def main():
"""Compute rank1 and rank5 accuracies
"""
# load the RGB means for the training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize the image preprocessors
simple_preprocessor = SimplePreprocessor(64, 64)
mean_preprocessor = MeanPreprocessor(means["R"], means["G"], means["B"])
image_to_array_preprocessor = ImageToArrayPreprocessor()
# initialize the testing dataset generator
test_gen = HDF5DatasetGenerator(
config.TEST_HDF5,
64,
preprocessors=[simple_preprocessor, mean_preprocessor, image_to_array_preprocessor],
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(test_gen.generator(), steps=test_gen.num_images // 64, max_queue_size=10)
# compute the rank-1 and rank-5 accuracies
(rank1, rank5) = rank5_accuracy(predictions, test_gen.database["labels"])
print("[INFO] rank-1: {:.2f}%".format(rank1 * 100))
print("[INFO] rank-5: {:.2f}%".format(rank5 * 100))
# close the database
test_gen.close()
def training(aug, means_path, train_hdf5_path, val_hdf5_path, fig_path, json_path, label_encoder_path, best_weight_path, checkpoint_path, cross_val=None):
# load RGB means
means = json.loads(open(means_path).read())
# initialize image preprocessors
sp, mp, pp, iap = SimplePreprocessor(227, 227), MeanPreprocessor(means['R'], means['G'], means['B']), PatchPreprocessor(227, 227), ImageToArrayPreprocessor()
# initialize training and validation image generator
train_gen = HDF5DatasetGenerator(train_hdf5_path, config.BATCH_SIZE, preprocessors=[pp, mp, iap], aug=aug, classes=config.NUM_CLASSES)
val_gen = HDF5DatasetGenerator(val_hdf5_path, config.BATCH_SIZE, preprocessors=[sp, mp, iap], aug=aug, classes=config.NUM_CLASSES)
metrics = ['accuracy']
if config.DATASET_TYPE == 'age':
le = pickle.loads(open(label_encoder_path, 'rb').read())
agh = AgeGenderHelper(config, deploy)
one_off_mappings = agh.build_oneoff_mappings(le)
one_off = OneOffAccuracy(one_off_mappings)
metrics.append(one_off.one_off_accuracy)
# construct callbacks
callbacks = [TrainingMonitor(fig_path, json_path=json_path, start_at=args['start_epoch']), EpochCheckpoint(checkpoint_path, every=5, start_at=args['start_epoch']), ModelCheckpointsAdvanced(best_weight_path, json_path=json_path, start_at=args['start_epoch'])] #, LearningRateScheduler(decay)
if cross_val is None:
print('[INFO] compiling model...')
else:
print(f'[INFO] compiling model for cross validation {cross_val}...')
if args['start_epoch'] == 0:
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
model = AgeGenderNet.build(227, 227, 3, config.NUM_CLASSES, reg=5e-4)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=metrics)
else:
model_path = os.path.sep.join([checkpoint_path, f"epoch_{args['start_epoch']}.hdf5"])
print(f"[INFO] loading {model_path}...")
if config.DATASET_TYPE == 'age':
model = load_model(model_path, custom_objects={'one_off_accuracy': one_off.one_off_accuracy})
elif config.DATASET_TYPE == 'gender':
model = load_model(model_path)
# update learning rate
print(f'[INFO] old learning rate: {K.get_value(model.optimizer.lr)}')
K.set_value(model.optimizer.lr, INIT_LR)
print(f'[INFO] new learning rate: {K.get_value(model.optimizer.lr)}')
# train the network
if cross_val is None:
print('[INFO] training the network...')
else:
print(f'[INFO] training the network for cross validation {cross_val}...')
model.fit_generator(train_gen.generator(), steps_per_epoch=train_gen.num_images//config.BATCH_SIZE, validation_data=val_gen.generator(), validation_steps=val_gen.num_images//config.BATCH_SIZE, epochs=MAX_EPOCH-args['start_epoch'], verbose=2, callbacks=callbacks)
# close dataset
train_gen.close()
val_gen.close()
# load model from disk
custom_objects = None
age_path = deploy.AGE_NETWORK_PATH
gender_path = deploy.GENDER_NETWORK_PATH
gender_model = load_model(gender_path)
agh = AgeGenderHelper(config, deploy)
one_off_mappings = agh.build_oneoff_mappings(age_le)
one_off = OneOffAccuracy(one_off_mappings)
custom_objects = {'one_off_accuracy': one_off.one_off_accuracy}
age_model = load_model(age_path, custom_objects=custom_objects)
# initialize image preprocessors
sp = SimplePreprocessor(256, 256, inter=cv2.INTER_CUBIC)
age_mp = MeanPreprocessor(age_means['R'], age_means['G'], age_means['B'])
gender_mp = MeanPreprocessor(gender_means['R'], gender_means['G'],
gender_means['B'])
cp = CropPreprocessor(227, 227)
iap = ImageToArrayPreprocessor()
# initialize dlib's face detector (HOG-based), then create facial landmark predictor and face aligner
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(deploy.DLIB_LANDMARK_PATH)
fa = FaceAligner(predictor)
# keep looping
while True:
# get the current frame
grabbed, frame = camera.read()
from pyimagesearch.preprocessing import CropPreprocessor
# extract a random crop from the image with the target width
# and height
from pyimagesearch.preprocessing import AddChannelPreprocessor
# subtitutes G and B channel for medianblur and Canny edge map
from pyimagesearch.preprocessing import MeanPreprocessor
# subtract the means for each channel
# initialize the image preprocessors
aap = AspectAwarePreprocessor(224, 224)
iap = ImageToArrayPreprocessor()
pp = PatchPreprocessor(224, 224)
sp = SimplePreprocessor(224, 224)
cp = CropPreprocessor(224, 224)
acp = AddChannelPreprocessor(10, 20)
mp = MeanPreprocessor(1, 1, 1)
if args["model"] in ("inception", "xception"):
aap = AspectAwarePreprocessor(299, 299)
iap = ImageToArrayPreprocessor()
pp = PatchPreprocessor(299, 299)
sp = SimplePreprocessor(299, 299)
cp = CropPreprocessor(299, 299)
acp = AddChannelPreprocessor(10, 20)
from pyimagesearch.datasets import SimpleDatasetLoader
# load the image (data) and extract the class label assuming
# that our path has the following format:
# /path/to/dataset/{class}/{image}.jpg
# print("[INFO] loading {}".format(imagePath))
ageModel = mx.model.FeedForward.load(agePath, deploy.AGE_EPOCH)
genderModel = mx.model.FeedForward.load(genderPath,
deploy.GENDER_EPOCH)
# now that the networks are loaded, we need to compile them
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)
# initialize the image pre-processors
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()
# load a sample of testing images
rows = open(config.TEST_MX_LIST).read().strip().split("\n")
rows = np.random.choice(rows, size=args["sample_size"])
# loop over the rows
for row in rows:
# unpack the row
(_, gtLabel, imagePath) = row.strip().split("\t")
image = cv2.imread(imagePath)
# pre-process the image, one for the age model and another for
# now that the networks are loaded, we need to compile them
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)
# initialize the image pre-processors
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")
# initialize dlib's face detector (HOG-based), then create the
# the facial landmark predictor and face aligner
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(deploy.DLIB_LANDMARK_PATH)
fa = FaceAligner(predictor)
# if a video path was not supplied, grab the reference to the webcam
if not args.get("video", False):
camera = VideoStream(src=0, usePiCamera=False).start()
# otherwise, load the video
def upload_file():
file = request.files['image']
image_path = os.path.sep.join([UPLOAD_FOLDER, file.filename])
file.save(image_path)
# image_url = uploader.upload(image_path)
# image = AgeGenderHelper.url_to_image(image_url['url'])
# initialize dlib's face detector (HOG-based), then create facial landmark predictor and face aligner
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(deploy.DLIB_LANDMARK_PATH)
fa = FaceAligner(predictor)
# initialize image preprocessors
sp, cp, iap = SimplePreprocessor(
256, 256, inter=cv2.INTER_CUBIC), CropPreprocessor(
config.IMAGE_SIZE, config.IMAGE_SIZE,
horiz=False), ImageToArrayPreprocessor()
# loop over image paths
# load image fron disk, resize it and convert it to grayscale
print(f'[INFO] processing {file.filename}')
image = cv2.imread(image_path)
image = imutils.resize(image, width=1024)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
clone = image.copy()
# detect faces in grayscale image
rects = detector(gray, 1)
# loop over face detections
for rect in rects:
# determine facial landmarks for face region, then align face
shape = predictor(gray, rect)
face = fa.align(image, gray, rect)
# draw bounding box around face
x, y, w, h = face_utils.rect_to_bb(rect)
cv2.rectangle(clone, (x, y), (x + w, y + h), (0, 255, 0), 2)
if config.DATASET == 'IOG':
# load Label Encoder and mean files
print('[INFO] loading label encoders and mean files...')
age_le = pickle.loads(open(deploy.AGE_LABEL_ENCODER, 'rb').read())
gender_le = pickle.loads(
open(deploy.GENDER_LABEL_ENCODER, 'rb').read())
age_means = json.loads(open(deploy.AGE_MEAN).read())
gender_means = json.loads(open(deploy.GENDER_MEAN).read())
# initialize image preprocessors
age_mp = MeanPreprocessor(age_means['R'], age_means['G'],
age_means['B'])
gender_mp = MeanPreprocessor(gender_means['R'], gender_means['G'],
gender_means['B'])
age_preds, gender_preds = predict(face, sp, age_mp, gender_mp, cp,
iap, deploy.AGE_NETWORK_PATH,
deploy.GENDER_NETWORK_PATH,
age_le, gender_le)
elif config.DATASET == 'ADIENCE':
# age_preds_cross, gender_preds_cross = [], []
i = 0
# load Label Encoder and mean files
print(
f'[INFO] loading label encoders and mean files for cross validation {i}...'
)
age_le = pickle.loads(
open(deploy.AGE_LABEL_ENCODERS[i], 'rb').read())
gender_le = pickle.loads(
open(deploy.GENDER_LABEL_ENCODERS[i], 'rb').read())
age_means = json.loads(open(deploy.AGE_MEANS[i]).read())
gender_means = json.loads(open(deploy.GENDER_MEANS[i]).read())
# initialize image preprocessors
age_mp = MeanPreprocessor(age_means['R'], age_means['G'],
age_means['B'])
gender_mp = MeanPreprocessor(gender_means['R'], gender_means['G'],
gender_means['B'])
age_preds, gender_preds = predict(face, sp, age_mp, gender_mp, cp,
iap, deploy.AGE_NETWORK_PATHS[i],
deploy.GENDER_NETWORK_PATHS[i],
age_le, gender_le)
# age_preds_cross.append(age_pred)
# gender_preds_cross.append(gender_pred)
# age_preds, gender_preds = np.mean(age_preds_cross, axis = 0), np.mean(gender_preds_cross, axis = 0)
clone = AgeGenderHelper.visualize_video(age_preds, gender_preds,
age_le, gender_le, clone,
(x, y))
# path = image_path.split('.')
# pred_path = '.'.join([f'{path[0]}_predict', path[1]])
# pred_filename = pred_path.split(os.path.sep)[-1]
pred_path = '.'.join([f"{image_path.split('.')[0]}_1", 'jpg'])
cv2.imwrite(pred_path, clone)
# image_url = uploader.upload(pred_path)
gc.collect()
K.clear_session()
return render_template('index.html',
filename=pred_path.split(os.path.sep)[-1])
import os
import pdb
aug = ImageDataGenerator(rotation_range=20,
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
horizontal_flip=True,
fill_mode="nearest")
means = json.loads(open(config.DATASET_MEAN).read())
sp = SimplePreprocessor(227, 227)
pp = PatchPreprocessor(227, 227)
mp = MeanPreprocessor(means['R'], means['G'], means['B'])
iap = ImageToArrayPreprocessor()
trainGen = HDF5DatasetGenerator(config.TRAIN_HDF5,
128,
aug=aug,
preprocessors=[pp, mp, iap],
classes=2)
valGen = HDF5DatasetGenerator(config.VAL_HDF5,
128,
preprocessors=[sp, mp, iap],
classes=2)
print("[INFO] compiling model...")
opt = Adam(lr=1e-3)
def main():
"""Train ResNet
"""
# construct the argument parse and parse the arguments
args = argparse.ArgumentParser()
args.add_argument("-c",
"--checkpoints",
required=True,
help="path to output checkpoint directory")
args.add_argument("-m",
"--model",
type=str,
help="path to *specific* model checkpoint to load")
args.add_argument("-s",
"--start-epoch",
type=int,
default=0,
help="epoch to restart training at")
args = vars(args.parse_args())
# construct the training image generator for data augmentation
aug = ImageDataGenerator(
rotation_range=18,
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
horizontal_flip=True,
fill_mode="nearest",
)
# load the RGB means for the training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize the image preprocessors
simple_preprocessor = SimplePreprocessor(64, 64)
mean_preprocessor = MeanPreprocessor(means["R"], means["G"], means["B"])
image_to_array_preprocessor = ImageToArrayPreprocessor()
# initialize the training and validation dataset generators
train_gen = HDF5DatasetGenerator(
config.TRAIN_HDF5,
64,
augmentation=aug,
preprocessors=[
simple_preprocessor, mean_preprocessor, image_to_array_preprocessor
],
classes=config.NUM_CLASSES,
)
val_gen = HDF5DatasetGenerator(
config.VAL_HDF5,
64,
preprocessors=[
simple_preprocessor, mean_preprocessor, image_to_array_preprocessor
],
classes=config.NUM_CLASSES,
)
# if there is no specific model checkpoint supplied, then initialize
# the network and compile the model
if args["model"] is None:
print("[INFO] compiling model...")
model = ResNet.build(64,
64,
3,
config.NUM_CLASSES, (3, 4, 6),
(64, 128, 256, 512),
reg=0.0005,
dataset="tiny_imagenet")
opt = SGD(lr=1e-1, momentum=0.9)
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
# otherwise, load the checkpoint from disk
else:
print("[INFO] loading {}...".format(args["model"]))
model = load_model(args["model"])
# update the learning rate
print("[INFO] old learning rate: {}".format(
K.get_value(model.optimizer.lr)))
K.set_value(model.optimizer.lr, 1e-5)
print("[INFO] new learning rate: {}".format(
K.get_value(model.optimizer.lr)))
# construct the set of callbacks
callbacks = [
EpochCheckpoint(args["checkpoints"],
every=5,
start_at=args["start_epoch"]),
TrainingMonitor(config.FIG_PATH,
json_path=config.JSON_PATH,
start_at=args["start_epoch"]),
]
# train the network
model.fit_generator(
train_gen.generator(),
steps_per_epoch=train_gen.num_images // 64,
validation_data=val_gen.generator(),
validation_steps=val_gen.num_images // 64,
epochs=50,
max_queue_size=10,
callbacks=callbacks,
verbose=1,
)
# close the databases
train_gen.close()
val_gen.close()
rows = np.random.choice(rows, size=args["sample_size"])
# load our pre-trained model
print("[INFO] loading pre-trained model...")
checkpointsPath = os.path.sep.join([args["checkpoints"], args["prefix"]])
model = mx.model.FeedForward.load(checkpointsPath, args["epoch"])
# compile the model
model = mx.model.FeedForward(ctx=[mx.gpu(0)],
symbol=model.symbol,
arg_params=model.arg_params,
aux_params=model.aux_params)
# initialize the image pre-processors
sp = AspectAwarePreprocessor(width=224, height=224)
mp = MeanPreprocessor(config.R_MEAN, config.G_MEAN, config.B_MEAN)
iap = ImageToArrayPreprocessor(dataFormat="channels_first")
# loop over the testing images
for row in rows:
# grab the target class label and the image path from the row
(target, imagePath) = row.split("\t")[1:]
target = int(target)
# load the image from disk and pre-process it by resizing the
# image and applying the pre-processors
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)
from pyimagesearch.utils.ranked import rank5_accuracy
from keras.models import load_model
import progressbar
import numpy as np
import os
import json
# load model
model = load_model(config.MODEL_PATH)
# load means of R, G, B
means = json.loads(open(config.DATASET_MEAN).read())
# initialize preprocessors
sp, mp, cp, iap = SimplePreprocessor(227, 227), MeanPreprocessor(
means['R'], means['G'],
means['B']), CropPreprocessor(227, 227), ImageToArrayPreprocessor()
# initialize test generator for evaluting without cropping
test_gen = HDF5DatasetGenerator(config.TEST_HDF5,
preprocessors=[sp, mp, iap],
batch_size=128)
print('[INFO] evaluating model without cropping...')
preds = model.predict_generator(test_gen.generator(),
steps=test_gen.num_images // 128)
rank_1, _ = rank5_accuracy(preds, test_gen.db['labels'])
print(f'[INFO] rank-1: f{rank_1*100:.2f}')
# close test_gen
test_gen.close()
import json
from shutil import rmtree
from config import affectnet_config as config
from pyimagesearch.preprocessing import AspectAwarePreprocessor, ImageToArrayPreprocessor, SimplePreprocessor, MeanPreprocessor, CropPreprocessor
from keras.models import load_model
# In[3]:
images = glob('/path/to/images/jpg/A/*/*.jpg')
print('Found {} images'.format(len(images)))
# In[4]:
means = json.loads(open(config.DATASET_MEAN).read())
sp = SimplePreprocessor(227, 227)
mp = MeanPreprocessor(means['R'], means['G'], means['B'])
cp = CropPreprocessor(227, 227)
iap = ImageToArrayPreprocessor()
#load the pretrained network
model = load_model(config.MODEL_PATH)
# In[5]:
predictions = []
for image in tqdm(images[:100]):
face = face_get(image)
crop = sp.preprocess(face)
crop = mp.preprocess(crop)
crop = np.expand_dims(crop, axis=0)
#crop = np.array([iap.preprocess(c) for c in crop], dtype = "float32")
# draw bounding box around face
clone = image.copy()
x, y, w, h = face_utils.rect_to_bb(rect)
cv2.rectangle(clone, (x, y), (x+w, y+h), (0, 255, 0), 2)
if config.DATASET == 'IOG':
# load Label Encoder and mean files
print('[INFO] loading label encoders and mean files...')
age_le = pickle.loads(open(deploy.AGE_LABEL_ENCODER, 'rb').read())
gender_le = pickle.loads(open(deploy.GENDER_LABEL_ENCODER, 'rb').read())
age_means = json.loads(open(deploy.AGE_MEAN).read())
gender_means = json.loads(open(deploy.GENDER_MEAN).read())
# initialize image preprocessors
age_mp = MeanPreprocessor(age_means['R'], age_means['G'], age_means['B'])
gender_mp = MeanPreprocessor(gender_means['R'], gender_means['G'], gender_means['B'])
age_preds, gender_preds = predict(face, sp, age_mp, gender_mp, cp, iap, deploy.AGE_NETWORK_PATH, deploy.GENDER_NETWORK_PATH, age_le, gender_le)
# visualize age and gender predictions
age_canvas = AgeGenderHelper.visualize_age(age_preds, age_le)
gender_canvas = AgeGenderHelper.visualize_gender(gender_preds, gender_le)
elif config.DATASET == 'ADIENCE':
# age_preds_cross, gender_preds_cross = [], []
i = 0
# load Label Encoder and mean files
print(f'[INFO] loading label encoders and mean files for cross validation {i}...')
age_le = pickle.loads(open(deploy.AGE_LABEL_ENCODERS[i], 'rb').read())
def main():
"""Train AlexNet on Dogs vs Cats
"""
# construct the training image generator for data augmentation
augmentation = ImageDataGenerator(
rotation_range=20,
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
horizontal_flip=True,
fill_mode="nearest",
)
# load the RGB means for the training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize the image preprocessors
simple_preprocessor = SimplePreprocessor(227, 227)
patch_preprocessor = PatchPreprocessor(227, 227)
mean_preprocessor = MeanPreprocessor(means["R"], means["G"], means["B"])
image_to_array_preprocessor = ImageToArrayPreprocessor()
# initialize the training and validation dataset generators
train_gen = HDF5DatasetGenerator(
config.TRAIN_HDF5,
128,
augmentation=augmentation,
preprocessors=[patch_preprocessor, mean_preprocessor, image_to_array_preprocessor],
classes=2,
)
val_gen = HDF5DatasetGenerator(
config.VAL_HDF5,
128,
preprocessors=[simple_preprocessor, mean_preprocessor, image_to_array_preprocessor],
classes=2,
)
# initialize the optimizer
print("[INFO] compiling model...")
opt = Adam(lr=1e-3)
model = AlexNet.build(width=227, height=227, depth=3, classes=2, regularization=0.0002)
model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"])
# construct the set of callbacks
path = os.path.sep.join([config.OUTPUT_PATH, "{}.png".format(os.getpid())])
callbacks = [TrainingMonitor(path)]
# train the network
model.fit_generator(
train_gen.generator(),
steps_per_epoch=train_gen.num_images // 128,
validation_data=val_gen.generator(),
validation_steps=val_gen.num_images // 128,
epochs=75,
max_queue_size=10,
callbacks=callbacks,
verbose=1,
)
# save the model to file
print("[INFO] serializing model...")
model.save(config.MODEL_PATH, overwrite=True)
# close the HDF5 datasets
train_gen.close()
val_gen.close()
help='epoch to restart training at')
args = vars(ap.parse_args())
# construct training image generator for data augmentation
aug = ImageDataGenerator(rotation_range=18,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
zoom_range=0.15,
horizontal_flip=True)
# load RGB means for training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize image preprocessors
sp, mp, iap = SimplePreprocessor(64, 64), MeanPreprocessor(
means['R'], means['G'], means['B']), ImageToArrayPreprocessor()
# initialize training and validation dataset generators
train_gen = HDF5DatasetGenerator(config.TRAIN_HDF5,
64,
preprocessors=[sp, mp, iap],
aug=aug,
classes=config.NUM_CLASSES)
val_gen = HDF5DatasetGenerator(config.VAL_HDF5,
64,
preprocessors=[sp, mp, iap],
aug=aug,
classes=config.NUM_CLASSES)
# if there is no specific model checkpoint supplied, initialize network and compile model
if args['model'] is None:
def main():
"""Evaluate AlexNet on Cats vs. Dogs
"""
# load the RGB means for the training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize the image preprocessors
simple_preprocessor = SimplePreprocessor(227, 227)
mean_preprocessor = MeanPreprocessor(means["R"], means["G"], means["B"])
crop_preprocessor = CropPreprocessor(227, 227)
image_to_array_preprocessor = ImageToArrayPreprocessor()
# load the pretrained network
print("[INFO] loading model...")
model = load_model(config.MODEL_PATH)
# initialize the testing dataset generator, then make predictions on the testing data
print("[INFO] predicting on test data (no crops)...")
test_gen = HDF5DatasetGenerator(config.TEST_HDF5,
64,
preprocessors=[
simple_preprocessor, mean_preprocessor,
image_to_array_preprocessor
],
classes=2)
predictions = model.predict_generator(test_gen.generator(),
steps=test_gen.num_images // 64,
max_queue_size=10)
# compute the rank-1 and rank-5 accuracies
(rank1, _) = rank5_accuracy(predictions, test_gen.database["labels"])
print("[INFO] rank-1: {:.2f}%".format(rank1 * 100))
test_gen.close()
# re-initialize the testing set generator, this time excluding the `SimplePreprocessor`
test_gen = HDF5DatasetGenerator(config.TEST_HDF5,
64,
preprocessors=[mean_preprocessor],
classes=2)
predictions = []
# initialize the progress bar
widgets = [
"Evaluating: ",
progressbar.Percentage(), " ",
progressbar.Bar(), " ",
progressbar.ETA()
]
progress_bar = progressbar.ProgressBar(maxval=test_gen.num_images // 64,
widgets=widgets).start()
# loop over a single pass of the test data
# passes=1 to indicate the testing data only needs to be looped over once
for (i, (images, _)) in enumerate(test_gen.generator(passes=1)):
# loop over each of the individual images
for image in images:
# apply the crop preprocessor to the image to generate 10
# separate crops, then convert them from images to arrays
crops = crop_preprocessor.preprocess(image)
crops = np.array(
[image_to_array_preprocessor.preprocess(c) for c in crops],
dtype="float32")
# make predictions on the crops and then average them
# together to obtain the final prediction
pred = model.predict(crops)
predictions.append(pred.mean(axis=0))
# update the progress bar
progress_bar.update(i)
# compute the rank-1 accuracy
progress_bar.finish()
print("[INFO] predicting on test data (with crops)...")
(rank1, _) = rank5_accuracy(predictions, test_gen.database["labels"])
print("[INFO] rank-1: {:.2f}%".format(rank1 * 100))
test_gen.close()
def main():
"""Train Resnet on TinyImageNet dataset
"""
# construct the argument parse and parse the arguments
args = argparse.ArgumentParser()
args.add_argument("-m",
"--model",
required=True,
help="path to output model")
args.add_argument("-o",
"--output",
required=True,
help="path to output directory (logs, plots, etc.)")
args = vars(args.parse_args())
# construct the training image generator for data augmentation
aug = ImageDataGenerator(
rotation_range=18,
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
horizontal_flip=True,
fill_mode="nearest",
)
# load the RGB means for the training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize the image preprocessors
simple_preprocessor = SimplePreprocessor(64, 64)
mean_preprocessor = MeanPreprocessor(means["R"], means["G"], means["B"])
image_to_array_preprocessor = ImageToArrayPreprocessor()
# initialize the training and validation dataset generators
train_gen = HDF5DatasetGenerator(
config.TRAIN_HDF5,
64,
augmentation=aug,
preprocessors=[
simple_preprocessor, mean_preprocessor, image_to_array_preprocessor
],
classes=config.NUM_CLASSES,
)
val_gen = HDF5DatasetGenerator(
config.VAL_HDF5,
64,
preprocessors=[
simple_preprocessor, mean_preprocessor, image_to_array_preprocessor
],
classes=config.NUM_CLASSES,
)
# construct the set of callbacks
fig_path = os.path.sep.join([args["output"], "{}.png".format(os.getpid())])
json_path = os.path.sep.join(
[args["output"], "{}.json".format(os.getpid())])
callbacks = [
TrainingMonitor(fig_path, json_path=json_path),
LearningRateScheduler(poly_decay)
]
# initialize the optimizer and model (ResNet-56)
print("[INFO] compiling model...")
model = ResNet.build(64,
64,
3,
config.NUM_CLASSES, (3, 4, 6), (64, 128, 256, 512),
reg=0.0005,
dataset="tiny_imagenet")
opt = SGD(lr=INIT_LR, momentum=0.9)
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
# train the network
print("[INFO] training network...")
model.fit_generator(
train_gen.generator(),
steps_per_epoch=train_gen.num_images // 64,
validation_data=val_gen.generator(),
validation_steps=val_gen.num_images // 64,
epochs=NUM_EPOCHS,
max_queue_size=10,
callbacks=callbacks,
verbose=1,
)
# save the network to disk
print("[INFO] serializing network...")
model.save(args["model"])
# close the databases
train_gen.close()
val_gen.close()
from pyimagesearch.io import HDF5DatasetGenerator from pyimagesearch.preprocessing import SimplePreprocessor, MeanPreprocessor, PatchPreprocessor from config import plant_seedlings_config as config from pyimagesearch.nn.conv import AlexNet from keras.preprocessing.image import ImageDataGenerator import json import pickle mean = json.loads(open(config.DATASET_MEAN).read()) le = pickle.loads(open(config.LABEL_MAPPINGS, 'rb').read()) sp, mp, pp = SimplePreprocessor(224, 224), MeanPreprocessor(mean['R'], mean['G'], mean['B']), PatchPreprocessor(224, 224) aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.15, zoom_range=0.1, horizontal_flip=True) train_gen = HDF5DatasetGenerator(config.TRAIN_HDF5, preprocessors=[pp, mp], aug=aug, batch_size=64, num_classes=len(le.classes_)) val_gen = HDF5DatasetGenerator(config.VAL_HDF5, preprocessors=[sp, mp], aug=aug, batch_size=64, num_classes=len(le.classes_)) model = AlexNet.build(224, 224, 3, len(le.classes_)) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.fit(train_gen.generator(), steps_per_epoch=train_gen.num_images//64, epochs=100, verbose=2, )
import argparse
import pandas as pd
# construct argument parser and parse the argument
ap = argparse.ArgumentParser()
ap.add_argument('-s',
'--submit',
required=True,
help='path to submission file')
args = vars(ap.parse_args())
# load RGB means for json
means = json.loads(open(config.DATASET_MEAN).read())
# initialize image preprocessors
mp, cp, iap = MeanPreprocessor(means['R'],
means['G'], means['B']), CropPreprocessor(
227, 227), ImageToArrayPreprocessor()
# load model
print('[INFO] loading model...')
model = load_model(config.MODEL_PATH)
# initialize dataset generator
test_gen = HDF5DatasetGenerator(config.PUBLIC_TEST_HDF5,
batch_size=64,
preprocessors=[mp])
preds = []
# initialize progressbar
widgets = [
'Evaluating: ',
class GenderRecognizer:
# load the label encoders and mean files
print("[INFO] loading label encoders and mean files...")
ageLE = pickle.loads(open(deploy.AGE_LABEL_ENCODER, "rb").read())
genderLE = pickle.loads(open(deploy.GENDER_LABEL_ENCODER, "rb").read())
ageMeans = json.loads(open(deploy.AGE_MEANS).read())
genderMeans = json.loads(open(deploy.GENDER_MEANS).read())
# load the models from disk
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)
# now that the networks are loaded, we need to compile them
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)
# initialize the image pre-processors
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")
def __init__(self):
# initialize dlib's face detector (HOG-based), then create the
# the facial landmark predictor and face aligner
self.detector = dlib.get_frontal_face_detector()
self.predictor = dlib.shape_predictor(deploy.DLIB_LANDMARK_PATH)
self.fa = FaceAligner(self.predictor)
def recognize(self, face):
# resize the face to a fixed size, then extract 10-crop
# patches from it
face = self.sp.preprocess(face)
patches = self.cp.preprocess(face)
# allocate memory for the age and gender patches
agePatches = np.zeros((patches.shape[0], 3, 227, 227), dtype="float")
genderPatches = np.zeros((patches.shape[0], 3, 227, 227),
dtype="float")
# loop over the patches
for j in np.arange(0, patches.shape[0]):
# perform mean subtraction on the patch
agePatch = self.ageMP.preprocess(patches[j])
genderPatch = self.genderMP.preprocess(patches[j])
agePatch = self.iap.preprocess(agePatch)
genderPatch = self.iap.preprocess(genderPatch)
# update the respective patches lists
agePatches[j] = agePatch
genderPatches[j] = genderPatch
# make predictions on age and gender based on the extracted
# patches
agePreds = self.ageModel.predict(agePatches)
genderPreds = self.genderModel.predict(genderPatches)
# compute the average for each class label based on the
# predictions for the patches
agePreds = agePreds.mean(axis=0)
genderPreds = genderPreds.mean(axis=0)
# visualize the age and gender predictions
age = GenderRecognizer.visAge(agePreds, self.ageLE)
gender = GenderRecognizer.visGender(genderPreds, self.genderLE)
image_to_show_path_GA = "images/{}/{}".format(gender, age)
return image_to_show_path_GA
@staticmethod
def visAge(agePreds, le):
# initialize the canvas and sort the predictions according
# to their probability
idxs = np.argsort(agePreds)[::-1]
# construct the text for the prediction
#ageLabel = le.inverse_transform(j) # Python 2.7
ageLabel = le.inverse_transform(idxs[0]).decode("utf-8")
ageLabel = ageLabel.replace("_", "-")
ageLabel = ageLabel.replace("-inf", "+")
age = "{}".format(ageLabel)
return age
@staticmethod
def visGender(genderPreds, le):
idxs = np.argsort(genderPreds)[::-1]
gender = le.inverse_transform(idxs[0])
gender = "Male" if gender == 0 else "Female"
text_gender = "{}".format(gender)
return text_gender
import json
from keras.models import load_model
from config import tiny_imagenet_config as config
from pyimagesearch.io import HDF5DatasetGenerator
from pyimagesearch.utils.ranked import rank5_accuracy
from pyimagesearch.preprocessing import MeanPreprocessor
from pyimagesearch.preprocessing import SimplePreprocessor
from pyimagesearch.preprocessing import ImageToArrayPreprocessor
# load the RGB means for the training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize the image preprocessors
sp = SimplePreprocessor(64, 64)
mp = MeanPreprocessor(means["R"], means["G"], means["B"])
iap = ImageToArrayPreprocessor()
# initialize the testing dataset generator
testGen = 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 testing data
print("[INFO] predicting on test data...")
predictions = model.predict_generator(testGen.generator(),
def calculate_score(means_path,
label_encoder_path,
best_weight_path,
test_hdf5_path,
cross_val=None,
preds_cross=None,
labels_cross=None,
is_mapped=False):
# load RGB means for training set
means = json.loads(open(means_path).read())
# load LabelEncoder
le = pickle.loads(open(label_encoder_path, 'rb').read())
# initialize image preprocessors
sp, mp, cp, iap = SimplePreprocessor(
config.IMAGE_SIZE, config.IMAGE_SIZE), MeanPreprocessor(
means['R'], means['G'], means['B']), CropPreprocessor(
config.IMAGE_SIZE,
config.IMAGE_SIZE), ImageToArrayPreprocessor()
custom_objects = None
agh = AgeGenderHelper(config, deploy)
if config.DATASET_TYPE == 'age':
one_off_mappings = agh.build_oneoff_mappings(le)
one_off = OneOffAccuracy(one_off_mappings)
custom_objects = {'one_off_accuracy': one_off.one_off_accuracy}
# load model
print(f'[INFO] loading {best_weight_path}...')
model = load_model(best_weight_path, custom_objects=custom_objects)
# initialize testing dataset generator, then predict
if cross_val is None:
print(
f'[INFO] predicting in testing data (no crops){config.SALIENCY_INFO}...'
)
else:
print(
f'[INFO] predicting in testing data (no crops) for cross validation {cross_val}{config.SALIENCY_INFO}...'
)
test_gen = HDF5DatasetGenerator(test_hdf5_path,
batch_size=config.BATCH_SIZE,
preprocessors=[sp, mp, iap],
classes=config.NUM_CLASSES)
preds = model.predict_generator(test_gen.generator(),
steps=test_gen.num_images //
config.BATCH_SIZE)
# compute rank-1 and one-off accuracies
labels = to_categorical(
test_gen.db['labels'][0:config.BATCH_SIZE *
(test_gen.num_images // config.BATCH_SIZE)],
num_classes=config.NUM_CLASSES)
preds_mapped = preds.argmax(axis=1)
if is_mapped == True:
preds_mapped = agh.build_mapping_to_iog_labels()[preds_mapped]
if cross_val is None:
print(
'[INFO] serializing all images classified incorrectly for testing dataset...'
)
prefix_path = os.path.sep.join(
[config.WRONG_BASE, config.DATASET_TYPE])
agh.plot_confusion_matrix_from_data(config,
labels.argmax(axis=1),
preds_mapped,
le=le,
save_path=os.path.sep.join([
config.OUTPUT_BASE,
f'cm_{config.DATASET_TYPE}.png'
]))
else:
print(
f'[INFO] serializing all images classified incorrectly for cross validation {cross_val} of testing dataset...'
)
prefix_path = os.path.sep.join(
[config.WRONG_BASE, f'Cross{cross_val}', config.DATASET_TYPE])
preds_cross.extend(preds_mapped.tolist())
labels_cross.extend(labels.argmax(axis=1).tolist())
if os.path.exists(prefix_path):
shutil.rmtree(prefix_path)
os.makedirs(prefix_path)
for i, (pred, label) in enumerate(zip(preds_mapped,
labels.argmax(axis=1))):
if pred != label:
image = test_gen.db['images'][i]
if config.DATASET_TYPE == 'age':
real_label, real_pred = le.classes_[label], le.classes_[pred]
real_label = real_label.replace('_', '-')
real_label = real_label.replace('-inf', '+')
real_pred = real_pred.replace('_', '-')
real_pred = real_pred.replace('-inf', '+')
elif config.DATASET_TYPE == 'gender':
real_label = 'Male' if label == 0 else 'Female'
real_pred = 'Male' if pred == 0 else 'Female'
cv2.putText(image, f'Actual: {real_label}, Predict: {real_pred}',
(15, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0),
2)
cv2.imwrite(os.path.sep.join([prefix_path, f'{i:05d}.jpg']), image)
score = accuracy_score(labels.argmax(axis=1), preds_mapped)
print(f'[INFO] rank-1: {score:.4f}')
score_one_off = None
if config.DATASET_TYPE == 'age':
score_one_off = one_off.one_off_compute(
labels, to_categorical(preds_mapped,
num_classes=config.NUM_CLASSES))
print(f'[INFO] one-off: {score_one_off:.4f}')
test_gen.close()
# re-initialize testing generator, now excluding SimplePreprocessor
test_gen = HDF5DatasetGenerator(test_hdf5_path,
config.BATCH_SIZE,
preprocessors=[mp],
classes=config.NUM_CLASSES)
preds = []
labels = to_categorical(test_gen.db['labels'],
num_classes=config.NUM_CLASSES)
print('[INFO] predicting in testing data (with crops)...')
# initialize progress bar
widgets = [
'Evaluating: ',
progressbar.Percentage(), ' ',
progressbar.Bar(), ' ',
progressbar.ETA()
]
pbar = progressbar.ProgressBar(maxval=math.ceil(test_gen.num_images /
config.BATCH_SIZE),
widgets=widgets).start()
for i, (images, _) in enumerate(test_gen.generator(passes=1)):
for image in images:
crops = cp.preprocess(image)
crops = np.array([iap.preprocess(c) for c in crops])
pred = model.predict(crops)
preds.append(pred.mean(axis=0))
pbar.update(i)
pbar.finish()
test_gen.close()
# compute rank-1 accuracy
preds_mapped = np.argmax(preds, axis=1)
if is_mapped == True:
preds_mapped = agh.build_mapping_to_iog_labels()[preds_mapped]
score_crops = accuracy_score(labels.argmax(axis=1), preds_mapped)
print(f'[INFO] rank-1: {score_crops:.4f}')
score_one_off_crops = None
if config.DATASET_TYPE == 'age':
score_one_off_crops = one_off.one_off_compute(
labels, to_categorical(preds_mapped,
num_classes=config.NUM_CLASSES))
print(f'[INFO] one-off: {score_one_off_crops:.4f}')
return score, score_one_off, score_crops, score_one_off_crops
