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Running
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Zero
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import os
import numpy as np
import cv2
import time
from tqdm import tqdm
import multiprocessing
import glob
import mediapipe as mp
from mediapipe import solutions
from mediapipe.framework.formats import landmark_pb2
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
from . import face_landmark
CUR_DIR = os.path.dirname(__file__)
class LMKExtractor():
def __init__(self, FPS=25):
# Create an FaceLandmarker object.
self.mode = mp.tasks.vision.FaceDetectorOptions.running_mode.IMAGE
base_options = python.BaseOptions(model_asset_path=os.path.join(CUR_DIR, 'mp_models/face_landmarker_v2_with_blendshapes.task'))
base_options.delegate = mp.tasks.BaseOptions.Delegate.CPU
options = vision.FaceLandmarkerOptions(base_options=base_options,
running_mode=self.mode,
output_face_blendshapes=True,
output_facial_transformation_matrixes=True,
num_faces=1)
self.detector = face_landmark.FaceLandmarker.create_from_options(options)
self.last_ts = 0
self.frame_ms = int(1000 / FPS)
det_base_options = python.BaseOptions(model_asset_path=os.path.join(CUR_DIR, 'mp_models/blaze_face_short_range.tflite'))
det_options = vision.FaceDetectorOptions(base_options=det_base_options)
self.det_detector = vision.FaceDetector.create_from_options(det_options)
def __call__(self, img):
frame = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame)
t0 = time.time()
if self.mode == mp.tasks.vision.FaceDetectorOptions.running_mode.VIDEO:
det_result = self.det_detector.detect(image)
if len(det_result.detections) != 1:
return None
self.last_ts += self.frame_ms
try:
detection_result, mesh3d = self.detector.detect_for_video(image, timestamp_ms=self.last_ts)
except:
return None
elif self.mode == mp.tasks.vision.FaceDetectorOptions.running_mode.IMAGE:
# det_result = self.det_detector.detect(image)
# if len(det_result.detections) != 1:
# return None
try:
detection_result, mesh3d = self.detector.detect(image)
except:
return None
bs_list = detection_result.face_blendshapes
if len(bs_list) == 1:
bs = bs_list[0]
bs_values = []
for index in range(len(bs)):
bs_values.append(bs[index].score)
bs_values = bs_values[1:] # remove neutral
trans_mat = detection_result.facial_transformation_matrixes[0]
face_landmarks_list = detection_result.face_landmarks
face_landmarks = face_landmarks_list[0]
lmks = []
for index in range(len(face_landmarks)):
x = face_landmarks[index].x
y = face_landmarks[index].y
z = face_landmarks[index].z
lmks.append([x, y, z])
lmks = np.array(lmks)
lmks3d = np.array(mesh3d.vertex_buffer)
lmks3d = lmks3d.reshape(-1, 5)[:, :3]
mp_tris = np.array(mesh3d.index_buffer).reshape(-1, 3) + 1
return {
"lmks": lmks,
'lmks3d': lmks3d,
"trans_mat": trans_mat,
'faces': mp_tris,
"bs": bs_values
}
else:
# print('multiple faces in the image: {}'.format(img_path))
return None
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