-
Notifications
You must be signed in to change notification settings - Fork 1
/
wf-face-detect.yaml
170 lines (151 loc) · 5.04 KB
/
wf-face-detect.yaml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
apiVersion: argoproj.io/v1alpha1
kind: WorkflowTemplate
metadata:
name: face-detect
spec:
entrypoint: main
arguments:
parameters:
- name: key # s3 key in the face-detect bucket
value: to-be-identified/obama_and_biden.jpg
serviceAccountName: face-detect
# Optional. Indicates the artifact repository (s3 bucket) to use for intermediate artifacts for
# the workflow (defined in the artifact-repositories ConfigMap). Uses the annotated default if omitted
# artifactRepositoryRef:
# key: face-detect
# artifactGC:
# strategy: OnWorkflowDeletion
# serviceAccountName: face-detect
templates:
- name: main
dag:
tasks:
- name: crop-faces
template: crop-faces
- name: iterate-faces
dependencies: [crop-faces]
template: iterate-faces
- name: identify-face
dependencies: [iterate-faces]
withParam: "{{tasks.iterate-faces.outputs.result}}"
template: identify-face
arguments:
artifacts:
- name: face
s3:
key: "{{item}}"
- name: approval
dependencies: [identify-face]
template: approval
- name: fail
dependencies: [approval]
when: "{{tasks.approval.outputs.parameters.approved}} != YES"
inline:
container:
image: busybox:1.36
command: [sh, -c]
args: ["exit 1"]
- name: finalize
dependencies: [approval]
when: "{{tasks.approval.outputs.parameters.approved}} == YES"
inline:
container:
image: busybox:1.36
command: [sh, -c]
args: ["echo success!"]
# Downloads the image supplied to the workflow (workflow.parameters.key)
# generates multiple cropped images of faces from the supplied image.
- name: crop-faces
inputs:
artifacts:
- name: images
path: /tmp/input/{{workflow.parameters.key}}
s3:
key: "{{workflow.parameters.key}}"
script:
image: hdgigante/python-opencv:4.7.0-ubuntu
command: [/usr/bin/python3]
source: |
import cv2
import os
input_image = "/tmp/input/{{workflow.parameters.key}}"
output_dir = "/tmp/cropped-faces/"
os.mkdir(output_dir)
# read the input image
img = cv2.imread(input_image)
# convert to grayscale of each frames
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# read the haarcascade to detect the faces in an image
face_cascade = cv2.CascadeClassifier('/usr/local/share/opencv4/haarcascades/haarcascade_frontalface_default.xml')
# detects faces in the input image
faces = face_cascade.detectMultiScale(gray, 1.3, 4)
print('Number of detected faces:', len(faces))
# loop over all detected faces
if len(faces) > 0:
for i, (x, y, w, h) in enumerate(faces):
# To draw a rectangle in a face
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 255), 2)
face = img[y:y + h, x:x + w]
#cv2.imshow("Cropped Face", face)
cv2.imwrite(output_dir + f'face{i}.jpg', face)
print(f"face{i}.jpg is saved")
outputs:
artifacts:
- name: cropped-faces
path: /tmp/cropped-faces
archive:
none: {}
# Iterates the cropped faces that were generated by the `crop-faces` step, then
# produces a list of S3 key locations so that they can be identified in parallel
- name: iterate-faces
data:
source:
artifactPaths:
name: workflow-artifacts
s3:
bucket: face-detect
key: "wf-artifacts/{{workflow.name}}/"
transformation:
- expression: "filter(data, {# endsWith \".jpg\"})"
outputs:
# Workaround necessary for artifactPaths to work
artifacts:
- name: file
path: /file
# Accepts a face artifact + and an s3 bucket of known faces, then outputs the identity (name)
# of the individual identified from the face
- name: identify-face
inputs:
artifacts:
- name: face
path: /tmp/face
- name: known-faces
path: /data/known-faces
s3:
key: known-faces
container:
image: iankoulski/face-recognition
command: [sh, -c]
args:
- face_recognition /data/known-faces /tmp/face | cut -d, -f2 | tee /tmp/name
outputs:
parameters:
- name: name
valueFrom:
path: /tmp/name
# Suspend template that
- name: approval
suspend: {}
inputs:
parameters:
- name: approved
default: 'NO'
enum:
- 'YES'
- 'NO'
description: Choose YES to continue workflow
outputs:
parameters:
- name: approved
valueFrom:
supplied: {}