外包 | LBP/HOG/CNN 实现对 CK/jaffe/fer2013 人脸表情数据集分类
文章目录
- 外包 | LBP/HOG/CNN 实现对 CK/jaffe/fer2013 人脸表情数据集分类
- 1. Data
- 2. Code
* - 2-1. LBP and HOG
– - 2-2. CNN
– -
Data
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三个数据集已经经过处理, 大小 resize 为 (48, 48), 并且分好了训练集和测试集, 有需要自取噢
; 2. Code
2-1. LBP and HOG
a. 读取数据集
def readData(dataName, label2id):
X, Y = [], []
path = f'./{dataName}/train'
for label in os.listdir(path):
for image in os.listdir(os.path.join(path, label)):
img = cv2.imread(os.path.join(path, label, image), cv2.IMREAD_GRAYSCALE)
img = img / 255.0
X.append(img)
Y.append(label2id[label])
return X, Y
b. LBP
LBP算法内容主要参考知乎, 代码重新写了如下:
def lbpSingle(img):
h, w = img.shape
temp_1 = np.zeros(img.shape)
for i in range(1, h - 1):
for j in range(1, w - 1):
temp_2 = (img[i - 1:i + 2, j - 1:j + 2] > img[i][j]).astype(np.int8)
temp_2 = temp_2.reshape(9)
temp_2 = np.delete(temp_2, 4)
temp_2[3], temp_2[4] = temp_2[4], temp_2[3]
temp_2[5], temp_2[7] = temp_2[7], temp_2[5]
temp_2 = ''.join('%s' % i for i in temp_2)
temp_1[i][j] = int(temp_2, 2)
return [temp_1.flatten()]
def lbpBatch(imgs):
imgsFeatureList = []
for img in imgs:
imgsFeatureList.append(lbpSingle(img)[0])
return imgsFeatureList
c. HOG
HOG算法直接调用sklearn
def hogSingle(img):
feature, _ = hog(img, orientations=9, pixels_per_cell=(6, 6), cells_per_block=(6, 6),
block_norm='L2-Hys', visualize=True)
return [feature]
def hogBatch(imgs):
featureList = []
for img in imgs:
feature, _ = hog(img, orientations=9, pixels_per_cell=(6, 6), cells_per_block=(6, 6),
block_norm='L2-Hys', visualize=True)
featureList.append(feature)
return featureList
d. 分类模型
def prepareModel(name):
if name == 'svm':
m = sklearn.svm.SVC(C=2, kernel='rbf', gamma=10, decision_function_shape='ovr')
elif name == 'knn':
m = KNeighborsClassifier(n_neighbors=1)
elif name == 'dt':
m = DecisionTreeClassifier()
elif name == 'nb':
m = GaussianNB()
elif name == 'lg':
m = LogisticRegression()
else:
m = RandomForestClassifier(n_estimators=180, random_state=0)
return m
2-2. CNN
a. 准备数据集
whichDataSet = 'CK'
trainDir = f'./{whichDataSet}/train/'
trainingDataGenerator = ImageDataGenerator(
rescale=1. / 255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
validation_split=0.25,
horizontal_flip=True,
fill_mode='nearest'
)
trainGenerator = trainingDataGenerator.flow_from_directory(
trainDir, subset='training', target_size=(48, 48), class_mode='categorical'
)
validGenerator = trainingDataGenerator.flow_from_directory(
trainDir, subset='validation', target_size=(48, 48), class_mode='categorical'
)
b. 准备模型
这里用的是三层 CNN:
model = tf.keras.models.Sequential([
Conv2D(16, (5, 5), activation='relu', input_shape=(48, 48, 3), padding='same'),
MaxPooling2D(2, 2),
Conv2D(32, (5, 5), activation='relu', padding='same'),
MaxPooling2D(2, 2),
Conv2D(32, (5, 5), activation='relu', padding='same'),
MaxPooling2D(2, 2),
Flatten(),
Dense(128, activation='relu'),
Dropout(0.5),
Dense(7, activation='softmax')
])
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])
c. 准备测试
读取test文件夹下没有标签的图片, 然后批量传进训练好的模型, 预测后将结果输出到一个csv文件
model = load_model(f'./{whichDataSet}/{whichDataSet}.h5')
with open(f'./{whichDataSet}/class2id.pkl', 'rb') as f:
class2id = pickle.load(f)
f.close()
fileList = os.listdir(f'./{whichDataSet}/test/')
testSet = []
for file in fileList:
img = image.load_img(f'./{whichDataSet}/test/{file}', target_size=(48, 48))
testSet.append(image.img_to_array(img))
testSet = np.array(testSet)
modelOutput = model.predict(testSet, batch_size=10)
getLabel = np.argmax(modelOutput, axis=1).flatten()
df = pd.DataFrame(columns=['file', 'label'])
for i in range(len(getLabel)):
df.append({
'file': fileList[i], 'label': class2id[getLabel[i]]
}, ignore_index=True)
df.to_csv(f'./{whichDataSet}/result.csv')
完整代码
完整代码在gitee上,点击跳转, 有需要自取, 欢迎⭐️star⭐️!
由于这个代码只是帮别人做的一个小毕设, 所以关于里面的 HOG和LBP 算法都没有深入了解, 因此这篇blog只是记录一下, 顺便分享一下数据集和代码~
如果数据集下载不了的欢迎底下留学邮箱地址, 看到后我会通过邮件发送给你
Original: https://blog.csdn.net/JackyAce6880/article/details/124451551
Author: Caffiny
Title: 外包 | LBP/HOG/CNN 实现对 CK/jaffe/fer2013 人脸表情数据集分类
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