-
-
Notifications
You must be signed in to change notification settings - Fork 48
/
OpenCVAnimOperator.py
251 lines (201 loc) · 11.3 KB
/
OpenCVAnimOperator.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
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
import bpy
import cv2
import time
import numpy
# Download trained model (lbfmodel.yaml)
# https://github.com/kurnianggoro/GSOC2017/tree/master/data
# Install prerequisites:
# Linux: (may vary between distro's and installation methods)
# This is for manjaro with Blender installed from the package manager
# python3 -m ensurepip
# python3 -m pip install --upgrade pip --user
# python3 -m pip install opencv-contrib-python numpy --user
# MacOS
# open the Terminal
# cd /Applications/Blender.app/Contents/Resources/2.81/python/bin
# ./python3.7m -m ensurepip
# ./python3.7m -m pip install --upgrade pip --user
# ./python3.7m -m pip install opencv-contrib-python numpy --user
# Windows:
# Open Command Prompt as Administrator
# cd "C:\Program Files\Blender Foundation\Blender 2.82\2.82\python\bin"
# python -m pip install --upgrade pip
# python -m pip install opencv-contrib-python numpy
class OpenCVAnimOperator(bpy.types.Operator):
"""Operator which runs its self from a timer"""
bl_idname = "wm.opencv_operator"
bl_label = "OpenCV Animation Operator"
# Set paths to trained models downloaded above
face_detect_path = cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
#landmark_model_path = "./data/lbfmodel.yaml" #Linux
#landmark_model_path = "./data/lbfmodel.yaml" #Mac
landmark_model_path = "C:\\Users\\Joe\\Documents\\AnimationUsingPython\\data\\lbfmodel.yaml" #Windows
# Load models
fm = cv2.face.createFacemarkLBF()
fm.loadModel(landmark_model_path)
cas = cv2.CascadeClassifier(face_detect_path)
_timer = None
_cap = None
stop = False
# Webcam resolution:
width = 640
height = 480
# 3D model points.
model_points = numpy.array([
(0.0, 0.0, 0.0), # Nose tip
(0.0, -330.0, -65.0), # Chin
(-225.0, 170.0, -135.0), # Left eye left corner
(225.0, 170.0, -135.0), # Right eye right corne
(-150.0, -150.0, -125.0), # Left Mouth corner
(150.0, -150.0, -125.0) # Right mouth corner
], dtype = numpy.float32)
# Camera internals
camera_matrix = numpy.array(
[[height, 0.0, width/2],
[0.0, height, height/2],
[0.0, 0.0, 1.0]], dtype = numpy.float32
)
# Keeps a moving average of given length
def smooth_value(self, name, length, value):
if not hasattr(self, 'smooth'):
self.smooth = {}
if not name in self.smooth:
self.smooth[name] = numpy.array([value])
else:
self.smooth[name] = numpy.insert(arr=self.smooth[name], obj=0, values=value)
if self.smooth[name].size > length:
self.smooth[name] = numpy.delete(self.smooth[name], self.smooth[name].size-1, 0)
sum = 0
for val in self.smooth[name]:
sum += val
return sum / self.smooth[name].size
# Keeps min and max values, then returns the value in a range 0 - 1
def get_range(self, name, value):
if not hasattr(self, 'range'):
self.range = {}
if not name in self.range:
self.range[name] = numpy.array([value, value])
else:
self.range[name] = numpy.array([min(value, self.range[name][0]), max(value, self.range[name][1])] )
val_range = self.range[name][1] - self.range[name][0]
if val_range != 0:
return (value - self.range[name][0]) / val_range
else:
return 0.0
# The main "loop"
def modal(self, context, event):
if (event.type in {'RIGHTMOUSE', 'ESC'}) or self.stop == True:
self.cancel(context)
return {'CANCELLED'}
if event.type == 'TIMER':
self.init_camera()
_, image = self._cap.read()
#gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#gray = cv2.equalizeHist(gray)
# find faces
faces = self.cas.detectMultiScale(image,
scaleFactor=1.05,
minNeighbors=3,
flags=cv2.CASCADE_SCALE_IMAGE,
minSize=(int(self.width/5), int(self.width/5)))
#find biggest face, and only keep it
if type(faces) is numpy.ndarray and faces.size > 0:
biggestFace = numpy.zeros(shape=(1,4))
for face in faces:
if face[2] > biggestFace[0][2]:
print(face)
biggestFace[0] = face
# find the landmarks.
_, landmarks = self.fm.fit(image, faces=biggestFace)
for mark in landmarks:
shape = mark[0]
#2D image points. If you change the image, you need to change vector
image_points = numpy.array([shape[30], # Nose tip - 31
shape[8], # Chin - 9
shape[36], # Left eye left corner - 37
shape[45], # Right eye right corne - 46
shape[48], # Left Mouth corner - 49
shape[54] # Right mouth corner - 55
], dtype = numpy.float32)
dist_coeffs = numpy.zeros((4,1)) # Assuming no lens distortion
# determine head rotation
if hasattr(self, 'rotation_vector'):
(success, self.rotation_vector, self.translation_vector) = cv2.solvePnP(self.model_points,
image_points, self.camera_matrix, dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE,
rvec=self.rotation_vector, tvec=self.translation_vector,
useExtrinsicGuess=True)
else:
(success, self.rotation_vector, self.translation_vector) = cv2.solvePnP(self.model_points,
image_points, self.camera_matrix, dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE,
useExtrinsicGuess=False)
if not hasattr(self, 'first_angle'):
self.first_angle = numpy.copy(self.rotation_vector)
# set bone rotation/positions
bones = bpy.data.objects["RIG-Vincent"].pose.bones
# head rotation
bones["head_fk"].rotation_euler[0] = self.smooth_value("h_x", 5, (self.rotation_vector[0] - self.first_angle[0])) / 1 # Up/Down
bones["head_fk"].rotation_euler[2] = self.smooth_value("h_y", 5, -(self.rotation_vector[1] - self.first_angle[1])) / 1.5 # Rotate
bones["head_fk"].rotation_euler[1] = self.smooth_value("h_z", 5, (self.rotation_vector[2] - self.first_angle[2])) / 1.3 # Left/Right
bones["head_fk"].keyframe_insert(data_path="rotation_euler", index=-1)
# mouth position
bones["mouth_ctrl"].location[2] = self.smooth_value("m_h", 2, -self.get_range("mouth_height", numpy.linalg.norm(shape[62] - shape[66])) * 0.06 )
bones["mouth_ctrl"].location[0] = self.smooth_value("m_w", 2, (self.get_range("mouth_width", numpy.linalg.norm(shape[54] - shape[48])) - 0.5) * -0.04)
bones["mouth_ctrl"].keyframe_insert(data_path="location", index=-1)
#eyebrows
bones["brow_ctrl_L"].location[2] = self.smooth_value("b_l", 3, (self.get_range("brow_left", numpy.linalg.norm(shape[19] - shape[27])) -0.5) * 0.04)
bones["brow_ctrl_R"].location[2] = self.smooth_value("b_r", 3, (self.get_range("brow_right", numpy.linalg.norm(shape[24] - shape[27])) -0.5) * 0.04)
bones["brow_ctrl_L"].keyframe_insert(data_path="location", index=2)
bones["brow_ctrl_R"].keyframe_insert(data_path="location", index=2)
# eyelids
l_open = self.smooth_value("e_l", 2, self.get_range("l_open", -numpy.linalg.norm(shape[48] - shape[44])) )
r_open = self.smooth_value("e_r", 2, self.get_range("r_open", -numpy.linalg.norm(shape[41] - shape[39])) )
eyes_open = (l_open + r_open) / 2.0 # looks weird if both eyes aren't the same...
bones["eyelid_up_ctrl_R"].location[2] = -eyes_open * 0.025 + 0.005
bones["eyelid_low_ctrl_R"].location[2] = eyes_open * 0.025 - 0.005
bones["eyelid_up_ctrl_L"].location[2] = -eyes_open * 0.025 + 0.005
bones["eyelid_low_ctrl_L"].location[2] = eyes_open * 0.025 - 0.005
bones["eyelid_up_ctrl_R"].keyframe_insert(data_path="location", index=2)
bones["eyelid_low_ctrl_R"].keyframe_insert(data_path="location", index=2)
bones["eyelid_up_ctrl_L"].keyframe_insert(data_path="location", index=2)
bones["eyelid_low_ctrl_L"].keyframe_insert(data_path="location", index=2)
# draw face markers
for (x, y) in shape:
cv2.circle(image, (int(x), int(y)), 2, (0, 255, 255), -1)
# draw detected face
for (x,y,w,h) in faces:
cv2.rectangle(image,(x,y),(x+w,y+h),(255,0,0),1)
# Show camera image in a window
cv2.imshow("Output", image)
cv2.waitKey(1)
return {'PASS_THROUGH'}
def init_camera(self):
if self._cap == None:
self._cap = cv2.VideoCapture(0)
self._cap.set(cv2.CAP_PROP_FRAME_WIDTH, self.width)
self._cap.set(cv2.CAP_PROP_FRAME_HEIGHT, self.height)
self._cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
time.sleep(1.0)
def stop_playback(self, scene):
print(format(scene.frame_current) + " / " + format(scene.frame_end))
if scene.frame_current == scene.frame_end:
bpy.ops.screen.animation_cancel(restore_frame=False)
def execute(self, context):
bpy.app.handlers.frame_change_pre.append(self.stop_playback)
wm = context.window_manager
self._timer = wm.event_timer_add(0.01, window=context.window)
wm.modal_handler_add(self)
return {'RUNNING_MODAL'}
def cancel(self, context):
wm = context.window_manager
wm.event_timer_remove(self._timer)
cv2.destroyAllWindows()
self._cap.release()
self._cap = None
def register():
bpy.utils.register_class(OpenCVAnimOperator)
def unregister():
bpy.utils.unregister_class(OpenCVAnimOperator)
if __name__ == "__main__":
register()
# test call
#bpy.ops.wm.opencv_operator()