카테고리 없음
[Tensorflow] [CNN] 요리 이미지 분류하기
설화님
2024. 9. 20. 19:59
코드
CNN을 활용한 이미지 분류 프로젝트¶
요리 이미지 판정 방법¶
- 사진에 어떤 요리가 있는지 분류 – 피자, 스파게티, 스시 3종류의 요리 학습
- 판정한 요리의 칼로리를 출력
요리 이미지 데이터 수집¶
- 피자, 스파게티, 스시 3가지 종류 요리 => 음식당 100장 이상의 사진 필요
- Flickr 사이트 이용
- Flickr가 제공하는 사진검색 API를 사용하여 요리사진 수집
- 사이트 : https://www.flickr.com/
CNN을 활용한 이미지 분류 프로젝트¶
1. 데이타 준비 및 정리¶
1) Flickr API를 사용해 사진 수집¶
(1) Flickr API 계정 만들기¶
- 구글 아이디로 계정 만들기
- Flickr API 계정 만들기 https://www.flickr.com/services/api/
Flickr API 모듈 설치하기¶
- pip install flickrapi
In [ ]:
# 경고(worning) 무시
import warnings
warnings.filterwarnings('ignore')
In [ ]:
# 현재 작업 디렉토리 확인하기
import os
os.getcwd()
Out[ ]:
'c:\\Users\\mgoog1.ko\\Desktop\\Downloads\\딥러닝+실무+프로젝트_down (1)\\1. CNN_요리이미지분류'
(2) Flickr API를 이용하여 요리 이미지 내려 받기¶
- 피자, 스파게티, 스시 3가지 요리당 75X75 픽셀의 정사각형 이미지를 300개씩 내려 받기
- ./image/pizza, ./image/spaghetti, ./image/sushi 폴더 생성
In [ ]:
# Flickr로 사진 검색해서 다운로드하기
from flickrapi import FlickrAPI
from urllib.request import urlretrieve
from pprint import pprint
import os, time, sys
# AP 키 지정하기
key = "6a77e04939b39922140fe0033ad87fbd"
secret = "867c278824b691dc"
wait_time = 1 # 대기 시간(초)
# image 디렉토리가 존재하지 않은 경우에만 디렉토리 생성
if not os.path.exists('./image/'):
os.mkdir('./image/')
# Flickr API로 사진 검색하고 './image' 아래에 요리 이름 폴더별로 저장하기
def go_download(keyword, dir): # go_download('피자', 'pizza')
# 저장 경로 지정하기
savedir = "./image/" + dir # "./image/pizza"
# 디렉토리가 존재하지 않은 경우에만 디렉토리 생성
if not os.path.exists(savedir):
os.mkdir(savedir)
# API를 사용해서 json 형태의 데이타 다운로드하기
flickr = FlickrAPI(key, secret, format='parsed-json')
res = flickr.photos.search(
text = keyword, # 키워드
per_page = 200, # 검색할 개수
media = 'photos', # 사진 검색
sort = "relevance", # 키워드 관련도 순서
safe_search = 1, # 안전 검색
extras = 'url_q, license')
# 결과 확인하기
photos = res['photos']
pprint(photos)
try:
# 1장씩 다운로드하기
for i, photo in enumerate(photos['photo']):
url_q = photo['url_q']
filepath = savedir + '/' + dir + '_' + str(i) + '.jpg'
if os.path.exists(filepath): continue
print(str(i+1) + ":download=", url_q)
urlretrieve(url_q, filepath)
time.sleep(wait_time)
except:
import traceback
traceback.print_exc()
# 키워드와 디렉터리 이름 지정해서 다운로드하기
def main():
go_download('피자', 'pizza')
# go_download('스파게티', 'spaghetti')
# go_download('스시', 'sushi')
# 메인 프로그램
if __name__ == '__main__':
main()
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(3) 사진 정리하기(클린징)¶
- 제대로 된 사진 100개만 남기기
- 대상이 크게 나오고 다른 물체가 안나오고 색이 선명한 것 위주로 선택
- 필터를 심하게 적용해 색상이 이상하거나 흑백 이미지는 제거
In [ ]:
# 디렉토리 안의 이미지 파일 수 확인하기
import os, glob
path1 = "./image/pizza/"
path2 = "./image/spaghetti/"
path3 = "./image/sushi/"
# for img in glob.glob(path1 + "/*.jpg"):
# print(img)
print("pizza: ", len(glob.glob(path1 + "/*.jpg")))
print("spaghetti: ", len(glob.glob(path2 + "/*.jpg")))
print("sushi: ", len(glob.glob(path3 + "/*.jpg")))
pizza: 129 spaghetti: 143 sushi: 111
In [ ]:
# 이미지들을 NumPy 형식으로 변환하기
import numpy as np
from PIL import Image
import os, glob, random
outfile = "image/photos.npz" # 저장할 파일 이름
max_photo = 100 # 사용할 이미지 수
photo_size = 32 # 이미지 크기
x = [] # 이미지 데이터
y = [] # 레이블 데이터
def main():
# 디렉터리 읽어 들이기
glob_files("./image/pizza", 0) # 레이블 설정 - glob_files("폴더명", 레이블)
glob_files("./image/spaghetti", 1)
glob_files("./image/sushi", 2)
# 파일로 저장하기
np.savez(outfile, x=x, y=y) # Numpy 압축파일로 저장
print(str(len(x)) + "개의 이미지를", "저장했습니다. 파일명 :" + outfile)
# path 내부의 이미지 읽어 들이기
def glob_files(path, label): # glob_files("./image/pizza", 0)
files = glob.glob(path + "/*.jpg")
random.shuffle(files)
# 파일 처리하기
num = 0
for f in files:
if num >= max_photo: break
num += 1
# 이미지 파일 읽어 들이기
img = Image.open(f)
img = img.convert("RGB") # RGB 컬러로 변환하기
img = img.resize((photo_size, photo_size)) # 크기 변경하기 - 32X32
img = np.asarray(img) # RBG의 각 색상 값을 0~1까지의 실수로 변환
x.append(img)
y.append(label)
# 메인 프로그램
if __name__ == '__main__':
main()
300개의 이미지를 저장했습니다. 파일명 :image/photos.npz
Numpy로 저장된 이미지 확인하기¶
In [ ]:
import matplotlib.pyplot as plt
import numpy as np
# 사진 데이타 읽어 들이기
photos = np.load("image/photos.npz")
x = photos["x"]
y = photos["y"]
# 시작 인덱스
idx = 0 # 100 or 200으로 변경하여 다른 이미지도 확인하기
# pyplot으로 출력하기
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5, 5, i+1)
plt.title(y[i+idx])
plt.imshow(x[i+idx])
plt.show()
In [ ]:
# 시작 인덱스
idx = 100 # 100 or 200으로 변경하여 다른 이미지도 확인하기
# pyplot으로 출력하기
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5, 5, i+1)
plt.title(y[i+idx])
plt.imshow(x[i+idx])
plt.show()
In [ ]:
# 시작 인덱스
idx = 200 # 100 or 200으로 변경하여 다른 이미지도 확인하기
# pyplot으로 출력하기
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5, 5, i+1)
plt.title(y[i+idx])
plt.imshow(x[i+idx])
plt.show()
3) 데이타 전처리¶
- 속성(피처) 데이터 - 1차원에서 3차원 배열로 변환, 정규화
- 레이블 - 원-핫 인코딩 형식으로 변환
In [ ]:
import tensorflow.keras
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plt
import numpy as np
# 입력 데이타와 출력 데이타 지정
im_rows = 32 # 이미지의 높이
im_cols = 32 # 이미지의 너비
im_color = 3 # 이미지의 색공간(RGB)
in_shape = (im_rows, im_cols, im_color) # (32, 32, 3) # 입력 데이타
nb_classes = 3 # 출력 데이타 수, 3종류의 요리
# 사진 데이터 읽어 들이기
photos = np.load('image/photos.npz')
X = photos['x']
y = photos['y']
# x 속성(피처) 데이터를 1차원에서 3차원 배열로 변환 및 정규화하기
X = x.reshape(-1, im_rows, im_cols, im_color)
X = x.astype('float32') / 255
# y 레이블을 원-핫 인코딩 형식으로 변환하기
y = to_categorical(y.astype('int32'), nb_classes) # nb_classes=3
4) 데이타 구분¶
- 학습데이타셋, 검증데이타셋, 테스트데이타셋으로 구분
In [ ]:
# 전체 데이타셋을 학습 데이타셋과 테스트셋으로 구분하기
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.2,
random_state=0)
# 학습데이타셋을 학습데이셋과 검증데이타셋으로 구분하기
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train,
test_size=0.2,
random_state=0)
# 데이타셋 형태 확인
print(X_train.shape, y_train.shape)
print(X_val.shape, y_val.shape)
print(X_test.shape, y_test.shape)
(192, 32, 32, 3) (192, 3) (48, 32, 32, 3) (48, 3) (60, 32, 32, 3) (60, 3)
2. CNN 모델 정의, 컴파일 설정¶
- cnn_model.py 파일로 저장
In [ ]:
# cnn_model.py 파일 코드
import tensorflow.keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.optimizers import Adam
# CNN 모델 정의하기
def def_model(in_shape, nb_classes):
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu',
input_shape=in_shape))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes, activation='softmax'))
return model
# 컴파일하고 모델 반환하기
def get_model(in_shape, nb_classes):
model = def_model(in_shape, nb_classes)
model.compile(
loss='categorical_crossentropy',
optimizer = Adam(1e-4),
metrics=['acc'])
return model
3. CNN 모델 학습¶
- 주피터에서 [Kernel --> Restart] 클릭하여 커널을 재시작한 후 실행
- 정답률이 너무 안좋은 경우는 이미지 클리닝을 한 후 다시 실행하기
1) ModelCheckpoint, Early_stopping 지정¶
In [ ]:
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
import os
# 딥러닝 모델을 저장할 디렉토리(폴더) 생성
MODEL_DIR = './model/'
if not os.path.exists(MODEL_DIR):
os.mkdir(MODEL_DIR)
modelpath="./model/CNN_Cooking.hdf5"
checkpointer = ModelCheckpoint(filepath=modelpath, monitor='val_loss', verbose=1, save_best_only=True)
early_stopping_callback = EarlyStopping(monitor='val_loss', patience=10)
2) 모델 학습¶
In [ ]:
import cnn_model # 내가 만든 CNN 모델 import 하기
im_rows = 32 # 이미지의 높이
im_cols = 32 # 이미지의 너비
im_color = 3 # 이미지의 색공간
in_shape = (im_rows, im_cols, im_color) # (32, 32, 3) # 입력 데이타
nb_classes = 3 # 출력 데이타 수, 3종류의 요리
# CNN 모델 만들기
model = cnn_model.get_model(in_shape, nb_classes)
# 모델 학습
history = model.fit(X_train, y_train,
batch_size=32,
epochs=30,
verbose=1,
validation_data=(X_val, y_val),
callbacks=[early_stopping_callback,checkpointer])
Epoch 1/30 5/6 [========================>.....] - ETA: 0s - loss: 1.1058 - acc: 0.2438 Epoch 1: val_loss improved from inf to 1.10462, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 1s 104ms/step - loss: 1.1023 - acc: 0.2552 - val_loss: 1.1046 - val_acc: 0.2708 Epoch 2/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0919 - acc: 0.3625 Epoch 2: val_loss did not improve from 1.10462 6/6 [==============================] - 0s 50ms/step - loss: 1.0947 - acc: 0.3490 - val_loss: 1.1057 - val_acc: 0.2500 Epoch 3/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0939 - acc: 0.3625 Epoch 3: val_loss did not improve from 1.10462 6/6 [==============================] - 0s 49ms/step - loss: 1.0938 - acc: 0.3646 - val_loss: 1.1048 - val_acc: 0.2708 Epoch 4/30 5/6 [========================>.....] - ETA: 0s - loss: 1.1048 - acc: 0.3063 Epoch 4: val_loss improved from 1.10462 to 1.10234, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 59ms/step - loss: 1.1020 - acc: 0.3333 - val_loss: 1.1023 - val_acc: 0.3542 Epoch 5/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0983 - acc: 0.3063 Epoch 5: val_loss improved from 1.10234 to 1.10088, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 58ms/step - loss: 1.0951 - acc: 0.3229 - val_loss: 1.1009 - val_acc: 0.3542 Epoch 6/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0978 - acc: 0.3750 Epoch 6: val_loss did not improve from 1.10088 6/6 [==============================] - 0s 48ms/step - loss: 1.0931 - acc: 0.3906 - val_loss: 1.1014 - val_acc: 0.2917 Epoch 7/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0861 - acc: 0.4250 Epoch 7: val_loss did not improve from 1.10088 6/6 [==============================] - 0s 48ms/step - loss: 1.0864 - acc: 0.4167 - val_loss: 1.1019 - val_acc: 0.2917 Epoch 8/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0874 - acc: 0.3562 Epoch 8: val_loss did not improve from 1.10088 6/6 [==============================] - 0s 50ms/step - loss: 1.0876 - acc: 0.3698 - val_loss: 1.1011 - val_acc: 0.2917 Epoch 9/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0908 - acc: 0.3750 Epoch 9: val_loss did not improve from 1.10088 6/6 [==============================] - 0s 50ms/step - loss: 1.0940 - acc: 0.3542 - val_loss: 1.1010 - val_acc: 0.2917 Epoch 10/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0981 - acc: 0.3875 Epoch 10: val_loss did not improve from 1.10088 6/6 [==============================] - 0s 50ms/step - loss: 1.0981 - acc: 0.3906 - val_loss: 1.1019 - val_acc: 0.3125 Epoch 11/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0825 - acc: 0.3562 Epoch 11: val_loss improved from 1.10088 to 1.10023, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 61ms/step - loss: 1.0855 - acc: 0.3646 - val_loss: 1.1002 - val_acc: 0.3125 Epoch 12/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0846 - acc: 0.4125 Epoch 12: val_loss improved from 1.10023 to 1.09729, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 60ms/step - loss: 1.0836 - acc: 0.4010 - val_loss: 1.0973 - val_acc: 0.2708 Epoch 13/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0813 - acc: 0.4062 Epoch 13: val_loss did not improve from 1.09729 6/6 [==============================] - 0s 50ms/step - loss: 1.0845 - acc: 0.3854 - val_loss: 1.0987 - val_acc: 0.3125 Epoch 14/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0903 - acc: 0.3750 Epoch 14: val_loss improved from 1.09729 to 1.09703, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 60ms/step - loss: 1.0852 - acc: 0.4010 - val_loss: 1.0970 - val_acc: 0.2917 Epoch 15/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0795 - acc: 0.4000 Epoch 15: val_loss did not improve from 1.09703 6/6 [==============================] - 0s 49ms/step - loss: 1.0787 - acc: 0.4062 - val_loss: 1.0979 - val_acc: 0.3125 Epoch 16/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0810 - acc: 0.4062 Epoch 16: val_loss improved from 1.09703 to 1.09379, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 60ms/step - loss: 1.0765 - acc: 0.4271 - val_loss: 1.0938 - val_acc: 0.3542 Epoch 17/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0671 - acc: 0.4938 Epoch 17: val_loss improved from 1.09379 to 1.09286, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 59ms/step - loss: 1.0652 - acc: 0.4896 - val_loss: 1.0929 - val_acc: 0.3542 Epoch 18/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0492 - acc: 0.5437 Epoch 18: val_loss did not improve from 1.09286 6/6 [==============================] - 0s 48ms/step - loss: 1.0596 - acc: 0.5260 - val_loss: 1.0966 - val_acc: 0.3125 Epoch 19/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0566 - acc: 0.4750 Epoch 19: val_loss improved from 1.09286 to 1.09043, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 61ms/step - loss: 1.0632 - acc: 0.4583 - val_loss: 1.0904 - val_acc: 0.3542 Epoch 20/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0548 - acc: 0.4250 Epoch 20: val_loss improved from 1.09043 to 1.08046, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 59ms/step - loss: 1.0507 - acc: 0.4375 - val_loss: 1.0805 - val_acc: 0.5000 Epoch 21/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0554 - acc: 0.4750 Epoch 21: val_loss did not improve from 1.08046 6/6 [==============================] - 0s 49ms/step - loss: 1.0526 - acc: 0.5000 - val_loss: 1.0805 - val_acc: 0.4583 Epoch 22/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0366 - acc: 0.4812 Epoch 22: val_loss did not improve from 1.08046 6/6 [==============================] - 0s 51ms/step - loss: 1.0377 - acc: 0.4740 - val_loss: 1.0906 - val_acc: 0.3333 Epoch 23/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0301 - acc: 0.4938 Epoch 23: val_loss did not improve from 1.08046 6/6 [==============================] - 0s 50ms/step - loss: 1.0333 - acc: 0.4844 - val_loss: 1.0909 - val_acc: 0.3125 Epoch 24/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0497 - acc: 0.4187 Epoch 24: val_loss improved from 1.08046 to 1.07602, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 59ms/step - loss: 1.0303 - acc: 0.4583 - val_loss: 1.0760 - val_acc: 0.4375 Epoch 25/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0150 - acc: 0.5500 Epoch 25: val_loss improved from 1.07602 to 1.07305, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 60ms/step - loss: 1.0181 - acc: 0.5521 - val_loss: 1.0730 - val_acc: 0.4583 Epoch 26/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0295 - acc: 0.5188 Epoch 26: val_loss improved from 1.07305 to 1.06345, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 59ms/step - loss: 1.0262 - acc: 0.5365 - val_loss: 1.0634 - val_acc: 0.4792 Epoch 27/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0204 - acc: 0.4812 Epoch 27: val_loss improved from 1.06345 to 1.06251, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 60ms/step - loss: 1.0063 - acc: 0.5104 - val_loss: 1.0625 - val_acc: 0.4583 Epoch 28/30 5/6 [========================>.....] - ETA: 0s - loss: 0.9853 - acc: 0.5312 Epoch 28: val_loss did not improve from 1.06251 6/6 [==============================] - 0s 50ms/step - loss: 1.0123 - acc: 0.4948 - val_loss: 1.0793 - val_acc: 0.3333 Epoch 29/30 5/6 [========================>.....] - ETA: 0s - loss: 1.0046 - acc: 0.5375 Epoch 29: val_loss improved from 1.06251 to 1.04789, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 59ms/step - loss: 1.0052 - acc: 0.5365 - val_loss: 1.0479 - val_acc: 0.5000 Epoch 30/30 5/6 [========================>.....] - ETA: 0s - loss: 0.9924 - acc: 0.5375 Epoch 30: val_loss improved from 1.04789 to 1.04737, saving model to ./model\CNN_Cooking.hdf5 6/6 [==============================] - 0s 63ms/step - loss: 0.9875 - acc: 0.5260 - val_loss: 1.0474 - val_acc: 0.4792
5. 모델 평가¶
In [ ]:
score = model.evaluate(X_test, y_test, verbose=1)
print('정답률 =', score[1], 'loss =', score[0])
2/2 [==============================] - 0s 12ms/step - loss: 1.0091 - acc: 0.5500 정답률 = 0.550000011920929 loss = 1.0090534687042236
학습 과정 시각화¶
In [ ]:
history.history.keys()
Out[ ]:
dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])
In [ ]:
# 학습 상태를 그래프로 그리기
import matplotlib.pyplot as plt
his_dict = history.history
loss = his_dict['loss']
val_loss = his_dict['val_loss']
epochs = range(1, len(loss) + 1)
fig = plt.figure(figsize = (10, 5))
# 훈련 및 검증 손실 그리기
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(epochs, loss, color = 'blue', label = 'train_loss')
ax1.plot(epochs, val_loss, color = 'orange', label = 'val_loss')
ax1.set_title('train and val loss')
ax1.set_xlabel('epochs')
ax1.set_ylabel('loss')
ax1.legend()
acc = his_dict['acc']
val_acc = his_dict['val_acc']
# 훈련 및 검증 정확도 그리기
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(epochs, acc, color = 'blue', label = 'train_acc')
ax2.plot(epochs, val_acc, color = 'orange', label = 'val_acc')
ax2.set_title('train and val acc')
ax2.set_xlabel('epochs')
ax2.set_ylabel('acc')
ax2.legend()
plt.show()
6. 이미지 증식 하기¶
- 정답률을 높이기 위해서 데이타 수량을 늘려서 진행
- 사진을 회전하거나 반전시켜서 데이타 수량을 늘리기
OpenCV를 사용하여 이미지를 0~180도까지 회전¶
- opencv-python 모듈 설치 : pip install opencv-python
In [ ]:
import matplotlib.pyplot as plt
import cv2
# 사진 데이터 읽어 들이기
photos = np.load('image/photos.npz')
x = photos['x']
img = x[250] # 사진 하나만 선택하기
plt.figure(figsize=(10,10))
for i in range(36):
plt.subplot(6,6,i+1)
# 회전 시키기
center = (16, 16) # 회전 중심
angle = i * 5 # 각도 조정
scale = 1.0 # 확대 비율
mtx = cv2.getRotationMatrix2D(center, angle, scale)
img2 = cv2.warpAffine(img, mtx, (32, 32))
# 화면에 출력하기
plt.imshow(img2)
plt.show()
7. 이미지 증식 후 CNN 모델 학습¶
In [ ]:
import cnn_model
import tensorflow.keras
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
import cv2
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
import os
# 입력과 출력 지정하기
im_rows = 32 # 이미지의 높이
im_cols = 32 # 이미지의 너비
im_color = 3 # 이미지의 색공간
in_shape = (im_rows, im_cols, im_color)
nb_classes = 3
# 사진 데이터 읽어 들이기
photos = np.load('image/photos.npz')
X = photos['x']
y = photos['y']
# X 속성 데이터를 3차원 배열로 변환, 정규화하기
X = x.reshape(-1, im_rows, im_cols, im_color)
X = x.astype('float32') / 255
# 레이블 데이터를 One-hot 형식으로 변환하기
y = to_categorical(y.astype('int32'), nb_classes)
# 학습 전용과 테스트 전용으로 구분하기
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.2,
random_state=0)
# 검증 데이타 만들기
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train,
test_size=0.2,
random_state=0)
# 이미지 증식 - 학습 데이타만 적용(주의-테스트 데이타 증식 하지 않는다.)
x_new = []
y_new = []
for i, xi in enumerate(X_train):
yi = y_train[i]
for ang in range(-30, 30, 5):
# 회전 시키기
center = (16, 16) # 회전 중심
mtx = cv2.getRotationMatrix2D(center, ang, 1.0)
xi2 = cv2.warpAffine(xi, mtx, (32, 32))
x_new.append(xi2)
y_new.append(yi)
# 좌우 반전
xi3 = cv2.flip(xi2, 1)
x_new.append(xi3)
y_new.append(yi)
# 이미지를 늘린 데이터를 학습 데이터로 사용하기
print('수량을 늘리기 전=', len(y_train))
X_train = np.array(x_new)
y_train = np.array(y_new)
print('수량을 늘린 후=', len(y_train))
print('-'*70)
수량을 늘리기 전= 192 수량을 늘린 후= 4608 ----------------------------------------------------------------------
In [ ]:
# CNN 모델 만들기
model = cnn_model.get_model(in_shape, nb_classes)
# ModelCheckpoint, EarlyStopping 지정
# 딥러닝 모델을 저장할 디렉토리(폴더) 생성
MODEL_DIR = './model/'
if not os.path.exists(MODEL_DIR):
os.mkdir(MODEL_DIR)
modelpath="./model/CNN_Cooking2.hdf5"
checkpointer = ModelCheckpoint(filepath=modelpath, monitor='val_loss', verbose=1, save_best_only=True)
early_stopping_callback = EarlyStopping(monitor='val_loss', patience=10)
# 모델 학습
history = model.fit(X_train, y_train,
batch_size=32,
epochs=30,
verbose=1,
validation_data=(X_val, y_val),
callbacks=[early_stopping_callback, checkpointer])
Epoch 1/30 143/144 [============================>.] - ETA: 0s - loss: 1.0878 - acc: 0.3805 Epoch 1: val_loss improved from inf to 1.10275, saving model to ./model\CNN_Cooking2.hdf5 144/144 [==============================] - 7s 43ms/step - loss: 1.0875 - acc: 0.3813 - val_loss: 1.1028 - val_acc: 0.3333 Epoch 2/30 144/144 [==============================] - ETA: 0s - loss: 0.9641 - acc: 0.5263 Epoch 2: val_loss improved from 1.10275 to 1.04126, saving model to ./model\CNN_Cooking2.hdf5 144/144 [==============================] - 6s 42ms/step - loss: 0.9641 - acc: 0.5263 - val_loss: 1.0413 - val_acc: 0.5417 Epoch 3/30 143/144 [============================>.] - ETA: 0s - loss: 0.8402 - acc: 0.6071 Epoch 3: val_loss did not improve from 1.04126 144/144 [==============================] - 6s 42ms/step - loss: 0.8392 - acc: 0.6076 - val_loss: 1.1161 - val_acc: 0.4792 Epoch 4/30 143/144 [============================>.] - ETA: 0s - loss: 0.7678 - acc: 0.6525 Epoch 4: val_loss did not improve from 1.04126 144/144 [==============================] - 6s 42ms/step - loss: 0.7671 - acc: 0.6532 - val_loss: 1.1064 - val_acc: 0.5000 Epoch 5/30 143/144 [============================>.] - ETA: 0s - loss: 0.6868 - acc: 0.6984 Epoch 5: val_loss did not improve from 1.04126 144/144 [==============================] - 6s 41ms/step - loss: 0.6851 - acc: 0.6994 - val_loss: 1.0656 - val_acc: 0.5000 Epoch 6/30 143/144 [============================>.] - ETA: 0s - loss: 0.5900 - acc: 0.7498 Epoch 6: val_loss did not improve from 1.04126 144/144 [==============================] - 6s 42ms/step - loss: 0.5896 - acc: 0.7498 - val_loss: 1.1462 - val_acc: 0.5417 Epoch 7/30 143/144 [============================>.] - ETA: 0s - loss: 0.5163 - acc: 0.7793 Epoch 7: val_loss did not improve from 1.04126 144/144 [==============================] - 6s 42ms/step - loss: 0.5164 - acc: 0.7795 - val_loss: 1.2973 - val_acc: 0.5417 Epoch 8/30 143/144 [============================>.] - ETA: 0s - loss: 0.4758 - acc: 0.8005 Epoch 8: val_loss did not improve from 1.04126 144/144 [==============================] - 5s 36ms/step - loss: 0.4750 - acc: 0.8008 - val_loss: 1.3910 - val_acc: 0.6250 Epoch 9/30 143/144 [============================>.] - ETA: 0s - loss: 0.4042 - acc: 0.8381 Epoch 9: val_loss did not improve from 1.04126 144/144 [==============================] - 5s 36ms/step - loss: 0.4036 - acc: 0.8385 - val_loss: 1.4131 - val_acc: 0.5833 Epoch 10/30 143/144 [============================>.] - ETA: 0s - loss: 0.3602 - acc: 0.8553 Epoch 10: val_loss did not improve from 1.04126 144/144 [==============================] - 5s 34ms/step - loss: 0.3604 - acc: 0.8553 - val_loss: 1.4942 - val_acc: 0.5833 Epoch 11/30 143/144 [============================>.] - ETA: 0s - loss: 0.3378 - acc: 0.8580 Epoch 11: val_loss did not improve from 1.04126 144/144 [==============================] - 5s 34ms/step - loss: 0.3377 - acc: 0.8579 - val_loss: 1.6179 - val_acc: 0.6250 Epoch 12/30 143/144 [============================>.] - ETA: 0s - loss: 0.3112 - acc: 0.8811 Epoch 12: val_loss did not improve from 1.04126 144/144 [==============================] - 5s 34ms/step - loss: 0.3108 - acc: 0.8815 - val_loss: 1.6454 - val_acc: 0.5833
이미지를 증식한 후 학습한 모델 평가하기¶
In [ ]:
# 모델 평가하기
score = model.evaluate(X_test, y_test, verbose=1)
print('정답률 =', score[1], 'loss =', score[0])
2/2 [==============================] - 0s 11ms/step - loss: 1.2103 - acc: 0.5833 정답률 = 0.5833333134651184 loss = 1.2102775573730469
학습 과정 시각화¶
In [ ]:
# 학습 과정 시각화
import matplotlib.pyplot as plt
his_dict = history.history
loss = his_dict['loss']
val_loss = his_dict['val_loss']
epochs = range(1, len(loss) + 1)
fig = plt.figure(figsize = (10, 5))
# 학습 및 데트스 손실(loss) 그리기
ax1 = fig.add_subplot(1, 2, 1)
ax1.plot(epochs, loss, color = 'blue', label = 'train_loss')
ax1.plot(epochs, val_loss, color = 'orange', label = 'val_loss')
ax1.set_title('train and val loss')
ax1.set_xlabel('epochs')
ax1.set_ylabel('loss')
ax1.legend()
acc = his_dict['acc']
val_acc = his_dict['val_acc']
# 학습 및 데이타 정확도(accuracy) 그리기
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(epochs, acc, color = 'blue', label = 'train_acc')
ax2.plot(epochs, val_acc, color = 'orange', label = 'val_acc')
ax2.set_title('train and val acc')
ax2.set_xlabel('epochs')
ax2.set_ylabel('acc')
ax2.legend()
plt.show()
모델과 학습된 가중치 데이타 저장하기¶
- CNN 모델 저장 => photos-cnn.h5
- 학습된 데이타 저장 => photos-cnn-weight.hdf5
In [ ]:
# 모델 저장
model.save('./model/photos-cnn-model.h5')
In [ ]:
# 학습된 가중치 데이타 저장
model.save_weights('./model/photos-cnn-weight.hdf5')
In [ ]:
import os
os.getcwd()
Out[ ]:
'c:\\python\\딥러닝 실무 프로젝트\\1. CNN_요리이미지분류'
In [ ]:
import cv2
import numpy as np
import tensorflow.keras
from tensorflow.keras.models import load_model
import matplotlib.pyplot as plt
im_rows = 32
im_cols = 32
in_shape = (im_rows, im_cols, 3)
# 레이블 정보 - 3개 레이블 클래스
labels = ["피자", "스파게티", "스시"]
# CNN 모델 읽기
model = load_model('./model/photos-cnn-model.h5')
# 학습된 데이타 불러오기
model.load_weights('./model/photos-cnn-weight.hdf5')
# OpenCV를 사용해서 이미지 읽어 들이기
im = cv2.imread('./img/test-pizza.jpg')
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
im = cv2.resize(im, (32, 32))
plt.imshow(im) # 이미지 출력하기
plt.show()
In [ ]:
# 학습한 이미지 데이터에 형태 맞추기
im = im.reshape(in_shape).astype('float32') / 255
# 예측하기
r = model.predict(np.array([im]), batch_size=32,verbose=1)
res = r[0]
# 결과 출력하기
for i, acc in enumerate(res):
print(labels[i], "=", round(float(acc * 100), 4))
print("---"*12)
idx = res.argmax()
per = float(round(res[idx] * 100, 4))
print("예측한 결과 = index :", idx, "\b,", str(round(per,4)) + "%")
1/1 [==============================] - 0s 87ms/step 피자 = 99.1389 스파게티 = 0.6162 스시 = 0.2449 ------------------------------------ 예측한 결과 = index : 0, 99.1389%
분류된 음식에 칼로리 출력하기¶
In [ ]:
import cnn_model
import tensorflow.keras
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from tensorflow.keras.models import load_model
# target_image = "test-sushi.jpg"
im_rows = 32 # 이미지의 높이
im_cols = 32 # 이미지의 너비
im_color = 3 # 이미지의 색공간
in_shape = (im_rows, im_cols, im_color)
nb_classes = 3
LABELS = ["피자", "스파게티", "스시"] # 레이블의 음식 이름
CALORIES = [266, 157, 228] # 각 레이블별 칼로리
# 저장한 CNN 모델 읽어 들이기
model = load_model('./model/photos-cnn-model.h5')
model.load_weights('./model/photos-cnn-weight.hdf5')
def check_photo(path):
# 이미지 읽어 들이기
img = Image.open(path)
img = img.convert("RGB") # 색공간 변환하기
img = img.resize((im_cols, im_rows)) # 크기 변경하기
plt.imshow(img)
plt.show()
# 3차원으로 데이터 변환하기 + 정규화
x = np.asarray(img)
x = x.reshape(-1, im_rows, im_cols, im_color)
x = x / 255
# 예측하기
pre = model.predict([x])[0]
# print(pre)
idx = pre.argmax()
per = int(pre[idx] * 100)
return (idx, per)
def check_photo_str(path):
idx, per = check_photo(path)
# 응답하기
print("이 사진은", LABELS[idx], "\b로(으로), 칼로리는", CALORIES[idx],"kcal입니다.")
print("정확도는", per, "%")
# 메인 프로그램
if __name__ == '__main__':
check_photo_str('./img/test-pizza.jpg')
# check_photo_str('./test-spaghetti.jpg')
# check_photo_str('./test-sushi.jpg')
1/1 [==============================] - 0s 89ms/step 이 사진은 피자로(으로), 칼로리는 266 kcal입니다. 정확도는 99 %
예측 결과를 html 파일로 저장하기 --> predict-result.html¶
In [ ]:
import cnn_model
import tensorflow.keras
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from tensorflow.keras.models import load_model
# target_image = "test-pizza.jpg"
im_rows = 32 # 이미지의 높이
im_cols = 32 # 이미지의 너비
im_color = 3 # 이미지의 색공간
in_shape = (im_rows, im_cols, im_color)
nb_classes = 3
LABELS = ["피자", "스파게티", "스시"] # 레이블의 음식 이름
CALORIES = [266, 157, 228] # 각 레이블별 칼로리
# 저장한 CNN 모델 읽어 들이기
# model = cnn_model.get_model(in_shape, nb_classes)
# CNN 모델 읽기
model = load_model('./model/photos-cnn-model.h5')
# 학습된 데이타 불러오기
model.load_weights('./model/photos-cnn-weight.hdf5')
# M학습한 이미지 데이터에 형태 맞추기
# im = im.reshape(in_shape).astype('float32') / 255
def check_photo(path):
# 이미지 읽어 들이기
img = Image.open(path)
img = img.convert("RGB") # 색공간 변환하기
img = img.resize((im_cols, im_rows)) # 크기 변경하기
plt.imshow(img)
plt.show()
# 데이터 변환하기
x = np.asarray(img)
x = x.reshape(-1, im_rows, im_cols, im_color)
x = x / 255
# 예측하기
pre = model.predict([x])[0]
idx = pre.argmax()
per = round(float(pre[idx] * 100),3)
return (idx, per)
result = [] # 여러개의 이미지 파일 예측결과를 리스트로 저장하기
def check_photo_str(path):
idx, per = check_photo(path)
# 응답하기
print("이 사진은", LABELS[idx], "로(으로), 칼로리는", CALORIES[idx],"kcal입니다.")
print("예측 정확도는", per, "%")
# HTML로 저장하기------------------------------
# 딥러닝 예측 결과
srcfile = path
html = ""
html = """<html><head><meta charset="utf8"></head>
<body><h3>원본 이미지</h3><p>
<img src='{0}' width=400></p>{1}""".format(path, html)
res = "이 사진은 " + LABELS[idx] + "로(으로), 칼로리는" + str(CALORIES[idx]) +"kcal입니다."
res = res + "<p>예측 정확도는" + str(per) + "% 입니다.</p><br>"
s = '<div style="float:center;"><p><h3>'+ "<딥러닝 결과> "+'</p></h3><h3>' + res + '</h3>'+ '</div>'
html += s
end = "</body></html>"
html += end
print('-'*100)
print(html)
print('-'*100)
result.append(html)
# html을 파일로 저장하기
with open("./predict-result.html", "w", encoding="utf-8") as f:
for item in result :
f.writelines(item)
print("성공")
if __name__ == '__main__':
check_photo_str('./img/test-pizza.jpg')
# check_photo_str('test-spaghetti.jpg')
# check_photo_str('test-sushi.jpg')
1/1 [==============================] - 0s 88ms/step
이 사진은 피자 로(으로), 칼로리는 266 kcal입니다.
예측 정확도는 99.155 %
----------------------------------------------------------------------------------------------------
<html><head><meta charset="utf8"></head>
<body><h3>원본 이미지</h3><p>
<img src='./img/test-pizza.jpg' width=400></p><div style="float:center;"><p><h3><딥러닝 결과> </p></h3><h3>이 사진은 피자로(으로), 칼로리는266kcal입니다.<p>예측 정확도는99.155% 입니다.</p><br></h3></div></body></html>
----------------------------------------------------------------------------------------------------
성공
In [ ]:
In [ ]:
In [ ]:
flickrapi.FlickrAPI
- flickrapi 라이브러리는 Flickr API와 상호작용하기 위한 라이브러리입니다. 이를 사용하면 Flickr의 이미지를 검색하고, 해당 이미지를 다운로드하거나 메타데이터를 가져올 수 있습니다.
- FlickrAPI 클래스는 Flickr와 연결하는 데 필요한 API 키를 사용하여 Flickr API에 접근할 수 있게 해 줍니다.
2. urllib.request.urlretrieve
- urllib.request 모듈의 urlretrieve 함수는 URL을 통해 데이터를 다운로드할 수 있게 해 줍니다. 이 함수는 주로 파일을 직접 다운로드하는 데 사용됩니다.
- 예를 들어, 이미지 URL을 사용하여 이미지를 컴퓨터에 저장할 수 있습니다.
3. pprint
- pprint는 Pretty Print의 약자입니다. 파이썬의 pprint 모듈을 사용하면 데이터를 보기 좋게 출력할 수 있습니다. 리스트나 딕셔너리 같은 복잡한 자료 구조를 읽기 쉽게 정렬해 보여줍니다.
4. os, time, sys
- os: 운영체제와 상호작용할 수 있는 모듈입니다. 예를 들어, 파일이나 디렉토리 경로를 처리하거나 시스템 관련 작업을 수행할 때 사용됩니다.
- time: 시간과 관련된 기능을 제공하는 모듈입니다. 특정 시간 동안 지연시키거나 현재 시간을 측정하는 등의 작업을 할 수 있습니다.
- sys: 파이썬 인터프리터와 관련된 정보를 제공하는 모듈입니다. 예를 들어, 명령행 인수에 접근하거나, 시스템 종료와 같은 작업을 수행할 수 있습니다.