x-circle" viewBox="0 0 16 16">
+                        <path d="M8 15A7 7 0 1 1 8 1a7 7 0 0 1 0 14zm0 1A8 8 0 1 0 8 0a8 8 0 0 0 0 16z"/>
+                        <path d="M4.646 4.646a.5.5 0 0 1 .708 0L8 7.293l2.646-2.647a.5.5 0 0 1 .708.708L8.707 8l2.647 2.646a.5.5 0 0 1-.708.708L8 8.707l-2.646 2.647a.5.5 0 0 1-.708-.708L7.293 8 4.646 5.354a.5.5 0 0 1 0-.708z"/>
+                      </svg>
+                    {%endif%}
+                </td>
+                <td>
+                    {{sample.created_at|naturaltime}}
+                </td>
+                <td>
+                    <a href="{% url "recognition:recognition_edit" sample.id %}" class="btn btn-outline-dark">Редактировать</a>
+                </td>
+            </tr>
+            {%endfor%}
+          </tbody>
+    </table>
+</div>
+
+{%endblock%}

app/apps/emotion_recognition/templates/recognition/real_time.html

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+{%  extends 'base.html' %}
+{% load static %}
+{% load crispy_forms_tags %}
+{% load crispy_forms_tags humanize %}
+
+{% block content %}
+
+<div class="container d-flex flex-wrap flex-column justify-content-center align-content-center">
+<!-- 
+    <form action="" method="POST" class='col-4'enctype="multipart/form-data">
+        {% csrf_token %}
+            <button type="submit" class="btn btn-primary mt-3">Включить камеру</button>
+    </form> -->
+
+    <a class="btn btn-primary w-75 " data-bs-toggle="collapse" href="#collapseCamera" role="button" aria-expanded="false" aria-controls="collapseCamera">
+        Включить/Выключить камеру
+      </a>
+
+    <div class="collapse mt-3" id="collapseCamera">
+        <div class="card card-body">
+            <div class="container">
+                <div class="row">
+                    <div class="col-lg-8  offset-lg-2">
+                        <img src="{% url 'recognition:real_time_video_stream' %}" width="100%">
+                    </div>
+                </div>
+            </div>
+        </div>
+      </div>
+
+</div>
+
+{%endblock%}

app/apps/emotion_recognition/templates/recognition/recognition_delete.html

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+{% extends 'base.html' %}
+{% load static %}
+{% load crispy_forms_tags %}
+{% load crispy_forms_tags humanize %}
+
+
+{%block content %}
+
+    <div class="pt-3 ps-3 bg-body-tertiary scrollspy-example overflow-auto min-vh-100" data-bs-spy="scroll" data-bs-smooth-scroll="true">
+        
+        <div class="container">
+            <div class="d-flex flex-column align-items-center justify-content-between mt">
+                <h2 class="mb-3">Подтверждение Удаления</h2>
+                <p class="lead">Вы уверены, что хотите удалить запись<strong>"{{ object}}"?</strong>
+                    <br>
+                    <u class="fw-bold">Восстановить будет невозможно.</u>
+                </p>
+                <form method="post">
+                    {% csrf_token %}
+                    {{form}}
+                    <button type="submit" class="btn btn-danger btn-lg">Подтверждаю</button>
+                    <a href="javascript:history.back()" class="btn btn-outline-secondary btn-lg" role="button">Вернуться Назад</a>
+                  </form>
+            </div>
+        </div>
+    </div>
+
+{%endblock%}

app/apps/emotion_recognition/templates/recognition/recognition_edit.html

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+{% extends 'base.html' %}
+{% load static %}
+{% load crispy_forms_tags humanize %}
+
+{%block content %}
+
+<div class="container">
+    <div class="pt-3 ps-3 bg-body-tertiary scrollspy-example overflow-auto min-vh-100" data-bs-spy="scroll" data-bs-smooth-scroll="true">
+        <form method="POST" enctype="multipart/form-data">
+                {% csrf_token %}
+                {{form.media}}
+                <fieldset class="form-group">
+                    <legend class="border-bottom mb-4"></legend>
+                    {{ form|crispy }}
+                </fieldset>
+                <div class="form-group" style="display: flex; flex-direction: column; align-items: center;">
+                    <button class="w-75 btn btn-lg btn-dark" type="submit">Применить Изменения</button>
+                </div>
+                
+                <div class="form-group" style="display: flex; flex-direction: column; align-items: center;">
+                    <a href="{% url 'recognition:recognition_delete' userimagerecognition.id%}" class="mt-3 w-75 btn btn-lg btn-danger " role="button">
+                        Удалить Запись
+                    </a>
+                    <a href="{% url 'recognition:index'%}" class="mt-3 w-75 btn btn-lg btn-outline-secondary " role="button">
+                        Вернуться Назад
+                    </a>
+                </div>
+        </form>
+    </div>
+</div>
+
+{%endblock%}

app/apps/emotion_recognition/tests.py

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+from django.test import TestCase
+
+# Create your tests here.

app/apps/emotion_recognition/urls.py

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+from django.urls import path
+from . import views
+
+
+app_name = 'recognition'
+urlpatterns = [
+    path('recognition/', views.index, name="index"),
+    path('api/emotion-recognition/', views.ImageProcessingView.as_view(), name='emotion_recognition'),
+    path('recognition/real-time', views.real_time_recognition, name="real_time"),
+    path('real-time-stream', views.real_time_stream, name="real_time_video_stream"),
+    path('recognition/edit/<pk>', views.RecognitionUpdateView.as_view(), name="recognition_edit"),
+    path('recognition/delete/<pk>', views.RecognitionDeleteView.as_view(), name="recognition_delete"),
+]

app/apps/emotion_recognition/views.py

@@ -0,0 +1,119 @@
+from django.shortcuts import render, redirect
+from django.views.decorators.http import require_http_methods
+from django.http import HttpResponse, StreamingHttpResponse
+from django.contrib.auth.mixins import LoginRequiredMixin
+from django.views.generic import UpdateView, DeleteView
+from django.contrib import messages
+from django.urls import reverse_lazy
+
+from rest_framework.views import APIView
+from rest_framework.response import Response
+from rest_framework import status
+from rest_framework.parsers import MultiPartParser, FormParser
+from .serializers import ImageSerializer
+from PIL import Image
+
+from .models import UserImageRecognition
+from .emotion_recognition import  emotionVideo
+from .tasks import proccess_uploaded_image, process_image_from_api, convert_image_to_bytes
+from .forms import RecognitionEditForm
+
+
+
+@require_http_methods(['GET', 'POST'])
+def index(request):
+    try:
+
+        # GET method, return HTML page
+        if request.method == 'GET':
+            samples = UserImageRecognition.objects.all()
+            return render(request, 'recognition/index.html', {'samples': samples, })
+
+        if request.FILES and request.method == 'POST':
+            for f in request.FILES.getlist('uploaded_file'):
+                uploaded_image = f
+                image_data = UserImageRecognition.objects.create(uploaded_image=uploaded_image)
+
+                proccess_uploaded_image(image_data.id)
+
+        return redirect('recognition:index')
+
+    except Exception as e:
+
+        image_data.status = 'ERR'
+        image_data.error_occurred = True
+        image_data.error_message = str(e)
+        image_data.save()
+
+        return HttpResponse(f'Error: {str(e)}')
+    
+
+
+@require_http_methods(['GET', 'POST'])
+def real_time_recognition(request):
+    return render(request, 'recognition/real_time.html')
+
+def real_time_stream(request):
+    return StreamingHttpResponse(emotionVideo(),content_type="multipart/x-mixed-replace;boundary=frame")
+
+
+class RecognitionUpdateView(LoginRequiredMixin, UpdateView):
+    model = UserImageRecognition
+    form_class = RecognitionEditForm
+    template_name = "recognition/recognition_edit.html"
+
+    def get(self, request, pk):
+        self.object = self.get_object()
+        
+        context = self.get_context_data(object=self.object)
+        return self.render_to_response(context)
+    
+    def get_success_url(self, **kwargs):
+        pk = self.object.pk
+        messages.success(self.request, 'Запись была успешно изменена!')         
+        return reverse_lazy('recognition:recognition_edit', args=(pk,))
+
+
+class RecognitionDeleteView(LoginRequiredMixin, DeleteView):
+    model = UserImageRecognition
+    template_name = "recognition/recognition_delete.html"
+
+    def delete(self, request, pk):
+        return super().delete(request, pk)
+
+    def get_success_url(self, **kwargs):
+        obj = self.get_object()
+        messages.success(self.request, 'Запись  была успешно удалёна!')         
+        return reverse_lazy('recognition:index')
+    
+    
+class ImageProcessingView(APIView):
+    parser_classes = (MultiPartParser, FormParser)  # Для обработки загруженных файлов
+    serializer_class = ImageSerializer
+
+    def post(self, request, format=None):
+        """
+        Обрабатывает изображение, переданное через API, и возвращает финальное изображение с эмоциями.
+        """
+        serializer = self.serializer_class(data=request.data)
+        if serializer.is_valid():
+            image_file = serializer.validated_data['image']
+            
+            try:
+                # Обработка изображения
+                final_image, predicted_emotions, recognized_emotion = process_image_from_api(image_file)
+                output_image = convert_image_to_bytes(final_image)
+
+                # Формируем ответ
+                response_data = {
+                    "predicted_emotions": predicted_emotions,
+                    "recognized_emotion": recognized_emotion,
+                }
+                response = Response(response_data, status=status.HTTP_200_OK)
+                response['Content-Type'] = 'image/jpeg'
+                response.content = output_image.getvalue()
+                return response
+            except Exception as e:
+                return Response({"error": str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
+
+        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)

app/django_app/settings.py

@@ -0,0 +1,147 @@
+"""
+Django settings for django_app project.
+
+Generated by 'django-admin startproject' using Django 4.0.4.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/4.0/topics/settings/
+
+For the full list of settings and their values, see
+https://docs.djangoproject.com/en/4.0/ref/settings/
+"""
+import os
+from pathlib import Path
+
+# Build paths inside the project like this: BASE_DIR / 'subdir'.
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+# Quick-start development settings - unsuitable for production
+# See https://docs.djangoproject.com/en/4.0/howto/deployment/checklist/
+
+SECRET_KEY = os.environ.get('SECRET_KEY', 'your-default-secret-key')
+
+# SECURITY WARNING: don't run with debug turned on in production!
+DEBUG = True
+
+ALLOWED_HOSTS = []
+
+
+# Application definition
+
+INSTALLED_APPS = [
+    'django.contrib.admin',
+    'django.contrib.auth',
+    'django.contrib.contenttypes',
+    'django.contrib.sessions',
+    'django.contrib.messages',
+    'django.contrib.staticfiles',
+    'django_extensions',
+    'django.contrib.humanize',
+    'rest_framework',
+    
+    'apps.emotion_recognition',
+
+    'crispy_forms',
+    'crispy_bootstrap5',
+]
+
+MIDDLEWARE = [
+    'django.middleware.security.SecurityMiddleware',
+    'whitenoise.middleware.WhiteNoiseMiddleware',
+    'django.contrib.sessions.middleware.SessionMiddleware',
+    'django.middleware.common.CommonMiddleware',
+    'django.middleware.csrf.CsrfViewMiddleware',
+    'django.contrib.auth.middleware.AuthenticationMiddleware',
+    'django.contrib.messages.middleware.MessageMiddleware',
+    'django.middleware.clickjacking.XFrameOptionsMiddleware',
+]
+
+ROOT_URLCONF = 'django_app.urls'
+
+TEMPLATES = [
+    {
+        'BACKEND': 'django.template.backends.django.DjangoTemplates',
+        'DIRS': [
+            os.path.join(BASE_DIR, 'templates')
+        ],
+        'APP_DIRS': True,
+        'OPTIONS': {
+            'context_processors': [
+                'django.template.context_processors.debug',
+                'django.template.context_processors.request',
+                'django.contrib.auth.context_processors.auth',
+                'django.contrib.messages.context_processors.messages',
+            ],
+        },
+    },
+]
+
+WSGI_APPLICATION = 'django_app.wsgi.application'
+
+
+# Database
+# https://docs.djangoproject.com/en/4.0/ref/settings/#databases
+
+DATABASES = {
+    'default': {
+        'ENGINE': 'django.db.backends.sqlite3',
+        'NAME': str(os.path.join(BASE_DIR, "db.sqlite3")),
+    }
+}
+
+
+# Password validation
+# https://docs.djangoproject.com/en/4.0/ref/settings/#auth-password-validators
+
+AUTH_USER_MODEL = 'auth.User'
+
+AUTH_PASSWORD_VALIDATORS = [
+    {
+        'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
+    },
+]
+
+
+# Internationalization
+# https://docs.djangoproject.com/en/4.0/topics/i18n/
+
+LANGUAGE_CODE = 'en-us'
+
+TIME_ZONE = 'UTC'
+
+USE_I18N = True
+
+USE_TZ = True
+
+
+CRISPY_ALLOWED_TEMPLATE_PACKS = "bootstrap5"
+CRISPY_TEMPLATE_PACK = "bootstrap5"
+
+# Static files (CSS, JavaScript, Images)
+# https://docs.djangoproject.com/en/4.0/howto/static-files/
+
+
+MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
+MEDIA_URL = "/media/"
+
+STATIC_URL = '/static/'
+STATICFILES_DIRS = [
+    os.path.join(BASE_DIR, 'static')
+]
+
+# Default primary key field type
+# https://docs.djangoproject.com/en/4.0/ref/settings/#default-auto-field
+
+DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
+
+

app/django_app/urls.py

@@ -0,0 +1,24 @@
+"""django_app URL Configuration
+
+The `urlpatterns` list routes URLs to views. For more information please see:
+    https://docs.djangoproject.com/en/4.0/topics/http/urls/
+Examples:
+Function views
+    1. Add an import:  from my_app import views
+    2. Add a URL to urlpatterns:  path('', views.home, name='home')
+Class-based views
+    1. Add an import:  from other_app.views import Home
+    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')
+Including another URLconf
+    1. Import the include() function: from django.urls import include, path
+    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))
+"""
+from django.contrib import admin
+from django.urls import path, include
+from django.conf import settings
+from django.conf.urls.static import static 
+
+urlpatterns = [
+    path('admin/', admin.site.urls),
+    path('', include('apps.emotion_recognition.urls', namespace='recognition')),
+]  + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

app/django_app/wsgi.py

@@ -0,0 +1,16 @@
+"""
+WSGI config for django_app project.
+
+It exposes the WSGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/4.0/howto/deployment/wsgi/
+"""
+
+import os
+
+from django.core.wsgi import get_wsgi_application
+
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'django_app.settings')
+
+application = get_wsgi_application()

app/manage.py

@@ -0,0 +1,22 @@
+#!/usr/bin/env python
+"""Django's command-line utility for administrative tasks."""
+import os
+import sys
+
+
+def main():
+    """Run administrative tasks."""
+    os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'django_app.settings')
+    try:
+        from django.core.management import execute_from_command_line
+    except ImportError as exc:
+        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?"
+        ) from exc
+    execute_from_command_line(sys.argv)
+
+
+if __name__ == '__main__':
+    main()

app/model/model_4layer_2_2_pool.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:731969c7f74aead9b001510a65c0f6febf6853af317266675e34a42eb08b1e81
+size 17977016

app/model/model_4layer_2_2_pool.json

@@ -0,0 +1 @@
+{"class_name": "Sequential", "keras_version": "2.0.8", "config": [{"class_name": "Conv2D", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "conv2d_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "linear", "trainable": true, "data_format": "channels_last", "filters": 64, "padding": "same", "strides": [1, 1], "dilation_rate": [1, 1], "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "batch_input_shape": [null, 48, 48, 1], "use_bias": true, "activity_regularizer": null, "kernel_size": [3, 3]}}, {"class_name": "BatchNormalization", "config": {"beta_constraint": null, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "name": "batch_normalization_1", "epsilon": 0.001, "trainable": true, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_initializer": {"class_name": "Zeros", "config": {}}, "scale": true, "axis": -1, "gamma_constraint": null, "gamma_regularizer": null, "beta_regularizer": null, "momentum": 0.99, "center": true}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_1"}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "data_format": "channels_last", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2]}}, {"class_name": "Dropout", "config": {"rate": 0.25, "trainable": true, "name": "dropout_1"}}, {"class_name": "Conv2D", "config": {"kernel_constraint": null, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "conv2d_2", "bias_regularizer": null, "bias_constraint": null, "activation": "linear", "trainable": true, "data_format": "channels_last", "padding": "same", "strides": [1, 1], "dilation_rate": [1, 1], "kernel_regularizer": null, "filters": 128, "bias_initializer": {"class_name": "Zeros", "config": {}}, "use_bias": true, "activity_regularizer": null, "kernel_size": [5, 5]}}, {"class_name": "BatchNormalization", "config": {"beta_constraint": null, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "name": "batch_normalization_2", "epsilon": 0.001, "trainable": true, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_initializer": {"class_name": "Zeros", "config": {}}, "scale": true, "axis": -1, "gamma_constraint": null, "gamma_regularizer": null, "beta_regularizer": null, "momentum": 0.99, "center": true}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_2"}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_2", "trainable": true, "data_format": "channels_last", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2]}}, {"class_name": "Dropout", "config": {"rate": 0.25, "trainable": true, "name": "dropout_2"}}, {"class_name": "Conv2D", "config": {"kernel_constraint": null, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "conv2d_3", "bias_regularizer": null, "bias_constraint": null, "activation": "linear", "trainable": true, "data_format": "channels_last", "padding": "same", "strides": [1, 1], "dilation_rate": [1, 1], "kernel_regularizer": null, "filters": 512, "bias_initializer": {"class_name": "Zeros", "config": {}}, "use_bias": true, "activity_regularizer": null, "kernel_size": [3, 3]}}, {"class_name": "BatchNormalization", "config": {"beta_constraint": null, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "name": "batch_normalization_3", "epsilon": 0.001, "trainable": true, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_initializer": {"class_name": "Zeros", "config": {}}, "scale": true, "axis": -1, "gamma_constraint": null, "gamma_regularizer": null, "beta_regularizer": null, "momentum": 0.99, "center": true}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_3"}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_3", "trainable": true, "data_format": "channels_last", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2]}}, {"class_name": "Dropout", "config": {"rate": 0.25, "trainable": true, "name": "dropout_3"}}, {"class_name": "Conv2D", "config": {"kernel_constraint": null, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "conv2d_4", "bias_regularizer": null, "bias_constraint": null, "activation": "linear", "trainable": true, "data_format": "channels_last", "padding": "same", "strides": [1, 1], "dilation_rate": [1, 1], "kernel_regularizer": null, "filters": 512, "bias_initializer": {"class_name": "Zeros", "config": {}}, "use_bias": true, "activity_regularizer": null, "kernel_size": [3, 3]}}, {"class_name": "BatchNormalization", "config": {"beta_constraint": null, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "name": "batch_normalization_4", "epsilon": 0.001, "trainable": true, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_initializer": {"class_name": "Zeros", "config": {}}, "scale": true, "axis": -1, "gamma_constraint": null, "gamma_regularizer": null, "beta_regularizer": null, "momentum": 0.99, "center": true}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_4"}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_4", "trainable": true, "data_format": "channels_last", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2]}}, {"class_name": "Dropout", "config": {"rate": 0.25, "trainable": true, "name": "dropout_4"}}, {"class_name": "Flatten", "config": {"trainable": true, "name": "flatten_1"}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "linear", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 256, "use_bias": true, "activity_regularizer": null}}, {"class_name": "BatchNormalization", "config": {"beta_constraint": null, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "name": "batch_normalization_5", "epsilon": 0.001, "trainable": true, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_initializer": {"class_name": "Zeros", "config": {}}, "scale": true, "axis": -1, "gamma_constraint": null, "gamma_regularizer": null, "beta_regularizer": null, "momentum": 0.99, "center": true}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_5"}}, {"class_name": "Dropout", "config": {"rate": 0.25, "trainable": true, "name": "dropout_5"}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "linear", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 512, "use_bias": true, "activity_regularizer": null}}, {"class_name": "BatchNormalization", "config": {"beta_constraint": null, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "name": "batch_normalization_6", "epsilon": 0.001, "trainable": true, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_initializer": {"class_name": "Zeros", "config": {}}, "scale": true, "axis": -1, "gamma_constraint": null, "gamma_regularizer": null, "beta_regularizer": null, "momentum": 0.99, "center": true}}, {"class_name": "Activation", "config": {"activation": "relu", "trainable": true, "name": "activation_6"}}, {"class_name": "Dropout", "config": {"rate": 0.25, "trainable": true, "name": "dropout_6"}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "sigmoid", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 7, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}

app/requirements.txt

@@ -0,0 +1,61 @@
+absl-py==1.3.0
+asgiref==3.8.1
+astunparse==1.6.3
+autopep8==2.0.0
+cachetools==5.2.0
+certifi==2022.9.24
+charset-normalizer==2.1.1
+crispy-bootstrap5==0.7
+Django==5.1.4
+django-crispy-forms==1.14.0
+django-extensions==3.2.1
+djangorestframework==3.15.2
+docopt==0.6.2
+flatbuffers==22.10.26
+gast==0.4.0
+google-auth==2.14.1
+google-auth-oauthlib==0.4.6
+google-pasta==0.2.0
+grpcio==1.50.0
+h5py==3.7.0
+idna==3.4
+importlib-metadata==5.0.0
+keras==2.11.0
+libclang==14.0.6
+Markdown==3.4.1
+MarkupSafe==2.1.1
+numpy==1.23.4
+oauthlib==3.2.2
+opencv-python==4.6.0.66
+opt-einsum==3.3.0
+packaging==21.3
+Pillow==9.3.0
+pipreqs==0.4.11
+protobuf==3.19.6
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pycodestyle==2.9.1
+pyparsing==3.0.9
+requests==2.28.1
+requests-oauthlib==1.3.1
+rsa==4.9
+six==1.16.0
+sqlparse==0.4.3
+tensorboard==2.11.0
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+tensorflow==2.11.0
+tensorflow-estimator==2.11.0
+tensorflow-intel==2.11.0
+tensorflow-io-gcs-filesystem==0.27.0
+termcolor==2.1.0
+tomli==2.0.1
+typing_extensions==4.4.0
+tzdata==2022.6
+urllib3==1.26.12
+Werkzeug==2.2.2
+wrapt==1.14.1
+yarg==0.1.9
+zipp==3.10.0
+gunicorn==20.1.0
+whitenoise==6.4.0

app/static/css/main.css

@@ -0,0 +1,6 @@
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@100;400;500;700;900&display=swap"');
+
+*{
+    font-family: 'Inter', sans-serif;
+
+}

app/templates/base.html

@@ -0,0 +1,48 @@
+{% load static %}
+{% load i18n %}
+<!doctype html>
+<html lang="en">
+  <head>
+    <!-- Required meta tags -->
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <link rel="stylesheet" type="text/css" href="{% static 'css/main.css' %}" />
+    <!-- Bootstrap CSS -->
+    <link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-EVSTQN3/azprG1Anm3QDgpJLIm9Nao0Yz1ztcQTwFspd3yD65VohhpuuCOmLASjC" crossorigin="anonymous">
+    
+    <title>Welcome!</title>
+  </head>
+  <body>
+    {% include 'header.html'%}
+    {% for message in messages %}
+        {% if forloop.last %}
+            {% if 'success' in message.tags %}
+                <div class="alert alert-primary alert-dissmisible fade show text-center" role="alert">
+                    <button type="button" class="btn-close" data-bs-dismiss="alert" aria-hidden="true"></button>
+                    {% blocktrans %} {{ message }} {% endblocktrans %}
+                </div>
+                {% elif 'error' in message.tags %}
+                <div class="alert alert-danger alert-general text-center" role="alert">
+                    <button type="button" class="btn-close" data-bs-dismiss="alert" aria-hidden="true"></button>
+                    {% blocktrans %} {{ message }} {% endblocktrans %}
+                </div>
+            {% endif %}
+        {% endif %}
+    {% endfor %}
+
+    <div class="container-fluid">
+        <div class="row">
+            
+            <div class="col-sm p-3 min-vh-100">
+                {% block content %}
+
+                {% endblock %}
+            </div>
+        </div>
+    </div>
+
+    <script src="https://cdn.jsdelivr.net/npm/@popperjs/[email protected]/dist/umd/popper.min.js" integrity="sha384-IQsoLXl5PILFhosVNubq5LC7Qb9DXgDA9i+tQ8Zj3iwWAwPtgFTxbJ8NT4GN1R8p" crossorigin="anonymous"></script>
+    <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.min.js" integrity="sha384-cVKIPhGWiC2Al4u+LWgxfKTRIcfu0JTxR+EQDz/bgldoEyl4H0zUF0QKbrJ0EcQF" crossorigin="anonymous"></script>
+   
+  </body>
+</html>

app/templates/header.html

@@ -0,0 +1,23 @@
+{% load static%}
+
+<div class="container">
+  <header class="d-flex flex-wrap align-items-center justify-content-center justify-content-md-between py-3 mb-4 border-bottom">
+      <a href="{% url 'recognition:index'%}" class="d-flex align-items-center col-md-1 mb-2 mb-md-0 ms-3 text-dark text-decoration-none">
+          <h1 class="h1">Распознавание эмоции</h1>
+      </a>
+
+      {% if request.user.is_authenticated %}
+      <div class="d-flex justify-content-around flex-column">
+        <a href="{% url 'recognition:index'%}" role='button'class="btn btn-outline-dark  mb-md-0 ">
+          На изображении
+      </a>
+    
+        <a href="{% url 'recognition:real_time'%}" role='button'class="btn btn-outline-dark  mt-2  mb-md-0 ">
+          В режиме реального времени
+      </a>
+      </div>
+      {% endif %}
+  </header>
+</div>
+
+