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PaddleDetection
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2f3950ee
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PaddleDetection
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2f3950ee
编写于
11月 19, 2021
作者:
F
Feng Ni
提交者:
GitHub
11月 19, 2021
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差异文件
[MOT] refine mtmct deploy (#4633)
* fix mtmct deploy * add enable_static
上级
7fc9614a
变更
2
显示空白变更内容
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Showing
2 changed file
with
19 addition
and
60 deletion
+19
-60
deploy/pptracking/python/mot/mtmct/utils.py
deploy/pptracking/python/mot/mtmct/utils.py
+1
-0
deploy/pptracking/python/mot_sde_infer.py
deploy/pptracking/python/mot_sde_infer.py
+18
-60
未找到文件。
deploy/pptracking/python/mot/mtmct/utils.py
浏览文件 @
2f3950ee
...
@@ -341,6 +341,7 @@ def visual_rerank(prb_feats,
...
@@ -341,6 +341,7 @@ def visual_rerank(prb_feats,
prb_feats
,
gal_feats
=
run_fac
(
prb_feats
,
gal_feats
,
prb_labels
,
prb_feats
,
gal_feats
=
run_fac
(
prb_feats
,
gal_feats
,
prb_labels
,
gal_labels
,
0.08
,
20
,
0.5
,
1
,
1
)
gal_labels
,
0.08
,
20
,
0.5
,
1
,
1
)
if
use_rerank
:
if
use_rerank
:
paddle
.
enable_static
()
print
(
'current use rerank finetuned parameters....'
)
print
(
'current use rerank finetuned parameters....'
)
# Step2: k-reciprocal. finetuned parameters: [k1,k2,lambda_value]
# Step2: k-reciprocal. finetuned parameters: [k1,k2,lambda_value]
sims
=
ReRank2
(
sims
=
ReRank2
(
...
...
deploy/pptracking/python/mot_sde_infer.py
浏览文件 @
2f3950ee
...
@@ -32,7 +32,7 @@ from benchmark_utils import PaddleInferBenchmark
...
@@ -32,7 +32,7 @@ from benchmark_utils import PaddleInferBenchmark
from
visualize
import
plot_tracking
from
visualize
import
plot_tracking
from
mot.tracker
import
DeepSORTTracker
from
mot.tracker
import
DeepSORTTracker
from
mot.utils
import
MOTTimer
,
write_mot_results
,
flow_statistic
from
mot.utils
import
MOTTimer
,
write_mot_results
,
flow_statistic
,
scale_coords
,
clip_box
,
preprocess_reid
from
mot.mtmct.utils
import
parse_bias
from
mot.mtmct.utils
import
parse_bias
from
mot.mtmct.postprocess
import
trajectory_fusion
,
sub_cluster
,
gen_res
,
print_mtmct_result
from
mot.mtmct.postprocess
import
trajectory_fusion
,
sub_cluster
,
gen_res
,
print_mtmct_result
...
@@ -59,50 +59,6 @@ def bench_log(detector, img_list, model_info, batch_size=1, name=None):
...
@@ -59,50 +59,6 @@ def bench_log(detector, img_list, model_info, batch_size=1, name=None):
log
(
name
)
log
(
name
)
def
scale_coords
(
coords
,
input_shape
,
im_shape
,
scale_factor
):
im_shape
=
im_shape
[
0
]
ratio
=
scale_factor
[
0
][
0
]
pad_w
=
(
input_shape
[
1
]
-
int
(
im_shape
[
1
]))
/
2
pad_h
=
(
input_shape
[
0
]
-
int
(
im_shape
[
0
]))
/
2
coords
[:,
0
::
2
]
-=
pad_w
coords
[:,
1
::
2
]
-=
pad_h
coords
[:,
0
:
4
]
/=
ratio
coords
[:,
:
4
]
=
np
.
clip
(
coords
[:,
:
4
],
a_min
=
0
,
a_max
=
coords
[:,
:
4
].
max
())
return
coords
.
round
()
def
clip_box
(
xyxy
,
input_shape
,
im_shape
,
scale_factor
):
im_shape
=
im_shape
[
0
]
ratio
=
scale_factor
[
0
][
0
]
img0_shape
=
[
int
(
im_shape
[
0
]
/
ratio
),
int
(
im_shape
[
1
]
/
ratio
)]
xyxy
[:,
0
::
2
]
=
np
.
clip
(
xyxy
[:,
0
::
2
],
a_min
=
0
,
a_max
=
img0_shape
[
1
])
xyxy
[:,
1
::
2
]
=
np
.
clip
(
xyxy
[:,
1
::
2
],
a_min
=
0
,
a_max
=
img0_shape
[
0
])
w
=
xyxy
[:,
2
:
3
]
-
xyxy
[:,
0
:
1
]
h
=
xyxy
[:,
3
:
4
]
-
xyxy
[:,
1
:
2
]
mask
=
np
.
logical_and
(
h
>
0
,
w
>
0
)
keep_idx
=
np
.
nonzero
(
mask
)
return
xyxy
[
keep_idx
[
0
]],
keep_idx
def
preprocess_reid
(
imgs
,
w
=
64
,
h
=
192
,
mean
=
[
0.485
,
0.456
,
0.406
],
std
=
[
0.229
,
0.224
,
0.225
]):
im_batch
=
[]
for
img
in
imgs
:
img
=
cv2
.
resize
(
img
,
(
w
,
h
))
img
=
img
[:,
:,
::
-
1
].
astype
(
'float32'
).
transpose
((
2
,
0
,
1
))
/
255
img_mean
=
np
.
array
(
mean
).
reshape
((
3
,
1
,
1
))
img_std
=
np
.
array
(
std
).
reshape
((
3
,
1
,
1
))
img
-=
img_mean
img
/=
img_std
img
=
np
.
expand_dims
(
img
,
axis
=
0
)
im_batch
.
append
(
img
)
im_batch
=
np
.
concatenate
(
im_batch
,
0
)
return
im_batch
class
SDE_Detector
(
Detector
):
class
SDE_Detector
(
Detector
):
"""
"""
Args:
Args:
...
@@ -146,8 +102,7 @@ class SDE_Detector(Detector):
...
@@ -146,8 +102,7 @@ class SDE_Detector(Detector):
assert
batch_size
==
1
,
"The JDE Detector only supports batch size=1 now"
assert
batch_size
==
1
,
"The JDE Detector only supports batch size=1 now"
self
.
pred_config
=
pred_config
self
.
pred_config
=
pred_config
def
postprocess
(
self
,
boxes
,
input_shape
,
im_shape
,
scale_factor
,
threshold
,
def
postprocess
(
self
,
boxes
,
ori_image_shape
,
threshold
,
scaled
):
scaled
):
over_thres_idx
=
np
.
nonzero
(
boxes
[:,
1
:
2
]
>=
threshold
)[
0
]
over_thres_idx
=
np
.
nonzero
(
boxes
[:,
1
:
2
]
>=
threshold
)[
0
]
if
len
(
over_thres_idx
)
==
0
:
if
len
(
over_thres_idx
)
==
0
:
pred_dets
=
np
.
zeros
((
1
,
6
),
dtype
=
np
.
float32
)
pred_dets
=
np
.
zeros
((
1
,
6
),
dtype
=
np
.
float32
)
...
@@ -165,8 +120,8 @@ class SDE_Detector(Detector):
...
@@ -165,8 +120,8 @@ class SDE_Detector(Detector):
else
:
else
:
pred_bboxes
=
boxes
[:,
2
:]
pred_bboxes
=
boxes
[:,
2
:]
pred_xyxys
,
keep_idx
=
clip_box
(
pred_bboxes
,
input_shape
,
im_shape
,
pred_xyxys
,
keep_idx
=
clip_box
(
pred_bboxes
,
ori_image_shape
)
scale_factor
)
if
len
(
keep_idx
[
0
])
==
0
:
if
len
(
keep_idx
[
0
])
==
0
:
pred_dets
=
np
.
zeros
((
1
,
6
),
dtype
=
np
.
float32
)
pred_dets
=
np
.
zeros
((
1
,
6
),
dtype
=
np
.
float32
)
pred_xyxys
=
np
.
zeros
((
1
,
4
),
dtype
=
np
.
float32
)
pred_xyxys
=
np
.
zeros
((
1
,
4
),
dtype
=
np
.
float32
)
...
@@ -183,10 +138,12 @@ class SDE_Detector(Detector):
...
@@ -183,10 +138,12 @@ class SDE_Detector(Detector):
return
pred_dets
,
pred_xyxys
return
pred_dets
,
pred_xyxys
def
predict
(
self
,
image
,
scaled
,
threshold
=
0.5
,
warmup
=
0
,
repeats
=
1
):
def
predict
(
self
,
image
_path
,
ori_image_shape
,
scaled
,
threshold
=
0.5
,
warmup
=
0
,
repeats
=
1
):
'''
'''
Args:
Args:
image (np.ndarray): image numpy data
image_path (list[str]): path of images, only support one image path
(batch_size=1) in tracking model
ori_image_shape (list[int]: original image shape
threshold (float): threshold of predicted box' score
threshold (float): threshold of predicted box' score
scaled (bool): whether the coords after detector outputs are scaled,
scaled (bool): whether the coords after detector outputs are scaled,
default False in jde yolov3, set True in general detector.
default False in jde yolov3, set True in general detector.
...
@@ -194,7 +151,7 @@ class SDE_Detector(Detector):
...
@@ -194,7 +151,7 @@ class SDE_Detector(Detector):
pred_dets (np.ndarray, [N, 6])
pred_dets (np.ndarray, [N, 6])
'''
'''
self
.
det_times
.
preprocess_time_s
.
start
()
self
.
det_times
.
preprocess_time_s
.
start
()
inputs
=
self
.
preprocess
(
image
)
inputs
=
self
.
preprocess
(
image
_path
)
self
.
det_times
.
preprocess_time_s
.
end
()
self
.
det_times
.
preprocess_time_s
.
end
()
input_names
=
self
.
predictor
.
get_input_names
()
input_names
=
self
.
predictor
.
get_input_names
()
...
@@ -221,12 +178,8 @@ class SDE_Detector(Detector):
...
@@ -221,12 +178,8 @@ class SDE_Detector(Detector):
pred_dets
=
np
.
zeros
((
1
,
6
),
dtype
=
np
.
float32
)
pred_dets
=
np
.
zeros
((
1
,
6
),
dtype
=
np
.
float32
)
pred_xyxys
=
np
.
zeros
((
1
,
4
),
dtype
=
np
.
float32
)
pred_xyxys
=
np
.
zeros
((
1
,
4
),
dtype
=
np
.
float32
)
else
:
else
:
input_shape
=
inputs
[
'image'
].
shape
[
2
:]
im_shape
=
inputs
[
'im_shape'
]
scale_factor
=
inputs
[
'scale_factor'
]
pred_dets
,
pred_xyxys
=
self
.
postprocess
(
pred_dets
,
pred_xyxys
=
self
.
postprocess
(
boxes
,
input_shape
,
im_shape
,
scale_factor
,
threshold
,
scaled
)
boxes
,
ori_image_shape
,
threshold
,
scaled
)
self
.
det_times
.
postprocess_time_s
.
end
()
self
.
det_times
.
postprocess_time_s
.
end
()
self
.
det_times
.
img_num
+=
1
self
.
det_times
.
img_num
+=
1
...
@@ -727,7 +680,9 @@ def predict_mtmct_seq(detector, reid_model, seq_name, output_dir):
...
@@ -727,7 +680,9 @@ def predict_mtmct_seq(detector, reid_model, seq_name, output_dir):
if
frame_id
%
40
==
0
:
if
frame_id
%
40
==
0
:
print
(
'Processing frame {} of seq {}.'
.
format
(
frame_id
,
seq_name
))
print
(
'Processing frame {} of seq {}.'
.
format
(
frame_id
,
seq_name
))
frame
=
cv2
.
imread
(
os
.
path
.
join
(
fpath
,
img_file
))
frame
=
cv2
.
imread
(
os
.
path
.
join
(
fpath
,
img_file
))
pred_dets
,
pred_xyxys
=
detector
.
predict
([
frame
],
FLAGS
.
scaled
,
ori_image_shape
=
list
(
frame
.
shape
[:
2
])
frame_path
=
os
.
path
.
join
(
fpath
,
img_file
)
pred_dets
,
pred_xyxys
=
detector
.
predict
([
frame_path
],
ori_image_shape
,
FLAGS
.
scaled
,
FLAGS
.
threshold
)
FLAGS
.
threshold
)
if
len
(
pred_dets
)
==
1
and
np
.
sum
(
pred_dets
)
==
0
:
if
len
(
pred_dets
)
==
1
and
np
.
sum
(
pred_dets
)
==
0
:
...
@@ -855,8 +810,6 @@ def predict_mtmct(detector, reid_model, mtmct_dir, mtmct_cfg):
...
@@ -855,8 +810,6 @@ def predict_mtmct(detector, reid_model, mtmct_dir, mtmct_cfg):
use_roi
=
use_roi
,
use_roi
=
use_roi
,
roi_dir
=
roi_dir
)
roi_dir
=
roi_dir
)
pred_mtmct_file
=
os
.
path
.
join
(
output_dir
,
'mtmct_result.txt'
)
if
FLAGS
.
save_images
:
if
FLAGS
.
save_images
:
carame_results
,
cid_tid_fid_res
=
get_mtmct_matching_results
(
carame_results
,
cid_tid_fid_res
=
get_mtmct_matching_results
(
pred_mtmct_file
)
pred_mtmct_file
)
...
@@ -872,6 +825,11 @@ def predict_mtmct(detector, reid_model, mtmct_dir, mtmct_cfg):
...
@@ -872,6 +825,11 @@ def predict_mtmct(detector, reid_model, mtmct_dir, mtmct_cfg):
save_dir
=
save_dir
,
save_dir
=
save_dir
,
save_videos
=
FLAGS
.
save_images
)
save_videos
=
FLAGS
.
save_images
)
# evalution metrics
data_root_gt
=
os
.
path
.
join
(
mtmct_dir
,
'..'
,
'gt'
,
'gt.txt'
)
if
os
.
path
.
exists
(
data_root_gt
):
print_mtmct_result
(
data_root_gt
,
pred_mtmct_file
)
def
main
():
def
main
():
pred_config
=
PredictConfig
(
FLAGS
.
model_dir
)
pred_config
=
PredictConfig
(
FLAGS
.
model_dir
)
...
...
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