def image_batch(image_path):
coder = ImageCoder()
image_data = tf.gfile.FastGFile(image_path, 'rb').read()
image = coder.decode_jpeg(image_data)
crop = tf.image.resize_images(image, (227, 227))
image_batch = tf.stack([crop])
# print(image_batch)
return image_batch
def guessGender(file_name): # pylint: disable=unused-argument
#检测单张照片的性别
with tf.Session() as sess:
with tf.device(FLAGS.device_id):
init = tf.global_variables_initializer()
requested_step = FLAGS.requested_step if FLAGS.requested_step else None
checkpoint_path = '%s' % (GENDER_MODEL_PATH)
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()
try:
best_choice = classify(sess, label_list, softmax_output, coder, images, file_name)
# print(best_choice)
return(best_choice)
except Exception as e:
print(e)
print('Failed to run image %s ' % file)
def __init__(
self,
model_dir='/usr/src/app/deps/rude-carnie/inception_gender_checkpoint',
model_type='inception',
class_type='gender'):
'''
Just a wrapper around guess.py.
'''
self.model_dir = model_dir
self.model_type = model_type
self.class_type = class_type
self.sess = tf.Session()
model_fn = select_model(self.model_type)
self.label_list = AGE_LIST if self.class_type == 'age' else GENDER_LIST
nlabels = len(self.label_list)
self.images = tf.placeholder(tf.string, [None])
standardize = tf.map_fn(self.decode, self.images, dtype=tf.float32)
logits = model_fn(nlabels, standardize, 1, False)
init = tf.global_variables_initializer()
requested_step = None #FLAGS.requested_step if FLAGS.requested_step else None
checkpoint_path = '%s' % (self.model_dir)
model_checkpoint_path, global_step = get_checkpoint(
checkpoint_path, requested_step, None)
#FLAGS.checkpoint)
saver = tf.train.Saver()
saver.restore(self.sess, model_checkpoint_path)
self.softmax_output = tf.nn.softmax(logits)
self.coder = ImageCoder()
def guessGender(path): # pylint: disable=unused-argument
with tf.Session() as sess:
# tf.reset_default_graph()
label_list = 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' % (GENDER_MODEL_PATH)
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()
files = deal_file.get_files(path)
font = cv2.FONT_HERSHEY_SIMPLEX
try:
for f in files:
best_choice = classify(sess, label_list, softmax_output,
coder, images, f)
# print(best_choice)
pic = cv2.imread(f)
fname = f[:-4] + '_test' + f[-4:]
cv2.putText(pic, best_choice[0], (5, 40), font, 1,
(100, 255, 50), 2, cv2.LINE_AA)
cv2.imwrite(fname, pic)
print(best_choice)
except Exception as e:
print(e)
print('Failed to run image %s ' % file)
def guessGender(path): # pylint: disable=unused-argument
# 检测文件夹中所有照片的性别
with tf.Session() as sess:
# tf.reset_default_graph()
label_list = 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' % (GENDER_MODEL_PATH)
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()
files = get_files(path)
gender_dict = {}
try:
for f in files:
best_choice = classify(sess, label_list, softmax_output, coder, images, f)
# print(best_choice)
gender_dict[f[len(path) + 1:]] = best_choice
return(best_choice)
except Exception as e:
print(e)
print('Failed to run image %s ' % file)
def guessAge(image_file):
#import!!!Fix the bug http://stackoverflow.com/questions/33765336/remove-nodes-from-graph-or-reset-entire-default-graph
tf.reset_default_graph()
with tf.Session() as sess:
age_label_list = AGE_LIST
agelabels = len(age_label_list)
# print('Executing on %s' % FLAGS.device_id)
model_fn = select_model('inception')
images = tf.placeholder(tf.float32,
[None, RESIZE_FINAL, RESIZE_FINAL, 3])
logits_age = model_fn(agelabels, images, 1, False)
init = tf.global_variables_initializer()
requested_step = FLAGS.requested_step if FLAGS.requested_step else None
checkpoint_path = '%s' % (AGE_MODEL_PATH)
# update in 0.11 version
model_checkpoint_path, global_step = get_checkpoint(
checkpoint_path, requested_step, FLAGS.checkpoint)
#print 'model_checkpoint_path is', model_checkpoint_path
#print model_checkpoint_path
saver = tf.train.Saver()
if not saver.last_checkpoints:
saver.restore(sess, model_checkpoint_path)
softmax_output = tf.nn.softmax(logits_age)
coder = ImageCoder()
files = []
# detect age
best_choice = classify(sess, age_label_list, softmax_output, coder, images,
image_file)
sess.close()
return best_choice
def main(argv=None): # pylint: disable=unused-argument
with tf.Session() 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)
images = tf.placeholder(tf.float32,
[None, RESIZE_FINAL, RESIZE_FINAL, 3])
logits = model_fn(nlabels, images, 1, False)
init = tf.initialize_all_variables()
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()
files = []
if FLAGS.face_detection_model:
print('Using face detector %s' % FLAGS.face_detection_model)
face_detect = FaceDetector(FLAGS.face_detection_model)
face_files, rectangles = face_detect.run(FLAGS.filename)
files += face_files
if len(files) == 0:
files.append(FLAGS.filename)
for f in files:
classify(sess, label_list, softmax_output, coder, images, f)
def guessGender(image_file):
tf.reset_default_graph()
with tf.Session() as sess:
#sess = tf.Session()
age_label_list = AGE_LIST
gender_label_list = GENDER_LIST
genderlabels = len(gender_label_list)
# print('Executing on %s' % FLAGS.device_id)
model_fn = select_model('')
with tf.device(FLAGS.device_id):
images = tf.placeholder(tf.float32,
[None, RESIZE_FINAL, RESIZE_FINAL, 3])
logits_gender = model_fn(genderlabels, images, 1, False)
init = tf.global_variables_initializer()
requested_step = FLAGS.requested_step if FLAGS.requested_step else None
checkpoint_path = '%s' % (GENDER_MODEL_PATH)
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_gender)
coder = ImageCoder()
files = []
# detect gender
#try:
best_choice = classifyGender(sess, gender_label_list,
softmax_output, coder, images,
image_file)
return best_choice
def guessGender(image_file):
with tf.Session() as sess:
sess = tf.Session()
age_label_list = AGE_LIST
gender_label_list = GENDER_LIST
genderlabels = len(gender_label_list)
# print('Executing on %s' % FLAGS.device_id)
model_fn = select_model('inception')
images = tf.placeholder(tf.float32,
[None, RESIZE_FINAL, RESIZE_FINAL, 3])
logits_gender = model_fn(genderlabels, 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_gender)
coder = ImageCoder()
files = []
# detect age
try:
best_choice = classify(sess, gender_label_list, softmax_output,
coder, images, image_file)
return best_choice
except Exception as e:
print(e)
print('Failed to run image %s ' % image_file)
def main(argv=None): # pylint: disable=unused-argument
with tf.Session() 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)
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()
files = []
if FLAGS.face_detection_model:
print('Using face detector %s' % FLAGS.face_detection_model)
face_detect = FaceDetector(FLAGS.face_detection_model)
face_files, rectangles = face_detect.run(FLAGS.filename)
files += face_files
# 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()
crops.append(standardize_image(cropped))
flipped = standardize_image(tf.image.flip_left_right(cropped))
crops.append(standardize_image(flipped))
image_batch = tf.stack(crops)
return image_batch
def make_single_image_batch(image_path, coder):
image_data = tf.gfile.FastGFile(image_path, 'rb').read()
image = coder.decode_jpeg(image_data)
crop = tf.image.resize_images(image, (227,227))
image_batch = tf.stack([crop])
return image_batch
with tf.Session() as sess:
coder = ImageCoder()
image_batch = make_multi_crop_batch(image_file, coder)
image_batch = image_batch.eval()
configuration = skil_client.Configuration()
configuration.host = 'http://192.168.1.128:9008'
configuration.username = '******'
configuration.password = '******'
r = requests.post("http://192.168.1.128:9008/login", json={"userId": "admin", "password": "******"})
token = r.json()['token']
configuration.api_key['authorization'] = f'Bearer {token}'
api_instance = skil_client.DefaultApi(skil_client.ApiClient(configuration))
def construct(filename,
class_type,
model_type,
model_dir,
checkpoint='checkpoint',
device='/cpu:0',
target=None,
classes=None): # pylint: disable=unused-argument
# sys.stdout = os.devnull
# sys.stderr = os.devnull
files = []
with tf.Graph().as_default():
with tf.Session() as sess:
#with tf.Session() as sess:
#print('\n111111111111111\n')
#tf.reset_default_graph()
label_list = AGE_LIST if class_type == 'age' else GENDER_LIST
nlabels = len(label_list)
#print('\n222222222222222\n')
#print('Executing on %s' % FLAGS.device_id)
model_fn = select_model(model_type)
with tf.device(device):
# sys.stdout = sys.__stdout__
# sys.stderr = sys.__stderr__
images = tf.placeholder(tf.float32,
[None, RESIZE_FINAL, RESIZE_FINAL, 3])
logits = model_fn(nlabels, images, 1, False)
init = tf.global_variables_initializer()
#print('\n333333333333333\n')
requested_step = None
checkpoint_path = '%s' % (model_dir)
model_checkpoint_path, global_step = get_checkpoint(
checkpoint_path, requested_step, checkpoint)
#print("\nglobal_step=",global_step)
saver = tf.train.Saver()
#print('\n44444444444444444\n')
#print("PATH=",model_checkpoint_path,'\n')
saver.restore(sess, model_checkpoint_path)
#print('\n55555555555555555\n')
softmax_output = tf.nn.softmax(logits)
coder = ImageCoder()
# Support a batch mode if no face detection model
if len(files) == 0:
files.append(filename)
# If it happens to be a list file, read the list and clobber the files
if one_of(filename, ('csv', 'tsv', 'txt')):
files = batchlist(filename)
for it, f in enumerate(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)
#results[it][0]=f
#print('f=%s\nresult='%f,results)
print("\nClass_type=", class_type)
if class_type == 'age':
#print("\n%s\n"%it)
classes[it].age = best_choice[0]
# print(best_choice[0],'\n')
# print(results,'\n')
target.writerow(
(f, classes[it].gender, classes[it].age,
'%.2f' % best_choice[1]))
if class_type == 'gender':
#print("\n222222222\n")
classes[it].name = f
classes[it].gender = best_choice[0]
except Exception as e:
print(e)
print('Failed to run image %s ' % image_file)
it += 1
#if output is not None:
# output.close()
# print(results)
sess.close()
def main(argv=None): # pylint: disable=unused-argument
# FIXME: Test by putting in multiple files in here.
# files = []
files = load_imgs(
'/Users/parimarjann/projects/face_recognizer/data/vgg_face_dataset/dataset_images'
)
random.seed(1234)
files = random.sample(files, 100)
print('single look: ', FLAGS.single_look)
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)
image_data = cv2.imread(image_file)
image_data = cv2.cvtColor(image_data, cv2.COLOR_RGB2BGR)
image_data = cv2.imencode('.jpeg', image_data)[1].tostring()
if image_file is None:
continue
try:
best_choice = classify(sess, label_list, softmax_output,
coder, images, image_data)
# if writer is not None:
# writer.writerow((f, best_choice[0], '%.2f' % best_choice[1]))
print(f)
print(best_choice)
except Exception as e:
print('exception!')
print(e)
print('Failed to run image %s ' % image_file)
if output is not None:
output.close()
AGE_LIST = [
'(0, 2)', '(4, 6)', '(8, 12)', '(15, 20)', '(25, 32)', '(38, 43)',
'(48, 53)', '(60, 100)'
]
with tf.Session() as sess:
nlabels = len(AGE_LIST)
from model import inception_v3
images = tf.placeholder(tf.float32, [None, 227, 227, 3])
logits = inception_v3(nlabels, images, 1, False)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
saver.restore(sess, 'D:\\model\\age\\inception\\checkpoint-14999')
softmax_output = tf.nn.softmax(logits)
coder = ImageCoder()
image_data = tf.gfile.FastGFile("test1.jpg", 'rb').read()
image = coder.decode_jpeg(image_data)
crop = tf.image.resize_images(image, (RESIZE_FINAL, RESIZE_FINAL))
image_batch = tf.stack([crop])
batch_results = sess.run(softmax_output,
feed_dict={images: image_batch.eval()})
output = batch_results[0]
batch_sz = batch_results.shape[0]
for i in range(1, batch_sz):
output = output + batch_results[i]
output /= batch_sz
best = np.argmax(output)
def construct(filename,
class_type,
model_type,
model_dir,
checkpoint='checkpoint',
device='/cpu:0',
target=None): # pylint: disable=unused-argument
files = []
with tf.Graph().as_default():
sess = tf.InteractiveSession()
#with tf.Session() as sess:
#print('\n111111111111111\n')
#tf.reset_default_graph()
label_list = AGE_LIST if class_type == 'age' else GENDER_LIST
nlabels = len(label_list)
#print('\n222222222222222\n')
#print('Executing on %s' % FLAGS.device_id)
model_fn = select_model(model_type)
with tf.device(device):
images = tf.placeholder(tf.float32,
[None, RESIZE_FINAL, RESIZE_FINAL, 3])
logits = model_fn(nlabels, images, 1, False)
init = tf.global_variables_initializer()
#print('\n333333333333333\n')
requested_step = None
checkpoint_path = '%s' % (model_dir)
model_checkpoint_path, global_step = get_checkpoint(
checkpoint_path, requested_step, checkpoint)
#print("\nglobal_step=",global_step)
saver = tf.train.Saver()
#print('\n44444444444444444\n')
#print("PATH=",model_checkpoint_path,'\n')
saver.restore(sess, model_checkpoint_path)
#print('\n55555555555555555\n')
softmax_output = tf.nn.softmax(logits)
coder = ImageCoder()
# Support a batch mode if no face detection model
if len(files) == 0:
files.append(filename)
# If it happens to be a list file, read the list and clobber the files
if one_of(filename, ('csv', 'tsv', 'txt')):
files = batchlist(filename)
writer = None
output = None
if target:
#print('Creating output file %s' % FLAGS.target)
output = open(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()
sess.close()