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OpenCVAnimOperator.py
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OpenCVAnimOperator.py
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import bpy
from imutils import face_utils
import dlib
import cv2
import time
import numpy
from bpy.props import FloatProperty
class OpenCVAnimOperator(bpy.types.Operator):
"""Operator which runs its self from a timer"""
bl_idname = "wm.opencv_operator"
bl_label = "OpenCV Animation Operator"
# p = our pre-treined model directory
#p = "D:\\Users\\jason\\shape_predictor_68_face_landmarks.dat" # Windows
#p = "/Users/jason/Downloads/shape_predictor_68_face_landmarks.dat" # macOS
p = "/home/jason/Downloads/shape_predictor_68_face_landmarks.dat" # linux
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(p)
# rig_name - take it from your scene collection tree
rig_name = "RIG-Vincent"
_timer = None
_cap = None
width = 800
height = 600
stop :bpy.props.BoolProperty()
# 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 ranve 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
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)
rects = self.detector(gray, 0)
# bpy.context.scene.frame_set(frame_num)
# For each detected face, find the landmark.
for (i, rect) in enumerate(rects):
shape = self.predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
#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
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)
bones = bpy.data.objects[self.rig_name].pose.bones
bones["head_fk"].rotation_euler[0] = self.smooth_value("h_x", 3, (self.rotation_vector[0] - self.first_angle[0])) / 1 # Up/Down
bones["head_fk"].rotation_euler[2] = self.smooth_value("h_y", 3, -(self.rotation_vector[1] - self.first_angle[1])) / 1.5 # Rotate
bones["head_fk"].rotation_euler[1] = self.smooth_value("h_z", 3, (self.rotation_vector[2] - self.first_angle[2])) / 1.3 # Left/Right
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["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["head_fk"].keyframe_insert(data_path="rotation_euler", index=-1)
bones["mouth_ctrl"].keyframe_insert(data_path="location", index=-1)
bones["brow_ctrl_L"].keyframe_insert(data_path="location", index=2)
bones["brow_ctrl_R"].keyframe_insert(data_path="location", index=2)
for (x, y) in shape:
cv2.circle(image, (x, y), 2, (0, 255, 255), -1)
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(0.5)
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(0.02, 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()