def main(argv=None): # pylint: disable=unused-argument
fd = face_detection_model(FLAGS.face_detection_type,
FLAGS.face_detection_model)
files = []
contains_faces = []
target = FLAGS.target = '%s.csv' % FLAGS.face_detection_type if FLAGS.target is None else FLAGS.target
print('Creating output file %s' % target)
output = open(target, 'w')
writer = csv.writer(output)
writer.writerow(('file_with_face', ))
if FLAGS.filename is not None:
if os.path.isdir(FLAGS.filename):
for relpath in os.listdir(FLAGS.filename):
abspath = os.path.join(FLAGS.filename, relpath)
if os.path.isfile(abspath) and any([
abspath.endswith('.' + ty)
for ty in ('jpg', 'png', 'JPG', 'PNG', 'jpeg')
]):
print(abspath)
files.append(abspath)
elif any([
FLAGS.filename.endswith('.' + ty)
for ty in ('csv', 'tsv', 'txt')
]):
files = list_images(FLAGS.filename)
else:
files = [FLAGS.filename]
for f in files:
try:
images, outfile = fd.run(f)
if len(images):
print(f, 'YES')
writer.writerow((f, ))
contains_faces.append(f)
else:
print(f, 'NO')
except Exception as e:
print(e)
def main(argv=None): # pylint: disable=unused-argument
files = []
if FLAGS.face_detection_model:
print('Using face detector (%s) %s' %
(FLAGS.face_detection_type, FLAGS.face_detection_model))
face_detect = face_detection_model(FLAGS.face_detection_type,
FLAGS.face_detection_model)
face_files, rectangles = face_detect.run(FLAGS.filename)
print(face_files)
files += face_files
with tf.Session() as sess:
#tf.reset_default_graph()
label_list = AGE_LIST if FLAGS.class_type == 'age' else GENDER_LIST
nlabels = len(label_list)
print('Executing on %s' % FLAGS.device_id)
model_fn = select_model(FLAGS.model_type)
with tf.device(FLAGS.device_id):
images = tf.placeholder(tf.float32,
[None, RESIZE_FINAL, RESIZE_FINAL, 3])
logits = model_fn(nlabels, images, 1, False)
init = tf.global_variables_initializer()
requested_step = FLAGS.requested_step if FLAGS.requested_step else None
checkpoint_path = '%s' % (FLAGS.model_dir)
model_checkpoint_path, global_step = get_checkpoint(
checkpoint_path, requested_step, FLAGS.checkpoint)
saver = tf.train.Saver()
saver.restore(sess, model_checkpoint_path)
softmax_output = tf.nn.softmax(logits)
coder = ImageCoder()
# Support a batch mode if no face detection model
if len(files) == 0:
files.append(FLAGS.filename)
# If it happens to be a list file, read the list and clobber the files
if one_of(FLAGS.filename, ('csv', 'tsv', 'txt')):
files = batchlist(FLAGS.filename)
writer = None
output = None
if FLAGS.target:
print('Creating output file %s' % FLAGS.target)
output = open(FLAGS.target, 'w')
writer = csv.writer(output)
writer.writerow(('file', 'label', 'score'))
for f in files:
image_file = resolve_file(f)
if image_file is None: continue
try:
best_choice = classify(sess, label_list, softmax_output,
coder, images, image_file)
if writer is not None:
writer.writerow(
(f, best_choice[0], '%.2f' % best_choice[1]))
except Exception as e:
print(e)
print('Failed to run image %s ' % image_file)
if output is not None:
output.close()
def main(argv=None): # pylint: disable=unused-argument
files = []
if FLAGS.face_detection_model:
print('Using face detector (%s) %s' % (FLAGS.face_detection_type, FLAGS.face_detection_model))
face_detect = face_detection_model(FLAGS.face_detection_type, FLAGS.face_detection_model)
face_files, rectangles = face_detect.run(FLAGS.filename)
print(face_files)
files += face_files
with tf.Session() as sess:
#tf.reset_default_graph()
label_list = AGE_LIST if FLAGS.class_type == 'age' else GENDER_LIST
nlabels = len(label_list)
print('Executing on %s' % FLAGS.device_id)
model_fn = select_model(FLAGS.model_type)
with tf.device(FLAGS.device_id):
images = tf.placeholder(tf.float32, [None, RESIZE_FINAL, RESIZE_FINAL, 3])
logits = model_fn(nlabels, images, 1, False)
init = tf.global_variables_initializer()
requested_step = FLAGS.requested_step if FLAGS.requested_step else None
checkpoint_path = '%s' % (FLAGS.model_dir)
model_checkpoint_path, global_step = get_checkpoint(checkpoint_path, requested_step, FLAGS.checkpoint)
saver = tf.train.Saver()
saver.restore(sess, model_checkpoint_path)
softmax_output = tf.nn.softmax(logits)
coder = ImageCoder()
# Support a batch mode if no face detection model
if len(files) == 0:
files.append(FLAGS.filename)
# If it happens to be a list file, read the list and clobber the files
if one_of(FLAGS.filename, ('csv', 'tsv', 'txt')):
files = batchlist(FLAGS.filename)
writer = None
output = None
if FLAGS.target:
print('Creating output file %s' % FLAGS.target)
output = open(FLAGS.target, 'w')
writer = csv.writer(output)
writer.writerow(('file', 'label', 'score'))
for f in files:
image_file = resolve_file(f)
if image_file is None: continue
try:
best_choice = classify(sess, label_list, softmax_output, coder, images, image_file)
if writer is not None:
writer.writerow((f, best_choice[0], '%.2f' % best_choice[1]))
except Exception as e:
print(e)
print('Failed to run image %s ' % image_file)
if output is not None:
output.close()
def main(argv=None): # pylint: disable=unused-argument
files = []
if FLAGS.face_detection_model:
print('Using face detector (%s) %s' %
(FLAGS.face_detection_type, FLAGS.face_detection_model))
face_detect = face_detection_model(FLAGS.face_detection_type,
FLAGS.face_detection_model)
face_files, rectangles = face_detect.run(FLAGS.filename)
print(face_files)
files += face_files
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
label_list = AGE_LIST if FLAGS.class_type == 'age' else GENDER_LIST
nlabels = len(label_list)
print('Executing on %s' % FLAGS.device_id)
model_fn = select_model(FLAGS.model_type)
with tf.device(FLAGS.device_id):
images = tf.placeholder(tf.float32,
[None, RESIZE_FINAL, RESIZE_FINAL, 3])
logits = model_fn(nlabels, images, 1, False)
init = tf.global_variables_initializer()
requested_step = FLAGS.requested_step if FLAGS.requested_step else None
checkpoint_path = '%s' % (FLAGS.model_dir)
model_checkpoint_path, global_step = get_checkpoint(
checkpoint_path, requested_step, FLAGS.checkpoint)
saver = tf.train.Saver()
saver.restore(sess, model_checkpoint_path)
softmax_output = tf.nn.softmax(logits)
coder = ImageCoder()
# Support a batch mode if no face detection model
if len(files) == 0:
if (os.path.isdir(FLAGS.filename)):
for relpath in os.listdir(FLAGS.filename):
abspath = os.path.join(FLAGS.filename, relpath)
if os.path.isfile(abspath) and any([
abspath.endswith('.' + ty)
for ty in ('jpg', 'png', 'JPG', 'PNG', 'jpeg')
]):
print(abspath)
files.append(abspath)
else:
files.append(FLAGS.filename)
# If it happens to be a list file, read the list and clobber the files
if any([
FLAGS.filename.endswith('.' + ty)
for ty in ('csv', 'tsv', 'txt')
]):
files = list_images(FLAGS.filename)
writer = None
output = None
if FLAGS.target:
print('Creating output file %s' % FLAGS.target)
output = open(FLAGS.target, 'w')
writer = csv.writer(output)
writer.writerow(('file', 'label', 'score'))
image_files = list(
filter(lambda x: x is not None,
[resolve_file(f) for f in files]))
print(image_files)
if FLAGS.single_look:
classify_many_single_crop(sess, label_list, softmax_output,
coder, images, image_files, writer)
else:
for image_file in image_files:
classify_one_multi_crop(sess, label_list, softmax_output,
coder, images, image_file, writer)
if output is not None:
output.close()