图像调整大小时 Keras 图像分类预测误差

Question

我有一个经过训练的模型，该模型经过训练可以识别不同的文档，我从 http://www.cs.cmu.edu/~aharley/rvl-cdip/ 获取数据集。

以下是我构建模型的方式

import numpy as np
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import pickle

from keras.optimizers import SGD
from keras.models import Sequential, save_model
from keras.layers import Dense, Dropout, Flatten, Activation
from keras.layers.convolutional import Conv2D, MaxPooling2D

# Set image information
channels = 1
height = 1000
width = 754

model = Sequential()
# Add a Conv2D layer with 32 nodes to the model
model.add(Conv2D(32, (3, 3), input_shape=(1000, 754, 3)))
# Add the reLU activation function to the model
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('relu'))

model.compile(loss='categorical_crossentropy',  # sparse_categorical_crossentropy
              # Adam(lr=.0001) SGD variation with learning rate
              optimizer='adam',
              metrics=['accuracy'])

# Image data generator to import iamges from data folder
datagen = ImageDataGenerator()

# Flowing images from folders sorting by labels, and generates batches of images
train_it = datagen.flow_from_directory(
    "data/train/", batch_size=16, target_size=(height, width), shuffle=True, class_mode='categorical')
test_it = datagen.flow_from_directory(
    "data/test/", batch_size=16, target_size=(height, width), shuffle=True, class_mode='categorical')
val_it = datagen.flow_from_directory(
    "data/validate/", batch_size=16, target_size=(height, width), shuffle=True, class_mode='categorical')

history = model.fit(
    train_it,
    epochs=2,
    batch_size=16,
    validation_data=val_it,
    shuffle=True,
    steps_per_epoch=2000 // 16,
    validation_steps=800 // 16)


save_model(model, "./ComplexDocumentModel")
model.save("my_model", save_format='h5')

与上一行一样，我将模型保存为 h5 格式。

我现在正在尝试使用经过训练的模型对单个图像进行预测，并通过以下脚本查看它属于哪个类别。

from keras.models import load_model
import cv2
import numpy as np
import keras
from keras.preprocessing import image

model = load_model('my_model')

# First try
def prepare(file):
    img_array = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
    new_array = cv2.resize(img_array, (1000, 754))
    return new_array.reshape(3, 1000, 754, 1)


# Second try
img = image.load_img(
    "/home/user1/Desktop/Office/image-process/test/0000113760.tif")
img = image.img_to_array(img)
img = np.expand_dims(img, axis=-1)


prediction = model.predict(
    [prepare("/home/user1/Desktop/Office/image-process/test/0000113760.tif")])

print(prediction)

我尝试用两种方式预测图像，但都给出了错误

    ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 3 but received input with shape (None, 762, 3, 1)

我还尝试使用 PIL 打开图像并将其转换为 NumPy 数组，这是在 google 上找到的一种方法。不幸的是，我发现没有其他答案、博客或视频教程对我有帮助。

Answer 1

您正在尝试将灰度图像馈送到需要具有 3 个通道的图像的网络。您可以将最后一个通道堆叠 3 次以获得兼容的形状，但预测可能会很差：

def prepare(file):
    img_array = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
    new_array = cv2.resize(img_array, (1000, 754)) # shape is (1000,754)
    # converting to RGB
    array_color = cv2.cvtColor(new_array, cv2.COLOR_GRAY2RGB) # shape is (1000,754,3)
    array_with_batch_dim = np.expand_dims(array_color, axis=0) # shape is (1,1000,754,3)
    return array_with_batch_dim

另一种解决方案是在阅读时不将图像转换为灰度，方法是省略标志 cv2.IMREAD_GRAYSCALE。 opencv 的默认行为是加载具有 3 个通道的图像。

def prepare(file):
    img_array = cv2.imread(file)
    new_array = cv2.resize(img_array, (1000, 754)) # shape is (1000,754, 3)
    # converting to RGB
    array_with_batch_dim = np.expand_dims(new_array, axis=0) # shape is (1,1000,754,3)
    return array_with_batch_dim

注意：根据您的预处理，您可能需要通过将图像除以 255 将图像在 0 和 1 之间进行归一化，然后再将其输入网络。