基于深度学习的122306验证码识别研究.zip

import matplotlib matplotlib.use("Agg") from keras.callbacks import Callback, EarlyStopping, ReduceLROnPlateau, ModelCheckpoint, CSVLogger from keras.preprocessing.image import ImageDataGenerator from keras.optimizers import Adam from keras.preprocessing.image import img_to_array from sklearn.preprocessing import LabelBinarizer from sklearn.model_selection import train_test_split from smallervggnet import SmallerVGGNet import matplotlib.pyplot as plt from imutils import paths import numpy as np import argparse import random import pickle import cv2 import os import time # ap = argparse.ArgumentParser() # ap.add_argument("-d", "--dataset", required=True, # help="path to input dataset") # ap.add_argument("-m", "--model", required=True, # help="path to output model") # ap.add_argument("-l", "--labelbin", required=True, # help="path to output label binarizer") # ap.add_argument("-p", "--plot", required=True, # help="path to output accuracy/loss plot") # args = vars(ap.parse_args()) DATASET_PATH = os.path.join(os.getcwd(), "data", "dataset") MODEL_SAVE_PATH = os.path.join(os.getcwd(), "model", "12306_eachDir3000.model") MODEL_CACHE_DIR = os.path.join(os.getcwd(), "model", "save_cache") LABELBIN_SAVE = os.path.join(os.getcwd(), "label", "12306.label") LOSS_PLOT_PATH = "accuracy_and_loss" EPOCHS = 1000 INIT_LR = 1e-3 BS = 8 IMAGE_DIMS = (67, 67, 3) data = [] labels = [] print("[INFO] loading images...") imagePaths = sorted(list(paths.list_images(DATASET_PATH))) random.seed(42) random.shuffle(imagePaths) for imagePath in imagePaths: image = cv2.imdecode(np.fromfile(imagePath, dtype=np.uint8),cv2.IMREAD_COLOR) image = cv2.resize(image, (IMAGE_DIMS[1], IMAGE_DIMS[0])) image = img_to_array(image) data.append(image) label = imagePath.split(os.path.sep)[-2] labels.append(label) data = np.array(data, dtype="float") / 255.0 labels = np.array(labels) print("[INFO] data matrix: {:.2f}MB".format( data.nbytes / (1024 * 1000.0) )) lb = LabelBinarizer() labels = lb.fit_transform(labels) (trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.2, random_state=42) aug = ImageDataGenerator(rotation_range=25, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode="nearest") print("[INFO] compiling model...") model = SmallerVGGNet.build(width=IMAGE_DIMS[1], height=IMAGE_DIMS[0], depth=IMAGE_DIMS[2], classes=len(lb.classes_)) opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS) model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"]) model.summary() print("[INFO] training network...") model_cache_path = os.path.join(MODEL_CACHE_DIR,"model_cache.model") H = model.fit_generator( aug.flow(trainX, trainY, batch_size=BS), validation_data=(testX, testY), steps_per_epoch=len(trainX) // BS, epochs=EPOCHS, verbose=1, callbacks=[ ModelCheckpoint( model_cache_path, monitor='val_loss', save_best_only=True, verbose=1), # EarlyStopping(patience=50), ReduceLROnPlateau(patience=10), CSVLogger("training.log") ] ) print("[INFO] serializing network...") model.save(MODEL_SAVE_PATH) print("[INFO] serializing label binarizer...") with open(LABELBIN_SAVE, "wb") as f: f.write(pickle.dumps(lb)) plt.style.use("ggplot") plt.figure() N = EPOCHS plt.plot(np.arange(0, N), H.history['loss'], label='train_loss') plt.plot(np.arange(0, N), H.history['val_loss'], label='val_loss') plt.plot(np.arange(0, N), H.history['acc'], label='train_acc') plt.plot(np.arange(0, N), H.history['val_acc'], label='val_acc') plt.title("Training Loss and Accuracy") plt.xlabel("Epoch #") plt.ylabel("Loss/Accuracy") plt.legend(loc="upper left") plt.savefig(LOSS_PLOT_PATH)