【Keras】完整实现‘交通标志’分类、‘票据’分类两个项目，让你掌握深度学习图像分类

# import the necessary packages

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

norm_size = 32def args_parse():# construct the argument parse and parse the arguments
    ap = argparse.ArgumentParser()
    ap.add_argument("-m", "--model", required=True,
        help="path to trained model model")
    ap.add_argument("-i", "--image", required=True,
        help="path to input image")
    ap.add_argument("-s", "--show", action="store_true",
        help="show predict image",default=False)
    args = vars(ap.parse_args())    
    return args    def predict(args):
    # load the trained convolutional neural network
    print("[INFO] loading network...")
    model = load_model(args["model"])    
    #load the image
    image = cv2.imread(args["image"])
    orig = image.copy()     
    # pre-process the image for classification
    image = cv2.resize(image, (norm_size, norm_size))
    image = image.astype("float") / 255.0
    image = img_to_array(image)
    image = np.expand_dims(image, axis=0)     
    # classify the input image
    result = model.predict(image)[0]    #print (result.shape)
    proba = np.max(result)
    label = str(np.where(result==proba)[0])
    label = "{}: {:.2f}%".format(label, proba * 100)
    print(label)    
    if args['show']:   
        # draw the label on the image
        output = imutils.resize(orig, width=400)
        cv2.putText(output, label, (10, 25),cv2.FONT_HERSHEY_SIMPLEX,            0.7, (0, 255, 0), 2)       
        # show the output image
        cv2.imshow("Output", output)
        cv2.waitKey(0)#python predict.py --model traffic_sign.model -i ../2.png -s

if __name__ == '__main__':
    args = args_parse()
    predict(args)

python predict.py --model traffic_sign.model -i ../2.png -s

def load_data2(path):
    print("[INFO] loading images...")
    data = []
    labels = []
    # grab the image paths and randomly shuffle them
    imagePaths = sorted(list(paths.list_images(path)))
    random.seed(42)
    random.shuffle(imagePaths)
    # loop over the input images
    for imagePath in imagePaths:
        # load the image, pre-process it, and store it in the data list
        image = cv2.imread(imagePath)
        image = cv2.resize(image, (norm_size, norm_size))
        image = img_to_array(image)        data.append(image)

        # extract the class label from the image path and update the
        # labels list
        label = int(imagePath.split(os.path.sep)[-2])       
        labels.append(label)  
        
    # scale the raw pixel intensities to the range [0, 1]    data = np.array(data, dtype="float") / 255.0
    labels = np.array(labels)


    # partition the data into training and testing splits using 75% of
    # the data for training and the remaining 25% for testing
    (trainX, testX, trainY, testY) = train_test_split(data,
            labels, test_size=0.25, random_state=42)

    # convert the labels from integers to vectors
    trainY = to_categorical(trainY, num_classes=CLASS_NUM)
    testY = to_categorical(testY, num_classes=CLASS_NUM)   
    return trainX,trainY,testX,testY

if __name__=='__main__':
    args = args_parse()
    file_path = args["dataset"]
    trainX,trainY,
testX,testY = load_data2(file_path)    # construct the image generator for data augmentation
    aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1,
        height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,
        horizontal_flip=True, fill_mode="nearest")
    train(aug,trainX,trainY,testX,testY,args)

python train.py --dataset ../../invoice_all/train  --model invoice.model