如何将灰度图像输入到预训练的神经网络模型中?

Question

这里没有。我一直在研究有丝分裂分类的灰度图像这是我正在研究的一个样本图像。我使用了VGGnet来实现这一点。我对如何将我的灰度图像输入到神经网络中有些疑问。我已经阅读了VGGnet关于在Imagenet上进行彩色图像训练的文档。

我使用cv2.imread()并通过压缩到一个数组来读取图像。我发现它的形状是(227,227,3)。当我处理灰度图像时，它不应该是(227,227,1)吗?模型的准确率也只有50%。我想知道是否数据集本身有问题，或者VGGnet不适合这个目的。或者我应该使用其他方法读取这些图像以获得灰度图像?

我已经尝试了类似问题中列出的解决方案。我看图片的方式对吗?

我在这里分享我的代码。

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Display Image data 
from PIL import Image
import cv2
from google.colab import drive
drive.mount('/content/drive')
mitotic_image = Image.open('/content/drive/MyDrive/Medical/actualmito1/trainactmito01.jpg')
nonmitotic_image=Image.open('/content/drive/MyDrive/Medical/actualnonmito1/trainactnonmito01.jpg')
# subplotting image data
fig = plt.figure(figsize=(15,9))
ax1 = fig.add_subplot(1, 2, 1)
img_plot = plt.imshow(nonmitotic_image, cmap = plt.cm.bone)
ax1.set_title("Non-Mitotic Image")
ax2 = fig.add_subplot(1, 2, 2)
img_plot = plt.imshow(mitotic_image, cmap = plt.cm.bone)
ax2.set_title("Mitotic Image")
plt.show()
import os
yes = os.listdir("/content/drive/MyDrive/Medical/actualmito1")
no = os.listdir("/content/drive/MyDrive/Medical/actualnonmito1")
data = np.concatenate([yes, no])
target_yes = np.full(len(yes), 1)
target_no = np.full(len(no), 0)
# Image Target
data_target = np.concatenate([target_yes, target_no])
# Generate Image Data
img = cv2.imread("/content/drive/MyDrive/Medical/actualmito1/trainactmito01.jpg")
mitosis = cv2.resize(img,(32,32))
plt.imshow(mitosis)
X_data = []
yes = os.listdir("/content/drive/MyDrive/Medical/actualmito1")
for file in yes:
img = cv2.imread("/content/drive/MyDrive/Medical/actualmito1/" + file)
# resizing image data to 32x32
img = cv2.resize(img, (224,224))
X_data.append(img) # This will store list of all image data in an array
no = os.listdir("/content/drive/MyDrive/Medical/actualnonmito1")
for file in no:
img = cv2.imread("/content/drive/MyDrive/Medical/actualnonmito1/" + file)
# resizing image data to 32x32
img = cv2.resize(img, (224,224))
X_data.append(img)        # This will store list of all image data in an array
X = np.squeeze(X_data)
X.shape
# Image Pixel Normalization
X = X.astype('float32')
X /= 255
X.shape
# Train & Test Data
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, data_target, test_size = 0.1, 
random_state = 3)
x_train2, x_val, y_train2, y_val = train_test_split(x_train, y_train, test_size = 0.15, 
random_state = 3)
# VGG16 - Transfer Learning
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Conv2D, GlobalAveragePooling2D, Flatten, ZeroPadding2D, 
Dropout, BatchNormalization
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.applications import VGG16
def build_model():
#use Imagenet = pre-trained models weights called knowledge transfer  
# image_shape = 32x32x3
vgg16_model = VGG16(weights = 'imagenet', include_top = False, input_shape=(224,224,3))
# Input Layer
model = Sequential()
# paadding = 'same' = ZeroPadding
model.add(Conv2D(filters=3, kernel_size=(3,3), padding='same', input_shape = (224,224,3)))
# add transfer learning model
model.add(vgg16_model)
# Average Pooling Layer
model.add(GlobalAveragePooling2D())
model.add(BatchNormalization())
model.add(Dropout(0.5))
# Fully Connected Layer
model.add(Dense(units = 512, activation='relu'))
model.add(Dropout(0.5))
# Output Layer
model.add(Dense(units = 1, activation='sigmoid'))
model.compile(optimizer = 'Adam', loss = 'binary_crossentropy', metrics = ['Accuracy'])
return model
model = build_model()
model.summary()
from tensorflow.keras import callbacks
filepath = "/content/drive/MyDrive/BestModelMRI3.hdf5"
checkpoint = callbacks.ModelCheckpoint(filepath, monitor = 'val_loss', save_best_only = True, 
mode = 'min',verbose = 1)

import datetime
import keras
import os
logdir = os.path.join("/content/drive/MyDrive/MRI_logs", 
datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
tensorboard_callback = keras.callbacks.TensorBoard(logdir)
history = model.fit(x_train2, y_train2, epochs = 200, batch_size = 32, shuffle = True, 
validation_data = (x_val, y_val), callbacks = [checkpoint, tensorboard_callback],verbose= 1)
model.load_weights("/content/drive/MyDrive/BestModelMRI3.hdf5")

model.evaluate(x_test, y_test)
predictions = model.predict(x_test)
yhat = np.round(predictions)
from sklearn.metrics import confusion_matrix, classification_report 
confusion_matrix(y_test, yhat)
sns.heatmap(confusion_matrix(y_test, yhat), annot = True, cmap = 'RdPu')
print(classification_report(y_test, yhat))

Answer 1

在RGB图像上训练VGG模型。CV2读取图像为BGR。所以添加代码

img=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

将BGR转换为RGB。此外，VGG要求像素在-1到+1的范围内，所以缩放

X=X/127.5 -1