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36152456
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36152456
编写于
8月 16, 2021
作者:
M
MissPenguin
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add benchmark log
上级
ecacf4c0
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
258 addition
and
187 deletion
+258
-187
deploy/cpp_infer/include/ocr_cls.h
deploy/cpp_infer/include/ocr_cls.h
+3
-3
deploy/cpp_infer/include/ocr_det.h
deploy/cpp_infer/include/ocr_det.h
+4
-4
deploy/cpp_infer/include/ocr_rec.h
deploy/cpp_infer/include/ocr_rec.h
+4
-4
deploy/cpp_infer/include/utility.h
deploy/cpp_infer/include/utility.h
+3
-0
deploy/cpp_infer/src/main.cpp
deploy/cpp_infer/src/main.cpp
+131
-164
deploy/cpp_infer/src/ocr_cls.cpp
deploy/cpp_infer/src/ocr_cls.cpp
+8
-2
deploy/cpp_infer/src/ocr_det.cpp
deploy/cpp_infer/src/ocr_det.cpp
+28
-6
deploy/cpp_infer/src/ocr_rec.cpp
deploy/cpp_infer/src/ocr_rec.cpp
+22
-4
deploy/cpp_infer/src/utility.cpp
deploy/cpp_infer/src/utility.cpp
+55
-0
未找到文件。
deploy/cpp_infer/include/ocr_cls.h
浏览文件 @
36152456
...
...
@@ -42,7 +42,7 @@ public:
const
int
&
gpu_id
,
const
int
&
gpu_mem
,
const
int
&
cpu_math_library_num_threads
,
const
bool
&
use_mkldnn
,
const
double
&
cls_thresh
,
const
bool
&
use_tensorrt
,
const
bool
&
use_fp16
)
{
const
bool
&
use_tensorrt
,
const
std
::
string
&
precision
)
{
this
->
use_gpu_
=
use_gpu
;
this
->
gpu_id_
=
gpu_id
;
this
->
gpu_mem_
=
gpu_mem
;
...
...
@@ -51,7 +51,7 @@ public:
this
->
cls_thresh
=
cls_thresh
;
this
->
use_tensorrt_
=
use_tensorrt
;
this
->
use_fp16_
=
use_fp16
;
this
->
precision_
=
precision
;
LoadModel
(
model_dir
);
}
...
...
@@ -75,7 +75,7 @@ private:
std
::
vector
<
float
>
scale_
=
{
1
/
0.5
f
,
1
/
0.5
f
,
1
/
0.5
f
};
bool
is_scale_
=
true
;
bool
use_tensorrt_
=
false
;
bool
use_fp16_
=
false
;
std
::
string
precision_
=
"fp32"
;
// pre-process
ClsResizeImg
resize_op_
;
Normalize
normalize_op_
;
...
...
deploy/cpp_infer/include/ocr_det.h
浏览文件 @
36152456
...
...
@@ -46,7 +46,7 @@ public:
const
double
&
det_db_box_thresh
,
const
double
&
det_db_unclip_ratio
,
const
bool
&
use_polygon_score
,
const
bool
&
visualize
,
const
bool
&
use_tensorrt
,
const
bool
&
use_fp16
)
{
const
bool
&
use_tensorrt
,
const
std
::
string
&
precision
)
{
this
->
use_gpu_
=
use_gpu
;
this
->
gpu_id_
=
gpu_id
;
this
->
gpu_mem_
=
gpu_mem
;
...
...
@@ -62,7 +62,7 @@ public:
this
->
visualize_
=
visualize
;
this
->
use_tensorrt_
=
use_tensorrt
;
this
->
use_fp16_
=
use_fp16
;
this
->
precision_
=
precision
;
LoadModel
(
model_dir
);
}
...
...
@@ -71,7 +71,7 @@ public:
void
LoadModel
(
const
std
::
string
&
model_dir
);
// Run predictor
void
Run
(
cv
::
Mat
&
img
,
std
::
vector
<
std
::
vector
<
std
::
vector
<
int
>>>
&
boxes
);
void
Run
(
cv
::
Mat
&
img
,
std
::
vector
<
std
::
vector
<
std
::
vector
<
int
>>>
&
boxes
,
std
::
vector
<
double
>
*
times
);
private:
std
::
shared_ptr
<
Predictor
>
predictor_
;
...
...
@@ -91,7 +91,7 @@ private:
bool
visualize_
=
true
;
bool
use_tensorrt_
=
false
;
bool
use_fp16_
=
false
;
std
::
string
precision_
=
"fp32"
;
std
::
vector
<
float
>
mean_
=
{
0.485
f
,
0.456
f
,
0.406
f
};
std
::
vector
<
float
>
scale_
=
{
1
/
0.229
f
,
1
/
0.224
f
,
1
/
0.225
f
};
...
...
deploy/cpp_infer/include/ocr_rec.h
浏览文件 @
36152456
...
...
@@ -44,14 +44,14 @@ public:
const
int
&
gpu_id
,
const
int
&
gpu_mem
,
const
int
&
cpu_math_library_num_threads
,
const
bool
&
use_mkldnn
,
const
string
&
label_path
,
const
bool
&
use_tensorrt
,
const
bool
&
use_fp16
)
{
const
bool
&
use_tensorrt
,
const
std
::
string
&
precision
)
{
this
->
use_gpu_
=
use_gpu
;
this
->
gpu_id_
=
gpu_id
;
this
->
gpu_mem_
=
gpu_mem
;
this
->
cpu_math_library_num_threads_
=
cpu_math_library_num_threads
;
this
->
use_mkldnn_
=
use_mkldnn
;
this
->
use_tensorrt_
=
use_tensorrt
;
this
->
use_fp16_
=
use_fp16
;
this
->
precision_
=
precision
;
this
->
label_list_
=
Utility
::
ReadDict
(
label_path
);
this
->
label_list_
.
insert
(
this
->
label_list_
.
begin
(),
...
...
@@ -64,7 +64,7 @@ public:
// Load Paddle inference model
void
LoadModel
(
const
std
::
string
&
model_dir
);
void
Run
(
cv
::
Mat
&
img
);
void
Run
(
cv
::
Mat
&
img
,
std
::
vector
<
double
>
*
times
);
private:
std
::
shared_ptr
<
Predictor
>
predictor_
;
...
...
@@ -81,7 +81,7 @@ private:
std
::
vector
<
float
>
scale_
=
{
1
/
0.5
f
,
1
/
0.5
f
,
1
/
0.5
f
};
bool
is_scale_
=
true
;
bool
use_tensorrt_
=
false
;
bool
use_fp16_
=
false
;
std
::
string
precision_
=
"fp32"
;
// pre-process
CrnnResizeImg
resize_op_
;
Normalize
normalize_op_
;
...
...
deploy/cpp_infer/include/utility.h
浏览文件 @
36152456
...
...
@@ -47,6 +47,9 @@ public:
static
void
GetAllFiles
(
const
char
*
dir_name
,
std
::
vector
<
std
::
string
>
&
all_inputs
);
static
cv
::
Mat
GetRotateCropImage
(
const
cv
::
Mat
&
srcimage
,
std
::
vector
<
std
::
vector
<
int
>>
box
);
};
}
// namespace PaddleOCR
\ No newline at end of file
deploy/cpp_infer/src/main.cpp
浏览文件 @
36152456
...
...
@@ -31,6 +31,7 @@
#include <include/ocr_det.h>
#include <include/ocr_cls.h>
#include <include/ocr_rec.h>
#include <include/utility.h>
#include <sys/stat.h>
#include <gflags/gflags.h>
...
...
@@ -41,7 +42,9 @@ DEFINE_int32(gpu_mem, 4000, "GPU id when infering with GPU.");
DEFINE_int32
(
cpu_math_library_num_threads
,
10
,
"Num of threads with CPU."
);
DEFINE_bool
(
use_mkldnn
,
false
,
"Whether use mkldnn with CPU."
);
DEFINE_bool
(
use_tensorrt
,
false
,
"Whether use tensorrt."
);
DEFINE_bool
(
use_fp16
,
false
,
"Whether use fp16 when use tensorrt."
);
DEFINE_string
(
precision
,
"fp32"
,
"Precision be one of fp32/fp16/int8"
);
DEFINE_bool
(
benchmark
,
true
,
"Whether use benchmark."
);
DEFINE_string
(
save_log_path
,
"./log_output/"
,
"Save benchmark log path."
);
// detection related
DEFINE_string
(
image_dir
,
""
,
"Dir of input image."
);
DEFINE_string
(
det_model_dir
,
""
,
"Path of det inference model."
);
...
...
@@ -65,6 +68,34 @@ using namespace cv;
using
namespace
PaddleOCR
;
void
PrintBenchmarkLog
(
std
::
string
model_name
,
int
batch_size
,
std
::
string
input_shape
,
std
::
vector
<
double
>
time_info
,
int
img_num
){
LOG
(
INFO
)
<<
"----------------------- Config info -----------------------"
;
LOG
(
INFO
)
<<
"runtime_device: "
<<
(
FLAGS_use_gpu
?
"gpu"
:
"cpu"
);
LOG
(
INFO
)
<<
"ir_optim: "
<<
"True"
;
LOG
(
INFO
)
<<
"enable_memory_optim: "
<<
"True"
;
LOG
(
INFO
)
<<
"enable_tensorrt: "
<<
FLAGS_use_tensorrt
;
LOG
(
INFO
)
<<
"enable_mkldnn: "
<<
(
FLAGS_use_mkldnn
?
"True"
:
"False"
);
LOG
(
INFO
)
<<
"cpu_math_library_num_threads: "
<<
FLAGS_cpu_math_library_num_threads
;
LOG
(
INFO
)
<<
"----------------------- Data info -----------------------"
;
LOG
(
INFO
)
<<
"batch_size: "
<<
batch_size
;
LOG
(
INFO
)
<<
"input_shape: "
<<
input_shape
;
LOG
(
INFO
)
<<
"data_num: "
<<
img_num
;
LOG
(
INFO
)
<<
"----------------------- Model info -----------------------"
;
LOG
(
INFO
)
<<
"model_name: "
<<
model_name
;
LOG
(
INFO
)
<<
"precision: "
<<
FLAGS_precision
;
LOG
(
INFO
)
<<
"----------------------- Perf info ------------------------"
;
LOG
(
INFO
)
<<
"Total time spent(ms): "
<<
std
::
accumulate
(
time_info
.
begin
(),
time_info
.
end
(),
0
);
LOG
(
INFO
)
<<
"preprocess_time(ms): "
<<
time_info
[
0
]
/
img_num
<<
", inference_time(ms): "
<<
time_info
[
1
]
/
img_num
<<
", postprocess_time(ms): "
<<
time_info
[
2
]
/
img_num
;
}
static
bool
PathExists
(
const
std
::
string
&
path
){
#ifdef _WIN32
struct
_stat
buffer
;
...
...
@@ -76,88 +107,15 @@ static bool PathExists(const std::string& path){
}
cv
::
Mat
GetRotateCropImage
(
const
cv
::
Mat
&
srcimage
,
std
::
vector
<
std
::
vector
<
int
>>
box
)
{
cv
::
Mat
image
;
srcimage
.
copyTo
(
image
);
std
::
vector
<
std
::
vector
<
int
>>
points
=
box
;
int
x_collect
[
4
]
=
{
box
[
0
][
0
],
box
[
1
][
0
],
box
[
2
][
0
],
box
[
3
][
0
]};
int
y_collect
[
4
]
=
{
box
[
0
][
1
],
box
[
1
][
1
],
box
[
2
][
1
],
box
[
3
][
1
]};
int
left
=
int
(
*
std
::
min_element
(
x_collect
,
x_collect
+
4
));
int
right
=
int
(
*
std
::
max_element
(
x_collect
,
x_collect
+
4
));
int
top
=
int
(
*
std
::
min_element
(
y_collect
,
y_collect
+
4
));
int
bottom
=
int
(
*
std
::
max_element
(
y_collect
,
y_collect
+
4
));
cv
::
Mat
img_crop
;
image
(
cv
::
Rect
(
left
,
top
,
right
-
left
,
bottom
-
top
)).
copyTo
(
img_crop
);
for
(
int
i
=
0
;
i
<
points
.
size
();
i
++
)
{
points
[
i
][
0
]
-=
left
;
points
[
i
][
1
]
-=
top
;
}
int
img_crop_width
=
int
(
sqrt
(
pow
(
points
[
0
][
0
]
-
points
[
1
][
0
],
2
)
+
pow
(
points
[
0
][
1
]
-
points
[
1
][
1
],
2
)));
int
img_crop_height
=
int
(
sqrt
(
pow
(
points
[
0
][
0
]
-
points
[
3
][
0
],
2
)
+
pow
(
points
[
0
][
1
]
-
points
[
3
][
1
],
2
)));
cv
::
Point2f
pts_std
[
4
];
pts_std
[
0
]
=
cv
::
Point2f
(
0.
,
0.
);
pts_std
[
1
]
=
cv
::
Point2f
(
img_crop_width
,
0.
);
pts_std
[
2
]
=
cv
::
Point2f
(
img_crop_width
,
img_crop_height
);
pts_std
[
3
]
=
cv
::
Point2f
(
0.
f
,
img_crop_height
);
cv
::
Point2f
pointsf
[
4
];
pointsf
[
0
]
=
cv
::
Point2f
(
points
[
0
][
0
],
points
[
0
][
1
]);
pointsf
[
1
]
=
cv
::
Point2f
(
points
[
1
][
0
],
points
[
1
][
1
]);
pointsf
[
2
]
=
cv
::
Point2f
(
points
[
2
][
0
],
points
[
2
][
1
]);
pointsf
[
3
]
=
cv
::
Point2f
(
points
[
3
][
0
],
points
[
3
][
1
]);
cv
::
Mat
M
=
cv
::
getPerspectiveTransform
(
pointsf
,
pts_std
);
cv
::
Mat
dst_img
;
cv
::
warpPerspective
(
img_crop
,
dst_img
,
M
,
cv
::
Size
(
img_crop_width
,
img_crop_height
),
cv
::
BORDER_REPLICATE
);
if
(
float
(
dst_img
.
rows
)
>=
float
(
dst_img
.
cols
)
*
1.5
)
{
cv
::
Mat
srcCopy
=
cv
::
Mat
(
dst_img
.
rows
,
dst_img
.
cols
,
dst_img
.
depth
());
cv
::
transpose
(
dst_img
,
srcCopy
);
cv
::
flip
(
srcCopy
,
srcCopy
,
0
);
return
srcCopy
;
}
else
{
return
dst_img
;
}
}
int
main_det
(
int
argc
,
char
**
argv
)
{
// Parsing command-line
google
::
ParseCommandLineFlags
(
&
argc
,
&
argv
,
true
);
if
(
FLAGS_det_model_dir
.
empty
()
||
FLAGS_image_dir
.
empty
())
{
std
::
cout
<<
"Usage[det]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<<
"--image_dir=/PATH/TO/INPUT/IMAGE/"
<<
std
::
endl
;
exit
(
1
);
}
if
(
!
PathExists
(
FLAGS_image_dir
))
{
std
::
cerr
<<
"[ERROR] image path not exist! image_dir: "
<<
FLAGS_image_dir
<<
endl
;
exit
(
1
);
}
std
::
vector
<
cv
::
String
>
cv_all_img_names
;
cv
::
glob
(
FLAGS_image_dir
,
cv_all_img_names
);
std
::
cout
<<
"total images num: "
<<
cv_all_img_names
.
size
()
<<
endl
;
int
main_det
(
std
::
vector
<
cv
::
String
>
cv_all_img_names
)
{
std
::
vector
<
double
>
time_info
=
{
0
,
0
,
0
};
DBDetector
det
(
FLAGS_det_model_dir
,
FLAGS_use_gpu
,
FLAGS_gpu_id
,
FLAGS_gpu_mem
,
FLAGS_cpu_math_library_num_threads
,
FLAGS_use_mkldnn
,
FLAGS_max_side_len
,
FLAGS_det_db_thresh
,
FLAGS_det_db_box_thresh
,
FLAGS_det_db_unclip_ratio
,
FLAGS_use_polygon_score
,
FLAGS_visualize
,
FLAGS_use_tensorrt
,
FLAGS_use_fp16
);
auto
start
=
std
::
chrono
::
system_clock
::
now
();
FLAGS_use_tensorrt
,
FLAGS_precision
);
for
(
int
i
=
0
;
i
<
cv_all_img_names
.
size
();
++
i
)
{
LOG
(
INFO
)
<<
"The predict img: "
<<
cv_all_img_names
[
i
];
...
...
@@ -167,46 +125,28 @@ int main_det(int argc, char **argv) {
exit
(
1
);
}
std
::
vector
<
std
::
vector
<
std
::
vector
<
int
>>>
boxes
;
std
::
vector
<
double
>
det_times
;
det
.
Run
(
srcimg
,
boxes
);
auto
end
=
std
::
chrono
::
system_clock
::
now
();
auto
duration
=
std
::
chrono
::
duration_cast
<
std
::
chrono
::
microseconds
>
(
end
-
start
);
std
::
cout
<<
"Cost "
<<
double
(
duration
.
count
())
*
std
::
chrono
::
microseconds
::
period
::
num
/
std
::
chrono
::
microseconds
::
period
::
den
<<
"s"
<<
std
::
endl
;
det
.
Run
(
srcimg
,
boxes
,
&
det_times
);
time_info
[
0
]
+=
det_times
[
0
];
time_info
[
1
]
+=
det_times
[
1
];
time_info
[
2
]
+=
det_times
[
2
];
}
if
(
FLAGS_benchmark
)
{
PrintBenchmarkLog
(
"det"
,
1
,
"dynamic"
,
time_info
,
cv_all_img_names
.
size
());
}
return
0
;
}
int
main_rec
(
int
argc
,
char
**
argv
)
{
// Parsing command-line
google
::
ParseCommandLineFlags
(
&
argc
,
&
argv
,
true
);
if
(
FLAGS_rec_model_dir
.
empty
()
||
FLAGS_image_dir
.
empty
())
{
std
::
cout
<<
"Usage[rec]: ./ppocr --rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<<
"--image_dir=/PATH/TO/INPUT/IMAGE/"
<<
std
::
endl
;
exit
(
1
);
}
if
(
!
PathExists
(
FLAGS_image_dir
))
{
std
::
cerr
<<
"[ERROR] image path not exist! image_dir: "
<<
FLAGS_image_dir
<<
endl
;
exit
(
1
);
}
std
::
vector
<
cv
::
String
>
cv_all_img_names
;
cv
::
glob
(
FLAGS_image_dir
,
cv_all_img_names
);
std
::
cout
<<
"total images num: "
<<
cv_all_img_names
.
size
()
<<
endl
;
int
main_rec
(
std
::
vector
<
cv
::
String
>
cv_all_img_names
)
{
std
::
vector
<
double
>
time_info
=
{
0
,
0
,
0
};
CRNNRecognizer
rec
(
FLAGS_rec_model_dir
,
FLAGS_use_gpu
,
FLAGS_gpu_id
,
FLAGS_gpu_mem
,
FLAGS_cpu_math_library_num_threads
,
FLAGS_use_mkldnn
,
FLAGS_char_list_file
,
FLAGS_use_tensorrt
,
FLAGS_use_fp16
);
auto
start
=
std
::
chrono
::
system_clock
::
now
();
FLAGS_use_tensorrt
,
FLAGS_precision
);
for
(
int
i
=
0
;
i
<
cv_all_img_names
.
size
();
++
i
)
{
LOG
(
INFO
)
<<
"The predict img: "
<<
cv_all_img_names
[
i
];
...
...
@@ -217,65 +157,42 @@ int main_rec(int argc, char **argv) {
exit
(
1
);
}
rec
.
Run
(
srcimg
);
std
::
vector
<
double
>
rec_times
;
rec
.
Run
(
srcimg
,
&
rec_times
);
auto
end
=
std
::
chrono
::
system_clock
::
now
();
auto
duration
=
std
::
chrono
::
duration_cast
<
std
::
chrono
::
microseconds
>
(
end
-
start
);
std
::
cout
<<
"Cost "
<<
double
(
duration
.
count
())
*
std
::
chrono
::
microseconds
::
period
::
num
/
std
::
chrono
::
microseconds
::
period
::
den
<<
"s"
<<
std
::
endl
;
time_info
[
0
]
+=
rec_times
[
0
];
time_info
[
1
]
+=
rec_times
[
1
];
time_info
[
2
]
+=
rec_times
[
2
];
}
if
(
FLAGS_benchmark
)
{
PrintBenchmarkLog
(
"rec"
,
1
,
"dynamic"
,
time_info
,
cv_all_img_names
.
size
());
}
return
0
;
}
int
main_system
(
int
argc
,
char
**
argv
)
{
// Parsing command-line
google
::
ParseCommandLineFlags
(
&
argc
,
&
argv
,
true
);
if
((
FLAGS_det_model_dir
.
empty
()
||
FLAGS_rec_model_dir
.
empty
()
||
FLAGS_image_dir
.
empty
())
||
(
FLAGS_use_angle_cls
&&
FLAGS_cls_model_dir
.
empty
()))
{
std
::
cout
<<
"Usage[system without angle cls]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<<
"--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<<
"--image_dir=/PATH/TO/INPUT/IMAGE/"
<<
std
::
endl
;
std
::
cout
<<
"Usage[system with angle cls]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<<
"--use_angle_cls=true "
<<
"--cls_model_dir=/PATH/TO/CLS_INFERENCE_MODEL/ "
<<
"--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<<
"--image_dir=/PATH/TO/INPUT/IMAGE/"
<<
std
::
endl
;
exit
(
1
);
}
if
(
!
PathExists
(
FLAGS_image_dir
))
{
std
::
cerr
<<
"[ERROR] image path not exist! image_dir: "
<<
FLAGS_image_dir
<<
endl
;
exit
(
1
);
}
std
::
vector
<
cv
::
String
>
cv_all_img_names
;
cv
::
glob
(
FLAGS_image_dir
,
cv_all_img_names
);
std
::
cout
<<
"total images num: "
<<
cv_all_img_names
.
size
()
<<
endl
;
int
main_system
(
std
::
vector
<
cv
::
String
>
cv_all_img_names
)
{
DBDetector
det
(
FLAGS_det_model_dir
,
FLAGS_use_gpu
,
FLAGS_gpu_id
,
FLAGS_gpu_mem
,
FLAGS_cpu_math_library_num_threads
,
FLAGS_use_mkldnn
,
FLAGS_max_side_len
,
FLAGS_det_db_thresh
,
FLAGS_det_db_box_thresh
,
FLAGS_det_db_unclip_ratio
,
FLAGS_use_polygon_score
,
FLAGS_visualize
,
FLAGS_use_tensorrt
,
FLAGS_
use_fp16
);
FLAGS_use_tensorrt
,
FLAGS_
precision
);
Classifier
*
cls
=
nullptr
;
if
(
FLAGS_use_angle_cls
)
{
cls
=
new
Classifier
(
FLAGS_cls_model_dir
,
FLAGS_use_gpu
,
FLAGS_gpu_id
,
FLAGS_gpu_mem
,
FLAGS_cpu_math_library_num_threads
,
FLAGS_use_mkldnn
,
FLAGS_cls_thresh
,
FLAGS_use_tensorrt
,
FLAGS_
use_fp16
);
FLAGS_use_tensorrt
,
FLAGS_
precision
);
}
CRNNRecognizer
rec
(
FLAGS_rec_model_dir
,
FLAGS_use_gpu
,
FLAGS_gpu_id
,
FLAGS_gpu_mem
,
FLAGS_cpu_math_library_num_threads
,
FLAGS_use_mkldnn
,
FLAGS_char_list_file
,
FLAGS_use_tensorrt
,
FLAGS_
use_fp16
);
FLAGS_use_tensorrt
,
FLAGS_
precision
);
auto
start
=
std
::
chrono
::
system_clock
::
now
();
...
...
@@ -288,17 +205,19 @@ int main_system(int argc, char **argv) {
exit
(
1
);
}
std
::
vector
<
std
::
vector
<
std
::
vector
<
int
>>>
boxes
;
det
.
Run
(
srcimg
,
boxes
);
std
::
vector
<
double
>
det_times
;
std
::
vector
<
double
>
rec_times
;
det
.
Run
(
srcimg
,
boxes
,
&
det_times
);
cv
::
Mat
crop_img
;
for
(
int
j
=
0
;
j
<
boxes
.
size
();
j
++
)
{
crop_img
=
GetRotateCropImage
(
srcimg
,
boxes
[
j
]);
crop_img
=
Utility
::
GetRotateCropImage
(
srcimg
,
boxes
[
j
]);
if
(
cls
!=
nullptr
)
{
crop_img
=
cls
->
Run
(
crop_img
);
}
rec
.
Run
(
crop_img
);
rec
.
Run
(
crop_img
,
&
rec_times
);
}
auto
end
=
std
::
chrono
::
system_clock
::
now
();
...
...
@@ -315,22 +234,70 @@ int main_system(int argc, char **argv) {
}
void
check_params
(
char
*
mode
)
{
if
(
strcmp
(
mode
,
"det"
)
==
0
)
{
if
(
FLAGS_det_model_dir
.
empty
()
||
FLAGS_image_dir
.
empty
())
{
std
::
cout
<<
"Usage[det]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<<
"--image_dir=/PATH/TO/INPUT/IMAGE/"
<<
std
::
endl
;
exit
(
1
);
}
}
if
(
strcmp
(
mode
,
"rec"
)
==
0
)
{
if
(
FLAGS_rec_model_dir
.
empty
()
||
FLAGS_image_dir
.
empty
())
{
std
::
cout
<<
"Usage[rec]: ./ppocr --rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<<
"--image_dir=/PATH/TO/INPUT/IMAGE/"
<<
std
::
endl
;
exit
(
1
);
}
}
if
(
strcmp
(
mode
,
"system"
)
==
0
)
{
if
((
FLAGS_det_model_dir
.
empty
()
||
FLAGS_rec_model_dir
.
empty
()
||
FLAGS_image_dir
.
empty
())
||
(
FLAGS_use_angle_cls
&&
FLAGS_cls_model_dir
.
empty
()))
{
std
::
cout
<<
"Usage[system without angle cls]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<<
"--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<<
"--image_dir=/PATH/TO/INPUT/IMAGE/"
<<
std
::
endl
;
std
::
cout
<<
"Usage[system with angle cls]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<<
"--use_angle_cls=true "
<<
"--cls_model_dir=/PATH/TO/CLS_INFERENCE_MODEL/ "
<<
"--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<<
"--image_dir=/PATH/TO/INPUT/IMAGE/"
<<
std
::
endl
;
exit
(
1
);
}
}
if
(
FLAGS_precision
!=
"fp32"
&&
FLAGS_precision
!=
"fp16"
&&
FLAGS_precision
!=
"int8"
)
{
cout
<<
"precison should be 'fp32'(default), 'fp16' or 'int8'. "
<<
endl
;
exit
(
1
);
}
}
int
main
(
int
argc
,
char
**
argv
)
{
if
(
strcmp
(
argv
[
1
],
"det"
)
!=
0
&&
strcmp
(
argv
[
1
],
"rec"
)
!=
0
&&
strcmp
(
argv
[
1
],
"system"
)
!=
0
)
{
std
::
cout
<<
"Please choose one mode of [det, rec, system] !"
<<
std
::
endl
;
return
-
1
;
}
std
::
cout
<<
"mode: "
<<
argv
[
1
]
<<
endl
;
if
(
strcmp
(
argv
[
1
],
"det"
)
==
0
)
{
return
main_det
(
argc
,
argv
);
}
if
(
strcmp
(
argv
[
1
],
"rec"
)
==
0
)
{
return
main_rec
(
argc
,
argv
);
}
if
(
strcmp
(
argv
[
1
],
"system"
)
==
0
)
{
return
main_system
(
argc
,
argv
);
}
if
(
argc
<=
1
||
(
strcmp
(
argv
[
1
],
"det"
)
!=
0
&&
strcmp
(
argv
[
1
],
"rec"
)
!=
0
&&
strcmp
(
argv
[
1
],
"system"
)
!=
0
))
{
std
::
cout
<<
"Please choose one mode of [det, rec, system] !"
<<
std
::
endl
;
return
-
1
;
}
std
::
cout
<<
"mode: "
<<
argv
[
1
]
<<
endl
;
// Parsing command-line
google
::
ParseCommandLineFlags
(
&
argc
,
&
argv
,
true
);
check_params
(
argv
[
1
]);
if
(
!
PathExists
(
FLAGS_image_dir
))
{
std
::
cerr
<<
"[ERROR] image path not exist! image_dir: "
<<
FLAGS_image_dir
<<
endl
;
exit
(
1
);
}
// return 0;
std
::
vector
<
cv
::
String
>
cv_all_img_names
;
cv
::
glob
(
FLAGS_image_dir
,
cv_all_img_names
);
std
::
cout
<<
"total images num: "
<<
cv_all_img_names
.
size
()
<<
endl
;
if
(
strcmp
(
argv
[
1
],
"det"
)
==
0
)
{
return
main_det
(
cv_all_img_names
);
}
if
(
strcmp
(
argv
[
1
],
"rec"
)
==
0
)
{
return
main_rec
(
cv_all_img_names
);
}
if
(
strcmp
(
argv
[
1
],
"system"
)
==
0
)
{
return
main_system
(
cv_all_img_names
);
}
}
deploy/cpp_infer/src/ocr_cls.cpp
浏览文件 @
36152456
...
...
@@ -77,10 +77,16 @@ void Classifier::LoadModel(const std::string &model_dir) {
if
(
this
->
use_gpu_
)
{
config
.
EnableUseGpu
(
this
->
gpu_mem_
,
this
->
gpu_id_
);
if
(
this
->
use_tensorrt_
)
{
auto
precision
=
paddle_infer
::
Config
::
Precision
::
kFloat32
;
if
(
this
->
precision_
==
"fp16"
)
{
precision
=
paddle_infer
::
Config
::
Precision
::
kHalf
;
}
if
(
this
->
precision_
==
"int8"
)
{
precision
=
paddle_infer
::
Config
::
Precision
::
kInt8
;
}
config
.
EnableTensorRtEngine
(
1
<<
20
,
10
,
3
,
this
->
use_fp16_
?
paddle_infer
::
Config
::
Precision
::
kHalf
:
paddle_infer
::
Config
::
Precision
::
kFloat32
,
precision
,
false
,
false
);
}
}
else
{
...
...
deploy/cpp_infer/src/ocr_det.cpp
浏览文件 @
36152456
...
...
@@ -26,10 +26,16 @@ void DBDetector::LoadModel(const std::string &model_dir) {
if
(
this
->
use_gpu_
)
{
config
.
EnableUseGpu
(
this
->
gpu_mem_
,
this
->
gpu_id_
);
if
(
this
->
use_tensorrt_
)
{
auto
precision
=
paddle_infer
::
Config
::
Precision
::
kFloat32
;
if
(
this
->
precision_
==
"fp16"
)
{
precision
=
paddle_infer
::
Config
::
Precision
::
kHalf
;
}
if
(
this
->
precision_
==
"int8"
)
{
precision
=
paddle_infer
::
Config
::
Precision
::
kInt8
;
}
config
.
EnableTensorRtEngine
(
1
<<
20
,
10
,
3
,
this
->
use_fp16_
?
paddle_infer
::
Config
::
Precision
::
kHalf
:
paddle_infer
::
Config
::
Precision
::
kFloat32
,
precision
,
false
,
false
);
std
::
map
<
std
::
string
,
std
::
vector
<
int
>>
min_input_shape
=
{
{
"x"
,
{
1
,
3
,
50
,
50
}},
...
...
@@ -91,13 +97,16 @@ void DBDetector::LoadModel(const std::string &model_dir) {
}
void
DBDetector
::
Run
(
cv
::
Mat
&
img
,
std
::
vector
<
std
::
vector
<
std
::
vector
<
int
>>>
&
boxes
)
{
std
::
vector
<
std
::
vector
<
std
::
vector
<
int
>>>
&
boxes
,
std
::
vector
<
double
>
*
times
)
{
float
ratio_h
{};
float
ratio_w
{};
cv
::
Mat
srcimg
;
cv
::
Mat
resize_img
;
img
.
copyTo
(
srcimg
);
auto
preprocess_start
=
std
::
chrono
::
steady_clock
::
now
();
this
->
resize_op_
.
Run
(
img
,
resize_img
,
this
->
max_side_len_
,
ratio_h
,
ratio_w
,
this
->
use_tensorrt_
);
...
...
@@ -106,14 +115,17 @@ void DBDetector::Run(cv::Mat &img,
std
::
vector
<
float
>
input
(
1
*
3
*
resize_img
.
rows
*
resize_img
.
cols
,
0.0
f
);
this
->
permute_op_
.
Run
(
&
resize_img
,
input
.
data
());
auto
preprocess_end
=
std
::
chrono
::
steady_clock
::
now
();
// Inference.
auto
inference_start
=
std
::
chrono
::
steady_clock
::
now
();
auto
input_names
=
this
->
predictor_
->
GetInputNames
();
auto
input_t
=
this
->
predictor_
->
GetInputHandle
(
input_names
[
0
]);
input_t
->
Reshape
({
1
,
3
,
resize_img
.
rows
,
resize_img
.
cols
});
input_t
->
CopyFromCpu
(
input
.
data
());
this
->
predictor_
->
Run
();
std
::
vector
<
float
>
out_data
;
auto
output_names
=
this
->
predictor_
->
GetOutputNames
();
auto
output_t
=
this
->
predictor_
->
GetOutputHandle
(
output_names
[
0
]);
...
...
@@ -123,7 +135,9 @@ void DBDetector::Run(cv::Mat &img,
out_data
.
resize
(
out_num
);
output_t
->
CopyToCpu
(
out_data
.
data
());
auto
inference_end
=
std
::
chrono
::
steady_clock
::
now
();
auto
postprocess_start
=
std
::
chrono
::
steady_clock
::
now
();
int
n2
=
output_shape
[
2
];
int
n3
=
output_shape
[
3
];
int
n
=
n2
*
n3
;
...
...
@@ -152,6 +166,14 @@ void DBDetector::Run(cv::Mat &img,
boxes
=
post_processor_
.
FilterTagDetRes
(
boxes
,
ratio_h
,
ratio_w
,
srcimg
);
std
::
cout
<<
"Detected boxes num: "
<<
boxes
.
size
()
<<
endl
;
auto
postprocess_end
=
std
::
chrono
::
steady_clock
::
now
();
std
::
chrono
::
duration
<
float
>
preprocess_diff
=
preprocess_end
-
preprocess_start
;
times
->
push_back
(
double
(
preprocess_diff
.
count
()
*
1000
));
std
::
chrono
::
duration
<
float
>
inference_diff
=
inference_end
-
inference_start
;
times
->
push_back
(
double
(
inference_diff
.
count
()
*
1000
));
std
::
chrono
::
duration
<
float
>
postprocess_diff
=
postprocess_end
-
postprocess_start
;
times
->
push_back
(
double
(
postprocess_diff
.
count
()
*
1000
));
//// visualization
if
(
this
->
visualize_
)
{
...
...
deploy/cpp_infer/src/ocr_rec.cpp
浏览文件 @
36152456
...
...
@@ -16,13 +16,13 @@
namespace
PaddleOCR
{
void
CRNNRecognizer
::
Run
(
cv
::
Mat
&
img
)
{
void
CRNNRecognizer
::
Run
(
cv
::
Mat
&
img
,
std
::
vector
<
double
>
*
times
)
{
cv
::
Mat
srcimg
;
img
.
copyTo
(
srcimg
);
cv
::
Mat
resize_img
;
float
wh_ratio
=
float
(
srcimg
.
cols
)
/
float
(
srcimg
.
rows
);
auto
preprocess_start
=
std
::
chrono
::
steady_clock
::
now
();
this
->
resize_op_
.
Run
(
srcimg
,
resize_img
,
wh_ratio
,
this
->
use_tensorrt_
);
this
->
normalize_op_
.
Run
(
&
resize_img
,
this
->
mean_
,
this
->
scale_
,
...
...
@@ -31,8 +31,10 @@ void CRNNRecognizer::Run(cv::Mat &img) {
std
::
vector
<
float
>
input
(
1
*
3
*
resize_img
.
rows
*
resize_img
.
cols
,
0.0
f
);
this
->
permute_op_
.
Run
(
&
resize_img
,
input
.
data
());
auto
preprocess_end
=
std
::
chrono
::
steady_clock
::
now
();
// Inference.
auto
inference_start
=
std
::
chrono
::
steady_clock
::
now
();
auto
input_names
=
this
->
predictor_
->
GetInputNames
();
auto
input_t
=
this
->
predictor_
->
GetInputHandle
(
input_names
[
0
]);
input_t
->
Reshape
({
1
,
3
,
resize_img
.
rows
,
resize_img
.
cols
});
...
...
@@ -49,8 +51,10 @@ void CRNNRecognizer::Run(cv::Mat &img) {
predict_batch
.
resize
(
out_num
);
output_t
->
CopyToCpu
(
predict_batch
.
data
());
auto
inference_end
=
std
::
chrono
::
steady_clock
::
now
();
// ctc decode
auto
postprocess_start
=
std
::
chrono
::
steady_clock
::
now
();
std
::
vector
<
std
::
string
>
str_res
;
int
argmax_idx
;
int
last_index
=
0
;
...
...
@@ -78,6 +82,14 @@ void CRNNRecognizer::Run(cv::Mat &img) {
std
::
cout
<<
str_res
[
i
];
}
std
::
cout
<<
"
\t
score: "
<<
score
<<
std
::
endl
;
auto
postprocess_end
=
std
::
chrono
::
steady_clock
::
now
();
std
::
chrono
::
duration
<
float
>
preprocess_diff
=
preprocess_end
-
preprocess_start
;
times
->
push_back
(
double
(
preprocess_diff
.
count
()
*
1000
));
std
::
chrono
::
duration
<
float
>
inference_diff
=
inference_end
-
inference_start
;
times
->
push_back
(
double
(
inference_diff
.
count
()
*
1000
));
std
::
chrono
::
duration
<
float
>
postprocess_diff
=
postprocess_end
-
postprocess_start
;
times
->
push_back
(
double
(
postprocess_diff
.
count
()
*
1000
));
}
void
CRNNRecognizer
::
LoadModel
(
const
std
::
string
&
model_dir
)
{
...
...
@@ -89,10 +101,16 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
if
(
this
->
use_gpu_
)
{
config
.
EnableUseGpu
(
this
->
gpu_mem_
,
this
->
gpu_id_
);
if
(
this
->
use_tensorrt_
)
{
auto
precision
=
paddle_infer
::
Config
::
Precision
::
kFloat32
;
if
(
this
->
precision_
==
"fp16"
)
{
precision
=
paddle_infer
::
Config
::
Precision
::
kHalf
;
}
if
(
this
->
precision_
==
"int8"
)
{
precision
=
paddle_infer
::
Config
::
Precision
::
kInt8
;
}
config
.
EnableTensorRtEngine
(
1
<<
20
,
10
,
3
,
this
->
use_fp16_
?
paddle_infer
::
Config
::
Precision
::
kHalf
:
paddle_infer
::
Config
::
Precision
::
kFloat32
,
precision
,
false
,
false
);
std
::
map
<
std
::
string
,
std
::
vector
<
int
>>
min_input_shape
=
{
{
"x"
,
{
1
,
3
,
32
,
10
}}};
...
...
deploy/cpp_infer/src/utility.cpp
浏览文件 @
36152456
...
...
@@ -92,4 +92,59 @@ void Utility::GetAllFiles(const char *dir_name,
}
}
cv
::
Mat
Utility
::
GetRotateCropImage
(
const
cv
::
Mat
&
srcimage
,
std
::
vector
<
std
::
vector
<
int
>>
box
)
{
cv
::
Mat
image
;
srcimage
.
copyTo
(
image
);
std
::
vector
<
std
::
vector
<
int
>>
points
=
box
;
int
x_collect
[
4
]
=
{
box
[
0
][
0
],
box
[
1
][
0
],
box
[
2
][
0
],
box
[
3
][
0
]};
int
y_collect
[
4
]
=
{
box
[
0
][
1
],
box
[
1
][
1
],
box
[
2
][
1
],
box
[
3
][
1
]};
int
left
=
int
(
*
std
::
min_element
(
x_collect
,
x_collect
+
4
));
int
right
=
int
(
*
std
::
max_element
(
x_collect
,
x_collect
+
4
));
int
top
=
int
(
*
std
::
min_element
(
y_collect
,
y_collect
+
4
));
int
bottom
=
int
(
*
std
::
max_element
(
y_collect
,
y_collect
+
4
));
cv
::
Mat
img_crop
;
image
(
cv
::
Rect
(
left
,
top
,
right
-
left
,
bottom
-
top
)).
copyTo
(
img_crop
);
for
(
int
i
=
0
;
i
<
points
.
size
();
i
++
)
{
points
[
i
][
0
]
-=
left
;
points
[
i
][
1
]
-=
top
;
}
int
img_crop_width
=
int
(
sqrt
(
pow
(
points
[
0
][
0
]
-
points
[
1
][
0
],
2
)
+
pow
(
points
[
0
][
1
]
-
points
[
1
][
1
],
2
)));
int
img_crop_height
=
int
(
sqrt
(
pow
(
points
[
0
][
0
]
-
points
[
3
][
0
],
2
)
+
pow
(
points
[
0
][
1
]
-
points
[
3
][
1
],
2
)));
cv
::
Point2f
pts_std
[
4
];
pts_std
[
0
]
=
cv
::
Point2f
(
0.
,
0.
);
pts_std
[
1
]
=
cv
::
Point2f
(
img_crop_width
,
0.
);
pts_std
[
2
]
=
cv
::
Point2f
(
img_crop_width
,
img_crop_height
);
pts_std
[
3
]
=
cv
::
Point2f
(
0.
f
,
img_crop_height
);
cv
::
Point2f
pointsf
[
4
];
pointsf
[
0
]
=
cv
::
Point2f
(
points
[
0
][
0
],
points
[
0
][
1
]);
pointsf
[
1
]
=
cv
::
Point2f
(
points
[
1
][
0
],
points
[
1
][
1
]);
pointsf
[
2
]
=
cv
::
Point2f
(
points
[
2
][
0
],
points
[
2
][
1
]);
pointsf
[
3
]
=
cv
::
Point2f
(
points
[
3
][
0
],
points
[
3
][
1
]);
cv
::
Mat
M
=
cv
::
getPerspectiveTransform
(
pointsf
,
pts_std
);
cv
::
Mat
dst_img
;
cv
::
warpPerspective
(
img_crop
,
dst_img
,
M
,
cv
::
Size
(
img_crop_width
,
img_crop_height
),
cv
::
BORDER_REPLICATE
);
if
(
float
(
dst_img
.
rows
)
>=
float
(
dst_img
.
cols
)
*
1.5
)
{
cv
::
Mat
srcCopy
=
cv
::
Mat
(
dst_img
.
rows
,
dst_img
.
cols
,
dst_img
.
depth
());
cv
::
transpose
(
dst_img
,
srcCopy
);
cv
::
flip
(
srcCopy
,
srcCopy
,
0
);
return
srcCopy
;
}
else
{
return
dst_img
;
}
}
}
// namespace PaddleOCR
\ No newline at end of file
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