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体验新版 GitCode,发现更多精彩内容 >>
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8f028d38
编写于
7月 13, 2020
作者:
L
LDOUBLEV
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add static_cast
上级
a5c095e0
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
88 addition
and
64 deletion
+88
-64
deploy/lite/crnn_process.cc
deploy/lite/crnn_process.cc
+11
-7
deploy/lite/db_post_process.cc
deploy/lite/db_post_process.cc
+49
-32
deploy/lite/ocr_db_crnn.cc
deploy/lite/ocr_db_crnn.cc
+28
-25
未找到文件。
deploy/lite/crnn_process.cc
浏览文件 @
8f028d38
...
...
@@ -27,12 +27,12 @@ cv::Mat CrnnResizeImg(cv::Mat img, float wh_ratio) {
imgW
=
int
(
32
*
wh_ratio
);
float
ratio
=
float
(
img
.
cols
)
/
float
(
img
.
rows
);
float
ratio
=
static_cast
<
float
>
(
img
.
cols
)
/
static_cast
<
float
>
(
img
.
rows
);
int
resize_w
,
resize_h
;
if
(
ceilf
(
imgH
*
ratio
)
>
imgW
)
resize_w
=
imgW
;
else
resize_w
=
int
(
ceilf
(
imgH
*
ratio
));
resize_w
=
static_cast
<
int
>
(
ceilf
(
imgH
*
ratio
));
cv
::
Mat
resize_img
;
cv
::
resize
(
img
,
resize_img
,
cv
::
Size
(
resize_w
,
imgH
),
0.
f
,
0.
f
,
cv
::
INTER_LINEAR
);
...
...
@@ -76,10 +76,12 @@ cv::Mat GetRotateCropImage(cv::Mat srcimage,
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
)));
int
img_crop_width
=
static_cast
<
int
>
(
sqrt
(
pow
(
points
[
0
][
0
]
-
points
[
1
][
0
],
2
)
+
pow
(
points
[
0
][
1
]
-
points
[
1
][
1
],
2
)));
int
img_crop_height
=
static_cast
<
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.
);
...
...
@@ -100,7 +102,9 @@ cv::Mat GetRotateCropImage(cv::Mat srcimage,
cv
::
Size
(
img_crop_width
,
img_crop_height
),
cv
::
BORDER_REPLICATE
);
if
(
float
(
dst_img
.
rows
)
>=
float
(
dst_img
.
cols
)
*
1.5
)
{
const
float
ratio
=
1.5
;
if
(
static_cast
<
float
>
(
dst_img
.
rows
)
>=
static_cast
<
float
>
(
dst_img
.
cols
)
*
ratio
)
{
cv
::
Mat
srcCopy
=
cv
::
Mat
(
dst_img
.
rows
,
dst_img
.
cols
,
dst_img
.
depth
());
cv
::
transpose
(
dst_img
,
srcCopy
);
cv
::
flip
(
srcCopy
,
srcCopy
,
0
);
...
...
deploy/lite/db_post_process.cc
浏览文件 @
8f028d38
...
...
@@ -42,10 +42,14 @@ cv::RotatedRect Unclip(std::vector<std::vector<float>> box,
ClipperLib
::
ClipperOffset
offset
;
ClipperLib
::
Path
p
;
p
<<
ClipperLib
::
IntPoint
(
int
(
box
[
0
][
0
]),
int
(
box
[
0
][
1
]))
<<
ClipperLib
::
IntPoint
(
int
(
box
[
1
][
0
]),
int
(
box
[
1
][
1
]))
<<
ClipperLib
::
IntPoint
(
int
(
box
[
2
][
0
]),
int
(
box
[
2
][
1
]))
<<
ClipperLib
::
IntPoint
(
int
(
box
[
3
][
0
]),
int
(
box
[
3
][
1
]));
p
<<
ClipperLib
::
IntPoint
(
static_cast
<
int
>
(
box
[
0
][
0
]),
static_cast
<
int
>
(
box
[
0
][
1
]))
<<
ClipperLib
::
IntPoint
(
static_cast
<
int
>
(
box
[
1
][
0
]),
static_cast
<
int
>
(
box
[
1
][
1
]))
<<
ClipperLib
::
IntPoint
(
static_cast
<
int
>
(
box
[
2
][
0
]),
static_cast
<
int
>
(
box
[
2
][
1
]))
<<
ClipperLib
::
IntPoint
(
static_cast
<
int
>
(
box
[
3
][
0
]),
static_cast
<
int
>
(
box
[
3
][
1
]));
offset
.
AddPath
(
p
,
ClipperLib
::
jtRound
,
ClipperLib
::
etClosedPolygon
);
ClipperLib
::
Paths
soln
;
...
...
@@ -149,23 +153,31 @@ float BoxScoreFast(std::vector<std::vector<float>> box_array, cv::Mat pred) {
float
box_x
[
4
]
=
{
array
[
0
][
0
],
array
[
1
][
0
],
array
[
2
][
0
],
array
[
3
][
0
]};
float
box_y
[
4
]
=
{
array
[
0
][
1
],
array
[
1
][
1
],
array
[
2
][
1
],
array
[
3
][
1
]};
int
xmin
=
clamp
(
int
(
std
::
floorf
(
*
(
std
::
min_element
(
box_x
,
box_x
+
4
)))),
0
,
width
-
1
);
int
xmax
=
clamp
(
int
(
std
::
ceilf
(
*
(
std
::
max_element
(
box_x
,
box_x
+
4
)))),
0
,
width
-
1
);
int
ymin
=
clamp
(
int
(
std
::
floorf
(
*
(
std
::
min_element
(
box_y
,
box_y
+
4
)))),
0
,
height
-
1
);
int
ymax
=
clamp
(
int
(
std
::
ceilf
(
*
(
std
::
max_element
(
box_y
,
box_y
+
4
)))),
0
,
height
-
1
);
int
xmin
=
clamp
(
static_cast
<
int
>
(
std
::
floorf
(
*
(
std
::
min_element
(
box_x
,
box_x
+
4
)))),
0
,
width
-
1
);
int
xmax
=
clamp
(
static_cast
<
int
>
(
std
::
ceilf
(
*
(
std
::
max_element
(
box_x
,
box_x
+
4
)))),
0
,
width
-
1
);
int
ymin
=
clamp
(
static_cast
<
int
>
(
std
::
floorf
(
*
(
std
::
min_element
(
box_y
,
box_y
+
4
)))),
0
,
height
-
1
);
int
ymax
=
clamp
(
static_cast
<
int
>
(
std
::
ceilf
(
*
(
std
::
max_element
(
box_y
,
box_y
+
4
)))),
0
,
height
-
1
);
cv
::
Mat
mask
;
mask
=
cv
::
Mat
::
zeros
(
ymax
-
ymin
+
1
,
xmax
-
xmin
+
1
,
CV_8UC1
);
cv
::
Point
root_point
[
4
];
root_point
[
0
]
=
cv
::
Point
(
int
(
array
[
0
][
0
])
-
xmin
,
int
(
array
[
0
][
1
])
-
ymin
);
root_point
[
1
]
=
cv
::
Point
(
int
(
array
[
1
][
0
])
-
xmin
,
int
(
array
[
1
][
1
])
-
ymin
);
root_point
[
2
]
=
cv
::
Point
(
int
(
array
[
2
][
0
])
-
xmin
,
int
(
array
[
2
][
1
])
-
ymin
);
root_point
[
3
]
=
cv
::
Point
(
int
(
array
[
3
][
0
])
-
xmin
,
int
(
array
[
3
][
1
])
-
ymin
);
root_point
[
0
]
=
cv
::
Point
(
static_cast
<
int
>
(
array
[
0
][
0
])
-
xmin
,
static_cast
<
int
>
(
array
[
0
][
1
])
-
ymin
);
root_point
[
1
]
=
cv
::
Point
(
static_cast
<
int
>
(
array
[
1
][
0
])
-
xmin
,
static_cast
<
int
>
(
array
[
1
][
1
])
-
ymin
);
root_point
[
2
]
=
cv
::
Point
(
static_cast
<
int
>
(
array
[
2
][
0
])
-
xmin
,
static_cast
<
int
>
(
array
[
2
][
1
])
-
ymin
);
root_point
[
3
]
=
cv
::
Point
(
static_cast
<
int
>
(
array
[
3
][
0
])
-
xmin
,
static_cast
<
int
>
(
array
[
3
][
1
])
-
ymin
);
const
cv
::
Point
*
ppt
[
1
]
=
{
root_point
};
int
npt
[]
=
{
4
};
cv
::
fillPoly
(
mask
,
ppt
,
npt
,
1
,
cv
::
Scalar
(
1
));
...
...
@@ -183,8 +195,8 @@ BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap,
std
::
map
<
std
::
string
,
double
>
Config
)
{
const
int
min_size
=
3
;
const
int
max_candidates
=
1000
;
const
float
box_thresh
=
float
(
Config
[
"det_db_box_thresh"
]);
const
float
unclip_ratio
=
float
(
Config
[
"det_db_unclip_ratio"
]);
const
float
box_thresh
=
static_cast
<
float
>
(
Config
[
"det_db_box_thresh"
]);
const
float
unclip_ratio
=
static_cast
<
float
>
(
Config
[
"det_db_unclip_ratio"
]);
int
width
=
bitmap
.
cols
;
int
height
=
bitmap
.
rows
;
...
...
@@ -233,12 +245,13 @@ BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap,
std
::
vector
<
std
::
vector
<
int
>>
intcliparray
;
for
(
int
num_pt
=
0
;
num_pt
<
4
;
num_pt
++
)
{
std
::
vector
<
int
>
a
{
int
(
clamp
(
roundf
(
cliparray
[
num_pt
][
0
]
/
float
(
width
)
*
float
(
dest_width
)),
float
(
0
),
float
(
dest_width
))),
int
(
clamp
(
roundf
(
cliparray
[
num_pt
][
1
]
/
float
(
height
)
*
float
(
dest_height
)),
float
(
0
),
float
(
dest_height
)))};
std
::
vector
<
int
>
a
{
static_cast
<
int
>
(
clamp
(
roundf
(
cliparray
[
num_pt
][
0
]
/
float
(
width
)
*
float
(
dest_width
)),
float
(
0
),
float
(
dest_width
))),
static_cast
<
int
>
(
clamp
(
roundf
(
cliparray
[
num_pt
][
1
]
/
float
(
height
)
*
float
(
dest_height
)),
float
(
0
),
float
(
dest_height
)))};
intcliparray
.
push_back
(
a
);
}
boxes
.
push_back
(
intcliparray
);
...
...
@@ -254,23 +267,27 @@ FilterTagDetRes(std::vector<std::vector<std::vector<int>>> boxes, float ratio_h,
int
oriimg_w
=
srcimg
.
cols
;
std
::
vector
<
std
::
vector
<
std
::
vector
<
int
>>>
root_points
;
for
(
int
n
=
0
;
n
<
boxes
.
size
(
);
n
++
)
{
for
(
int
n
=
0
;
n
<
static_cast
<
int
>
(
boxes
.
size
()
);
n
++
)
{
boxes
[
n
]
=
OrderPointsClockwise
(
boxes
[
n
]);
for
(
int
m
=
0
;
m
<
boxes
[
0
].
size
(
);
m
++
)
{
for
(
int
m
=
0
;
m
<
static_cast
<
int
>
(
boxes
[
0
].
size
()
);
m
++
)
{
boxes
[
n
][
m
][
0
]
/=
ratio_w
;
boxes
[
n
][
m
][
1
]
/=
ratio_h
;
boxes
[
n
][
m
][
0
]
=
int
(
std
::
min
(
std
::
max
(
boxes
[
n
][
m
][
0
],
0
),
oriimg_w
-
1
));
boxes
[
n
][
m
][
1
]
=
int
(
std
::
min
(
std
::
max
(
boxes
[
n
][
m
][
1
],
0
),
oriimg_h
-
1
));
boxes
[
n
][
m
][
0
]
=
static_cast
<
int
>
(
std
::
min
(
std
::
max
(
boxes
[
n
][
m
][
0
],
0
),
oriimg_w
-
1
));
boxes
[
n
][
m
][
1
]
=
static_cast
<
int
>
(
std
::
min
(
std
::
max
(
boxes
[
n
][
m
][
1
],
0
),
oriimg_h
-
1
));
}
}
for
(
int
n
=
0
;
n
<
boxes
.
size
();
n
++
)
{
int
rect_width
,
rect_height
;
rect_width
=
int
(
sqrt
(
pow
(
boxes
[
n
][
0
][
0
]
-
boxes
[
n
][
1
][
0
],
2
)
+
pow
(
boxes
[
n
][
0
][
1
]
-
boxes
[
n
][
1
][
1
],
2
)));
rect_height
=
int
(
sqrt
(
pow
(
boxes
[
n
][
0
][
0
]
-
boxes
[
n
][
3
][
0
],
2
)
+
pow
(
boxes
[
n
][
0
][
1
]
-
boxes
[
n
][
3
][
1
],
2
)));
rect_width
=
static_cast
<
int
>
(
sqrt
(
pow
(
boxes
[
n
][
0
][
0
]
-
boxes
[
n
][
1
][
0
],
2
)
+
pow
(
boxes
[
n
][
0
][
1
]
-
boxes
[
n
][
1
][
1
],
2
)));
rect_height
=
static_cast
<
int
>
(
sqrt
(
pow
(
boxes
[
n
][
0
][
0
]
-
boxes
[
n
][
3
][
0
],
2
)
+
pow
(
boxes
[
n
][
0
][
1
]
-
boxes
[
n
][
3
][
1
],
2
)));
if
(
rect_width
<=
10
||
rect_height
<=
10
)
continue
;
root_points
.
push_back
(
boxes
[
n
]);
...
...
deploy/lite/ocr_db_crnn.cc
浏览文件 @
8f028d38
...
...
@@ -22,9 +22,9 @@ using namespace paddle::lite_api; // NOLINT
using
namespace
std
;
// fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up
void
neon_mean_s
cale
(
const
float
*
din
,
float
*
dout
,
int
size
,
const
std
::
vector
<
float
>
mean
,
const
std
::
vector
<
float
>
scale
)
{
void
NeonMeanS
cale
(
const
float
*
din
,
float
*
dout
,
int
size
,
const
std
::
vector
<
float
>
mean
,
const
std
::
vector
<
float
>
scale
)
{
if
(
mean
.
size
()
!=
3
||
scale
.
size
()
!=
3
)
{
std
::
cerr
<<
"[ERROR] mean or scale size must equal to 3
\n
"
;
exit
(
1
);
...
...
@@ -75,14 +75,14 @@ cv::Mat DetResizeImg(const cv::Mat img, int max_size_len,
int
max_wh
=
w
>=
h
?
w
:
h
;
if
(
max_wh
>
max_size_len
)
{
if
(
h
>
w
)
{
ratio
=
float
(
max_size_len
)
/
float
(
h
);
ratio
=
static_cast
<
float
>
(
max_size_len
)
/
static_cast
<
float
>
(
h
);
}
else
{
ratio
=
float
(
max_size_len
)
/
float
(
w
);
ratio
=
static_cast
<
float
>
(
max_size_len
)
/
static_cast
<
float
>
(
w
);
}
}
int
resize_h
=
int
(
float
(
h
)
*
ratio
);
int
resize_w
=
int
(
float
(
w
)
*
ratio
);
int
resize_h
=
static_cast
<
int
>
(
float
(
h
)
*
ratio
);
int
resize_w
=
static_cast
<
int
>
(
float
(
w
)
*
ratio
);
if
(
resize_h
%
32
==
0
)
resize_h
=
resize_h
;
else
if
(
resize_h
/
32
<
1
+
1e-5
)
...
...
@@ -100,8 +100,8 @@ cv::Mat DetResizeImg(const cv::Mat img, int max_size_len,
cv
::
Mat
resize_img
;
cv
::
resize
(
img
,
resize_img
,
cv
::
Size
(
resize_w
,
resize_h
));
ratio_hw
.
push_back
(
float
(
resize_h
)
/
float
(
h
));
ratio_hw
.
push_back
(
float
(
resize_w
)
/
float
(
w
));
ratio_hw
.
push_back
(
static_cast
<
float
>
(
resize_h
)
/
static_cast
<
float
>
(
h
));
ratio_hw
.
push_back
(
static_cast
<
float
>
(
resize_w
)
/
static_cast
<
float
>
(
w
));
return
resize_img
;
}
...
...
@@ -121,7 +121,8 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
int
index
=
0
;
for
(
int
i
=
boxes
.
size
()
-
1
;
i
>=
0
;
i
--
)
{
crop_img
=
GetRotateCropImage
(
srcimg
,
boxes
[
i
]);
float
wh_ratio
=
float
(
crop_img
.
cols
)
/
float
(
crop_img
.
rows
);
float
wh_ratio
=
static_cast
<
float
>
(
crop_img
.
cols
)
/
static_cast
<
float
>
(
crop_img
.
rows
);
resize_img
=
CrnnResizeImg
(
crop_img
,
wh_ratio
);
resize_img
.
convertTo
(
resize_img
,
CV_32FC3
,
1
/
255.
f
);
...
...
@@ -133,8 +134,7 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
input_tensor0
->
Resize
({
1
,
3
,
resize_img
.
rows
,
resize_img
.
cols
});
auto
*
data0
=
input_tensor0
->
mutable_data
<
float
>
();
neon_mean_scale
(
dimg
,
data0
,
resize_img
.
rows
*
resize_img
.
cols
,
mean
,
scale
);
NeonMeanScale
(
dimg
,
data0
,
resize_img
.
rows
*
resize_img
.
cols
,
mean
,
scale
);
//// Run CRNN predictor
predictor_crnn
->
Run
();
...
...
@@ -147,8 +147,9 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
auto
shape_out
=
output_tensor0
->
shape
();
std
::
vector
<
int
>
pred_idx
;
for
(
int
n
=
int
(
rec_idx_lod
[
0
][
0
]);
n
<
int
(
rec_idx_lod
[
0
][
1
]);
n
+=
1
)
{
pred_idx
.
push_back
(
int
(
rec_idx
[
n
]));
for
(
int
n
=
static_cast
<
int
>
(
rec_idx_lod
[
0
][
0
]);
n
<
static_cast
<
int
>
(
rec_idx_lod
[
0
][
1
]);
n
+=
1
)
{
pred_idx
.
push_back
(
static_cast
<
int
>
(
rec_idx
[
n
]));
}
if
(
pred_idx
.
size
()
<
1e-3
)
...
...
@@ -169,16 +170,15 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
auto
predict_lod
=
output_tensor1
->
lod
();
int
argmax_idx
;
int
blank
=
predict_shape
[
1
];
float
score
=
0.
f
;
int
count
=
0
;
float
max_value
=
0.0
f
;
for
(
int
n
=
predict_lod
[
0
][
0
];
n
<
predict_lod
[
0
][
1
]
-
1
;
n
++
)
{
argmax_idx
=
int
(
Argmax
(
&
predict_batch
[
n
*
predict_shape
[
1
]],
&
predict_batch
[(
n
+
1
)
*
predict_shape
[
1
]]));
max_value
=
int
argmax_idx
=
static_cast
<
int
>
(
Argmax
(
&
predict_batch
[
n
*
predict_shape
[
1
]],
&
predict_batch
[(
n
+
1
)
*
predict_shape
[
1
]]));
float
max_value
=
float
(
*
std
::
max_element
(
&
predict_batch
[
n
*
predict_shape
[
1
]],
&
predict_batch
[(
n
+
1
)
*
predict_shape
[
1
]]));
...
...
@@ -214,7 +214,7 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
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
};
const
float
*
dimg
=
reinterpret_cast
<
const
float
*>
(
img_fp
.
data
);
neon_mean_s
cale
(
dimg
,
data0
,
img_fp
.
rows
*
img_fp
.
cols
,
mean
,
scale
);
NeonMeanS
cale
(
dimg
,
data0
,
img_fp
.
rows
*
img_fp
.
cols
,
mean
,
scale
);
// Run predictor
predictor
->
Run
();
...
...
@@ -230,12 +230,14 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
unsigned
char
cbuf
[
shape_out
[
2
]
*
shape_out
[
3
]];
for
(
int
i
=
0
;
i
<
int
(
shape_out
[
2
]
*
shape_out
[
3
]);
i
++
)
{
pred
[
i
]
=
float
(
outptr
[
i
]);
cbuf
[
i
]
=
(
unsigned
char
)
((
outptr
[
i
])
*
255
);
pred
[
i
]
=
static_cast
<
float
>
(
outptr
[
i
]);
cbuf
[
i
]
=
static_cast
<
unsigned
char
>
((
outptr
[
i
])
*
255
);
}
cv
::
Mat
cbuf_map
(
shape_out
[
2
],
shape_out
[
3
],
CV_8UC1
,
(
unsigned
char
*
)
cbuf
);
cv
::
Mat
pred_map
(
shape_out
[
2
],
shape_out
[
3
],
CV_32F
,
(
float
*
)
pred
);
cv
::
Mat
cbuf_map
(
shape_out
[
2
],
shape_out
[
3
],
CV_8UC1
,
reinterpret_cast
<
unsigned
char
*>
cbuf
);
cv
::
Mat
pred_map
(
shape_out
[
2
],
shape_out
[
3
],
CV_32F
,
reinterpret_cast
<
float
*>
pred
);
const
double
threshold
=
double
(
Config
[
"det_db_thresh"
])
*
255
;
const
double
maxvalue
=
255
;
...
...
@@ -264,7 +266,8 @@ cv::Mat Visualization(cv::Mat srcimg,
cv
::
Point
rook_points
[
boxes
.
size
()][
4
];
for
(
int
n
=
0
;
n
<
boxes
.
size
();
n
++
)
{
for
(
int
m
=
0
;
m
<
boxes
[
0
].
size
();
m
++
)
{
rook_points
[
n
][
m
]
=
cv
::
Point
(
int
(
boxes
[
n
][
m
][
0
]),
int
(
boxes
[
n
][
m
][
1
]));
rook_points
[
n
][
m
]
=
cv
::
Point
(
static_cast
<
int
>
(
boxes
[
n
][
m
][
0
]),
static_cast
<
int
>
(
boxes
[
n
][
m
][
1
]));
}
}
cv
::
Mat
img_vis
;
...
...
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