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submodule
opencv
Commits
ea31a14c
Commit
ea31a14c
authored
Jan 10, 2020
by
Liubov Batanina
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29 additions
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human_parsing.py
samples/dnn/human_parsing.py
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samples/dnn/human_parsing.py
View file @
ea31a14c
...
@@ -3,8 +3,8 @@ import numpy as np
...
@@ -3,8 +3,8 @@ import numpy as np
import
argparse
import
argparse
backends
=
(
cv
.
dnn
.
DNN_BACKEND_DEFAULT
,
cv
.
dnn
.
DNN_BACKEND_
HALIDE
,
backends
=
(
cv
.
dnn
.
DNN_BACKEND_DEFAULT
,
cv
.
dnn
.
DNN_BACKEND_
INFERENCE_ENGINE_NN_BUILDER_2019
,
cv
.
dnn
.
DNN_BACKEND_
OPENCV
,
cv
.
dnn
.
DNN_BACKEND_INFERENCE_ENGINE
)
cv
.
dnn
.
DNN_BACKEND_
INFERENCE_ENGINE_NGRAPH
,
cv
.
dnn
.
DNN_BACKEND_OPENCV
)
targets
=
(
cv
.
dnn
.
DNN_TARGET_CPU
,
cv
.
dnn
.
DNN_TARGET_OPENCL
,
cv
.
dnn
.
DNN_TARGET_OPENCL_FP16
,
cv
.
dnn
.
DNN_TARGET_MYRIAD
)
targets
=
(
cv
.
dnn
.
DNN_TARGET_CPU
,
cv
.
dnn
.
DNN_TARGET_OPENCL
,
cv
.
dnn
.
DNN_TARGET_OPENCL_FP16
,
cv
.
dnn
.
DNN_TARGET_MYRIAD
)
parser
=
argparse
.
ArgumentParser
(
description
=
'Use this script to run human parsing using JPPNet'
,
parser
=
argparse
.
ArgumentParser
(
description
=
'Use this script to run human parsing using JPPNet'
,
...
@@ -36,26 +36,27 @@ parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU,
...
@@ -36,26 +36,27 @@ parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU,
# 2. Create input
# 2. Create input
# image = cv2.imread(path/to/image)
# image = cv2.imread(path/to/image)
# image_rev = np.flip(image, axis=1)
# image_rev = np.flip(image, axis=1)
# image_h, image_w = image.shape[:2]
# input = np.stack([image, image_rev], axis=0)
# input = np.stack([image, image_rev], axis=0)
#
#
# 3. Hardcode image_h and image_w shapes to determine output shapes
# 3. Hardcode image_h and image_w shapes to determine output shapes.
# - parsing_out1 = tf.reduce_mean(tf.stack([tf.image.resize_images(parsing_out1_100, [image_h, image_w]),
# We use default INPUT_SIZE = (384, 384) from evaluate_parsing_JPPNet-s2.py.
# tf.image.resize_images(parsing_out1_075, [image_h, image_w]),
# - parsing_out1 = tf.reduce_mean(tf.stack([tf.image.resize_images(parsing_out1_100, INPUT_SIZE),
# tf.image.resize_images(parsing_out1_125, [image_h, image_w])]), axis=0)
# tf.image.resize_images(parsing_out1_075, INPUT_SIZE),
# Do similarly with parsing_out2, parsing_out3
# tf.image.resize_images(parsing_out1_125, INPUT_SIZE)]), axis=0)
# 4. Remove postprocessing
# Do similarly with parsing_out2, parsing_out3
# - parsing_ = sess.run(raw_output, feed_dict={'input:0': input})
# 4. Remove postprocessing. Last net operation:
# raw_output = tf.reduce_mean(tf.stack([parsing_out1, parsing_out2, parsing_out3]), axis=0)
# Change:
# parsing_ = sess.run(raw_output, feed_dict={'input:0': input})
#
#
# 5. To save model after sess.run(...) add:
# 5. To save model after sess.run(...) add:
# - input_graph_def = tf.get_default_graph().as_graph_def()
# input_graph_def = tf.get_default_graph().as_graph_def()
# - output_node = "Mean_3"
# output_node = "Mean_3"
# - output_graph_def = tf.graph_util.convert_variables_to_constants(sess, input_graph_def, output_node)
# output_graph_def = tf.graph_util.convert_variables_to_constants(sess, input_graph_def, output_node)
# -
#
# - output_graph = "LIP_JPPNet.pb"
# output_graph = "LIP_JPPNet.pb"
# - with tf.gfile.GFile(output_graph, "wb") as f:
# with tf.gfile.GFile(output_graph, "wb") as f:
# - f.write(output_graph_def.SerializeToString())
# f.write(output_graph_def.SerializeToString())
def
preprocess
(
image_path
):
def
preprocess
(
image_path
):
...
@@ -73,6 +74,8 @@ def run_net(input, model_path, backend, target):
...
@@ -73,6 +74,8 @@ def run_net(input, model_path, backend, target):
"""
"""
Read network and infer model
Read network and infer model
:param model_path: path to JPPNet model
:param model_path: path to JPPNet model
:param backend: computation backend
:param target: computation device
"""
"""
net
=
cv
.
dnn
.
readNet
(
model_path
)
net
=
cv
.
dnn
.
readNet
(
model_path
)
net
.
setPreferableBackend
(
backend
)
net
.
setPreferableBackend
(
backend
)
...
@@ -82,10 +85,11 @@ def run_net(input, model_path, backend, target):
...
@@ -82,10 +85,11 @@ def run_net(input, model_path, backend, target):
return
out
return
out
def
postprocess
(
out
):
def
postprocess
(
out
,
input_shape
):
"""
"""
Create a grayscale human segmentation
Create a grayscale human segmentation
:param out: network output
:param out: network output
:param input_shape: input image width and height
"""
"""
# LIP classes
# LIP classes
# 0 Background
# 0 Background
...
@@ -111,6 +115,10 @@ def postprocess(out):
...
@@ -111,6 +115,10 @@ def postprocess(out):
head_output
,
tail_output
=
np
.
split
(
out
,
indices_or_sections
=
[
1
],
axis
=
0
)
head_output
,
tail_output
=
np
.
split
(
out
,
indices_or_sections
=
[
1
],
axis
=
0
)
head_output
=
head_output
.
squeeze
(
0
)
head_output
=
head_output
.
squeeze
(
0
)
tail_output
=
tail_output
.
squeeze
(
0
)
tail_output
=
tail_output
.
squeeze
(
0
)
head_output
=
np
.
stack
([
cv
.
resize
(
img
,
dsize
=
input_shape
)
for
img
in
head_output
[:,
...
]])
tail_output
=
np
.
stack
([
cv
.
resize
(
img
,
dsize
=
input_shape
)
for
img
in
tail_output
[:,
...
]])
tail_list
=
np
.
split
(
tail_output
,
indices_or_sections
=
list
(
range
(
1
,
20
)),
axis
=
0
)
tail_list
=
np
.
split
(
tail_output
,
indices_or_sections
=
list
(
range
(
1
,
20
)),
axis
=
0
)
tail_list
=
[
arr
.
squeeze
(
0
)
for
arr
in
tail_list
]
tail_list
=
[
arr
.
squeeze
(
0
)
for
arr
in
tail_list
]
tail_list_rev
=
[
tail_list
[
i
]
for
i
in
range
(
14
)]
tail_list_rev
=
[
tail_list
[
i
]
for
i
in
range
(
14
)]
...
@@ -149,8 +157,9 @@ def parse_human(image_path, model_path, backend, target):
...
@@ -149,8 +157,9 @@ def parse_human(image_path, model_path, backend, target):
:param target: name of computation target
:param target: name of computation target
"""
"""
input
=
preprocess
(
image_path
)
input
=
preprocess
(
image_path
)
input_h
,
input_w
=
input
.
shape
[
2
:]
output
=
run_net
(
input
,
model_path
,
backend
,
target
)
output
=
run_net
(
input
,
model_path
,
backend
,
target
)
grayscale_out
=
postprocess
(
output
)
grayscale_out
=
postprocess
(
output
,
(
input_w
,
input_h
)
)
segmentation
=
decode_labels
(
grayscale_out
)
segmentation
=
decode_labels
(
grayscale_out
)
return
segmentation
return
segmentation
...
...
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