-
-
Notifications
You must be signed in to change notification settings - Fork 11
/
app_sfu_filters.py
173 lines (139 loc) · 6.1 KB
/
app_sfu_filters.py
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
171
172
173
import urllib.request
from pathlib import Path
import av
import cv2
import numpy as np
import streamlit as st
from streamlit_server_state import server_state, server_state_lock
from streamlit_webrtc import ClientSettings, WebRtcMode, webrtc_streamer
cv2_path = Path(cv2.__file__).parent
def imread_from_url(url: str):
req = urllib.request.urlopen(url)
encoded = np.asarray(bytearray(req.read()), dtype="uint8")
image_bgra = cv2.imdecode(encoded, cv2.IMREAD_UNCHANGED)
return image_bgra
def overlay_bgra(background: np.ndarray, overlay: np.ndarray, roi):
roi_x, roi_y, roi_w, roi_h = roi
roi_aspect_ratio = roi_w / roi_h
# Calc overlay x, y, w, h that cover the ROI keeping the original aspect ratio
ov_org_h, ov_org_w = overlay.shape[:2]
ov_aspect_ratio = ov_org_w / ov_org_h
if ov_aspect_ratio >= roi_aspect_ratio:
ov_h = roi_h
ov_w = int(ov_aspect_ratio * ov_h)
ov_y = roi_y
ov_x = int(roi_x - (ov_w - roi_w) / 2)
else:
ov_w = roi_w
ov_h = int(ov_w / ov_aspect_ratio)
ov_x = roi_x
ov_y = int(roi_y - (ov_h - roi_h) / 2)
resized_overlay = cv2.resize(overlay, (ov_w, ov_h))
# Cut out the pixels of the overlay image outside the background frame.
margin_x0 = -min(0, ov_x)
margin_y0 = -min(0, ov_y)
margin_x1 = max(background.shape[1], ov_x + ov_w) - background.shape[1]
margin_y1 = max(background.shape[0], ov_y + ov_h) - background.shape[0]
resized_overlay = resized_overlay[
margin_y0 : resized_overlay.shape[0] - margin_y1,
margin_x0 : resized_overlay.shape[1] - margin_x1,
]
ov_x += margin_x0
ov_w -= margin_x0 + margin_x1
ov_y += margin_y0
ov_h -= margin_y0 + margin_y1
# Overlay
foreground = resized_overlay[:, :, :3]
mask = resized_overlay[:, :, 3]
overlaid_area = background[ov_y : ov_y + ov_h, ov_x : ov_x + ov_w]
overlaid_area[:] = np.where(mask[:, :, np.newaxis], foreground, overlaid_area)
@st.experimental_singleton
def get_face_classifier():
return cv2.CascadeClassifier(
str(cv2_path / "data/haarcascade_frontalface_alt2.xml")
)
@st.experimental_singleton
def get_filters():
return {
"ironman": imread_from_url(
"https://i.pinimg.com/originals/0c/c0/50/0cc050fd99aad66dc434ce772a0449a9.png" # noqa: E501
),
"laughing_man": imread_from_url(
"https://images-wixmp-ed30a86b8c4ca887773594c2.wixmp.com/f/3a17e5a4-9610-4fa3-a4bd-cb7d94d6f7e1/darwcty-d989aaf1-3cfa-4576-b2ac-305209346162.png/v1/fill/w_944,h_847,strp/laughing_man_logo_by_aggressive_vector_darwcty-pre.png?token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJ1cm46YXBwOjdlMGQxODg5ODIyNjQzNzNhNWYwZDQxNWVhMGQyNmUwIiwiaXNzIjoidXJuOmFwcDo3ZTBkMTg4OTgyMjY0MzczYTVmMGQ0MTVlYTBkMjZlMCIsIm9iaiI6W1t7ImhlaWdodCI6Ijw9OTE5IiwicGF0aCI6IlwvZlwvM2ExN2U1YTQtOTYxMC00ZmEzLWE0YmQtY2I3ZDk0ZDZmN2UxXC9kYXJ3Y3R5LWQ5ODlhYWYxLTNjZmEtNDU3Ni1iMmFjLTMwNTIwOTM0NjE2Mi5wbmciLCJ3aWR0aCI6Ijw9MTAyNCJ9XV0sImF1ZCI6WyJ1cm46c2VydmljZTppbWFnZS5vcGVyYXRpb25zIl19.5SDBnNZF6ktZM7Mk5gJfpHNQswRba3eqpvUn6FMHyW4" # noqa: E501
),
"cat": imread_from_url(
"https://i.pinimg.com/originals/29/cd/fd/29cdfdf2248ce2465598b2cc9e357579.png" # noqa: E501
),
}
def main():
if "webrtc_contexts" not in server_state:
server_state["webrtc_contexts"] = []
face_cascade = get_face_classifier()
filters = get_filters()
filter_type = st.radio(
"Select filter type",
("ironman", "laughing_man", "cat"),
key="filter-type",
)
draw_rect = st.checkbox("Draw rect (for debug)")
def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
img = frame.to_ndarray(format="bgr24")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(
gray, scaleFactor=1.11, minNeighbors=3, minSize=(30, 30)
)
overlay = filters[filter_type]
for (x, y, w, h) in faces:
# Ad-hoc adjustment of the ROI for each filter type
if filter_type == "ironman":
roi = (x, y, w, h)
elif filter_type == "laughing_man":
roi = (x, y, int(w * 1.15), h)
elif filter_type == "cat":
roi = (x, y - int(h * 0.3), w, h)
overlay_bgra(img, overlay, roi)
if draw_rect:
img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
return av.VideoFrame.from_ndarray(img, format="bgr24")
self_ctx = webrtc_streamer(
key="self",
mode=WebRtcMode.SENDRECV,
client_settings=ClientSettings(
rtc_configuration={
"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]
},
media_stream_constraints={"video": True, "audio": True},
),
video_frame_callback=video_frame_callback,
sendback_audio=False,
)
with server_state_lock["webrtc_contexts"]:
webrtc_contexts = server_state["webrtc_contexts"]
if self_ctx.state.playing and self_ctx not in webrtc_contexts:
webrtc_contexts.append(self_ctx)
server_state["webrtc_contexts"] = webrtc_contexts
elif not self_ctx.state.playing and self_ctx in webrtc_contexts:
webrtc_contexts.remove(self_ctx)
server_state["webrtc_contexts"] = webrtc_contexts
active_other_ctxs = [
ctx for ctx in webrtc_contexts if ctx != self_ctx and ctx.state.playing
]
for ctx in active_other_ctxs:
webrtc_streamer(
key=str(id(ctx)),
mode=WebRtcMode.RECVONLY,
client_settings=ClientSettings(
rtc_configuration={
"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]
},
media_stream_constraints={
"video": True,
"audio": True,
},
),
source_audio_track=ctx.output_audio_track,
source_video_track=ctx.output_video_track,
desired_playing_state=ctx.state.playing,
)
if __name__ == "__main__":
main()