Hello, maybe my question will seem silly because I’m just starting to learn and I try to do it with interest for myself. The code is not mine, I found it on the Internet, but I have been trying to fix errors in it for 3 days. And it was possible to do it, but it is with this one that I have been suffering for 2 days in a row. I read the documentation, turned to ChatGPT, watched the lessons, but I can’t figure it out. When running the script, everything goes well, the webcam turns on and it should read my face and transfer it to a 3D model. But as soon as I move the camera to the face, everything freezes and an error comes out. I would be very grateful if you could help me figure it out.
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\\kroek\\Downloads\\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()
This is what the error looks like:
[ WARN:1@1425.438] global cap_msmf.cpp:471 anonymous-namespace'::SourceReaderCB::OnReadSample videoio(MSMF): OnReadSample() is called with error status: -1072875772 [ WARN:1@1425.442] global cap_msmf.cpp:483 anonymous-namespace’::SourceReaderCB::OnReadSample videoio(MSMF): async ReadSample() call is failed with error status: -1072875772 [ WARN:0@1425.445] global cap_msmf.cpp:1759 CvCapture_MSMF::grabFrame videoio(MSMF): can’t grab frame. Error: -1072875772 Traceback (most recent call last): File “C:\Users\kroek\Downloads\vincent.blend\OpenCVAnimOperator.py”, line 203, in modal cv2.error: OpenCV(4.7.0) D:\a\opencv-python\opencv-python\opencv\modules\highgui\src\window.cpp:971: error: (-215:Assertion failed) size.width>0 && size.height>0 in function ‘cv::imshow’ Error: Python: Traceback (most recent call last): File “C:\Users\kroek\Downloads\vincent.blend\OpenCVAnimOperator.py”, line 203, in modal cv2.error: OpenCV(4.7.0) D:\a\opencv-python\opencv-python\opencv\modules\highgui\src\window.cpp:971: error: (-215:Assertion failed) size.width>0 && size.height>0 in function ‘cv::imshow’