def predict(self, img):
""" # Arguments
img: a numpy array
# Returns
The url to an image with the segmentation
"""
with self.graph.as_default():
img = Image.fromarray(img)
# RGB -> BGR
b, g, r = img.split()
img = Image.merge("RGB", (r, g, b))
img -= self.IMG_MEAN
# Predictions.
raw_output = self.net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output, tf.shape(img)[0:2, ])
raw_output_up = tf.argmax(raw_output_up, axis=3)
self.pred = tf.expand_dims(raw_output_up, dim=3)
preds = self.sess.run(self.pred, feed_dict={self.image_placeholder: np.expand_dims(img, axis=0)})
msk = decode_labels(preds, num_classes=self.NUM_CLASSES)
im = Image.fromarray(msk[0])
filename = str(uuid.uuid4()) + '.jpg'
save_dir = './outputs'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path = os.path.join(save_dir, filename)
im.save(save_path)
return json.dumps({'output': filename})
def _process(self, req):
# type: (DeepLabRequest) -> None
# img_path = os.path.join(self.manager.upload_path, req.prefix, req.input_file_name)
# ret_path = os.path.join(self.manager.result_path, req.prefix, req.result_file_name)
# Prepare image.
# img_rgb = tf.image.decode_jpeg(tf.read_file(img_path), channels=3)
jpg_tensor, jpg_data = self.manager.load_img(req.input_file_name)
img_rgb = tf.image.decode_jpeg(jpg_tensor, channels=3)
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(img_rgb, 3, axis=2)
img_bgr = tf.cast(tf.concat([img_b, img_g, img_r], 2),
dtype=tf.float32)
# Extract mean.
img_bgr -= self.manager.img_mean
# Create network.
net = DeepLabResNetModel({'data': tf.expand_dims(img_bgr, dim=0)},
is_training=False)
tf.get_variable_scope().reuse_variables()
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output,
tf.shape(img_bgr)[0:2, ])
# CRF.
raw_output_up = tf.nn.softmax(raw_output_up)
raw_output_up = tf.py_func(
dense_crf,
[raw_output_up, tf.expand_dims(img_rgb, dim=0)], tf.float32)
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
if not self.initialized:
# Set up TF session and initialize variables.
config = tf.ConfigProto(device_count={'GPU': self.gpu_id})
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
init = tf.global_variables_initializer()
self.sess.run(init)
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
loader.restore(self.sess, self.manager.weights_model_path)
self.initialized = True
# Perform inference.
preds = self.sess.run(pred, feed_dict={jpg_tensor: jpg_data})
msk = decode_labels(preds)
im = Image.fromarray(msk[0])
mask_path = req.result_file_name
# os.makedirs(os.path.dirname(mask_path), exist_ok=True)
# im.save(mask_path)
self.manager.save_img(mask_path, im)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
filename = os.path.basename(args.img_path).split('.')[0]
# Prepare image.
img = tf.image.decode_jpeg(tf.read_file(args.img_path), channels=3)
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(axis=2, num_or_size_splits=3, value=img)
img = tf.cast(tf.concat(axis=2, values=[img_b, img_g, img_r]),
dtype=tf.float32)
# Extract mean.
img -= IMG_MEAN
# Create network.
net = DeepLabResNetModel({'data': tf.expand_dims(img, dim=0)},
is_training=False,
num_classes=args.num_classes)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output, tf.shape(img)[0:2, ])
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, args.model_weights)
# Perform inference.
preds = sess.run(pred)
msk = decode_labels(preds, num_classes=args.num_classes)
im = Image.fromarray(msk[0])
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
mask_path = args.save_dir + filename + '_mask.png'
im.save(mask_path)
apply_mask_all_classes(args.img_path, mask_path, args.save_dir)
# im_png = Image.open(args.save_dir + filename + '_result.png')
# im_png.convert('RGB').save(args.save_dir + filename + '_result.jpg',"JPEG")
print('The output file has been saved to {}'.format(args.save_dir +
filename +
'_result.jpeg'))
def infer(img_path, save_dir=SAVE_DIR, model_weights=MODEL_WEIGHTS, num_classes=NUM_CLASSES):
"""Create the model and start the evaluation process."""
# Prepare image.
img = tf.image.decode_jpeg(tf.read_file(img_path), channels=3)
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(axis=2, num_or_size_splits=3, value=img)
img = tf.cast(tf.concat(axis=2, values=[img_b, img_g, img_r]), dtype=tf.float32)
# Extract mean.
img -= IMG_MEAN
# Create network.
net = DeepLabResNetModel({'data': tf.expand_dims(img, dim=0)}, is_training=False, num_classes=num_classes)
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output, tf.shape(img)[0:2,])
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
# Which variables to load.
restore_var = tf.global_variables()
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, model_weights)
# for path in img_path:
# print("img_path {}, save path {}".format(path, save_dir))
# pred = prepare_pred(path, num_classes)
# Perform inference.
preds = sess.run(pred)
msk = decode_labels(preds, num_classes=num_classes)
im = Image.fromarray(msk[0])
if not os.path.exists(save_dir):
os.makedirs(save_dir)
im.save(save_dir + os.path.basename(img_path))
print('The segmentation mask has been saved to {}'.format(save_dir + os.path.basename(img_path)))
tf.reset_default_graph()
sess.close()
def process_frame(frame):
frame = cv2.resize(frame, (IMG_SIZE, IMG_SIZE))
input_img_feed = np.array(frame, dtype=float)
input_img_feed = np.expand_dims(input_img_feed, axis=0)
start_time = time.time()
preds = sess.run(pred, feed_dict={img_tf: input_img_feed})
elapsed_time = time.time() - start_time
print(("FPS: ", 1 / elapsed_time))
msk = decode_labels(preds, num_classes=NUM_CLASSES)
im = msk[0]
final = cv2.addWeighted(im, 0.9, frame, 0.7, 0)
return final
def process_frame(frame):
frame = cv2.resize(frame, (IMG_SIZE, IMG_SIZE))
input_img_feed = np.array(frame, dtype=float)
input_img_feed = np.expand_dims(input_img_feed, axis=0)
start_time = time.time()
preds = sess.run(pred, feed_dict={img_tf: input_img_feed})
elapsed_time = time.time() - start_time
print(("FPS: ", 1 / elapsed_time))
msk = decode_labels(preds, num_classes=NUM_CLASSES)
im = msk[0]
final = cv2.addWeighted(im,0.9,frame,0.7,0)
return final
def main():
"""主函数：模型构建和evaluate."""
args = get_arguments()
# 读取图片.
img = tf.image.decode_jpeg(tf.read_file(args.img_path), channels=3)
# 通道转换： RGB --> BGR.
img_r, img_g, img_b = tf.split(axis=2, num_or_size_splits=3, value=img)
img = tf.cast(tf.concat(axis=2, values=[img_b, img_g, img_r]),
dtype=tf.float32)
# 减去像素均值.
img -= IMG_MEAN
# 构建DeepLab-ResNet-101网络.
net = DeepLabResNetModel({'data': tf.expand_dims(img, dim=0)},
is_training=False,
num_classes=args.num_classes)
# 设定要预加载的网络权重参数
restore_var = tf.global_variables()
# 执行预测.
raw_output = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output, tf.shape(img)[0:2, ])
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
# 建立tf session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# 执行权重变量初始化
init = tf.global_variables_initializer()
sess.run(init)
# 加载已有的checkpoint文件
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, args.model_weights)
# 执行推断.
preds = sess.run(pred)
msk = decode_labels(preds, num_classes=args.num_classes)
im = Image.fromarray(msk[0])
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
im.save(args.save_dir + 'mask.png')
print('The output file has been saved to {}'.format(args.save_dir +
'mask.png'))
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
# Prepare image.
img = tf.image.decode_jpeg(tf.read_file(args.img_path), channels=3)
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(value=img, num_or_size_splits=3, axis=2)
img = tf.cast(tf.concat([img_b, img_g, img_r], axis=2), dtype=tf.float32)
# Extract mean.
img -= IMG_MEAN
# Create network.
net = DeepLabResNetModel({'data': tf.expand_dims(img, dim=0)},
is_training=False)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output, tf.shape(img)[0:2, ])
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
# Set up TF session and initialize variables.
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
graph = tf.get_default_graph()
with tf.Session(graph=graph) as session:
init = tf.global_variables_initializer()
session.run(init)
# Load weights.
loader = tf.train.Saver(tf.global_variables())
load(loader, session, args.model_weights)
# Perform inference.
preds = session.run(pred)
msk = decode_labels(preds)
im = Image.fromarray(msk[0])
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
im.save(args.save_dir + 'mask.png')
print('The output file has been saved to {}'.format(args.save_dir +
'mask.png'))
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
# Prepare image.
my_img = mpimg.imread(args.img_path)
input_data = tf.placeholder(tf.float32, shape=list(my_img.shape))
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(axis=2, num_or_size_splits=3, value=input_data)
img = tf.cast(tf.concat(axis=2, values=[img_b, img_g, img_r]), dtype=tf.float32)
# Extract mean.
img -= IMG_MEAN
# Create network.
net = DeepLabResNetModel({'data': tf.expand_dims(img, dim=0)}, is_training=False, num_classes=args.num_classes)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output, tf.shape(img)[0:2,])
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, args.model_weights)
# Perform inference.
preds = sess.run(pred, feed_dict={input_data: my_img})
msk = decode_labels(preds, num_classes=args.num_classes)
im = Image.fromarray(msk[0])
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
im.save(args.save_dir + 'mask.png')
print('The output file has been saved to {}'.format(args.save_dir + 'mask.png'))
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
# Prepare image.
img = tf.image.decode_jpeg(tf.read_file(args.img_path), channels=3)
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(axis=2, num_or_size_splits=3, value=img)
img = tf.cast(tf.concat(axis=2, values=[img_b, img_g, img_r]), dtype=tf.float32)
# Extract mean.
img -= IMG_MEAN
# Create network.
net = DeepLabResNetModel({'data': tf.expand_dims(img, dim=0)}, is_training=False, num_classes=args.num_classes)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output, tf.shape(img)[0:2,])
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, args.model_weights)
# Perform inference.
preds = sess.run(pred)
msk = decode_labels(preds, num_classes=args.num_classes)
im = Image.fromarray(msk[0])
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
im.save(args.save_dir + 'mask.png')
print('The output file has been saved to {}'.format(args.save_dir + 'mask.png'))
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
# Prepare image.
img = tf.image.decode_jpeg(tf.read_file(args.img_path), channels=3)
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(split_dim=2, num_split=3, value=img)
img = tf.cast(tf.concat(2, [img_b, img_g, img_r]), dtype=tf.float32)
# Extract mean.
img -= IMG_MEAN
# Create network.
net = DeepLabResNetModel({'data': tf.expand_dims(img, dim=0)})
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output, tf.shape(img)[0:2,])
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
# Which variables to load.
trainable = tf.trainable_variables()
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.initialize_all_variables()
sess.run(init)
# Load weights.
saver = tf.train.Saver(var_list=trainable)
load(saver, sess, args.model_weights)
# Perform inference.
preds = sess.run([pred])
msk = decode_labels(np.array(preds)[0, 0, :, :, 0])
im = Image.fromarray(msk)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
im.save(args.save_dir + 'mask.png')
print('The output file has been saved to {}'.format(args.save_dir + 'mask.png'))
def infer_and_save_colormask(img_path, model_weights, use_crf):
# Perform inference.
preds = infer_absolute_path(img_path, model_weights, use_crf)
# Have to add an extra dimension in the front because `infer_absolute_path` removed the batch dimension
preds = np.expand_dims(preds, axis=0)
msk = decode_labels(preds, num_classes=NUM_CLASSES)
im = Image.fromarray(msk[0])
# Save the image
head, tail = os.path.split(img_path)
filename, file_extension = os.path.splitext(tail)
save_path = COLOR_MASK_SAVE_DIR + filename + '_colormask.png'
if not os.path.exists(COLOR_MASK_SAVE_DIR):
os.makedirs(COLOR_MASK_SAVE_DIR)
im.save(save_path)
print('The output file has been saved to {}'.format(save_path))
def predict(self, img):
""" # Arguments
img: a numpy array
# Returns
The url to an image with the segmentation
"""
with self.graph.as_default():
img = Image.fromarray(img)
# RGB -> BGR
b, g, r = img.split()
img = Image.merge("RGB", (r, g, b))
img -= self.IMG_MEAN
# Predictions.
raw_output = self.net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output,
tf.shape(img)[0:2, ])
raw_output_up = tf.argmax(raw_output_up, axis=3)
self.pred = tf.expand_dims(raw_output_up, dim=3)
preds = self.sess.run(self.pred,
feed_dict={
self.image_placeholder:
np.expand_dims(img, axis=0)
})
msk = decode_labels(preds, num_classes=self.NUM_CLASSES)
im = Image.fromarray(msk[0])
filename = str(uuid.uuid4()) + '.jpg'
save_dir = './outputs'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path = os.path.join(save_dir, filename)
im.save(save_path)
return json.dumps({'output': filename})
for line in lines:
img_path, seg_path = line.split('\t')
img_path = os.path.join(args.data_dir, img_path)
seg_path = os.path.join(args.data_dir, seg_path)
if not os.path.exists(seg_path):
print("skip non-existent: "+ seg_path)
continue
if not os.path.exists(img_path):
print("skip non-existent: "+ img_path)
continue
seg = cv2.imread(seg_path, cv2.IMREAD_UNCHANGED)
seg = np.expand_dims(np.expand_dims(seg, 0), -1)
msk = decode_labels(seg, num_classes=np.max(seg) + 1)
im = msk[0]
img_o = cv2.imread(img_path)
img_path = str(img_path)
print(im.shape, im.dtype)
print(img_o.shape, img_o.dtype)
# img = np.array(im) * 0.9 + np.array(img_o) * 0.7
img = np.hstack([im, img_o])
img[img > 255] = 255
cv2.imshow("labels", img.astype(np.uint8))
cv2.waitKey(0)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
print(args)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_mask
with open(args.data_list) as f:
list_of_filenames = [line.rstrip() for line in f]
num_steps = len(list_of_filenames)
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(
args.data_dir,
args.data_list,
(512, 512), # No defined input size.
False, # No random scale.
False, # No random mirror.
args.ignore_label,
IMG_MEAN,
coord,
shuffle=False)
image, label = reader.image, reader.label
image_batch, label_batch = tf.expand_dims(image, dim=0), tf.expand_dims(label,
dim=0) # Add one batch dimension.
# # Prepare image.
# img = tf.image.decode_jpeg(tf.read_file(args.img_path), channels=3)
# # Convert RGB to BGR.
# img_r, img_g, img_b = tf.split(axis=2, num_or_size_splits=3, value=img)
# img = tf.cast(tf.concat(axis=2, values=[img_b, img_g, img_r]), dtype=tf.float32)
# # Extract mean.
# img -= IMG_MEAN
# Create network.
net = DeepLabResNetModel({'data': image_batch}, is_training=False, num_classes=args.num_classes)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
# raw_output = net.layers['concat_conv6']
raw_output_up = tf.image.resize_area(raw_output, tf.shape(label_batch)[1:3, ])
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
# Set up TF session and initialize variables.
sess = tf.Session()
init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init)
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
if '.ckpt' in args.model_weights:
load(loader, sess, args.model_weights)
else:
load(loader, sess, tf.train.latest_checkpoint(args.model_weights))
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
# Perform inference.
for step in range(num_steps):
preds = sess.run([pred])
msk = decode_labels(preds[0], num_classes=args.num_classes)
im = Image.fromarray(msk[0])
im.save(args.save_dir + list_of_filenames[step].split("/")[-1] + '_mask.png')
print('The output file has been saved to {}'.format(
args.save_dir + list_of_filenames[step].split("/")[-1] + '_mask.png'))
coord.request_stop()
coord.join(threads)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
num_steps = file_len(args.data_list)
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(
args.img_path,
args.data_list,
None, # No defined input size.
False, # No random scale.
False, # No random mirror.
255,
IMG_MEAN,
coord)
image, label = reader.image, reader.label
title = reader.queue[0]
image_batch, label_batch = tf.expand_dims(image, dim=0), tf.expand_dims(
label, dim=0) # Add one batch dimension.
# Create network.
net = DeepLabResNetModel({'data': image_batch},
is_training=False,
num_classes=args.num_classes)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output,
tf.shape(image_batch)[1:3, ])
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, args.model_weights)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
start_time = time.time()
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
# Perform inference.
for step in range(num_steps):
preds, jpg_path = sess.run([pred, title])
msk = decode_labels(preds, num_classes=args.num_classes)
im = Image.fromarray(msk[0])
img_o = Image.open(jpg_path)
jpg_path = jpg_path.decode()
jpg_path = jpg_path.split('/')[-1].split('.')[0]
img = np.array(im) * 0.9 + np.array(img_o) * 0.7
img[img > 255] = 255
img = Image.fromarray(np.uint8(img))
img.save(args.save_dir + jpg_path + '.png')
print('Image processed {}.png'.format(jpg_path))
total_time = time.time() - start_time
print('The output files have been saved to {}'.format(args.save_dir))
print('It took {} sec on each image.'.format(total_time / num_steps))
def main():
args, preds = get_arguments(), []
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(
args.data_dir,
args.target_list,
None, # No defined input size.
False, # No random scale.
False, # No random mirror.
args.ignore_label,
IMG_MEAN,
coord)
image_orig = reader.image
for rots in range(4):
image = tf.image.rot90(image_orig, k=rots)
image_batch = tf.expand_dims(image, dim=0) # Add one batch dimension.
# Create network.
net = DeepLabResNetModel({'data': image_batch},
is_training=False,
num_classes=args.num_classes)
tf.get_variable_scope().reuse_variables()
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output = tf.image.resize_bilinear(raw_output,
tf.shape(image_batch)[1:3, ])
# CRF.
if args.crf:
inv_image = tf.py_func(inv_preprocess, [image_batch, 1, IMG_MEAN],
tf.uint8)
raw_output = tf.py_func(dense_crf,
[tf.nn.softmax(raw_output), inv_image],
tf.float32)
# Rotate to original
raw_output = tf.image.rot90(tf.squeeze(raw_output), k=(4 - rots))
raw_output = tf.expand_dims(raw_output, dim=0)
preds.append(raw_output)
if not args.augment:
break
pred = tf.reduce_mean(tf.concat(preds, axis=0), axis=0)
if args.heatmap < 0:
pred = tf.argmax(tf.expand_dims(pred, dim=0), dimension=3)
pred = tf.cast(tf.expand_dims(pred, dim=3), tf.int32)
else:
pred = tf.expand_dims(pred[:, :, args.heatmap], dim=0)
pred = tf.cast(pred, tf.int32)
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
sess.run(tf.local_variables_initializer())
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, args.restore_from)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
# Iterate over training steps.
for step in tqdm(range(args.num_steps)):
preds, img_path = sess.run([pred, reader.queue[0]])
if args.heatmap < 0:
preds = decode_labels(preds, num_classes=args.num_classes)
im = Image.fromarray(preds[0])
else:
pr = np.zeros((1, preds.shape[1], preds.shape[2], 3))
preds += abs(np.min(preds))
preds *= 255 / np.max(preds)
pr[:, :, :, 0] = preds
pr[:, :, :, 1] = preds
pr[:, :, :, 2] = preds
im = Image.fromarray(pr[0].astype('uint8'))
img_name = os.path.split(img_path)[-1]
im.save(os.path.join(args.save_dir + img_name))
coord.request_stop()
coord.join(threads)
def main():
"""Create the model and start the evaluation process."""
args, rot_preds = get_arguments(), []
# Prepare image.
img_orig = tf.image.decode_png(tf.read_file(args.img_path), channels=3)
for rots in range(4):
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(axis=2,
num_or_size_splits=3,
value=img_orig)
img = tf.cast(tf.concat(axis=2, values=[img_b, img_g, img_r]),
dtype=tf.float32)
# Extract mean.
img -= IMG_MEAN
img = tf.image.rot90(img, k=rots)
# Create network.
net = DeepLabResNetModel({'data': tf.expand_dims(img, dim=0)},
is_training=False,
num_classes=args.num_classes)
tf.get_variable_scope().reuse_variables()
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output,
tf.shape(img)[0:2, ])
# CRF.
if args.crf:
raw_output_up = tf.nn.softmax(raw_output_up)
raw_output_up = tf.py_func(
dense_crf,
[raw_output_up, tf.expand_dims(img_orig, dim=0)], tf.float32)
raw_output_up = tf.image.rot90(tf.squeeze(raw_output_up), k=(4 - rots))
raw_output_up = tf.expand_dims(raw_output_up, dim=0)
rot_preds.append(raw_output_up)
if not args.augment:
break
pred = tf.reduce_mean(tf.concat(rot_preds, axis=0), axis=0)
pred = tf.argmax(tf.expand_dims(pred, dim=0), dimension=3)
pred = tf.cast(tf.expand_dims(pred, dim=3), tf.int32)
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, args.model_weights)
# Perform inference.
preds = sess.run(pred)
msk = decode_labels(preds, num_classes=args.num_classes)
im = Image.fromarray(msk[0])
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
im.save(args.save_dir + 'mask.png')
print('The output file has been saved to {}'.format(args.save_dir +
'mask.png'))
def deeplabProcessing(gpuId):
"""Create the model and start the evaluation process."""
print("Starting worker on GPU " + str(gpuId) + "...")
def printWorker(msg):
print(str(timestampMs()) + " [gpu-worker-" + str(gpuId) + "] " + msg)
printWorker("Waiting for segmentation requests...")
initialized = False
while (not quit):
fileId = requestQueue.get() # will block
if fileId == "quit" + str(gpuId):
printWorker("Received quit command")
break
printWorker("Received request for DL segmentaiton: " + fileId)
printWorker("Requests queue size: " + str(requestQueue.qsize()))
t1 = timestampMs() #datetime.datetime.now()
imgPath = os.path.join(uploadPath, fileId)
# Prepare image.
imgRGB = tf.image.decode_jpeg(tf.read_file(imgPath), channels=3)
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(imgRGB, 3, axis=2)
imgBGR = tf.cast(tf.concat([img_b, img_g, img_r], 2), dtype=tf.float32)
# Extract mean.
imgBGR -= IMG_MEAN
printWorker("Will create network")
# Create network.
net = DeepLabResNetModel({'data': tf.expand_dims(imgBGR, dim=0)},
is_training=False)
tf.get_variable_scope().reuse_variables()
printWorker("Network created")
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output,
tf.shape(imgBGR)[0:2, ])
printWorker("Predictions")
# CRF.
raw_output_up = tf.nn.softmax(raw_output_up)
raw_output_up = tf.py_func(
dense_crf,
[raw_output_up, tf.expand_dims(imgRGB, dim=0)], tf.float32)
printWorker("CRF")
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
if not initialized:
printWorker("Setup tf session")
# Set up TF session and initialize variables.
config = tf.ConfigProto(device_count={'GPU': gpuId})
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
printWorker("TF session initialized")
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, weightsModelPath)
initialized = True
# Perform inference.
preds = sess.run(pred)
msk = decode_labels(preds)
im = Image.fromarray(msk[0])
maskPath = os.path.join(resultsPath, fileId) + ".png"
im.save(maskPath)
originalFile = os.path.join(uploadPath, fileId)
os.remove(originalFile)
t2 = timestampMs() #datetime.datetime.now()
printWorker("Processing took " + str(t2 - t1) + "ms. Result is at " +
maskPath)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
num_steps = file_len(args.data_list)
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(
args.img_path,
args.data_list,
None, # No defined input size.
False, # No random scale.
False, # No random mirror.
255,
IMG_MEAN,
coord)
image, label = reader.image, reader.label
title = reader.queue[0]
image_batch, label_batch = tf.expand_dims(image, dim=0), tf.expand_dims(label, dim=0) # Add one batch dimension.
# Create network.
net = DeepLabResNetModel({'data': image_batch}, is_training=False, num_classes=args.num_classes)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output, tf.shape(image_batch)[1:3,])
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, args.model_weights)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
start_time = time.time()
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
# Perform inference.
for step in range(num_steps):
preds, jpg_path = sess.run([pred, title])
msk = decode_labels(preds, num_classes=args.num_classes)
im = Image.fromarray(msk[0])
img_o = Image.open(jpg_path)
jpg_path = jpg_path.split('/')[-1].split('.')[0]
img = np.array(im)*0.9 + np.array(img_o)*0.7
img[img>255] = 255
img = Image.fromarray(np.uint8(img))
img.save(args.save_dir + jpg_path + '.png')
print('Image processed {}.png'.format(jpg_path))
total_time = time.time() - start_time
print('The output files have been saved to {}'.format(args.save_dir))
print('It took {} sec on each image.'.format(total_time/num_steps))
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
sess.run(tf.local_variables_initializer())
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
if args.restore_from is not None:
load(loader, sess, args.restore_from)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
# make output dir if
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
# Iterate over training steps.
for step in tqdm(range(len(reader.image_list))):
preds = sess.run(pred)
msk = decode_labels(preds, num_classes=args.num_classes)
im = Image.fromarray(msk[0])
im.save(os.path.join(args.output_dir, os.path.basename(reader.image_list[step])+'_mask.png'))
coord.request_stop()
coord.join(threads)
def predict(preds, scores, img_path, scale, num_classes, save_dir, heavy, t1):
msk = decode_labels(preds, num_classes=num_classes)
"""
print('The output file has been saved to {}'.format( save_dir + 'mask.png'))
print(time.time() - t1)"""
means, labels_means = filter_label(msk, scores, 0.98)
my_msk = msk
labels = []
#Rule 1 : (skirt - shirt) vs (skirt - t-shirt) vs (dress)
try:
_sk = means[labels_means.index(18)]
except ValueError:
_sk = 0
try:
_sh = means[labels_means.index(15)]
except ValueError:
_sh = 0
try:
_tsh = means[labels_means.index(22)]
except ValueError:
_tsh = 0
try:
_dr = means[labels_means.index(7)]
except ValueError:
_dr = 0
_out1 = np.mean([_sk, _sh])
_out2 = np.mean([_sk, _tsh])
my_max = np.argmax([_out1, _out2, _dr])
if my_max == 0:
change_label(my_msk, scores, [7, 22], 15, _sh)
elif my_max == 1:
change_label(my_msk, scores, [7, 15], 22, _tsh)
else:
change_label(my_msk, scores, [18, 22, 15], 7, _dr)
#Rule 2 : (pants) vs (leggins) vs (shorts)
try:
_pa = means[labels_means.index(13)]
except ValueError:
_pa = 0
try:
_leg = means[labels_means.index(21)]
except ValueError:
_leg = 0
try:
_sh = means[labels_means.index(17)]
except ValueError:
_sh = 0
my_max = np.argmax([_pa, _leg, _sh])
if my_max == 0:
change_label(my_msk, scores, [21, 17], 13, _pa)
elif my_max == 1:
change_label(my_msk, scores, [13, 17], 21, _leg)
else:
change_label(my_msk, scores, [13, 21], 17, _sh)
#Rule 3 : (boots) vs (shoes) vs (socks)
try:
_bo = means[labels_means.index(5)]
except ValueError:
_bo = 0
try:
_sh = means[labels_means.index(16)]
except ValueError:
_sh = 0
try:
_so = means[labels_means.index(19)]
except ValueError:
_so = 0
my_max = np.argmax([_bo, _sh, _so])
if my_max == 0:
change_label(my_msk, scores, [16, 19], 5, _bo)
elif my_max == 1:
change_label(my_msk, scores, [5, 19], 16, _sh)
else:
change_label(my_msk, scores, [5, 16], 19, _so)
#Rule 4 : (cardigan) vs (blazer)
try:
_ca = means[labels_means.index(20)]
except ValueError:
_ca = 0
try:
_bl = means[labels_means.index(11)]
except ValueError:
_bl = 0
my_max = np.argmax([_ca, _bl])
if heavy:
if my_max == 0:
if _ca > 0:
change_label(my_msk, scores, [11, 15, 22], 20, _ca)
else:
if _bl > 0:
change_label(my_msk, scores, [20, 15, 22], 11, _bl)
else:
if my_max == 0:
change_label(my_msk, scores, [11], 20, _ca)
else:
change_label(my_msk, scores, [20], 11, _bl)
#Rule 5: Coat over the all
if heavy:
try:
_coat = means[labels_means.index(6)]
except ValueError:
_coat = 0
if _coat != 0:
change_label(my_msk, scores, [20, 11, 15, 22], 6, _coat)
# Filter POST edit
means, labels_means = filter_label(my_msk, scores, 0.98)
image = Image.fromarray(msk[0])
if not os.path.exists(save_dir):
os.makedirs(save_dir)
image.save(save_dir + 'mask.png')
# Create Dictionary of time
data = {}
data['time'] = str(time.time() - t1)
# Create Dictionary of pic
data['pic'] = img_path
# Create Dictionary of labels used
data['labels'] = []
idx = 0
for l in labels_means:
label = {}
label['label'] = code_colours[l]
label['num'] = str(l)
label['score'] = str(means[idx])
data.get('labels').append(label)
idx += 1
# Create Dictionary of BoundingBox
bboxes = extract_region(my_msk[0], labels_means)
data['bounding_box'] = []
for bbox in bboxes:
bb = {}
bb['min_x'] = int(bbox[0] * scale)
bb['max_x'] = int(bbox[1] * scale)
bb['min_y'] = int(bbox[2] * scale)
bb['max_y'] = int(bbox[3] * scale)
data.get('bounding_box').append(bb)
bb_ins, rate = extract_inside_region(my_msk[0, :, :, 0], labels_means,
scale)
data['bounding_box_inside'] = []
for bbox in bb_ins:
bb = {}
bb['min_x'] = bbox[0]
bb['max_x'] = bbox[0] + 50
bb['min_y'] = bbox[1]
bb['max_y'] = bbox[1] + 50
data.get('bounding_box_inside').append(bb)
return image, data
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
img_list = utils.generate_filelist_test(args.test_set)
img_queue = tf.train.slice_input_producer([img_list], shuffle=False)
img_content = tf.read_file(img_queue[0])
# Prepare image.
img = tf.image.decode_png(img_content, channels=3)
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(axis=2, num_or_size_splits=3, value=img)
img = tf.cast(tf.concat(axis=2, values=[img_b, img_g, img_r]),
dtype=tf.float32)
# Extract mean.
img -= IMG_MEAN
img = tf.reshape(img, [pic_height, pic_width, 3])
image_test, image_name = tf.train.batch([img, img_queue[0]], 1)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# Create network.
net = DeepLabResNetModel_50({'data': image_test},
is_training=False,
num_classes=args.num_classes)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output, tf.shape(img)[0:2, ])
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
# Set up TF session and initialize variables.
init = tf.global_variables_initializer()
sess.run(init)
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, args.model_weights)
for time in range(test_batch_num):
# Perform inference.
preds, test_img_name = sess.run([pred, image_name])
test_img_name = test_img_name[0].decode()
test_img_name = test_img_name.split('/')[-1].strip('.png\n')
print(test_img_name)
msk = decode_labels(preds, num_classes=args.num_classes)
im = Image.fromarray(msk[0])
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
im.save(args.save_dir + test_img_name + '.png')
print('The output file has been saved to {}'.format(args.save_dir +
'mask.png'))
coord.request_stop()
coord.join(threads)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
num_steps = file_len(os.path.join(args.img_path, args.data_list))
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(
os.path.join(args.img_path, "texture"),
os.path.join(args.img_path, args.data_list),
None, # No defined input size.
False, # No random scale.
False, # No random mirror.
255,
IMG_MEAN,
coord,
)
image, label = reader.image, reader.label
title = reader.queue[0]
image_batch, label_batch = (
tf.expand_dims(image, axis=0),
tf.expand_dims(label, axis=0),
) # Add one batch dimension.
# Create network.
net = DeepLabResNetModel(
{"data": image_batch}, is_training=False, num_classes=args.num_classes
)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers["fc1_voc12"]
before_argmax = tf.image.resize_bilinear(raw_output, tf.shape(image_batch)[1:3,])
raw_output_up = tf.argmax(before_argmax, dimension=3)
pred = tf.expand_dims(raw_output_up, axis=3)
hw_only = pred[0, :, :, 0]
class_0 = tf.where(tf.equal(hw_only, 0))
class_1 = tf.where(tf.equal(hw_only, 1))
class_2 = tf.where(tf.equal(hw_only, 2))
class_3 = tf.where(tf.equal(hw_only, 3))
class_4 = tf.where(tf.equal(hw_only, 4))
class_5 = tf.where(tf.equal(hw_only, 5))
class_6 = tf.where(tf.equal(hw_only, 6))
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, args.model_weights)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
start_time = time.time()
os.makedirs(os.path.join(args.img_path, args.body_dir), exist_ok=True)
os.makedirs(os.path.join(args.img_path, args.vis_dir), exist_ok=True)
# write the header
rois_file = os.path.join(args.img_path, "rois.csv")
if os.path.isfile(rois_file):
print(f"The rois file {rois_file} already exists...")
ans = None
while all(ans != choice for choice in ("a", "o", "q")):
ans = input("Do you want to (a)ppend, (o)verwrite, or (q)uit? ")
if ans == "o":
print("Overwriting existing rois file...")
write_header(rois_file)
elif ans == "q":
sys.exit(1)
else:
write_header(rois_file)
# Perform inference.
t = trange(num_steps, desc="Inference progress", unit="img")
for step in t:
# run through the model
jpg_path, c0, c1, c2, c3, c4, c5, c6, raw_output_up_ = sess.run(
[
title,
class_0,
class_1,
class_2,
class_3,
class_4,
class_5,
class_6,
raw_output_up,
]
)
# == First, save the body segmentation ==
if not args.no_body:
# convert to a 2D compressed matrix, because we have a lot of 0's for the
# background
compressed = sparse.csr_matrix(np.squeeze(raw_output_up_))
fname = os.path.splitext(os.path.basename(str(jpg_path)))[0]
out = os.path.join(args.img_path, args.body_dir, fname)
sparse.save_npz(out, compressed)
# == Next, save the ROIs ==
if not args.no_rois:
img_id = extract_nums_only(fname)
for c in (c0, c1, c2, c3, c4, c5, c6):
try:
min_x = np.min(c[:, 1])
except ValueError:
min_x = None
try:
min_y = np.min(c[:, 0])
except ValueError:
min_y = None
try:
max_x = np.max(c[:, 1])
except ValueError:
max_x = None
try:
max_y = np.max(c[:, 0])
except ValueError:
max_y = None
# write out the stuff
with open(rois_file, "a") as f:
f.write(
",".join(
(img_id, str(min_x), str(min_y), str(max_x), str(max_y), "\n")
)
)
# Save an image of the mask for our own reference every 1000 steps
if not args.no_vis and step % args.visualize_step == 0:
preds = np.expand_dims(raw_output_up_, axis=3)
msk = decode_labels(preds, num_classes=args.num_classes)
# the mask
im = Image.fromarray(msk[0])
# # Save the mask separately
# jpg_path = str(jpg_path).split('/')[-1].split('.')[0]
# out = os.path.join(args.vis_dir, jpg_path + '.png')
# im.save(out)
# Save the mask with background
img_orig = Image.open(jpg_path)
# create the final result using the mask and the original
img = np.array(im) * 0.9 + np.array(img_orig) * 0.7
# clip surpassed colors
img[img > 255] = 255
img = Image.fromarray(np.uint8(img))
out = os.path.join(args.img_path, args.vis_dir, fname + ".png")
img.save(out)
# # print('Image processed {}.png'.format(jpg_path))
t.set_description("Finished " + fname)
total_time = time.time() - start_time
print(f"The output files have been saved to {args.img_path}/{args.body_dir}")
print(f"It took {total_time / num_steps} sec on each image.")
def main():
"""Create the model and start the evaluation process."""
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
args = get_arguments()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_mask
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(
args.data_dir,
args.data_list,
None, # No defined input size.
False, # No random scale.
False, # No random mirror.
args.ignore_label,
IMG_MEAN,
coord,
shuffle=False)
image, label = reader.image, reader.label
image_batch, label_batch = tf.expand_dims(image, dim=0), tf.expand_dims(
label, dim=0) # Add one batch dimension.
# Create network.
net = DeepLabResNetModel({'data': image_batch},
is_training=False,
num_classes=args.num_classes)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output = tf.image.resize_bilinear(raw_output,
tf.shape(image_batch)[1:3, ])
raw_output = tf.argmax(raw_output, dimension=3)
pred = tf.expand_dims(raw_output, dim=3) # Create 4-d tensor.
# img-list = reader.images
# mIoU
# pred = tf.reshape(pred, [-1,])
# gt = tf.reshape(label_batch, [-1,])
# weights = tf.cast(tf.less_equal(gt, args.num_classes - 1), tf.int32) # Ignoring all labels greater than or equal to n_classes.
# mIoU, update_op = tf.contrib.metrics.streaming_mean_iou(pred, gt, num_classes=args.num_classes, weights=weights)
#
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
sess.run(tf.local_variables_initializer())
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
if args.restore_from is not None:
load(loader, sess, args.restore_from)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
image_fns = reader.images.eval(session=sess)
for image_fn in image_fns:
start_time = time.time()
# print (image_fn)
# Iterate over training steps.
# for step in range(args.num_steps):
# Perform inference.
preds = sess.run(pred)
msk = decode_labels(preds, num_classes=args.num_classes)
im = Image.fromarray(msk[0])
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
# print (args.save_dir + os.path.split(image_fn)[1].replace('jpg','png'))
im.save(args.save_dir +
os.path.split(image_fn)[1].replace('jpg', 'png'))
duration = time.time() - start_time
print('duration per pred ({:.3f} sec/step)'.format(duration))
print('The output file has been saved to {}'.format(args.save_dir))
coord.request_stop()
coord.join(threads)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load validation
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(
args.data_dir,
args.data_list,
None, # No defined input size.
False, # No random scale.
False, # No random mirror.
args.ignore_label,
IMG_MEAN,
coord)
image, label, file, mask = reader.image, reader.label, reader.image_list, reader.label_list
image_batch, label_batch, file_batch, mask_batch = tf.expand_dims(image, dim=0), \
tf.expand_dims(label, dim=0), \
tf.expand_dims(file, dim=0), \
tf.expand_dims(mask, dim=0)# Add one batch dimension.
# Create network.
net = DeepLabResNetModel({'data': image_batch},
is_training=False,
num_classes=args.num_classes)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output = tf.image.resize_bilinear(raw_output,
tf.shape(image_batch)[1:3, ])
probabilities = tf.nn.softmax(raw_output)
raw_output = tf.argmax(raw_output, dimension=3)
preds = tf.expand_dims(raw_output, dim=3) # Create 4-d tensor.
# mIoU
# pred = tf.reshape(preds, [-1,])
# gt = tf.reshape(label_batch, [-1,])
# weights = tf.cast(tf.less_equal(gt, args.num_classes - 1), tf.int32) # Ignoring all labels greater than or equal to n_classes.
# mIoU, update_op = tf.contrib.metrics.streaming_mean_iou(pred, gt, num_classes=args.num_classes, weights=weights)
file_name = file_batch
mask_link = mask_batch
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
sess.run(tf.local_variables_initializer())
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
if args.restore_from is not None:
load(loader, sess, args.restore_from)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
# Iterate over training steps.
result_data = {}
total_miuo_score = 0
nb = 0
global OUT_DIR
global IMAGE
global IMAGE_PATH
for step in range(args.num_steps):
predict, probmap, fb, mk = sess.run(
[preds, probabilities, file_name, mask_link])
print('step {:d}'.format(step))
if IS_POD:
IMAGE = fb[0][step].decode('utf8').replace(
"D:\Data\POD/JpegImages/", "").replace(".jpg", "")
IMAGE_PATH = "D:\Data\POD\JpegImages\\" + IMAGE + ".jpg"
labels, bounding_boxs, confidence_score, miuo_score, pixel_score = \
get_bounding_boxs(probmap)
result_data[IMAGE] = {
"labels": labels,
"confidence_score": confidence_score,
"boxes": bounding_boxs,
"pixel_accuracy": pixel_score,
"mIoU": miuo_score
}
# Draw bouding boxes to image
# drw_img(cv2.imread(IMAGE_PATH), labels, bounding_boxs)
OUT_DIR = "mask_pod/"
else:
# print(mk[0][step].decode('utf8'))
IMAGE = mk[0][step].decode('utf8').replace(
"D:\Data\PLAD/MaskImage/", "")
result_data[IMAGE] = probabilities_map_evaluate(
probmap, mk[0][step].decode('utf8'))
OUT_DIR = "mask_plad/"
print(result_data)
# Draw mask image
msk = decode_labels(predict, num_classes=args.num_classes)
im = Image.fromarray(msk[0])
im.save(OUT_DIR + IMAGE + '.png')
# with open('predicted_boxes.json', 'w') as outfile:
# json.dump(result_data, outfile)
# print('Mean IoU: {:.3f}'.format(mIoU.eval(session=sess)))
coord.request_stop()
coord.join(threads)
def main():
"""Create the model and start the training."""
args = get_arguments()
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(args.data_dir, args.data_list, input_size,
RANDOM_SCALE, coord)
image_batch, label_batch = reader.dequeue(args.batch_size)
# Create network.
net = DeepLabResNetModel({'data': image_batch})
# Predictions.
raw_output = net.layers['fc1_voc12']
prediction = tf.reshape(raw_output, [-1, n_classes])
label_proc = prepare_label(label_batch,
tf.pack(raw_output.get_shape()[1:3]))
gt = tf.reshape(label_proc, [-1, n_classes])
# Pixel-wise softmax loss.
loss = tf.nn.softmax_cross_entropy_with_logits(prediction, gt)
reduced_loss = tf.reduce_mean(loss)
# Processed predictions.
raw_output_up = tf.image.resize_bilinear(raw_output,
tf.shape(image_batch)[1:3, ])
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
# Define loss and optimisation parameters.
optimiser = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
trainable = tf.trainable_variables()
optim = optimiser.minimize(reduced_loss, var_list=trainable)
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.initialize_all_variables()
sess.run(init)
# Saver for storing checkpoints of the model.
saver = tf.train.Saver(var_list=trainable, max_to_keep=40)
if args.restore_from is not None:
load(saver, sess, args.restore_from)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
# Iterate over training steps.
for step in range(args.num_steps):
start_time = time.time()
if step % args.save_pred_every == 0:
loss_value, images, labels, preds, _ = sess.run(
[reduced_loss, image_batch, label_batch, pred, optim])
fig, axes = plt.subplots(args.save_num_images, 3, figsize=(16, 12))
for i in xrange(args.save_num_images):
axes.flat[i * 3].set_title('data')
axes.flat[i * 3].imshow(
(images[i] + IMG_MEAN)[:, :, ::-1].astype(np.uint8))
axes.flat[i * 3 + 1].set_title('mask')
axes.flat[i * 3 + 1].imshow(decode_labels(labels[i, :, :, 0]))
axes.flat[i * 3 + 2].set_title('pred')
axes.flat[i * 3 + 2].imshow(decode_labels(preds[i, :, :, 0]))
plt.savefig(args.save_dir + str(start_time) + ".png")
plt.close(fig)
save(saver, sess, args.snapshot_dir, step)
else:
loss_value, _ = sess.run([reduced_loss, optim])
duration = time.time() - start_time
print('step {:d} \t loss = {:.3f}, ({:.3f} sec/step)'.format(
step, loss_value, duration))
coord.request_stop()
coord.join(threads)
def main(argv=None):
# 1.获取测试集数据
# map()会根据提供的函数对指定序列做映射。FLAGS.input_size.split(',')得到['321','321']
image_height, image_width = map(int, FLAGS.input_size.split(','))
input_size = (image_height, image_width)
# 创建一个线程管理器
coord = tf.train.Coordinator()
with tf.name_scope("create_inputs"):
reader = ImageReader(FLAGS.data_dir,
FLAGS.data_list,
input_size=None,
random_scale=False,
coord=coord)
image, label = reader.image, reader.label
image_batch, label_batch = tf.expand_dims(image,
dim=0), tf.expand_dims(label,
dim=0)
# 2.建立模型
net = DeepLabV3Model(is_training=False, num_classes=FLAGS.num_classes)
# 3.预测结果
raw_output_up = net.preds(image_batch)
#
pred = tf.expand_dims(raw_output_up, dim=3)
# 4.定义一个初始化变量op
init_variable = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_variable)
# 5.
restore_var = tf.global_variables()
loader = tf.train.Saver(var_list=restore_var)
loader.restore(sess, FLAGS.model_weights)
print("Restored model parameters from {}".format(FLAGS.model_weights))
# 6.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
# 7.mIoU
predictions = tf.reshape(pred, [
-1,
])
labels = tf.reshape(tf.cast(label_batch, tf.int32), [
-1,
])
mask = labels <= FLAGS.num_classes - 1
predictions = tf.boolean_mask(predictions, mask)
labels = tf.boolean_mask(labels, mask)
# Define the evaluation metric.
mIoU, update_op = tf.contrib.metrics.streaming_mean_iou(
predictions, labels, FLAGS.num_classes)
# 一定要有
sess.run(tf.local_variables_initializer())
# ###
# 8.Iterate over images.
for step in range(FLAGS.num_steps):
# Perform inference.
preds, img, _ = sess.run(
[pred, tf.cast(image_batch, tf.uint8), update_op])
msk = decode_labels(preds, num_classes=FLAGS.num_classes)
im0 = Image.fromarray(msk[0])
im0.save(FLAGS.save_image_dir + 'mask' + str(step) + '.png')
print('The output file has been saved to {}'.format(
FLAGS.save_image_dir + 'mask' + str(step) + '.png'))
# CRF
raw_im = dense_crf(img[0], msk[0])
#
# plt.subplot(1,2,1)
# plt.imshow(msk[0])
# plt.subplot(1, 2, 2)
# plt.imshow(raw_im)
# plt.show()
im = Image.fromarray(raw_im)
im.save(FLAGS.save_image_dir + 'mask_crf' + str(step) + '.png')
print('The output file has been saved to {}'.format(
FLAGS.save_image_dir + 'mask_crf' + str(step) + '.png'))
print('Mean IoU: {:.3f}'.format(mIoU.eval(session=sess)))
coord.request_stop()
coord.join(threads)
return None
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(
args.data_dir,
args.data_list,
None, # No defined input size.
False, # No random scale.
False, # No random mirror.
args.ignore_label,
IMG_MEAN,
coord)
image, label = reader.image, reader.label
# Add one batch dimension.
image_batch, label_batch = tf.expand_dims(image,
dim=0), tf.expand_dims(label,
dim=0)
# Create network.
net = DeepLabResNetModel({'data': image_batch},
is_training=False,
num_classes=args.num_classes)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output = tf.image.resize_bilinear(raw_output,
tf.shape(image_batch)[1:3, ])
raw_output = tf.argmax(raw_output, dimension=3)
pred = tf.expand_dims(raw_output, dim=3) # Create 4-d tensor.
# mIoU
pred = tf.reshape(pred, [
-1,
])
gt = tf.reshape(label_batch, [
-1,
])
# Ignoring all labels greater than or equal to n_classes.
weights = tf.cast(tf.less_equal(gt, args.num_classes - 1), tf.int32)
mIoU, update_op = tf.contrib.metrics.streaming_mean_iou(
pred, gt, num_classes=args.num_classes, weights=weights)
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
sess.run(tf.local_variables_initializer())
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
if args.restore_from is not None:
load(loader, sess, args.restore_from)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
# Iterate over training steps.
for step in range(args.num_steps):
preds, _ = sess.run([pred, update_op])
if args.save_dir is not None:
img = decode_labels(preds[0, :, :, 0])
im = Image.fromarray(img)
im.save(args.save_dir + str(step) + '_vis.png')
cv2.imwrite(args.save_dir + str(step) + '.png', preds[0, :, :, 0])
if step % 100 == 0:
print('step {:d}'.format(step))
print('Mean IoU: {:.3f}'.format(mIoU.eval(session=sess)))
coord.request_stop()
coord.join(threads)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader_MultiClass_Loss(
args.data_dir,
args.data_list,
None, # No defined input size.
RANDOM_SEED,
False, # No random scale.
False, # No random mirror.
coord)
image, l2_catg, binary_catg, hinge_catg = reader.image, reader.l2_catg, reader.binary_catg, reader.hinge_catg
image_batch = tf.expand_dims(image, dim=0)
binary_catg_batch = tf.expand_dims(binary_catg, dim=0)
# Create network.
net = DeepLabResNetModel({'data': image_batch}, is_training=False)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
# Do the global average pooling
raw_output_bcgd_rmvd = raw_output[:, :, :, 1:]
g_avg_pool = tf.reduce_mean(tf.reduce_mean(raw_output_bcgd_rmvd, axis=1, keep_dims=True),\
axis=2, keep_dims=True) # Avg across the width and height dimension -> [Bx21]
g_avg_pool_sqzd = tf.squeeze(g_avg_pool, axis=[1, 2])
pred = tf.nn.softmax(g_avg_pool_sqzd)
# Get the class activation map
raw_output_up = tf.image.resize_bilinear(raw_output_bcgd_rmvd,
tf.shape(image_batch)[1:3, ])
raw_output_up = raw_output_up - tf.reduce_min(tf.reduce_min(
raw_output_up, axis=1, keep_dims=True),
axis=2,
keep_dims=True) + EPSILON
raw_output_up = raw_output_up / tf.reduce_max(tf.reduce_max(
raw_output_up, axis=1, keep_dims=True),
axis=2,
keep_dims=True)
cam_m_1 = tf.argmax(raw_output_up, dimension=3) + 1
raw_output_catgs_rmvd = raw_output_up * tf.expand_dims(
tf.expand_dims(binary_catg_batch, 1), 2)
cam_m_2 = tf.argmax(raw_output_catgs_rmvd, dimension=3) + 1
cam = tf.cast(tf.equal(cam_m_1, cam_m_2), tf.int64) * cam_m_1
cam_batch = tf.expand_dims(cam, dim=3)
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
sess.run(tf.local_variables_initializer())
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
if args.restore_from is not None:
load(loader, sess, args.restore_from)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
# Iterate over training steps.
for step in range(args.num_steps):
preds, images, cams, bin_catg = sess.run(
[pred, image_batch, cam_batch, binary_catg])
"""
print(bin_catg)
print(np.unique(np.unique(cams)))
"""
img = inv_preprocess(images)
attMap = decode_labels(cams)
output_dir = './output_maps_binary_without_norm/'
img_name = output_dir + str(step) + '.jpg'
map_name = output_dir + str(step) + '.png'
misc.imsave(img_name, img[0, :, :, :])
misc.imsave(map_name, attMap[0, :, :, :])
coord.request_stop()
coord.join(threads)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
# Prepare image.
img = tf.image.decode_jpeg(tf.read_file(args.img_path), channels=3)
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(axis=2, num_or_size_splits=3, value=img)
img = tf.cast(tf.concat(axis=2, values=[img_b, img_g, img_r]),
dtype=tf.float32)
# Extract mean.
img -= IMG_MEAN
# Create network.
net = DeepLabResNetModel({'data': tf.expand_dims(img, dim=0)},
is_training=False,
num_classes=args.num_classes)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(raw_output, tf.shape(img)[0:2, ])
raw_output_up_squeeze = tf.squeeze(raw_output_up, axis=0)
raw_output_up_squeeze = tf.nn.softmax(raw_output_up_squeeze, )
# raw_output_up = tf.argmax(raw_output_up, dimension=3)
# print(raw_output_up.get_shape()) #(1, ?, ?)
# pred = tf.expand_dims(raw_output_up, dim=3)
# print(pred.get_shape()) #(1, ?, ?, 1)
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, args.model_weights)
# Perform inference.
start = time.time()
processed_probabilities = sess.run(raw_output_up_squeeze)
img = tf.image.decode_jpeg(tf.read_file(args.img_path), channels=3)
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(axis=2, num_or_size_splits=3, value=img)
img = tf.cast(tf.concat(axis=2, values=[img_b, img_g, img_r]),
dtype=tf.float32)
img = sess.run(img)
#---------------------------------CRF
import sys
import pydensecrf.densecrf as dcrf
from pydensecrf.utils import compute_unary, create_pairwise_bilateral, \
create_pairwise_gaussian, softmax_to_unary
import skimage.io as io
softmax = processed_probabilities.transpose((2, 0, 1))
# The input should be the negative of the logarithm of probability values
# Look up the definition of the softmax_to_unary for more information
unary = softmax_to_unary(softmax)
# The inputs should be C-continious -- we are using Cython wrapper
unary = np.ascontiguousarray(unary) #(21,n)
d = dcrf.DenseCRF(img.shape[0] * img.shape[1], 21)
d.setUnaryEnergy(unary)
# This potential penalizes small pieces of segmentation that are
# spatially isolated -- enforces more spatially consistent segmentations
feats = create_pairwise_gaussian(sdims=(10, 10), shape=img.shape[:2])
d.addPairwiseEnergy(feats,
compat=3,
kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC)
# This creates the color-dependent features --
# because the segmentation that we get from CNN are too coarse
# and we can use local color features to refine them
feats = create_pairwise_bilateral(sdims=(50, 50),
schan=(20, 20, 20),
img=img,
chdim=2)
d.addPairwiseEnergy(feats,
compat=10,
kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC)
#迭代次数,对于IMG_1702(2592*1456)这张图,迭代5 16.807087183s 迭代20 37.5700438023s
Q = d.inference(5)
res = np.argmax(Q, axis=0).reshape((img.shape[0], img.shape[1]))
#-----------------------------------
# res = tf.expand_dims(res, dim=3)
res = res[np.newaxis, :, :, np.newaxis]
msk = decode_labels(res, num_classes=args.num_classes)
im = Image.fromarray(msk[0])
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
im.save(args.save_dir + '16_crf.png')
end = time.time()
print('{}'.format(end - start))
def infer_and_save_matlab_for_filelist(filelist=FILELIST,
also_save_colormask=False,
color_mask_save_dir=COLOR_MASK_SAVE_DIR,
matlab_save_dir=MATLAB_SAVE_DIR,
model_weights=MODEL_WEIGHTS,
use_crf=False):
# Open and parse the filelist
text_file = open(filelist, "r")
lines = text_file.read().splitlines()
text_file.close()
# Perform inference.
print('Starting inference')
preds = infer_multiple_images(filelist, model_weights, use_crf)
# Get the color values for the labels if the colormask is also saved
if also_save_colormask == True:
print('Decoding the labels')
masks = decode_labels(preds,
num_images=preds.shape[0],
num_classes=NUM_CLASSES)
# Prepare the location for saving the colormask
if use_crf == True:
crf_used_string = 'with_crf'
else:
crf_used_string = ''
if not os.path.exists(color_mask_save_dir):
os.makedirs(color_mask_save_dir)
if not os.path.exists(matlab_save_dir):
os.makedirs(matlab_save_dir)
for index, img_name in enumerate(lines):
# Save the colormask as .png
if also_save_colormask == True:
im = Image.fromarray(masks[index])
# os.path.splitext(img_name)[0] removes the '.png' extension from the image name
colormask_save_path = color_mask_save_dir + os.path.splitext(
img_name)[0] + crf_used_string + '_colormask.png'
im.save(colormask_save_path)
print('The colormask has been saved to {}'.format(
colormask_save_path))
# Save the mask as MATLAB .mat
# os.path.splitext(img_name)[0] removes the '.png' extension from the image name
matlab_labels_save_path = matlab_save_dir + os.path.splitext(
img_name)[0] + '.mat'
if use_crf == False:
scipy.io.savemat(matlab_labels_save_path,
mdict={'labels': preds[index].astype(np.uint16)})
print('The matlab file has been saved to {}'.format(
matlab_labels_save_path))
continue
# TODO Save the inference correctly in the case that CRF is used
# preds_crf = infer_absolute_path(img_absolute_path, model_weights, use_crf=True)
# scipy.io.savemat(labels_absolute_path,
# mdict={'labels': preds.astype(np.uint16), 'labels_crf': preds_crf.astype(np.uint16)})
# print('The output file has been saved to {}'.format(labels_absolute_path))
return
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(
args.data_dir,
args.data_list,
None, # No defined input size.
False, # No random scale.
False, # No random mirror.
args.ignore_label,
IMG_MEAN,
coord)
image, label = reader.image, reader.label
image_batch, label_batch = tf.expand_dims(image, dim=0), tf.expand_dims(
label, dim=0) # Add one batch dimension.
# Create network.
net = DeepLabResNetModel({'data': image_batch},
is_training=False,
num_classes=args.num_classes)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc_out']
raw_output = tf.image.resize_bilinear(raw_output,
tf.shape(image_batch)[1:3, ])
raw_output = tf.argmax(raw_output, dimension=3)
pred = tf.expand_dims(raw_output, dim=3) # Create 4-d tensor.
# mIoU
pred_flatten = tf.reshape(pred, [
-1,
])
gt = tf.reshape(label_batch, [
-1,
])
weights = tf.cast(
tf.less_equal(gt, args.num_classes - 1),
tf.int32) # Ignoring all labels greater than or equal to n_classes.
mIoU, update_op = tf.contrib.metrics.streaming_mean_iou(
predictions=pred_flatten,
labels=gt,
num_classes=args.num_classes,
weights=weights)
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
sess.run(tf.local_variables_initializer())
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
ckpt = tf.train.get_checkpoint_state(SNAPSHOT_DIR)
if ckpt and ckpt.model_checkpoint_path:
loader = tf.train.Saver(var_list=restore_var)
load_step = int(
os.path.basename(ckpt.model_checkpoint_path).split('-')[1])
load(loader, sess, ckpt.model_checkpoint_path)
else:
print('No checkpoint file found.')
load_step = 0
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
if not os.path.exists(SAVE_DIR):
os.makedirs(SAVE_DIR)
for step in range(args.num_steps):
preds, _ = sess.run([pred, update_op])
if IS_SAVE == True:
msk = decode_labels(preds, num_classes=args.num_classes)
im = Image.fromarray(msk[0])
filename = 'mask' + str(step) + '.png'
im.save(SAVE_DIR + filename)
if step % 10 == 0:
print('step {0} mIoU: {1}'.format(step, mIoU.eval(session=sess)))
coord.request_stop()
coord.join(threads)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
num_steps = file_len(os.path.join(args.img_path, args.data_list))
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(
os.path.join(args.img_path, "texture"),
os.path.join(args.img_path, args.data_list),
None, # No defined input size.
False, # No random scale.
False, # No random mirror.
255,
IMG_MEAN,
coord,
)
image, label = reader.image, reader.label
title = reader.queue[0]
image_batch, label_batch = (
tf.expand_dims(image, axis=0),
tf.expand_dims(label, axis=0),
) # Add one batch dimension.
# Create network.
net = DeepLabResNetModel({"data": image_batch},
is_training=False,
num_classes=args.num_classes)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers["fc1_voc12"]
before_argmax = tf.image.resize_bilinear(raw_output,
tf.shape(image_batch)[1:3, ])
raw_output_up = tf.argmax(before_argmax, dimension=3)
pred = tf.expand_dims(raw_output_up, axis=3)
hw_only = pred[0, :, :, 0]
class_0 = tf.where(tf.equal(hw_only, 0))
class_1 = tf.where(tf.equal(hw_only, 1))
class_2 = tf.where(tf.equal(hw_only, 2))
class_3 = tf.where(tf.equal(hw_only, 3))
class_4 = tf.where(tf.equal(hw_only, 4))
class_5 = tf.where(tf.equal(hw_only, 5))
class_6 = tf.where(tf.equal(hw_only, 6))
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, args.model_weights)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
start_time = time.time()
os.makedirs(os.path.join(args.img_path, args.body_dir), exist_ok=True)
os.makedirs(os.path.join(args.img_path, args.vis_dir), exist_ok=True)
# write the header
rois_file = os.path.join(args.img_path, "rois.csv")
if os.path.isfile(rois_file):
print(f"The rois file {rois_file} already exists...")
ans = None
while all(ans != choice for choice in ("a", "o", "q")):
ans = input("Do you want to (a)ppend, (o)verwrite, or (q)uit? ")
if ans == "o":
print("Overwriting existing rois file...")
write_header(rois_file)
elif ans == "q":
sys.exit(1)
else:
write_header(rois_file)
# Perform inference.
t = trange(num_steps, desc="Inference progress", unit="img")
for step in t:
# run through the model
jpg_path, c0, c1, c2, c3, c4, c5, c6, raw_output_up_ = sess.run([
title,
class_0,
class_1,
class_2,
class_3,
class_4,
class_5,
class_6,
raw_output_up,
])
# == First, save the body segmentation ==
if not args.no_body:
# convert to a 2D compressed matrix, because we have a lot of 0's for the
# background
compressed = sparse.csr_matrix(np.squeeze(raw_output_up_))
fname = os.path.splitext(os.path.basename(str(jpg_path)))[0]
out = os.path.join(args.img_path, args.body_dir, fname)
sparse.save_npz(out, compressed)
# == Next, save the ROIs ==
if not args.no_rois:
img_id = extract_nums_only(fname)
for c in (c0, c1, c2, c3, c4, c5, c6):
try:
min_x = np.min(c[:, 1])
except ValueError:
min_x = None
try:
min_y = np.min(c[:, 0])
except ValueError:
min_y = None
try:
max_x = np.max(c[:, 1])
except ValueError:
max_x = None
try:
max_y = np.max(c[:, 0])
except ValueError:
max_y = None
# write out the stuff
with open(rois_file, "a") as f:
f.write(",".join((img_id, str(min_x), str(min_y),
str(max_x), str(max_y), "\n")))
# Save an image of the mask for our own reference every 1000 steps
if not args.no_vis and step % args.visualize_step == 0:
preds = np.expand_dims(raw_output_up_, axis=3)
msk = decode_labels(preds, num_classes=args.num_classes)
# the mask
im = Image.fromarray(msk[0])
# # Save the mask separately
# jpg_path = str(jpg_path).split('/')[-1].split('.')[0]
# out = os.path.join(args.vis_dir, jpg_path + '.png')
# im.save(out)
# Save the mask with background
img_orig = Image.open(jpg_path)
# create the final result using the mask and the original
img = np.array(im) * 0.9 + np.array(img_orig) * 0.7
# clip surpassed colors
img[img > 255] = 255
img = Image.fromarray(np.uint8(img))
out = os.path.join(args.img_path, args.vis_dir, fname + ".png")
img.save(out)
# # print('Image processed {}.png'.format(jpg_path))
t.set_description("Finished " + fname)
total_time = time.time() - start_time
print(
f"The output files have been saved to {args.img_path}/{args.body_dir}")
print(f"It took {total_time / num_steps} sec on each image.")
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
# remove_huge_images(args.data_list, args.img_path)
num_steps = file_len(args.data_list)
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
print(args.img_path, ' ', file_len(args.data_list))
with tf.name_scope("create_inputs"):
reader = ImageReader(
args.img_path,
args.data_list,
None, # No defined input size.
False, # No random scale.
False, # No random mirror.
255,
IMG_MEAN,
coord)
image, label = reader.image, reader.label
title = reader.queue[0]
image_batch, label_batch = tf.expand_dims(image, dim=0), tf.expand_dims(
label, dim=0) # Add one batch dimension.
# Create network.
net = DeepLabResNetModel({'data': image_batch},
is_training=False,
num_classes=args.num_classes)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
fc1_voc12_layer = net.layers['fc1_voc12']
raw_output_up = tf.image.resize_bilinear(fc1_voc12_layer,
tf.shape(image_batch)[1:3, ])
# uncomment to see only stock segmentation
# raw_output_up = tf.slice(raw_output_up, [0,0,0,0], [-1,-1,-1,7])
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
# Load weights.
loader = tf.train.Saver(var_list=restore_var)
load(loader, sess, args.model_weights)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
start_time = time.time()
os.makedirs(args.save_dir, exist_ok=True)
path_parts = args.img_path.split("/")
if path_parts[-1].strip() == "":
path_parts = path_parts[:-1]
if path_parts[0] == "":
path_parts[0] = "/"
bottleneck_dir = os.path.join(*path_parts[:-1],
path_parts[-1] + "_hp_bottlenecks")
os.makedirs(bottleneck_dir, exist_ok=True)
# Perform inference.
for step in range(num_steps):
jpg_name = None
try:
preds, jpg_path, fc1_voc12_val = sess.run(
[pred, title, fc1_voc12_layer])
msk = decode_labels(preds, num_classes=args.num_classes)
im = Image.fromarray(msk[0])
img_o = Image.open(jpg_path)
jpg_path = str(jpg_path)
jpg_name = Path(jpg_path).name.split('.')[0]
img = np.array(im) * 0.9 + np.array(img_o) * 0.7
img[img > 255] = 255
img = Image.fromarray(np.uint8(img))
img.save(os.path.join(args.save_dir, str(jpg_name + '.png')))
img_bgr = cv2.cvtColor(np.array(img_o), cv2.COLOR_BGR2RGB)
cv2.imwrite(
os.path.join(args.save_dir,
"stacked_" + str(jpg_name + '.png')),
np.hstack([img_bgr, im]))
bottleneck_path = os.path.join(bottleneck_dir,
jpg_name + "_hp_bottleneck.h5")
with h5py.File(bottleneck_path, "w") as bottleneck_file:
bottleneck_file.create_dataset("fc1_voc12", data=fc1_voc12_val)
print('Image processed {}.png'.format(jpg_name))
print(
'Wrote human parsing bottleneck to {}'.format(bottleneck_path))
except Exception as e:
print(e)
print('Image failed: ', jpg_name)
total_time = time.time() - start_time
print('The output files have been saved to {}'.format(args.save_dir))
print('It took {} sec on each image.'.format(total_time / num_steps))
