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face_recognition
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23d2d656
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face_recognition
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face_recognition
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前往新版Gitcode,体验更适合开发者的 AI 搜索 >>
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23d2d656
编写于
4月 06, 2019
作者:
D
DE_DZ_TBH
提交者:
Adam Geitgey
4月 15, 2019
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差异文件
Use the best match for better accuracy in examples
上级
f9f95baf
变更
3
隐藏空白更改
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并排
Showing
3 changed file
with
31 addition
and
10 deletion
+31
-10
examples/facerec_from_webcam.py
examples/facerec_from_webcam.py
+10
-3
examples/facerec_from_webcam_faster.py
examples/facerec_from_webcam_faster.py
+11
-4
examples/identify_and_draw_boxes_on_faces.py
examples/identify_and_draw_boxes_on_faces.py
+10
-3
未找到文件。
examples/facerec_from_webcam.py
浏览文件 @
23d2d656
import
face_recognition
import
face_recognition
import
cv2
import
cv2
import
numpy
as
np
# This is a super simple (but slow) example of running face recognition on live video from your webcam.
# This is a super simple (but slow) example of running face recognition on live video from your webcam.
# There's a second example that's a little more complicated but runs faster.
# There's a second example that's a little more complicated but runs faster.
...
@@ -48,9 +49,15 @@ while True:
...
@@ -48,9 +49,15 @@ while True:
name
=
"Unknown"
name
=
"Unknown"
# If a match was found in known_face_encodings, just use the first one.
# If a match was found in known_face_encodings, just use the first one.
if
True
in
matches
:
# if True in matches:
first_match_index
=
matches
.
index
(
True
)
# first_match_index = matches.index(True)
name
=
known_face_names
[
first_match_index
]
# name = known_face_names[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances
=
face_recognition
.
face_distance
(
known_face_encodings
,
face_encoding
)
best_match_index
=
np
.
argmin
(
face_distances
)
if
matches
[
best_match_index
]:
name
=
known_face_names
[
best_match_index
]
# Draw a box around the face
# Draw a box around the face
cv2
.
rectangle
(
frame
,
(
left
,
top
),
(
right
,
bottom
),
(
0
,
0
,
255
),
2
)
cv2
.
rectangle
(
frame
,
(
left
,
top
),
(
right
,
bottom
),
(
0
,
0
,
255
),
2
)
...
...
examples/facerec_from_webcam_faster.py
浏览文件 @
23d2d656
import
face_recognition
import
face_recognition
import
cv2
import
cv2
import
numpy
as
np
# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
# other example, but it includes some basic performance tweaks to make things run a lot faster:
...
@@ -59,10 +60,16 @@ while True:
...
@@ -59,10 +60,16 @@ while True:
matches
=
face_recognition
.
compare_faces
(
known_face_encodings
,
face_encoding
)
matches
=
face_recognition
.
compare_faces
(
known_face_encodings
,
face_encoding
)
name
=
"Unknown"
name
=
"Unknown"
# If a match was found in known_face_encodings, just use the first one.
# # If a match was found in known_face_encodings, just use the first one.
if
True
in
matches
:
# if True in matches:
first_match_index
=
matches
.
index
(
True
)
# first_match_index = matches.index(True)
name
=
known_face_names
[
first_match_index
]
# name = known_face_names[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances
=
face_recognition
.
face_distance
(
known_face_encodings
,
face_encoding
)
best_match_index
=
np
.
argmin
(
face_distances
)
if
matches
[
best_match_index
]:
name
=
known_face_names
[
best_match_index
]
face_names
.
append
(
name
)
face_names
.
append
(
name
)
...
...
examples/identify_and_draw_boxes_on_faces.py
浏览文件 @
23d2d656
import
face_recognition
import
face_recognition
from
PIL
import
Image
,
ImageDraw
from
PIL
import
Image
,
ImageDraw
import
numpy
as
np
# This is an example of running face recognition on a single image
# This is an example of running face recognition on a single image
# and drawing a box around each person that was identified.
# and drawing a box around each person that was identified.
...
@@ -43,9 +44,15 @@ for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodi
...
@@ -43,9 +44,15 @@ for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodi
name
=
"Unknown"
name
=
"Unknown"
# If a match was found in known_face_encodings, just use the first one.
# If a match was found in known_face_encodings, just use the first one.
if
True
in
matches
:
# if True in matches:
first_match_index
=
matches
.
index
(
True
)
# first_match_index = matches.index(True)
name
=
known_face_names
[
first_match_index
]
# name = known_face_names[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances
=
face_recognition
.
face_distance
(
known_face_encodings
,
face_encoding
)
best_match_index
=
np
.
argmin
(
face_distances
)
if
matches
[
best_match_index
]:
name
=
known_face_names
[
best_match_index
]
# Draw a box around the face using the Pillow module
# Draw a box around the face using the Pillow module
draw
.
rectangle
(((
left
,
top
),
(
right
,
bottom
)),
outline
=
(
0
,
0
,
255
))
draw
.
rectangle
(((
left
,
top
),
(
right
,
bottom
)),
outline
=
(
0
,
0
,
255
))
...
...
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