model 생성 후 db에 저장

djangoServer/djangoServer/admin.py

@@ -0,0 +1,6 @@
+from django.contrib import admin
+from .models import PrintLog
+
+@admin.register(PrintLog)
+class LogAdmin(admin.ModelAdmin):
+    list_display = ('id','wav_file', 'image', 'result', 'create_dt', 'update_dt')

djangoServer/djangoServer/models.py

@@ -0,0 +1,11 @@
+from django.db import models
+
+class PrintLog(models.Model):
+    wav_file = models.FileField(upload_to='audio/')
+    image = models.ImageField('IMAGE',upload_to='specImage/%Y/%m/',blank=True,null=True)
+    result = models.CharField(max_length=50)
+    create_dt = models.DateTimeField('CREATE DT', auto_now_add=True)
+    update_dt = models.DateTimeField('UPDATE DT', auto_now=True)
+
+    def __str__(self):
+        return self.wav_file.name

djangoServer/djangoServer/settings.py

@@ -16,7 +16,6 @@
 # Build paths inside the project like this: BASE_DIR / 'subdir'.
 BASE_DIR = Path(__file__).resolve().parent.parent
 
-
 # Quick-start development settings - unsuitable for production
 # See https://docs.djangoproject.com/en/4.2/howto/deployment/checklist/
 
@@ -26,8 +25,7 @@
 # SECURITY WARNING: don't run with debug turned on in production!
 DEBUG = True
 
-ALLOWED_HOSTS = []
-
+ALLOWED_HOSTS = ['223.194.130.187']
 
 # Application definition
 
@@ -40,6 +38,7 @@
     'django.contrib.staticfiles',
 
     'rest_framework',
+    'djangoServer',
 ]
 
 MIDDLEWARE = [
@@ -72,7 +71,6 @@
 
 WSGI_APPLICATION = 'djangoServer.wsgi.application'
 
-
 # Database
 # https://docs.djangoproject.com/en/4.2/ref/settings/#databases
 
@@ -83,7 +81,6 @@
     }
 }
 
-
 # Password validation
 # https://docs.djangoproject.com/en/4.2/ref/settings/#auth-password-validators
 
@@ -102,7 +99,6 @@
     },
 ]
 
-
 REST_FRAMEWORK = {
     'DEFAULT_PERMISSION_CLASSES': [
         'rest_framework.permissions.AllowAny',
@@ -124,7 +120,6 @@
 
 USE_TZ = True
 
-
 # Static files (CSS, JavaScript, Images)
 # https://docs.djangoproject.com/en/4.2/howto/static-files/
 
@@ -137,4 +132,4 @@
 DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
 
 MEDIA_URL = '/media/'
-MEDIA_ROOT = BASE_DIR / 'media'
+MEDIA_ROOT = BASE_DIR / 'media'

djangoServer/djangoServer/urls.py

@@ -26,6 +26,7 @@
 
 urlpatterns = [
     path('', include(router.urls)),
+    path('admin/', admin.site.urls),
     path('process_audio/', views.process_audio, name='process_audio'),
     path('get_spectrogram/', views.get_spectrogram, name='get_spectrogram'),
 

djangoServer/djangoServer/views.py

@@ -1,59 +1,84 @@
-from io import BytesIO
+import os
+
 from PIL import Image
-from django.http import HttpResponse, HttpResponseServerError
+from django.core.files import File
+from django.http import HttpResponseServerError
 from django.views.decorators.csrf import csrf_exempt
+from django.http import JsonResponse
 
 import requests
 import traceback
-import os
+import json
 
 def get_spectrogram(request):
     if request.method == 'GET':
         # 클라이언트에서 전송한 파일을 가져옵니다.
 
-        audio_path = 'djangoServer/audio/m.m4a'
-        url = 'http://localhost:8000/process_audio/'
+        audio_path = 'djangoServer/audio/W.m4a'
+        url = 'http://223.194.130.187:8000/process_audio/'
+        headers = {"Content-Type": "application/json"}
+
         try:
             with open(audio_path, 'rb') as f:
-                # audio_file = {'m4a' : f}
+                byte_array = bytearray(f.read())  # 파일을 바이트 배열로 읽음
+
                 if not f.closed:
                     print(f"{audio_path} is opened.")
-                spectrogram = requests.post(url, files={'m4a': f})
-                # print(spectrogram)
+
+                data = {'recordData': byte_array}
+                #response = requests.post(url, data=json.dumps(data), headers=headers)
+                response = requests.post(url, data= json.dumps(data))
+                #response = requests.post(url, data=byte_array)
+
+                print(response.json())
+
+                if response.status_code == 200:
+                    response_data = response.json()
+                    predicted_alphabet = response_data['predicted_alphabet']
+                    print("(get) response_data : ", response_data)
+                    print("(get) predicted_alphabet : ", predicted_alphabet)
+
+                    return JsonResponse({'predicted_alphabet': predicted_alphabet})
+                else:
+                    print(f"An error occurred: {response.status_code}")
+                    return JsonResponse({'error': f"An error occurred: {response.status_code}"})
 
         except FileNotFoundError:
             # 파일이 존재하지 않을 때의 예외 처리
             print(f"{audio_path} does not exist.")
+            return JsonResponse({'error': f"An error occurred: FileNotFoundError"})
         except Exception as e:
             # 그 외의 예외 처리
             print(f"An error occurred while opening {audio_path}: {e}")
-        # audio_file = request.FILES.get('audio_file')
-
-        # 스펙트로그램 이미지를 생성합니다.
-        # spectrogram = process_audio(audio_file)
-
-        # spectrogram = requests.post(url, files=audio_file)
-
-        # 스펙트로그램 이미지를 응답으로 반환합니다.
-        response = HttpResponse(spectrogram, content_type='image/jpeg')
-        return response
+            return JsonResponse({'error': f"An error occurred: FileNotFoundError"})
 
 
 # POST 응답 처리
 from pydub import AudioSegment
+import torch
+import torchvision.transforms as transforms
 import numpy as np
 import librosa, librosa.display
 import matplotlib.pyplot as plt
 import matplotlib
-matplotlib.use('Agg')
+import subprocess
+import struct
+from .models import PrintLog
 
-from django.conf import settings
+matplotlib.use('Agg')
 
 FIG_SIZE = (15, 10)
 DATA_NUM = 30
 
+alpha = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v',
+         'w', 'x', 'y', 'z']
+
+# 음성 데이터의 샘플링 레이트, 채널 수, 샘플링 포맷 등을 정의합니다.
+SAMPLE_RATE = 44100
+CHANNELS = 1
+SAMPLE_WIDTH = 2
 
-# m4a -> wav -> spectrogram / -> model -> result
+# m4a -> wav -> spectrogram / -> resnetModel -> result
 @csrf_exempt
 def process_audio(request):
     global peekIndex, image_url
@@ -62,22 +87,32 @@ def process_audio(request):
     try:
         if request.method == 'POST':
             print("POST")
-            # POST 요청에서 이미지 파일을 가져옵니다.
-            m4a_file = request.FILES['m4a']
-            #print("m4a_file : ", m4a_file)
 
-            # 소리 + 묵음
-            # load the audio files
-            audio1 = AudioSegment.from_file(m4a_file, format="m4a")
-            audio2 = AudioSegment.from_file("djangoServer/slienceSound.m4a", format="m4a")
+            # POST 요청에서 biteArray 데이터를 가져옵니다.
+            requestBody = json.loads(request.body)  # 안드로이드 앱에서 보낸 데이터를 가져옵니다.
+            byte_data = requestBody['recordData']
+            byte_array = bytes([struct.pack('b', x)[0] for x in byte_data])
+
+            with open('my_audio_file.aac', 'wb+') as destination:
+                for i in range(0, len(byte_array), 32):
+                    chunk = byte_array[i:i + 32]
+                    destination.write(chunk)
+
+            # aac -> wav
+            input_file = "my_audio_file.aac"
+            output_file = "my_audio_file.wav"
+
+            # Run the ffmpeg command to convert the AAC file to WAV
+            subprocess.run(["ffmpeg", "-y", "-i", input_file, output_file])
 
-            # concatenate the audio files
-            combined_audio = audio1 + audio2
+            audio1 = AudioSegment.from_file("my_audio_file.wav", format="wav")
+            silence = AudioSegment.silent(duration=1000)  # 1초 묵음
+            combined_audio = audio1 + silence
 
             # export the concatenated audio as a new file
             file_handle = combined_audio.export("combined.wav", format="wav")
 
-            # paths.append(file_path)
+            # 신호 및 샘플링 레이트 가져오기
             sig, sr = librosa.load(file_handle, sr=22050)
 
             # 에너지 평균 구하기
@@ -91,15 +126,15 @@ def process_audio(request):
                 if (sig[i] ** 2 >= mean):
                     peekIndex = i
                     break
-
+            #
             START_LEN = 1102
             END_LEN = 20948
             if peekIndex > 1102:
-                print(peekIndex)
+                #print(peekIndex)
                 startPoint = peekIndex - START_LEN
                 endPoint = peekIndex + 22050
             else:
-                print(peekIndex)
+                #print(peekIndex)
                 startPoint = peekIndex
                 endPoint = peekIndex + END_LEN
 
@@ -125,6 +160,7 @@ def process_audio(request):
             n_fft_duration = float(n_fft) / sr
 
             # STFT
+            # stft = librosa.stft(sig[startPoint:endPoint], n_fft=n_fft, hop_length=hop_length)
             stft = librosa.stft(sig[startPoint:endPoint], n_fft=n_fft, hop_length=hop_length)
 
             # 복소공간 값 절댓값 취하기
@@ -139,37 +175,55 @@ def process_audio(request):
             plt.figure(figsize=FIG_SIZE)
             librosa.display.specshow(log_spectrogram, sr=sr, hop_length=hop_length, cmap='magma')
 
-
             # matplotlib 라이브러리를 사용하여 생성된 spectrogram 이미지를 jpg 형식으로 저장
-            #name_end_pos = file_handle.find('.')
-            # print(name_end_pos)
-            # image_path = 'images/' + file_handle[:name_end_pos] + '.jpg'
-            # print(image_path)
-            # image_url = settings.STATIC_URL + image_path
-            # print(image_url)
             image_path = 'static/images/' + 'test.jpg'
-
-            # save spectrogram image
-            #plt.savefig('static/images/' + file_handle[:name_end_pos] + '.jpg')
-            # spectrogram 이미지 저장
             plt.savefig(image_path)
 
             plt.close()
 
+            # 모델 입히기
+            model = torch.load('djangoServer/resnetModel/resnet32.pth')
+            # switch resnetModel to evaluation mode
+            model.eval()
+            # define the image transforms
+            image_transforms = transforms.Compose([
+                transforms.Resize((224, 224)),
+                transforms.ToTensor(),
+                transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+            ])
+
             # 이미지 열기
             image = Image.open(image_path)
-            # 저장된 이미지를 열어서 확인
-            #os.system('open ' + image_path)  # Mac OS 기준
-
-            # 이미지를 바이트 형태로 변환하여 메모리에 저장
-            image_bytes = BytesIO()
-            image.save(image_bytes, format='JPEG')
-            image_bytes = image_bytes.getvalue()
-
-            # 이미지를 HttpResponse 객체에 첨부 파일로 반환
-            response = HttpResponse(image_bytes, content_type='image/jpeg')
-            response['Content-Disposition'] = 'inline; filename="spectrogram.jpeg"'
-            return response
+
+            # apply the transforms to the test image
+            test_image_tensor = image_transforms(image)
+            # add batch dimension to the image tensor
+            test_image_tensor = test_image_tensor.unsqueeze(0)
+
+            # get the resnetModel's prediction
+            with torch.no_grad():
+                prediction = model(test_image_tensor)
+
+            # get the predicted class index
+            predicted_class_index = torch.argmax(prediction).item()
+
+            # 모델 객체 생성 및 데이터 저장
+            print_log = PrintLog()
+            print_log.wav_file.save("combined.wav", file_handle)
+            # 이미지 파일을 열고 File 객체 생성
+            with open(image_path, 'rb') as f:
+                file_name = os.path.basename(image_path)
+                django_file = File(f, name=file_name)
+
+                # 모델 객체 생성 및 이미지 파일 저장
+                print_log.image.save(file_name, django_file, save=True)
+            print_log.result = alpha[predicted_class_index]
+            print_log.save()
+
+            response = {'predicted_alphabet': alpha[predicted_class_index]}
+            # 예측값 알파벳 출력
+            print("post: ", response)
+            return JsonResponse(response)
     except Exception as e:
         print(traceback.format_exc())  # 예외 발생시 traceback 메시지 출력
-        return HttpResponseServerError()  # 500 Internal Server Error 응답 반환
+        return HttpResponseServerError()  # 500 Internal Server Error 응답 반환