def load_logo(data_dir):
image_files = os.listdir(data_dir)
dataset = np.ndarray(
shape=(len(image_files), common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH,
common.CNN_IN_CH),
dtype=np.float32)
print(data_dir)
num_images = 0
for image in image_files:
image_file = os.path.join(data_dir, image)
try:
if common.CNN_IN_CH == 1:
image_data = skimage.io.imread(image_file, as_grey=True)
image_data = image_data.reshape(common.CNN_IN_HEIGHT,
common.CNN_IN_WIDTH,
common.CNN_IN_CH)
else:
image_data = skimage.io.imread(image_file)
image_data = preprocess.scaling(image_data)
if image_data.shape != (common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH,
common.CNN_IN_CH):
raise Exception(
'Unexpected image shape: %s' % str(image_data.shape))
dataset[num_images, :, :] = image_data
num_images = num_images + 1
except IOError as e:
print('Could not read:', image_file, ':', e,
'-it\'s ok, skipping.')
dataset = dataset[0:num_images, :, :]
print('Full dataset tensor:', dataset.shape)
print('Mean:', np.mean(dataset))
print('Standard deviation:', np.std(dataset))
return dataset
def logo_recognition(sess, img, obj_proposal, graph_params):
# recognition results
recog_results = {}
recog_results['obj_proposal'] = obj_proposal
# Resize image
if img.shape != common.CNN_SHAPE:
img = imresize(img, common.CNN_SHAPE, interp='bicubic')
# Pre-processing
img = preprocess.scaling(img)
img = img.reshape((1, common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH,
common.CNN_IN_CH)).astype(np.float32)
# Logo recognition
pred = sess.run([graph_params['pred']],
feed_dict={graph_params['target_image']: img})
recog_results['pred_class'] = common.CLASS_NAME[np.argmax(pred)]
recog_results['pred_prob'] = np.max(pred)
return recog_results
def main():
if len(sys.argv) > 1:
test_image_fn = sys.argv[1]
if not os.path.exists(test_image_fn):
print("Not found:", test_image_fn)
sys.exit(-1)
else:
# Select a test image from a test directory
test_dirs = [
os.path.join(common.CROPPED_AUG_IMAGE_DIR, class_name, 'test')
for class_name in common.CLASS_NAME
]
test_dir = np.random.choice(test_dirs)
test_images_fn = [test_image for test_image in os.listdir(test_dir)]
test_image_fn = np.random.choice(test_images_fn, 1)[0]
test_image_fn = os.path.join(test_dir, test_image_fn)
print("Test image:", test_image_fn)
# Open and resize a test image
if common.CNN_IN_CH == 1:
test_image_org = skimage.io.imread(test_image_fn, as_grey=True)
test_image_org = test_image_org.reshape(
common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH, common.CNN_IN_CH)
else:
test_image_org = skimage.io.imread(test_image_fn)
if test_image_org.shape != (common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH,
common.CNN_IN_CH):
test_image_org = imresize(
test_image_org, (common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH),
interp='bicubic')
test_image_org = preprocess.scaling(test_image_org)
test_image = test_image_org.reshape(
(1, common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH,
common.CNN_IN_CH)).astype(np.float32)
# Training model
graph = tf.Graph()
with graph.as_default():
# Weights and biases
model_params = model.params()
# restore weights
f = "weights.npz"
if os.path.exists(f):
initial_weights = load_initial_weights(f)
else:
initial_weights = None
if initial_weights is not None:
assert len(initial_weights) == len(model_params)
assign_ops = [
w.assign(v) for w, v in zip(model_params, initial_weights)
]
# A placeholder for a test image
tf_test_image = tf.constant(test_image)
# model
logits = model.cnn(tf_test_image, model_params, keep_prob=1.0)
test_pred = tf.nn.softmax(logits)
# Restore ops
saver = tf.train.Saver()
# Recognize a brand logo of test image
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
if initial_weights is not None:
session.run(assign_ops)
print('initialized by pre-learned weights')
elif os.path.exists("models"):
save_path = "models/deep_logo_model"
saver.restore(session, save_path)
print('Model restored')
else:
print('initialized')
pred = session.run([test_pred])
print("Class name:", common.CLASS_NAME[np.argmax(pred)])
print("Probability:", np.max(pred))
def main():
if len(sys.argv) > 1:
test_image_fn = sys.argv[1]
if not os.path.exists(test_image_fn):
print("Not found:", test_image_fn)
sys.exit(-1)
else:
# Select a test image from a test directory
test_dirs = [
os.path.join(common.CROPPED_AUG_IMAGE_DIR, class_name, 'test')
for class_name in common.CLASS_NAME
]
test_dir = np.random.choice(test_dirs)
test_images_fn = [test_image for test_image in os.listdir(test_dir)]
test_image_fn = np.random.choice(test_images_fn, 1)[0]
test_image_fn = os.path.join(test_dir, test_image_fn)
print("Test image:", test_image_fn)
# Open and resize a test image
if common.CNN_IN_CH == 1:
test_image_org = skimage.io.imread(test_image_fn, as_grey=True)
test_image_org = test_image_org.reshape(common.CNN_IN_HEIGHT,
common.CNN_IN_WIDTH,
common.CNN_IN_CH)
else:
test_image_org = skimage.io.imread(test_image_fn)
if test_image_org.shape != (common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH,
common.CNN_IN_CH):
test_image_org = imresize(test_image_org,
(common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH),
interp='bicubic')
test_image_org = preprocess.scaling(test_image_org)
test_image = test_image_org.reshape(
(1, common.CNN_IN_HEIGHT, common.CNN_IN_WIDTH,
common.CNN_IN_CH)).astype(np.float32)
# Training model
graph = tf.Graph()
with graph.as_default():
# Weights and biases
model_params = model.params()
# restore weights
f = "weights.npz"
if os.path.exists(f):
initial_weights = load_initial_weights(f)
else:
initial_weights = None
if initial_weights is not None:
assert len(initial_weights) == len(model_params)
assign_ops = [
w.assign(v) for w, v in zip(model_params, initial_weights)
]
# A placeholder for a test image
tf_test_image = tf.constant(test_image)
# model
logits = model.cnn(tf_test_image, model_params, keep_prob=1.0)
test_pred = tf.nn.softmax(logits)
# Restore ops
saver = tf.train.Saver()
# Recognize a brand logo of test image
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
if initial_weights is not None:
session.run(assign_ops)
print('initialized by pre-learned weights')
elif os.path.exists("models"):
save_path = "models/deep_logo_model"
saver.restore(session, save_path)
print('Model restored')
else:
print('initialized')
pred = session.run([test_pred])
print("Class name:", common.CLASS_NAME[np.argmax(pred)])
print("Probability:", np.max(pred))