def get_depth(image_path):
# Default input size
height = 228
width = 304
channels = 3
batch_size = 1
# Read image
img = Image.open(image_path)
img = img.resize([width, height], Image.ANTIALIAS)
img = np.array(img).astype('float32')
img = np.expand_dims(np.asarray(img), axis=0)
# Create a placeholder for the input image
input_node = tf.placeholder(tf.float32,
shape=(None, height, width, channels))
# Construct the network
net = models.ResNet50UpProj({'data': input_node}, batch_size, 1, False)
with tf.Session() as sess:
tf.get_variable_scope().reuse_variables()
# Load the converted parameters
# Use to load from ckpt file
saver = tf.train.Saver()
saver.restore(sess, model_data_path)
# Use to load from npy file
# net.load(model_data_path, sess)
# Evaluate the network for the given image
pred = sess.run(net.get_output(), feed_dict={input_node: img})
return pred[0, :, :, 0]
def predict(model_data_path, image_path):
# Default input size
height = 228
width = 304
channels = 3
batch_size = 1
# Read image
img = Image.open(image_path)
img = img.resize([width, height], Image.ANTIALIAS)
img = np.array(img).astype('float32')
img = np.expand_dims(np.asarray(img), axis=0)
# Create a placeholder for the input image
input_node = tf.placeholder(tf.float32,
shape=(None, height, width, channels))
# Construct the network
net = models.ResNet50UpProj({'data': input_node}, batch_size, 1, False)
with tf.Session() as sess:
tf.get_variable_scope().reuse_variables()
# Load the converted parameters
# Use to load from ckpt file
saver = tf.train.Saver()
saver.restore(sess, model_data_path)
# Use to load from npy file
# net.load(model_data_path, sess)
# Evaluate the network for the given image
pred = sess.run(net.get_output(), feed_dict={input_node: img})
# Plot result
fig = plt.figure()
# 可修改colormap，cmap=plt.cm.jet
ii = plt.imshow(pred[0, :, :, 0],
interpolation='nearest',
cmap=plt.cm.jet)
# 去掉坐标轴
plt.axis('off')
# 去掉图像边缘
plt.subplots_adjust(left=0, bottom=0, right=1, top=1)
# 保存生成图片
plt.savefig('pred.jpg')
return pred
def predict(model_data_path, image_path):
# Default input size
height = 228
width = 304
channels = 3
batch_size = 1
# Read image
img = Image.open(image_path)
img = img.resize([width, height], Image.ANTIALIAS)
img = np.array(img).astype('float32')
img = np.expand_dims(np.asarray(img), axis=0)
# Create a placeholder for the input image
input_node = tf.placeholder(tf.float32,
shape=(None, height, width, channels))
# Construct the network
net = models.ResNet50UpProj({'data': input_node}, batch_size, 1, False)
with tf.Session() as sess:
# Load the converted parameters
print('Loading the model')
# Use to load from ckpt file
saver = tf.train.Saver()
saver.restore(sess, model_data_path)
# Use to load from npy file
#net.load(model_data_path, sess)
# Evalute the network for the given image
pred = sess.run(net.get_output(), feed_dict={input_node: img})
# Plot result
fig = plt.figure()
ii = plt.imshow(pred[0, :, :, 0], interpolation='nearest')
fig.colorbar(ii)
plt.show()
return pred
# im.save(name_path + ".png")
numpngw.write_png(name_path + ".png", rescaled)
# np.save(name_path,disp)
# Get rgb images
dataset = tf.data.Dataset.list_files(args.path + "/rgb/*")
dataset = dataset.map(_parse_fn)
dataset = dataset.batch(batch_size)
dataset.prefetch(2 * batch_size)
dataset = dataset.make_one_shot_iterator()
example, image = dataset.get_next()
# Instantiate depth FCRN model
net = models.ResNet50UpProj({'data': image}, batch_size, 1, False)
# GPU options
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
# Load the converted parameters
print('Loading the model')
# Use to load from ckpt file
saver = tf.train.Saver()
saver.restore(sess, args.checkpoint_path)
i = 1
# Run dataset iterator