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sample_face_landmark_detection.py
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sample_face_landmark_detection.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import copy
import argparse
from typing import List, Any, Dict, Tuple, Union
import cv2
import mediapipe as mp # type:ignore
from mediapipe.tasks import python # type:ignore
from mediapipe.tasks.python import vision # type:ignore
from utils import CvFpsCalc
from utils.download_file import download_file
def get_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--device", type=int, default=0)
parser.add_argument("--video", type=str, default=None)
parser.add_argument("--width", help='cap width', type=int, default=960)
parser.add_argument("--height", help='cap height', type=int, default=540)
parser.add_argument(
"--model",
type=int,
choices=[0],
default=0,
help='''
0:FaceLandscapeer
''',
)
parser.add_argument("--num_faces", type=int, default=1)
parser.add_argument("--unuse_output_face_blendshapes", action="store_true")
parser.add_argument("--unuse_output_facial_transformation_matrixes",
action="store_true")
args = parser.parse_args()
return args
def main() -> None:
# 引数解析
args: argparse.Namespace = get_args()
cap_device: int = args.device
cap_width: int = args.width
cap_height: int = args.height
model: int = args.model
num_faces = args.num_faces
unuse_output_face_blendshapes = args.unuse_output_face_blendshapes
unuse_output_facial_transformation_matrixes = args.unuse_output_facial_transformation_matrixes
use_output_face_blendshapes = not unuse_output_face_blendshapes
use_output_facial_transformation_matrixes = not unuse_output_facial_transformation_matrixes
if args.video is not None:
cap_device = args.video
model_url: List[str] = [
'https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/latest/face_landmarker.task',
]
# ダウンロードファイル名生成
model_name: str = model_url[model].split('/')[-1]
quantize_type: str = model_url[model].split('/')[-3]
split_name: List[str] = model_name.split('.')
model_name = split_name[0] + '_' + quantize_type + '.' + split_name[1]
# 重みファイルダウンロード
model_path: str = os.path.join('model', model_name)
if not os.path.exists(model_path):
download_file(url=model_url[model], save_path=model_path)
# カメラ準備
cap: cv2.VideoCapture = cv2.VideoCapture(cap_device)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, cap_width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, cap_height)
# Face Detector生成
base_options: python.BaseOptions = python.BaseOptions(
model_asset_path=model_path)
options: vision.FaceLandmarkerOptions = vision.FaceLandmarkerOptions(
base_options=base_options,
output_face_blendshapes=use_output_face_blendshapes,
output_facial_transformation_matrixes=
use_output_facial_transformation_matrixes,
num_faces=num_faces,
)
detector: vision.FaceLandmarker = vision.FaceLandmarker.create_from_options(
options) # type:ignore
# FPS計測モジュール
cvFpsCalc: CvFpsCalc = CvFpsCalc(buffer_len=10)
while True:
display_fps: float = cvFpsCalc.get()
# カメラキャプチャ
ret: bool
frame: Any
ret, frame = cap.read()
if not ret:
break
# 推論実施
rgb_frame: mp.Image = mp.Image(
image_format=mp.ImageFormat.SRGBA,
data=cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA),
)
detection_result: vision.FaceLandmarkerResult = detector.detect(
rgb_frame)
# 描画
debug_image: Any = copy.deepcopy(frame)
debug_image = draw_debug(
debug_image,
detection_result,
display_fps,
)
# 画面反映
cv2.imshow('MediaPipe Face Landmark Detection Demo', debug_image)
# キー処理(ESC：終了)
key: int = cv2.waitKey(1)
if key == 27: # ESC
break
cap.release()
cv2.destroyAllWindows()
def draw_debug(
image: Any,
detection_result, # type:ignore
display_fps: float,
) -> Any:
image_width: int = image.shape[1]
image_height: int = image.shape[0]
# ランドマーク表示
landmark_dict: Dict[int, List[Union[int, float]]] = {}
for face_landmarks in detection_result.face_landmarks:
for index, face_landmark in enumerate(face_landmarks):
face_landmark_x: int = int(image_width * face_landmark.x)
face_landmark_y: int = int(image_height * face_landmark.y)
cv2.circle(image, (face_landmark_x, face_landmark_y), 1,
(0, 255, 0), -1, cv2.LINE_AA)
landmark_dict[index] = [face_landmark_x, face_landmark_y]
# 左眉毛(55：内側、46：外側)
cv2.line(image, landmark_dict[55], landmark_dict[65], (0, 255, 0), 2)
cv2.line(image, landmark_dict[65], landmark_dict[52], (0, 255, 0), 2)
cv2.line(image, landmark_dict[52], landmark_dict[53], (0, 255, 0), 2)
cv2.line(image, landmark_dict[53], landmark_dict[46], (0, 255, 0), 2)
# 右眉毛(285：内側、276：外側)
cv2.line(image, landmark_dict[285], landmark_dict[295], (0, 255, 0), 2)
cv2.line(image, landmark_dict[295], landmark_dict[282], (0, 255, 0), 2)
cv2.line(image, landmark_dict[282], landmark_dict[283], (0, 255, 0), 2)
cv2.line(image, landmark_dict[283], landmark_dict[276], (0, 255, 0), 2)
# 左目 (133：目頭、246：目尻)
cv2.line(image, landmark_dict[133], landmark_dict[173], (0, 255, 0), 2)
cv2.line(image, landmark_dict[173], landmark_dict[157], (0, 255, 0), 2)
cv2.line(image, landmark_dict[157], landmark_dict[158], (0, 255, 0), 2)
cv2.line(image, landmark_dict[158], landmark_dict[159], (0, 255, 0), 2)
cv2.line(image, landmark_dict[159], landmark_dict[160], (0, 255, 0), 2)
cv2.line(image, landmark_dict[160], landmark_dict[161], (0, 255, 0), 2)
cv2.line(image, landmark_dict[161], landmark_dict[246], (0, 255, 0), 2)
cv2.line(image, landmark_dict[246], landmark_dict[163], (0, 255, 0), 2)
cv2.line(image, landmark_dict[163], landmark_dict[144], (0, 255, 0), 2)
cv2.line(image, landmark_dict[144], landmark_dict[145], (0, 255, 0), 2)
cv2.line(image, landmark_dict[145], landmark_dict[153], (0, 255, 0), 2)
cv2.line(image, landmark_dict[153], landmark_dict[154], (0, 255, 0), 2)
cv2.line(image, landmark_dict[154], landmark_dict[155], (0, 255, 0), 2)
cv2.line(image, landmark_dict[155], landmark_dict[133], (0, 255, 0), 2)
# 右目 (362：目頭、466：目尻)
cv2.line(image, landmark_dict[362], landmark_dict[398], (0, 255, 0), 2)
cv2.line(image, landmark_dict[398], landmark_dict[384], (0, 255, 0), 2)
cv2.line(image, landmark_dict[384], landmark_dict[385], (0, 255, 0), 2)
cv2.line(image, landmark_dict[385], landmark_dict[386], (0, 255, 0), 2)
cv2.line(image, landmark_dict[386], landmark_dict[387], (0, 255, 0), 2)
cv2.line(image, landmark_dict[387], landmark_dict[388], (0, 255, 0), 2)
cv2.line(image, landmark_dict[388], landmark_dict[466], (0, 255, 0), 2)
cv2.line(image, landmark_dict[466], landmark_dict[390], (0, 255, 0), 2)
cv2.line(image, landmark_dict[390], landmark_dict[373], (0, 255, 0), 2)
cv2.line(image, landmark_dict[373], landmark_dict[374], (0, 255, 0), 2)
cv2.line(image, landmark_dict[374], landmark_dict[380], (0, 255, 0), 2)
cv2.line(image, landmark_dict[380], landmark_dict[381], (0, 255, 0), 2)
cv2.line(image, landmark_dict[381], landmark_dict[382], (0, 255, 0), 2)
cv2.line(image, landmark_dict[382], landmark_dict[362], (0, 255, 0), 2)
# 口 (308：右端、78：左端)
cv2.line(image, landmark_dict[308], landmark_dict[415], (0, 255, 0), 2)
cv2.line(image, landmark_dict[415], landmark_dict[310], (0, 255, 0), 2)
cv2.line(image, landmark_dict[310], landmark_dict[311], (0, 255, 0), 2)
cv2.line(image, landmark_dict[311], landmark_dict[312], (0, 255, 0), 2)
cv2.line(image, landmark_dict[312], landmark_dict[13], (0, 255, 0), 2)
cv2.line(image, landmark_dict[13], landmark_dict[82], (0, 255, 0), 2)
cv2.line(image, landmark_dict[82], landmark_dict[81], (0, 255, 0), 2)
cv2.line(image, landmark_dict[81], landmark_dict[80], (0, 255, 0), 2)
cv2.line(image, landmark_dict[80], landmark_dict[191], (0, 255, 0), 2)
cv2.line(image, landmark_dict[191], landmark_dict[78], (0, 255, 0), 2)
cv2.line(image, landmark_dict[78], landmark_dict[95], (0, 255, 0), 2)
cv2.line(image, landmark_dict[95], landmark_dict[88], (0, 255, 0), 2)
cv2.line(image, landmark_dict[88], landmark_dict[178], (0, 255, 0), 2)
cv2.line(image, landmark_dict[178], landmark_dict[87], (0, 255, 0), 2)
cv2.line(image, landmark_dict[87], landmark_dict[14], (0, 255, 0), 2)
cv2.line(image, landmark_dict[14], landmark_dict[317], (0, 255, 0), 2)
cv2.line(image, landmark_dict[317], landmark_dict[402], (0, 255, 0), 2)
cv2.line(image, landmark_dict[402], landmark_dict[318], (0, 255, 0), 2)
cv2.line(image, landmark_dict[318], landmark_dict[324], (0, 255, 0), 2)
cv2.line(image, landmark_dict[324], landmark_dict[308], (0, 255, 0), 2)
# 左目：中心
cv2.circle(image, landmark_dict[468], 2, (0, 0, 255), -1)
# 左目：目頭側
cv2.circle(image, landmark_dict[469], 2, (0, 0, 255), -1)
# 左目：上側
cv2.circle(image, landmark_dict[470], 2, (0, 0, 255), -1)
# 左目：目尻側
cv2.circle(image, landmark_dict[471], 2, (0, 0, 255), -1)
# 左目：下側
cv2.circle(image, landmark_dict[472], 2, (0, 0, 255), -1)
# 右目：中心
cv2.circle(image, landmark_dict[473], 2, (0, 0, 255), -1)
# 右目：目尻側
cv2.circle(image, landmark_dict[474], 2, (0, 0, 255), -1)
# 右目：上側
cv2.circle(image, landmark_dict[475], 2, (0, 0, 255), -1)
# 右目：目頭側
cv2.circle(image, landmark_dict[476], 2, (0, 0, 255), -1)
# 右目：下側
cv2.circle(image, landmark_dict[477], 2, (0, 0, 255), -1)
# FPS
cv2.putText(
image,
"FPS:" + str(display_fps),
(10, 30),
cv2.FONT_HERSHEY_SIMPLEX,
1.0,
(0, 255, 0),
2,
cv2.LINE_AA,
)
return image
if __name__ == '__main__':
main()