import numpy as np
import argparse
import os
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True)
ap.add_argument("-m", "--model", required=True)
args = vars(ap.parse_args())
print("[INFO] Loading")
imagePaths = list(paths.list_images(args["dataset"]))
classNames = [pt.split(os.path.sep)[-2] for pt in imagePaths]
classNames = [str(x) for x in np.unique(classNames)]
aap = AspectAwarePreprocessor(64, 64)
iap = ImageToArrayPreprocessor()
sdl = SimpleDatasetLoader(preprocessors=[aap, iap])
(data, labels) = sdl.load(imagePaths, verbose=500)
data = data.astype("float") / 255.0
(trainX, testX, trainY, testY) = train_test_split(data,
labels,
test_size=0.25,
random_state=42)
trainY = LabelBinarizer().fit_transform(trainY)
testY = LabelBinarizer().fit_transform(testY)
aug = ImageDataGenerator(rotation_range=30,
width_shift_range=0.1,
le = pickle.loads(open(config.LABEL_ENCODER_PATH, "rb").read())
rows = open(config.TEST_MX_LIST).read().strip().split("\n")
rows = np.random.choice(rows, size=args["sample_size"])
print("[INFO] loading pre-trained model")
checkpointsPath = os.path.sep.join([args["checkpoints"], args["prefix"]])
model = mx.model.FeedForward.load(checkpointsPath, args["epoch"])
model = mx.model.FeedForward(ctx=[mx.gpu(0)],
symbol=model.symbol,
arg_params=model.arg_params,
aux_params=model.aux_params)
sp = AspectAwarePreprocessor(width=224, height=224)
mp = MeanPreprocessor(config.R_MEAN, config.G_MEAN, config.B_MEAN)
iap = ImageToArrayPreprocessor(dataFormat="channels_first")
for row in rows:
(target, imagePath) = row.split("\t")[1:]
target = int(target)
image = cv2.imread(imagePath)
orig = image.copy()
orig = imutils.resize(orig, width=min(500, orig.shape[1]))
image = iap.preprocess(mp.preprocess(sp.preprocess(image)))
image = np.expand_dims(image, axis=0)
preds = model.predict(image)[0]
idxs = np.argsort(preds)[::-1][:5]
print("[INFO] actual={}".format(le.inverse_transform(target)))
print("[INFO] loading models")
agePath = os.path.sep.join([deploy.AGE_NETWORK_PATH, deploy.AGE_PREFIX])
genderPath = os.path.sep.join([deploy.GENDER_NETWORK_PATH, deploy.GENDER_PREFIX])
ageModel = mx.model.FeedForward.load(agePath, deploy.AGE_EPOCH)
genderModel = mx.model.FeedForward.load(genderPath, deploy.GENDER_EPOCH)
print("[INFO] compiling models")
ageModel = mx.model.FeedForward(ctx = [mx.gpu(0)], symbol = ageModel.symbol, arg_params = ageModel.arg_params, aux_params = ageModel.aux_params)
genderModel = mx.model.FeedForward(ctx = [mx.gpu(0)], symbol = genderModel.symbol, arg_params = genderModel.arg_params, aux_params = genderModel.aux_params)
sp = SimplePreprocessor(width = 256, height = 256, inter = cv2.INTER_CUBIC)
cp = CropPreprocessor(width = 227, height = 227, horiz = True)
ageMP = MeanPreprocessor(ageMeans["R"], ageMeans["G"], ageMeans["B"])
genderMP = MeanPreprocessor(genderMeans["R"], genderMeans["G"], genderMeans["B"])
iap = ImageToArrayPreprocessor(dataFormat = "channels_first")
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(deploy.DLIB_LANDMARK_PATH)
fa = FaceAligner(predictor)
imagePaths = [args["image"]]
if os.path.isdir(args["image"]):
imagePaths = sorted(list(paths.list_files(args["image"])))
for imagePath in imagePaths:
print("[INFO] processing {}".format(imagePath))
image = cv2.imread(imagePath)
image = imutils.resize(image, width=800)
print("[INFO] compiling models")
ageModel = mx.model.FeedForward(ctx=[mx.gpu(0)],
symbol=ageModel.symbol,
arg_params=ageModel.arg_params,
aux_params=ageModel.aux_params)
genderModel = mx.model.FeedForward(ctx=[mx.gpu(0)],
symbol=genderModel.symbol,
arg_params=genderModel.arg_params,
aux_params=genderModel.aux_params)
sp = SimplePreprocessor(width=227, height=227, inter=cv2.INTER_CUBIC)
ageMP = MeanPreprocessor(ageMeans["R"], ageMeans["G"], ageMeans["B"])
genderMP = MeanPreprocessor(genderMeans["R"], genderMeans["G"],
genderMeans["B"])
iap = ImageToArrayPreprocessor()
rows = open(config.TEST_MX_LIST).read().strip().split("\n")
rows = np.random.choice(rows, size=args["sample_size"])
for row in rows:
(_, gtLabel, imagePath) = row.strip().split("\t")
image = cv2.imread(imagePath)
ageImage = iap.preprocess(ageMP.preprocess(sp.preprocess(image)))
genderImage = iap.preprocess(genderMP.preprocess(sp.preprocess(image)))
ageImage = np.expand_dims(ageImage, axis=0)
genderImage = np.expand_dims(genderImage, axis=0)
agePreds = ageModel.predict(ageImage)[0]