from config import tiny_imagenet_config as config
from pyimagesearch.preprocessing import imagetoarraypreprocessor as ITA
from pyimagesearch.preprocessing import simplespreprocessor as SP
from pyimagesearch.preprocessing import meanpreprocessor as MP
from pyimagesearch.io import hdf5datasetgenerator as HDFG
from pyimagesearch.utils.ranked import rank5_accuracy
from keras.models import load_model
import json
# load the RGB means for the training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize the image preprocessors
sp = SP.SimplePreprocessor(64,64)
mp = MP.MeanPreprocessor(means['R'],means['G'],means['B'])
iap = ITA.ImageToArrayPreprocessor()
# initialize the testing dataset generator
testGen = HDFG.HDF5DatasetGenerator(config.TEST_HDF5, 64,preprocessors=[sp, mp, iap], classes=config.NUM_CLASSES)
# load the pre-trained network
print("[INFO] loading model...")
model = load_model(config.MODEL_PATH)
# make predictions on the testing data
print("[INFO] predicting on test data...")
predictions = model.predict_generator(testGen.generator(),steps=testGen.numImages // 64, max_queue_size=64 * 2)
# compute the rank-1 and rank-5 accuracies
(rank1,rank5) = rank5_accuracy(predictions,testGen.db['labels'])
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("-o", "--output", required=True, help="path to the output image files")
args = vars(ap.parse_args())
classLabels = ["cat", "dog", "panda"]
print("[INFO] sampling images...")
imagePaths = np.array(list(paths.list_images(args["dataset"])))
idxs = np.random.randint(0, len(imagePaths), size=(10,))
imagePaths = imagePaths[idxs]
sp = simplepreprocessor.SimplePreprocessor(32, 32)
iap = imagetoarraypreprocessor.ImageToArrayPreprocessor()
sdl = simpledatasetloader.SimpleDatasetLoader(preprocessors=[sp, iap])
(data, labels) = sdl.load(imagePaths)
data = data.astype("float") / 255.0
print("[INFO] loading pre-trained network...")
model = load_model(args["model"])
print("[INFO] predicting...")
preds = model.predict(data, batch_size=32).argmax(axis=1)
for (i, imagePath) in enumerate(imagePaths):
image = cv2.imread(imagePath)
cv2.putText(image, "Label: {}".format(classLabels[preds[i]]), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
cv2.imwrite("{}/image_{}.jpg".format(args["output"]), i), image)
from pyimagesearch.io import hdf5datasetgenerator as hdf5dg
from keras.preprocessing.image import ImageDataGenerator
from keras.models import load_model
import argparse
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-m",
"--model",
type=str,
help="path to model checkpoint to load")
args = vars(ap.parse_args())
# initialize the testing data generator and image preprocessor
testAug = ImageDataGenerator(rescale=1 / 255.0)
iap = imagetoap.ImageToArrayPreprocessor()
# initialize the testing dataset generator
testGen = hdf5dg.HDF5DatasetGenerator(config.TEST_HDF5,
config.BATCH_SIZE,
aug=testAug,
preprocessors=[iap],
classes=config.NUM_CLASSES)
# load the model from disk
print("[INFO] loading {}...".format(args["model"]))
model = load_model(args["model"])
# evaluate the network
(loss,
acc) = model.evaluate_generator(testGen.generator(),
from pyimagesearch.preprocessing import croppreprocessor as CP
from pyimagesearch.io import hdf5datasetgenerator as HDF
from pyimagesearch.utils.ranked import rank5_accuracy
from keras.models import load_model
import numpy as np
import progressbar
import json
# 加载RGB均值数据
means = json.loads(open(config.DATASET_MEAN).read())
# 初始化预处理
sp = SP.SimplePreprocessor(227, 227)
mp = MP.MeanPreprocessor(means['R'], means['G'], means['B'])
cp = CP.CropPreprocessor(227, 227)
iap = IAP.ImageToArrayPreprocessor()
# 加载训练好的模型
print("[INFO] loading model ...")
model = load_model(config.MODEL_PATH)
# 初始化测试数据集生成器，并进行预测
print("[INFO] predicting on test data (no crops)...")
testGen = HDF.HDF5DatasetGenerator(config.TEST_HDF5,
64,
preprocessors=[sp, mp, iap],
classes=2)
predictions = model.predict_generator(testGen.generator(),
steps=testGen.numImages // 64,
max_queue_size=64 * 2)
#计算rank-1和rank-5准确度
ap.add_argument("-s",
"--start-epoch",
type=int,
default=0,
help="epoch to restart training at")
args = vars(ap.parse_args())
# construct the training and testing image generators for data
# augmentation, then initialize the image preprocessor
trainAug = ImageDataGenerator(rotation_range=10,
zoom_range=0.1,
horizontal_flip=True,
rescale=1 / 255.0,
fill_mode="nearest")
valAug = ImageDataGenerator(rescale=1 / 255.0)
iap = image2arrayProc.ImageToArrayPreprocessor()
trainGen = hdf5DG.HDF5DatasetGenerator(config.TRAIN_HDF5,
config.BATCH_SIZE,
aug=trainAug,
preprocessors=[iap],
classes=config.NUM_CLASSES)
valGen = hdf5DG.HDF5DatasetGenerator(config.VAL_HDF5,
config.BATCH_SIZE,
aug=valAug,
preprocessors=[iap],
classes=config.NUM_CLASSES)
if args["model"] is None:
print("[INFO] compiling model...")
model = EVGG.EmotionVGGNet.build(width=48,
