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# Copyright (c) 2020 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 cv2
import numpy as np
import time
import paddle
import paddle.nn.functional as F
from paddleseg.utils import TimeAverager, calculate_eta, logger, progbar
from ppmatting.metrics import metric
from pymatting.util.util import load_image, save_image, stack_images
from pymatting.foreground.estimate_foreground_ml import estimate_foreground_ml
np.set_printoptions(suppress=True)
def save_alpha_pred(alpha, path):
"""
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)
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][0] == 'resize':
h, w = item[1][0].numpy()[0], item[1][1].numpy()[0]
alpha = cv2.resize(alpha, dsize=(w, h))
elif item[0][0] == 'padding':
h, w = item[1][0].numpy()[0], item[1][1].numpy()[0]
alpha = alpha[0:h, 0:w]
else:
raise Exception("Unexpected info '{}' in im_info".format(item[0]))
return alpha
def evaluate_ml(model,
eval_dataset,
num_workers=0,
print_detail=True,
save_dir='output/results',
save_results=True):
loader = paddle.io.DataLoader(
eval_dataset,
batch_size=1,
drop_last=False,
num_workers=num_workers,
return_list=True, )
total_iters = len(loader)
mse_metric = metric.MSE()
sad_metric = metric.SAD()
grad_metric = metric.Grad()
conn_metric = metric.Conn()
if print_detail:
logger.info("Start evaluating (total_samples: {}, total_iters: {})...".
format(len(eval_dataset), total_iters))
progbar_val = progbar.Progbar(target=total_iters, verbose=1)
reader_cost_averager = TimeAverager()
batch_cost_averager = TimeAverager()
batch_start = time.time()
img_name = ''
i = 0
ignore_cnt = 0
for iter, data in enumerate(loader):
reader_cost_averager.record(time.time() - batch_start)
image_rgb_chw = data['img'].numpy()[0]
image_rgb_hwc = np.transpose(image_rgb_chw, (1, 2, 0))
trimap = data['trimap'].numpy().squeeze() / 255.0
image = image_rgb_hwc * 0.5 + 0.5 # reverse normalize (x/255 - mean) / std
is_fg = trimap >= 0.9
is_bg = trimap <= 0.1
if is_fg.sum() == 0 or is_bg.sum() == 0:
ignore_cnt += 1
logger.info(str(iter))
continue
alpha_pred = model(image, trimap)
alpha_pred = reverse_transform(alpha_pred, data['trans_info'])
alpha_gt = data['alpha'].numpy().squeeze() * 255
trimap = data['ori_trimap'].numpy().squeeze()
alpha_pred = np.round(alpha_pred * 255)
mse = mse_metric.update(alpha_pred, alpha_gt, trimap)
sad = sad_metric.update(alpha_pred, alpha_gt, trimap)
grad = grad_metric.update(alpha_pred, alpha_gt, trimap)
conn = conn_metric.update(alpha_pred, alpha_gt, trimap)
if sad > 1000:
print(data['img_name'][0])
if save_results:
alpha_pred_one = alpha_pred
alpha_pred_one[trimap == 255] = 255
alpha_pred_one[trimap == 0] = 0
save_name = data['img_name'][0]
name, ext = os.path.splitext(save_name)
if save_name == img_name:
save_name = name + '_' + str(i) + ext
i += 1
else:
img_name = save_name
save_name = name + '_' + str(0) + ext
i = 1
save_alpha_pred(alpha_pred_one, os.path.join(save_dir, save_name))
batch_cost_averager.record(
time.time() - batch_start, num_samples=len(alpha_gt))
batch_cost = batch_cost_averager.get_average()
reader_cost = reader_cost_averager.get_average()
if print_detail:
progbar_val.update(iter + 1,
[('SAD', sad), ('MSE', mse), ('Grad', grad),
('Conn', conn), ('batch_cost', batch_cost),
('reader cost', reader_cost)])
reader_cost_averager.reset()
batch_cost_averager.reset()
batch_start = time.time()
mse = mse_metric.evaluate()
sad = sad_metric.evaluate()
grad = grad_metric.evaluate()
conn = conn_metric.evaluate()
logger.info('[EVAL] SAD: {:.4f}, MSE: {:.4f}, Grad: {:.4f}, Conn: {:.4f}'.
format(sad, mse, grad, conn))
logger.info('{}'.format(ignore_cnt))
return sad, mse, grad, conn
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