ADD moving average, x angle control
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GunwooHan committed
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@@ -1,3 +1,6 @@
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pretrained
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character
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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@@ -1,32 +1,3 @@
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# import time
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#
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# import pyvirtualcam
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# import numpy as np
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# import cv2
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# import mediapipe as mp
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#
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# mp_drawing = mp.solutions.drawing_utils
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# mp_holistic = mp.solutions.holistic
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#
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# cap = cv2.VideoCapture(0)
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#
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# with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:
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# with pyvirtualcam.Camera(width=1280, height=720, fps=30) as cam:
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# print(f'Using virtual camera: {cam.device}')
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# while cap.isOpened():
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# ret, frame = cap.read()
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# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# frame = cv2.resize(frame, dsize=None, fx=720/480, fy=720/480)
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# results = holistic.process(frame)
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# mp_drawing.draw_landmarks(frame, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION)
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# mp_drawing.draw_landmarks(frame, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
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# mp_drawing.draw_landmarks(frame, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
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# mp_drawing.draw_landmarks(frame, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
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# pad_frame = np.zeros((720, 1280, 3), dtype=np.uint8)
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# pad_frame[:, (1280-960)//2:(1280-960)//2+960] = frame
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# cam.send(pad_frame)
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# cam.sleep_until_next_frame()
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import time
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import argparse
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@@ -50,10 +21,10 @@ from mediapipe.python.solutions.face_mesh_connections import FACEMESH_RIGHT_IRIS
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from mediapipe.python.solutions.face_mesh_connections import FACEMESH_TESSELATION
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from tha2.poser.modes.mode_20 import load_face_morpher, load_face_rotater, load_combiner
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from utils import preprocessing_image, postprocessing_image
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from utils import preprocessing_image, postprocessing_image, get_distance
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parser = argparse.ArgumentParser()
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parser.add_argument('--debug', action='store_true')
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parser.add_argument('--debug', action='store_false')
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parser.add_argument('--output', type=str, default='window')
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args = parser.parse_args()
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@@ -74,11 +45,96 @@ class TalkingAnimeLight(nn.Module):
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return output_image
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def get_pose(facial_landmarks):
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iris_r_center = [0, 0, 0]
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iris_l_center = [0, 0, 0]
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for idx in list(map(lambda x: x[0], FACEMESH_RIGHT_IRIS)):
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pos_x, pos_y, pos_z = facial_landmarks[idx].x, facial_landmarks[idx].y, facial_landmarks[idx].z
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iris_r_center[0] += pos_x
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iris_r_center[1] += pos_y
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iris_r_center[2] += pos_z
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iris_r_center = np.array(iris_r_center) / len(FACEMESH_RIGHT_IRIS)
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for idx in list(map(lambda x: x[0], FACEMESH_LEFT_IRIS)):
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pos_x, pos_y, pos_z = facial_landmarks[idx].x, facial_landmarks[idx].y, facial_landmarks[idx].z
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iris_l_center[0] += pos_x
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iris_l_center[1] += pos_y
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iris_l_center[2] += pos_z
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iris_l_center = np.array(iris_l_center) / len(FACEMESH_LEFT_IRIS)
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# if args.debug:
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# h, w, c = frame.shape
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# cv2.line(frame, [int(iris_l_center[0] * w), int(iris_l_center[1] * h)],
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# [int(iris_r_center[0] * w), int(iris_r_center[1] * h)], (0, 128, 255), 2)
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z_angle = np.arctan2(iris_l_center[1] - iris_r_center[1], iris_l_center[0] - iris_r_center[0]) # * 180 / np.pi
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y_angle = np.arctan2(iris_l_center[2] - iris_r_center[2], iris_l_center[0] - iris_r_center[0]) # * 180 / np.pi
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iris_middle_center = np.mean(np.array([iris_l_center, iris_r_center]), axis=0)
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mouth_center = [0, 0, 0]
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for idx in list(map(lambda x: x[0], FACEMESH_LIPS)):
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pos_x, pos_y, pos_z = facial_landmarks[idx].x, facial_landmarks[idx].y, facial_landmarks[idx].z
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mouth_center[0] += pos_x
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mouth_center[1] += pos_y
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mouth_center[2] += pos_z
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mouth_center = np.array(mouth_center) / len(FACEMESH_LIPS)
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mouth_h = facial_landmarks[13].y - facial_landmarks[14].y
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mouth_w = facial_landmarks[78].x - (facial_landmarks[409].x + facial_landmarks[375].x) / 2
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mouth_ratio = mouth_h / mouth_w
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x_angle = np.arctan2(iris_middle_center[2] - mouth_center[2], iris_middle_center[1] - mouth_center[1]) # * 180 / np.pi
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x_angle = np.arctan2(iris_middle_center[1] - mouth_center[1], iris_middle_center[2] - mouth_center[2]) # * 180 / np.pi
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# if args.debug:
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# h, w, c = frame.shape
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# cv2.line(frame, [int(iris_middle_center[0] * w), int(iris_middle_center[1] * h)],
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# [int(mouth_center[0] * w), int(mouth_center[1] * h)], (0, 128, 255), 2)
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# cv2.putText(frame, f'z angle:{z_angle * 180 / np.pi}',
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# [int(iris_middle_center[0] * w), int(iris_middle_center[1] * h) + 30], cv2.FONT_HERSHEY_SIMPLEX,
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# 0.5, (255, 0, 0), 1)
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iris_rotation_l_h = get_distance(facial_landmarks[386], facial_landmarks[374])
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iris_rotation_l_w = get_distance(facial_landmarks[382], facial_landmarks[263])
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iris_rotation_r_h = get_distance(facial_landmarks[159], facial_landmarks[145])
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iris_rotation_r_w = get_distance(facial_landmarks[33], facial_landmarks[155])
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iris_rotation_l_h_temp = np.sqrt(
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(iris_l_center[0] - facial_landmarks[386].x) ** 2 + (iris_l_center[1] - facial_landmarks[386].y) ** 2)
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iris_rotation_l_w_temp = np.sqrt(
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(iris_l_center[0] - facial_landmarks[382].x) ** 2 + (iris_l_center[1] - facial_landmarks[382].y) ** 2)
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iris_rotation_r_h_temp = np.sqrt(
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(iris_r_center[0] - facial_landmarks[159].x) ** 2 + (iris_r_center[1] - facial_landmarks[159].y) ** 2)
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iris_rotation_r_w_temp = np.sqrt(
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(iris_r_center[0] - facial_landmarks[33].x) ** 2 + (iris_r_center[1] - facial_landmarks[33].y) ** 2)
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# if args.debug:
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# h, w, c = frame.shape
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# cv2.line(frame, [int(iris_l_center[0] * w), int(iris_l_center[1] * h)],
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# [int(iris_l_center[0] * w), int(iris_l_center[1] * h)], (0, 128, 128), 3)
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# cv2.line(frame, [int(iris_r_center[0] * w), int(iris_r_center[1] * h)],
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# [int(iris_r_center[0] * w), int(iris_r_center[1] * h)], (255, 128, 0), 3)
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eye_x_ratio = ((iris_rotation_l_w_temp / iris_rotation_l_w + iris_rotation_r_w_temp / iris_rotation_r_w) - 1) * 3
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eye_y_ratio = ((iris_rotation_l_h_temp / iris_rotation_l_h + iris_rotation_r_h_temp / iris_rotation_r_h) - 1) * 3
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eye_l_h_temp = 1 - 2 * (facial_landmarks[145].y - facial_landmarks[159].y) / (
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facial_landmarks[155].x - facial_landmarks[33].x)
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eye_r_h_temp = 1 - 2 * (facial_landmarks[374].y - facial_landmarks[386].y) / (
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facial_landmarks[263].x - facial_landmarks[382].x)
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return eye_l_h_temp, eye_r_h_temp, mouth_ratio, eye_y_ratio, eye_x_ratio, y_angle, z_angle, x_angle
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def main():
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model = TalkingAnimeLight().cuda()
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model = model.eval()
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model = model.half()
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img = Image.open("character/sleeping_rat.png")
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img = img.resize((256, 256))
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input_image = preprocessing_image(img).unsqueeze(0)
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mp_facemesh = mp.solutions.face_mesh
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@@ -92,21 +148,14 @@ def main():
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if ret is None:
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raise Exception("Can't find Camera")
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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results = facemesh.process(frame)
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input_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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results = facemesh.process(input_frame)
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if results.multi_face_landmarks is None:
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raise Exception("Fail to initailize parameters")
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facial_landmarks = results.multi_face_landmarks[0].landmark
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# initial parameters
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eye_l_h = facial_landmarks[145].y - facial_landmarks[159].y
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eye_r_h = facial_landmarks[374].y - facial_landmarks[386].y
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eye_l_initial = facial_landmarks[159].y
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eye_r_initial = facial_landmarks[386].y
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mouth_eye_vector = torch.empty(1, 27)
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pose_vector = torch.empty(1, 3)
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@@ -118,93 +167,142 @@ def main():
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mouth_eye_vector = mouth_eye_vector.cuda()
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pose_vector = pose_vector.cuda()
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pose_queue = []
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while cap.isOpened():
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ret, frame = cap.read()
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results = facemesh.process(frame)
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input_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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results = facemesh.process(input_frame)
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if results.multi_face_landmarks is None:
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continue
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facial_landmarks = results.multi_face_landmarks[0].landmark
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#
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# iris_r_center = [0, 0, 0]
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# iris_l_center = [0, 0, 0]
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#
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# for idx in list(map(lambda x: x[0], FACEMESH_RIGHT_IRIS)):
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# pos_x, pos_y, pos_z = facial_landmarks[idx].x, facial_landmarks[idx].y, facial_landmarks[idx].z
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# iris_r_center[0] += pos_x
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# iris_r_center[1] += pos_y
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# iris_r_center[2] += pos_z
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#
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# iris_r_center = np.array(iris_r_center) / len(FACEMESH_RIGHT_IRIS)
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#
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# for idx in list(map(lambda x: x[0], FACEMESH_LEFT_IRIS)):
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# pos_x, pos_y, pos_z = facial_landmarks[idx].x, facial_landmarks[idx].y, facial_landmarks[idx].z
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# iris_l_center[0] += pos_x
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# iris_l_center[1] += pos_y
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# iris_l_center[2] += pos_z
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#
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# iris_l_center = np.array(iris_l_center) / len(FACEMESH_LEFT_IRIS)
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#
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# if args.debug:
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# h, w, c = frame.shape
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# cv2.line(frame, [int(iris_l_center[0] * w), int(iris_l_center[1] * h)],
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# [int(iris_r_center[0] * w), int(iris_r_center[1] * h)], (0, 128, 255), 2)
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#
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# z_angle = np.arctan2(iris_l_center[1] - iris_r_center[1],
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# iris_l_center[0] - iris_r_center[0]) # * 180 / np.pi
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# y_angle = np.arctan2(iris_l_center[2] - iris_r_center[2],
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# iris_l_center[0] - iris_r_center[0]) # * 180 / np.pi
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#
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# iris_middle_center = np.mean(np.array([iris_l_center, iris_r_center]), axis=0)
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# mouth_center = [0, 0, 0]
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#
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# for idx in list(map(lambda x: x[0], FACEMESH_LIPS)):
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# pos_x, pos_y, pos_z = facial_landmarks[idx].x, facial_landmarks[idx].y, facial_landmarks[idx].z
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# mouth_center[0] += pos_x
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# mouth_center[1] += pos_y
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# mouth_center[2] += pos_z
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#
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# mouth_center = np.array(mouth_center) / len(FACEMESH_LIPS)
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# mouth_h = facial_landmarks[13].y - facial_landmarks[14].y
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# mouth_w = facial_landmarks[78].x - (facial_landmarks[409].x + facial_landmarks[375].x) / 2
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# mouth_ratio = mouth_h / mouth_w
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#
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# if args.debug:
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# h, w, c = frame.shape
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# cv2.line(frame, [int(iris_middle_center[0] * w), int(iris_middle_center[1] * h)],
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# [int(mouth_center[0] * w), int(mouth_center[1] * h)], (0, 128, 255), 2)
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# cv2.putText(frame, f'z angle:{z_angle * 180 / np.pi}',
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# [int(iris_middle_center[0] * w), int(iris_middle_center[1] * h) + 30],
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# cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
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#
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# iris_rotation_l_h = get_distance(facial_landmarks[386], facial_landmarks[374])
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# iris_rotation_l_w = get_distance(facial_landmarks[382], facial_landmarks[263])
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# iris_rotation_r_h = get_distance(facial_landmarks[159], facial_landmarks[145])
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# iris_rotation_r_w = get_distance(facial_landmarks[33], facial_landmarks[155])
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#
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# iris_rotation_l_h_temp = np.sqrt(
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# (iris_l_center[0] - facial_landmarks[386].x) ** 2 + (iris_l_center[1] - facial_landmarks[386].y) ** 2)
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# iris_rotation_l_w_temp = np.sqrt(
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# (iris_l_center[0] - facial_landmarks[382].x) ** 2 + (iris_l_center[1] - facial_landmarks[382].y) ** 2)
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# iris_rotation_r_h_temp = np.sqrt(
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# (iris_r_center[0] - facial_landmarks[159].x) ** 2 + (iris_r_center[1] - facial_landmarks[159].y) ** 2)
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# iris_rotation_r_w_temp = np.sqrt(
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# (iris_r_center[0] - facial_landmarks[33].x) ** 2 + (iris_r_center[1] - facial_landmarks[33].y) ** 2)
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#
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# if args.debug:
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# h, w, c = frame.shape
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# cv2.line(frame, [int(iris_l_center[0] * w), int(iris_l_center[1] * h)],
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# [int(iris_l_center[0] * w), int(iris_l_center[1] * h)], (0, 128, 128), 3)
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# cv2.line(frame, [int(iris_r_center[0] * w), int(iris_r_center[1] * h)],
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# [int(iris_r_center[0] * w), int(iris_r_center[1] * h)], (255, 128, 0), 3)
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#
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# eye_x_ratio = ((
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# iris_rotation_l_w_temp / iris_rotation_l_w + iris_rotation_r_w_temp / iris_rotation_r_w) - 1) * 3
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# eye_y_ratio = ((
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# iris_rotation_l_h_temp / iris_rotation_l_h + iris_rotation_r_h_temp / iris_rotation_r_h) - 1) * 3
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#
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# eye_l_h_temp = 0.9 - 2 * (facial_landmarks[145].y - facial_landmarks[159].y) / (
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# facial_landmarks[155].x - facial_landmarks[33].x)
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# eye_r_h_temp = 0.9 - 2 * (facial_landmarks[374].y - facial_landmarks[386].y) / (
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# facial_landmarks[263].x - facial_landmarks[382].x)
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iris_r_center = [0, 0, 0]
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iris_l_center = [0, 0, 0]
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pose = get_pose(facial_landmarks)
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for idx in list(map(lambda x: x[0], FACEMESH_RIGHT_IRIS)):
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pos_x, pos_y, pos_z = facial_landmarks[idx].x, facial_landmarks[idx].y, facial_landmarks[idx].z
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iris_r_center[0] += pos_x
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iris_r_center[1] += pos_y
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iris_r_center[2] += pos_z
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if len(pose_queue) < 3:
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pose_queue.append(pose)
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pose_queue.append(pose)
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pose_queue.append(pose)
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else:
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pose_queue.pop(0)
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pose_queue.append(pose)
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iris_r_center = np.array(iris_r_center) / len(FACEMESH_RIGHT_IRIS)
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np_pose = np.average(np.array(pose_queue), axis=0, weights=[0.6, 0.3, 0.1])
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for idx in list(map(lambda x: x[0], FACEMESH_LEFT_IRIS)):
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pos_x, pos_y, pos_z = facial_landmarks[idx].x, facial_landmarks[idx].y, facial_landmarks[idx].z
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iris_l_center[0] += pos_x
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iris_l_center[1] += pos_y
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iris_l_center[2] += pos_z
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iris_l_center = np.array(iris_l_center) / len(FACEMESH_LEFT_IRIS)
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z_angle = np.arctan2(iris_l_center[1] - iris_r_center[1], iris_l_center[0] - iris_r_center[0]) #* 180 / np.pi
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y_angle = np.arctan2(iris_l_center[2] - iris_r_center[2], iris_l_center[0] - iris_r_center[0]) #* 180 / np.pi
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iris_middle_center = np.mean([iris_l_center, iris_r_center])
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mouth_center = [0, 0, 0]
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for idx in list(map(lambda x: x[0], FACEMESH_LIPS)):
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pos_x, pos_y, pos_z = facial_landmarks[idx].x, facial_landmarks[idx].y, facial_landmarks[idx].z
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mouth_center[0] += pos_x
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mouth_center[1] += pos_y
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mouth_center[2] += pos_z
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mouth_center = np.array(mouth_center) / len(FACEMESH_LIPS)
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mouth_h = facial_landmarks[13].y - facial_landmarks[14].y
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mouth_w = facial_landmarks[78].x - (facial_landmarks[409].x + facial_landmarks[375].x)/2
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mouth_ratio = mouth_h / mouth_w
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eye_l_x_min_boundary = min(facial_landmarks[33].x, facial_landmarks[145].x, facial_landmarks[159].x, facial_landmarks[155].x)
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||||
eye_l_x_max_boundary = max(facial_landmarks[33].x, facial_landmarks[145].x, facial_landmarks[159].x, facial_landmarks[155].x)
|
||||
eye_l_y_min_boundary = min(facial_landmarks[33].y, facial_landmarks[145].y, facial_landmarks[159].y, facial_landmarks[155].y)
|
||||
eye_l_y_max_boundary = max(facial_landmarks[33].y, facial_landmarks[145].y, facial_landmarks[159].y, facial_landmarks[155].y)
|
||||
|
||||
eye_r_x_min_boundary = min(facial_landmarks[382].x, facial_landmarks[386].x, facial_landmarks[374].x, facial_landmarks[263].x)
|
||||
eye_r_x_max_boundary = max(facial_landmarks[382].x, facial_landmarks[386].x, facial_landmarks[374].x, facial_landmarks[263].x)
|
||||
eye_r_y_min_boundary = min(facial_landmarks[382].y, facial_landmarks[386].y, facial_landmarks[374].y, facial_landmarks[263].y)
|
||||
eye_r_y_max_boundary = max(facial_landmarks[382].y, facial_landmarks[386].y, facial_landmarks[374].y, facial_landmarks[263].y)
|
||||
|
||||
eye_l_x_ratio = (iris_l_center[0] - eye_l_x_min_boundary) / (eye_l_x_max_boundary - eye_l_x_min_boundary)
|
||||
eye_l_y_ratio = (iris_l_center[1] - eye_l_y_min_boundary) / (eye_l_y_max_boundary - eye_l_y_min_boundary)
|
||||
|
||||
eye_r_x_ratio = (iris_r_center[0] - eye_r_x_min_boundary) / (eye_r_x_max_boundary - eye_r_x_min_boundary)
|
||||
eye_r_y_ratio = (iris_r_center[1] - eye_r_y_min_boundary) / (eye_r_y_max_boundary - eye_r_y_min_boundary)
|
||||
|
||||
eye_x_ratio = (eye_l_x_ratio + eye_r_x_ratio) / 2
|
||||
eye_y_ratio = (eye_l_y_ratio + eye_r_y_ratio) / 2
|
||||
|
||||
eye_l_h_temp = (facial_landmarks[159].y - eye_l_initial) / eye_l_h
|
||||
eye_r_h_temp = (facial_landmarks[386].y - eye_r_initial) / eye_r_h
|
||||
|
||||
# eye_l_h_temp = (facial_landmarks[145].x - facial_landmarks[159].x) / (facial_landmarks[155].x - facial_landmarks[33].x) * 5
|
||||
# eye_r_h_temp = (facial_landmarks[374].x - facial_landmarks[386].x) / (facial_landmarks[263].x - facial_landmarks[382].x) * 5
|
||||
eye_l_h_temp = np_pose[0]
|
||||
eye_r_h_temp = np_pose[1]
|
||||
mouth_ratio = np_pose[2]
|
||||
eye_y_ratio = np_pose[3]
|
||||
eye_x_ratio = np_pose[4]
|
||||
y_angle = np_pose[5]
|
||||
z_angle = np_pose[6]
|
||||
x_angle = np_pose[7]
|
||||
|
||||
mouth_eye_vector[0, :] = 0
|
||||
|
||||
mouth_eye_vector[0, 2] = eye_l_h_temp * 1.2
|
||||
mouth_eye_vector[0, 3] = eye_r_h_temp * 1.2
|
||||
mouth_eye_vector[0, 2] = eye_l_h_temp
|
||||
mouth_eye_vector[0, 3] = eye_r_h_temp
|
||||
|
||||
mouth_eye_vector[0, 14] = mouth_ratio * 1.5
|
||||
|
||||
mouth_eye_vector[0, 25] = eye_x_ratio
|
||||
mouth_eye_vector[0, 26] = eye_y_ratio
|
||||
mouth_eye_vector[0, 25] = eye_y_ratio
|
||||
mouth_eye_vector[0, 26] = eye_x_ratio
|
||||
|
||||
# pose_vector[0, 0] = x_angle
|
||||
pose_vector[0, 0] = 0
|
||||
pose_vector[0, 0] = (1.5 + x_angle) * 2.0
|
||||
pose_vector[0, 1] = y_angle * 2.0 #temp weight
|
||||
pose_vector[0, 2] = z_angle * 1.5 #temp weight
|
||||
|
||||
cv2.putText(frame, f'z angle:{x_angle * 180 / np.pi}, {x_angle}', (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
|
||||
|
||||
output_image = model(input_image, mouth_eye_vector, pose_vector)
|
||||
cv2.imshow("frame", cv2.cvtColor(postprocessing_image(output_image.cpu()), cv2.COLOR_RGBA2BGRA))
|
||||
if args.debug:
|
||||
cv2.imshow("camera", frame)
|
||||
cv2.waitKey(1)
|
||||
|
||||
# if args.output == 'window':
|
||||
@@ -229,5 +327,6 @@ def main():
|
||||
|
||||
# x,y,z 27, 28, 29
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -1,6 +1,7 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
|
||||
|
||||
def preprocessing_image(image):
|
||||
np_image = np.array(image) / 255
|
||||
clipped_image = np.clip(np_image, 0, 1)
|
||||
@@ -27,3 +28,7 @@ def postprocessing_image(image):
|
||||
rgba_image = np.concatenate([rgb_image, alpha_image[..., np.newaxis]], axis=2)
|
||||
rgba_image = rgba_image * 255
|
||||
return rgba_image.astype(np.uint8)
|
||||
|
||||
|
||||
def get_distance(a, b):
|
||||
return np.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2)
|
||||
Reference in new issue
Block a user