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decode_sampler_MRI.py
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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 random
import numpy as np
from PIL import Image
try:
import SimpleITK as sitk
except ImportError as e:
print(
f"{e}, [SimpleITK] package and it's dependencies is required for PP-Care."
)
import cv2
from ..registry import PIPELINES
@PIPELINES.register()
class SFMRI_DecodeSampler(object):
"""
Sample frames id.
NOTE: Use PIL to read image here, has diff with CV2
Args:
num_seg(int): number of segments.
seg_len(int): number of sampled frames in each segment.
valid_mode(bool): True or False.
select_left: Whether to select the frame to the left in the middle when the sampling interval is even in the test mode.
Returns:
frames_idx: the index of sampled #frames.
"""
def __init__(self,
num_seg,
seg_len,
valid_mode=False,
select_left=False,
dense_sample=False,
linspace_sample=False):
self.num_seg = num_seg
self.seg_len = seg_len
self.valid_mode = valid_mode
self.select_left = select_left
self.dense_sample = dense_sample
self.linspace_sample = linspace_sample
def _get(self, frames_idx_s, frames_idx_f, results):
frame_dir = results['frame_dir']
imgs_s = []
imgs_f = []
MRI = sitk.GetArrayFromImage(sitk.ReadImage(frame_dir))
for idx in frames_idx_s:
item = MRI[idx]
item = cv2.resize(item, (224, 224))
imgs_s.append(item)
for idx in frames_idx_f:
item = MRI[idx]
item = cv2.resize(item, (224, 224))
imgs_f.append(item)
results['imgs'] = [imgs_s, imgs_f]
return results
def __call__(self, results):
"""
Args:
frames_len: length of frames.
return:
sampling id.
"""
frames_len = int(results['frames_len'])
average_dur1 = int(frames_len / self.num_seg[0])
average_dur2 = int(frames_len / self.num_seg[1])
frames_idx_s = []
frames_idx_f = []
if self.linspace_sample:
if 'start_idx' in results and 'end_idx' in results:
offsets_s = np.linspace(results['start_idx'],
results['end_idx'], self.num_seg[0])
offsets_f = np.linspace(results['start_idx'],
results['end_idx'], self.num_seg[1])
else:
offsets_s = np.linspace(0, frames_len - 1, self.num_seg[0])
offsets_f = np.linspace(0, frames_len - 1, self.num_seg[1])
offsets_s = np.clip(offsets_s, 0, frames_len - 1).astype(np.int64)
offsets_f = np.clip(offsets_f, 0, frames_len - 1).astype(np.int64)
frames_idx_s = list(offsets_s)
frames_idx_f = list(offsets_f)
return self._get(frames_idx_s, frames_idx_f, results)
if not self.select_left:
if self.dense_sample: # For ppTSM
if not self.valid_mode: # train
sample_pos = max(1, 1 + frames_len - 64)
t_stride1 = 64 // self.num_seg[0]
t_stride2 = 64 // self.num_seg[1]
start_idx = 0 if sample_pos == 1 else np.random.randint(
0, sample_pos - 1)
offsets_s = [(idx * t_stride1 + start_idx) % frames_len + 1
for idx in range(self.num_seg[0])]
offsets_f = [(idx * t_stride2 + start_idx) % frames_len + 1
for idx in range(self.num_seg[1])]
frames_idx_s = offsets_s
frames_idx_f = offsets_f
else:
sample_pos = max(1, 1 + frames_len - 64)
t_stride1 = 64 // self.num_seg[0]
t_stride2 = 64 // self.num_seg[1]
start_list = np.linspace(0,
sample_pos - 1,
num=10,
dtype=int)
offsets_s = []
offsets_f = []
for start_idx in start_list.tolist():
offsets_s += [
(idx * t_stride1 + start_idx) % frames_len + 1
for idx in range(self.num_seg[0])
]
for start_idx in start_list.tolist():
offsets_f += [
(idx * t_stride2 + start_idx) % frames_len + 1
for idx in range(self.num_seg[1])
]
frames_idx_s = offsets_s
frames_idx_f = offsets_f
else:
for i in range(self.num_seg[0]):
idx = 0
if not self.valid_mode:
if average_dur1 >= self.seg_len:
idx = random.randint(0, average_dur1 - self.seg_len)
idx += i * average_dur1
elif average_dur1 >= 1:
idx += i * average_dur1
else:
idx = i
else:
if average_dur1 >= self.seg_len:
idx = (average_dur1 - 1) // 2
idx += i * average_dur1
elif average_dur1 >= 1:
idx += i * average_dur1
else:
idx = i
for jj in range(idx, idx + self.seg_len):
frames_idx_s.append(jj)
for i in range(self.num_seg[1]):
idx = 0
if not self.valid_mode:
if average_dur2 >= self.seg_len:
idx = random.randint(0, average_dur2 - self.seg_len)
idx += i * average_dur2
elif average_dur2 >= 1:
idx += i * average_dur2
else:
idx = i
else:
if average_dur2 >= self.seg_len:
idx = (average_dur2 - 1) // 2
idx += i * average_dur2
elif average_dur2 >= 1:
idx += i * average_dur2
else:
idx = i
for jj in range(idx, idx + self.seg_len):
frames_idx_f.append(jj)
return self._get(frames_idx_s, frames_idx_f, results)
else: # for TSM
if not self.valid_mode:
if average_dur2 > 0:
offsets_s = np.multiply(list(range(
self.num_seg[0])), average_dur1) + np.random.randint(
average_dur1, size=self.num_seg[0])
offsets_f = np.multiply(list(range(
self.num_seg[1])), average_dur2) + np.random.randint(
average_dur2, size=self.num_seg[1])
elif frames_len > self.num_seg[1]:
offsets_s = np.sort(
np.random.randint(frames_len, size=self.num_seg[0]))
offsets_f = np.sort(
np.random.randint(frames_len, size=self.num_seg[1]))
else:
offsets_s = np.zeros(shape=(self.num_seg[0], ))
offsets_f = np.zeros(shape=(self.num_seg[1], ))
else:
if frames_len > self.num_seg[1]:
average_dur_float_s = frames_len / self.num_seg[0]
offsets_s = np.array([
int(average_dur_float_s / 2.0 + average_dur_float_s * x)
for x in range(self.num_seg[0])
])
average_dur_float_f = frames_len / self.num_seg[1]
offsets_f = np.array([
int(average_dur_float_f / 2.0 + average_dur_float_f * x)
for x in range(self.num_seg[1])
])
else:
offsets_s = np.zeros(shape=(self.num_seg[0], ))
offsets_f = np.zeros(shape=(self.num_seg[1], ))
frames_idx_s = list(offsets_s)
frames_idx_f = list(offsets_f)
return self._get(frames_idx_s, frames_idx_f, results)