I've just tracked and saved on the timeline the facial movements of Victor using OPEN CV and following this tutorial :
https://www.youtube.com/watch?v=RY_eErKlilw
The result has been good,as u can see :
https://drive.google.com/open?id=1ErXgMbFdy_bc7przM0LNe1BX2ud0GAxA
What I did next has been to apply the armature of Victor to a new character because I wanted to achieve the same goal as before with a new body,but the result has not been good :
https://drive.google.com/open?id=159YpyOV5Hxmu6b2drKVhIi4YWiQgU7XJ
The mouth moves in a totally wrong way and also the eyebrows. This happens because I'm not able to change the values and the maths formulas used on the python code that has been used to create the facial movements of Victor. Below you can read the code :
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 2.81\2.81\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 = "/home/username/Documents/Vincent/lbfmodel.yaml" #Linux
#landmark_model_path = "/Users/username/Downloads/lbfmodel.yaml" #Mac
landmark_model_path = "C:\\Users\\marietto2020\\Desktop\\OpenCV\\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, (x, 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()
I tried to change some value,but since I don't understand what piece of code makes what,I haven't been able to fix the movements. Can some one help me to do that ? thanks.