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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import math
import time
import cv2
import numpy as np
import paddle
import paddle.nn.functional as F
from paddleseg import utils
from paddleseg.core import infer
from paddleseg.utils import logger, progbar, TimeAverager
from matting.utils import mkdir
def partition_list(arr, m):
"""split the list 'arr' into m pieces"""
n = int(math.ceil(len(arr) / float(m)))
return [arr[i:i + n] for i in range(0, len(arr), n)]
def save_alpha_pred(alpha, path, trimap=None):
"""
The value of alpha is range [0, 1], shape should be [h,w]
"""
dirname = os.path.dirname(path)
if not os.path.exists(dirname):
os.makedirs(dirname)
trimap = cv2.imread(trimap, 0)
alpha[trimap == 0] = 0
alpha[trimap == 255] = 255
alpha = (alpha).astype('uint8')
cv2.imwrite(path, alpha)
def reverse_transform(alpha, trans_info):
"""recover pred to origin shape"""
for item in trans_info[::-1]:
if item[0] == 'resize':
h, w = item[1][0], item[1][1]
alpha = F.interpolate(alpha, [h, w], mode='bilinear')
elif item[0] == 'padding':
h, w = item[1][0], item[1][1]
alpha = alpha[:, :, 0:h, 0:w]
else:
raise Exception("Unexpected info '{}' in im_info".format(item[0]))
return alpha
def preprocess(img, transforms, trimap=None):
data = {}
data['img'] = img
if trimap is not None:
data['trimap'] = trimap
data['gt_fields'] = ['trimap']
data['trans_info'] = []
data = transforms(data)
data['img'] = paddle.to_tensor(data['img'])
data['img'] = data['img'].unsqueeze(0)
if trimap is not None:
data['trimap'] = paddle.to_tensor(data['trimap'])
data['trimap'] = data['trimap'].unsqueeze((0, 1))
return data
def predict(model,
model_path,
transforms,
image_list,
image_dir=None,
trimap_list=None,
save_dir='output'):
"""
predict and visualize the image_list.
Args:
model (nn.Layer): Used to predict for input image.
model_path (str): The path of pretrained model.
transforms (transforms.Compose): Preprocess for input image.
image_list (list): A list of image path to be predicted.
image_dir (str, optional): The root directory of the images predicted. Default: None.
trimap_list (list, optional): A list of trimap of image_list. Default: None.
save_dir (str, optional): The directory to save the visualized results. Default: 'output'.
"""
utils.utils.load_entire_model(model, model_path)
model.eval()
nranks = paddle.distributed.get_world_size()
local_rank = paddle.distributed.get_rank()
if nranks > 1:
img_lists = partition_list(image_list, nranks)
trimap_lists = partition_list(
trimap_list, nranks) if trimap_list is not None else None
else:
img_lists = [image_list]
trimap_lists = [trimap_list] if trimap_list is not None else None
logger.info("Start to predict...")
progbar_pred = progbar.Progbar(target=len(img_lists[0]), verbose=1)
preprocess_cost_averager = TimeAverager()
infer_cost_averager = TimeAverager()
postprocess_cost_averager = TimeAverager()
batch_start = time.time()
with paddle.no_grad():
for i, im_path in enumerate(img_lists[local_rank]):
preprocess_start = time.time()
trimap = trimap_lists[local_rank][
i] if trimap_list is not None else None
data = preprocess(img=im_path, transforms=transforms, trimap=trimap)
preprocess_cost_averager.record(time.time() - preprocess_start)
infer_start = time.time()
alpha_pred = model(data)
infer_cost_averager.record(time.time() - infer_start)
postprocess_start = time.time()
alpha_pred = reverse_transform(alpha_pred, data['trans_info'])
alpha_pred = (alpha_pred.numpy()).squeeze()
alpha_pred = (alpha_pred * 255).astype('uint8')
# get the saved name
# if image_dir is not None:
# im_file = im_path.replace(image_dir, '')
# else:
# im_file = os.path.basename(im_path)
# if im_file[0] == '/' or im_file[0] == '\\':
# im_file = im_file[1:]
# save_path = os.path.join(save_dir, im_file)
# mkdir(save_path)
# save_alpha_pred(alpha_pred, save_path, trimap=trimap)
postprocess_cost_averager.record(time.time() - postprocess_start)
preprocess_cost = preprocess_cost_averager.get_average()
infer_cost = infer_cost_averager.get_average()
postprocess_cost = postprocess_cost_averager.get_average()
if local_rank == 0:
progbar_pred.update(i + 1,
[('preprocess_cost', preprocess_cost),
('infer_cost cost', infer_cost),
('postprocess_cost', postprocess_cost)])
preprocess_cost_averager.reset()
infer_cost_averager.reset()
postprocess_cost_averager.reset()
return alpha_pred
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