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Stephane Chavin
RAVEN2YOLO
Commits
46d7d3b1
Commit
46d7d3b1
authored
2 years ago
by
Stephane Chavin
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yolov5/models/utils/segment/plots.py
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yolov5/models/utils/segment/plots.py
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46d7d3b1
import
contextlib
import
math
from
pathlib
import
Path
import
cv2
import
matplotlib.pyplot
as
plt
import
numpy
as
np
import
pandas
as
pd
import
torch
from
..
import
threaded
from
..general
import
xywh2xyxy
from
..plots
import
Annotator
,
colors
@threaded
def
plot_images_and_masks
(
images
,
targets
,
masks
,
paths
=
None
,
fname
=
'
images.jpg
'
,
names
=
None
):
# Plot image grid with labels
if
isinstance
(
images
,
torch
.
Tensor
):
images
=
images
.
cpu
().
float
().
numpy
()
if
isinstance
(
targets
,
torch
.
Tensor
):
targets
=
targets
.
cpu
().
numpy
()
if
isinstance
(
masks
,
torch
.
Tensor
):
masks
=
masks
.
cpu
().
numpy
().
astype
(
int
)
max_size
=
1920
# max image size
max_subplots
=
16
# max image subplots, i.e. 4x4
bs
,
_
,
h
,
w
=
images
.
shape
# batch size, _, height, width
bs
=
min
(
bs
,
max_subplots
)
# limit plot images
ns
=
np
.
ceil
(
bs
**
0.5
)
# number of subplots (square)
if
np
.
max
(
images
[
0
])
<=
1
:
images
*=
255
# de-normalise (optional)
# Build Image
mosaic
=
np
.
full
((
int
(
ns
*
h
),
int
(
ns
*
w
),
3
),
255
,
dtype
=
np
.
uint8
)
# init
for
i
,
im
in
enumerate
(
images
):
if
i
==
max_subplots
:
# if last batch has fewer images than we expect
break
x
,
y
=
int
(
w
*
(
i
//
ns
)),
int
(
h
*
(
i
%
ns
))
# block origin
im
=
im
.
transpose
(
1
,
2
,
0
)
mosaic
[
y
:
y
+
h
,
x
:
x
+
w
,
:]
=
im
# Resize (optional)
scale
=
max_size
/
ns
/
max
(
h
,
w
)
if
scale
<
1
:
h
=
math
.
ceil
(
scale
*
h
)
w
=
math
.
ceil
(
scale
*
w
)
mosaic
=
cv2
.
resize
(
mosaic
,
tuple
(
int
(
x
*
ns
)
for
x
in
(
w
,
h
)))
# Annotate
fs
=
int
((
h
+
w
)
*
ns
*
0.01
)
# font size
annotator
=
Annotator
(
mosaic
,
line_width
=
round
(
fs
/
10
),
font_size
=
fs
,
pil
=
True
,
example
=
names
)
for
i
in
range
(
i
+
1
):
x
,
y
=
int
(
w
*
(
i
//
ns
)),
int
(
h
*
(
i
%
ns
))
# block origin
annotator
.
rectangle
([
x
,
y
,
x
+
w
,
y
+
h
],
None
,
(
255
,
255
,
255
),
width
=
2
)
# borders
if
paths
:
annotator
.
text
((
x
+
5
,
y
+
5
+
h
),
text
=
Path
(
paths
[
i
]).
name
[:
40
],
txt_color
=
(
220
,
220
,
220
))
# filenames
if
len
(
targets
)
>
0
:
idx
=
targets
[:,
0
]
==
i
ti
=
targets
[
idx
]
# image targets
boxes
=
xywh2xyxy
(
ti
[:,
2
:
6
]).
T
classes
=
ti
[:,
1
].
astype
(
'
int
'
)
labels
=
ti
.
shape
[
1
]
==
6
# labels if no conf column
conf
=
None
if
labels
else
ti
[:,
6
]
# check for confidence presence (label vs pred)
if
boxes
.
shape
[
1
]:
if
boxes
.
max
()
<=
1.01
:
# if normalized with tolerance 0.01
boxes
[[
0
,
2
]]
*=
w
# scale to pixels
boxes
[[
1
,
3
]]
*=
h
elif
scale
<
1
:
# absolute coords need scale if image scales
boxes
*=
scale
boxes
[[
0
,
2
]]
+=
x
boxes
[[
1
,
3
]]
+=
y
for
j
,
box
in
enumerate
(
boxes
.
T
.
tolist
()):
cls
=
classes
[
j
]
color
=
colors
(
cls
)
cls
=
names
[
cls
]
if
names
else
cls
if
labels
or
conf
[
j
]
>
0.25
:
# 0.25 conf thresh
label
=
f
'
{
cls
}
'
if
labels
else
f
'
{
cls
}
{
conf
[
j
]
:
.
1
f
}
'
annotator
.
box_label
(
box
,
label
,
color
=
color
)
# Plot masks
if
len
(
masks
):
if
masks
.
max
()
>
1.0
:
# mean that masks are overlap
image_masks
=
masks
[[
i
]]
# (1, 640, 640)
nl
=
len
(
ti
)
index
=
np
.
arange
(
nl
).
reshape
(
nl
,
1
,
1
)
+
1
image_masks
=
np
.
repeat
(
image_masks
,
nl
,
axis
=
0
)
image_masks
=
np
.
where
(
image_masks
==
index
,
1.0
,
0.0
)
else
:
image_masks
=
masks
[
idx
]
im
=
np
.
asarray
(
annotator
.
im
).
copy
()
for
j
,
box
in
enumerate
(
boxes
.
T
.
tolist
()):
if
labels
or
conf
[
j
]
>
0.25
:
# 0.25 conf thresh
color
=
colors
(
classes
[
j
])
mh
,
mw
=
image_masks
[
j
].
shape
if
mh
!=
h
or
mw
!=
w
:
mask
=
image_masks
[
j
].
astype
(
np
.
uint8
)
mask
=
cv2
.
resize
(
mask
,
(
w
,
h
))
mask
=
mask
.
astype
(
bool
)
else
:
mask
=
image_masks
[
j
].
astype
(
bool
)
with
contextlib
.
suppress
(
Exception
):
im
[
y
:
y
+
h
,
x
:
x
+
w
,
:][
mask
]
=
im
[
y
:
y
+
h
,
x
:
x
+
w
,
:][
mask
]
*
0.4
+
np
.
array
(
color
)
*
0.6
annotator
.
fromarray
(
im
)
annotator
.
im
.
save
(
fname
)
# save
def
plot_results_with_masks
(
file
=
'
path/to/results.csv
'
,
dir
=
''
,
best
=
True
):
# Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
save_dir
=
Path
(
file
).
parent
if
file
else
Path
(
dir
)
fig
,
ax
=
plt
.
subplots
(
2
,
8
,
figsize
=
(
18
,
6
),
tight_layout
=
True
)
ax
=
ax
.
ravel
()
files
=
list
(
save_dir
.
glob
(
'
results*.csv
'
))
assert
len
(
files
),
f
'
No results.csv files found in
{
save_dir
.
resolve
()
}
, nothing to plot.
'
for
f
in
files
:
try
:
data
=
pd
.
read_csv
(
f
)
index
=
np
.
argmax
(
0.9
*
data
.
values
[:,
8
]
+
0.1
*
data
.
values
[:,
7
]
+
0.9
*
data
.
values
[:,
12
]
+
0.1
*
data
.
values
[:,
11
])
s
=
[
x
.
strip
()
for
x
in
data
.
columns
]
x
=
data
.
values
[:,
0
]
for
i
,
j
in
enumerate
([
1
,
2
,
3
,
4
,
5
,
6
,
9
,
10
,
13
,
14
,
15
,
16
,
7
,
8
,
11
,
12
]):
y
=
data
.
values
[:,
j
]
# y[y == 0] = np.nan # don't show zero values
ax
[
i
].
plot
(
x
,
y
,
marker
=
'
.
'
,
label
=
f
.
stem
,
linewidth
=
2
,
markersize
=
2
)
if
best
:
# best
ax
[
i
].
scatter
(
index
,
y
[
index
],
color
=
'
r
'
,
label
=
f
'
best:
{
index
}
'
,
marker
=
'
*
'
,
linewidth
=
3
)
ax
[
i
].
set_title
(
s
[
j
]
+
f
'
\n
{
round
(
y
[
index
],
5
)
}
'
)
else
:
# last
ax
[
i
].
scatter
(
x
[
-
1
],
y
[
-
1
],
color
=
'
r
'
,
label
=
'
last
'
,
marker
=
'
*
'
,
linewidth
=
3
)
ax
[
i
].
set_title
(
s
[
j
]
+
f
'
\n
{
round
(
y
[
-
1
],
5
)
}
'
)
# if j in [8, 9, 10]: # share train and val loss y axes
# ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
except
Exception
as
e
:
print
(
f
'
Warning: Plotting error for
{
f
}
:
{
e
}
'
)
ax
[
1
].
legend
()
fig
.
savefig
(
save_dir
/
'
results.png
'
,
dpi
=
200
)
plt
.
close
()
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