CNN - 检测手写笑脸：ValueError：无法将输入数组从形状 (26,26,3) 广播到形状 (26)

Question

In my root folder images I have three folders called 0 , 1 , 2 . In folder 0 there are no smilies . In folder 1 there are happy handwritten smilies and in folder 2 there are sad handwritten smilies . The images are jpg color images with the dimension 26x26 .

This is my code

from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten
import numpy as np
import os
import cv2
from sklearn.model_selection import train_test_split

def getImages(path, classes):
    folder = os.listdir(path)
    classes_counter = 0
    images = []
    images_classes = []

    for x in range (0,len(folder)):
        myPicList = os.listdir(path+"/"+ str(classes[classes_counter]))
        for pic in myPicList:
            img_path = path+"/" + str(classes[classes_counter]) + "/" + pic
            img = cv2.imread(img_path)
            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            images.append(img)
            images_classes.append(classes_counter)
        classes_counter +=1

    images = np.array(images, dtype="float") / 255
    return images, images_classes

def createModel(classes, images_dimension):
    classes_amount = len(np.unique(classes))
    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu', input_shape=images_dimension))
    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))
    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))
    model.add(Conv2D(64, kernel_size=(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(classes_amount, activation='softmax'))

    return model


labels = [0,1,2]
images, images_classes = getImages('training-images', labels)
images_dimension=(26,26,3)

X_train, X_test, Y_train, Y_test = train_test_split(images, images_classes, test_size=0.2)  # if 1000 images split will 200 for testing
X_train, X_validation, Y_train, Y_validation = train_test_split(X_train, Y_train, test_size=0.2) # if 1000 images 20% of remaining 800 will be 160 for validation

model = createModel(labels, images_dimension)
batch_size = 20
epochs = 100
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, Y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(X_validation, Y_validation))
model.evaluate(X_test,Y_test,verbose=0)

In the Line images = np.array(images, dtype="float") / 255 I get the error:

Traceback (most recent call last):
  File "train-nn.py", line 54, in <module>
    images, images_classes = getImages('training-images', labels)
  File "train-nn.py", line 24, in getImages
    images = np.array(images, dtype="float") / 255
ValueError: could not broadcast input array from shape (26,26) into shape (26)

I think something is wrong with the datastructure or with the array structure. I have no clue what I did wrong. Maybe someone known this problem and can give me a hint!

Here you can download the whole project as zip file. http://fileshare.mynotiz.de/cnn-handwritten-smilies.zip

Answer 1

I found the problem. One of my test-data images was not in the format of 26x26 it was 26x23.