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data_preparation_mini.py
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294 lines (247 loc) · 9.96 KB
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import subprocess
import tqdm
import numpy as np
import cv2
import sys
import os
import math
import pickle
import mediapipe as mp
import shutil
# 自定义异常类
class VideoProcessingError(Exception):
"""视频处理基类异常"""
pass
class FFmpegError(VideoProcessingError):
"""FFmpeg处理异常"""
pass
class FaceDetectionError(VideoProcessingError):
"""人脸检测异常"""
pass
class FirstFrameFaceDetectionError(FaceDetectionError):
"""首帧人脸检测异常"""
pass
class FaceMeshDetectionError(VideoProcessingError):
"""面部网格检测异常"""
pass
class EnvironmentError(VideoProcessingError):
"""环境配置错误"""
pass
mp_face_mesh = mp.solutions.face_mesh
mp_face_detection = mp.solutions.face_detection
def detect_face(frame: np.ndarray, min_detection_confidence: float = 0.5) -> list:
"""人脸检测并验证有效性"""
with mp_face_detection.FaceDetection(
model_selection=1,
min_detection_confidence=min_detection_confidence
) as face_detection:
results = face_detection.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
# 人脸数量检查
if not results.detections:
raise FaceDetectionError("未检测到人脸")
if len(results.detections) > 1:
raise FaceDetectionError("检测到多个人脸")
detection = results.detections[0]
rect = detection.location_data.relative_bounding_box
out_rect = [
rect.xmin,
rect.xmin + rect.width,
rect.ymin,
rect.ymin + rect.height
]
# 关键点验证
nose = mp_face_detection.get_key_point(
detection, mp_face_detection.FaceKeyPoint.NOSE_TIP)
left_eye = mp_face_detection.get_key_point(
detection, mp_face_detection.FaceKeyPoint.LEFT_EYE)
right_eye = mp_face_detection.get_key_point(
detection, mp_face_detection.FaceKeyPoint.RIGHT_EYE)
if nose.x > left_eye.x or nose.x < right_eye.x:
raise FaceDetectionError("人脸角度不符合要求，请提供正脸图片")
# 边界检查
h, w = frame.shape[:2]
if (out_rect[0] < 0 or out_rect[2] < 0
or out_rect[1] > 1 or out_rect[3] > 1):
raise FaceDetectionError("人脸区域超出画面边界")
# 尺寸检查
if rect.width * w < 80 or rect.height * h < 80:
raise FaceDetectionError("人脸尺寸不能低于80*80像素")
return out_rect
def calc_face_interact(face0, face1):
x_min = min(face0[0], face1[0])
x_max = max(face0[1], face1[1])
y_min = min(face0[2], face1[2])
y_max = max(face0[3], face1[3])
tmp0 = ((face0[1] - face0[0]) * (face0[3] - face0[2])) / ((x_max - x_min) * (y_max - y_min))
tmp1 = ((face1[1] - face1[0]) * (face1[3] - face1[2])) / ((x_max - x_min) * (y_max - y_min))
return min(tmp0, tmp1)
def detect_face_mesh(frame: np.ndarray) -> np.ndarray:
"""面部网格检测"""
with mp_face_mesh.FaceMesh(
static_image_mode=True,
max_num_faces=1,
refine_landmarks=True,
min_detection_confidence=0.5
) as face_mesh:
results = face_mesh.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
pts_3d = np.zeros((478, 3))
if not results.multi_face_landmarks:
raise FaceMeshDetectionError("未检测到面部网格")
image_height, image_width = frame.shape[:2]
for idx, landmark in enumerate(results.multi_face_landmarks[0].landmark):
pts_3d[idx] = [
min(math.floor(landmark.x * image_width), image_width - 1),
min(math.floor(landmark.y * image_height), image_height - 1),
min(math.floor(landmark.z * image_width), image_width - 1)
]
return pts_3d
def extract_from_video(
video_path: str,
output_pkl_path: str
) -> None:
"""从视频提取关键点"""
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
raise VideoProcessingError("无法打开视频文件")
try:
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
vid_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
vid_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
pts_3d = np.zeros((total_frames, 478, 3))
face_rect = None
for frame_index in tqdm.tqdm(range(total_frames)):
ret, frame = cap.read() # 按帧读取视频
# #到视频结尾时终止
if ret is False:
break
if frame_index == 0:
try:
rect = detect_face(frame, 0.25)
x_min = int(rect[0] * vid_width)
y_min = int(rect[2] * vid_height)
x_max = int(rect[1] * vid_width)
y_max = int(rect[3] * vid_height)
except FaceDetectionError:
# 尝试裁剪后检测
cropped = frame[
int(0.1 * vid_height):int(0.9 * vid_height),
int(0.1 * vid_width):int(0.9 * vid_width)
]
try:
rect = detect_face(cropped, 0.25)
except FaceDetectionError as e:
raise FirstFrameFaceDetectionError("首帧人脸检测失败") from e
# 转换坐标到原图
x_min = int(rect[0] * vid_width + 0.1 * vid_width)
y_min = int(rect[2] * vid_height + 0.1 * vid_height)
x_max = int(rect[1] * vid_width + 0.1 * vid_width)
y_max = int(rect[3] * vid_height + 0.1 * vid_height)
y_mid = (y_min + y_max) / 2.
x_mid = (x_min + x_max) / 2.
len_ = max(x_max - x_min, y_max - y_min)
face_rect = [x_mid - len_, y_mid - len_, x_mid + len_, y_mid + len_]
x_min, y_min, x_max, y_max = face_rect
seq_w, seq_h = x_max - x_min, y_max - y_min
x_mid, y_mid = (x_min + x_max) / 2, (y_min + y_max) / 2
crop_size = int(max(seq_w * 1.35, seq_h * 1.35))
x_min = int(max(0, x_mid - crop_size * 0.5))
y_min = int(max(0, y_mid - crop_size * 0.45))
x_max = int(min(vid_width, x_min + crop_size))
y_max = int(min(vid_height, y_min + crop_size))
face_rect = (x_min, y_min, x_max, y_max)
# 裁剪人脸区域
x0, y0, x1, y1 = face_rect
face_region = frame[y0:y1, x0:x1]
# print(y_min, y_max, x_min, x_max)
# cv2.imshow("s", frame_face)
# cv2.waitKey(10)
try:
frame_kps = detect_face_mesh(face_region)
except FaceMeshDetectionError as e:
raise VideoProcessingError(f"第{frame_index}帧面部网格检测失败") from e
pts_3d[frame_index] = frame_kps + [x0, y0, 0]
# point_size = 1
# point_color = (0, 0, 255) # BGR
# thickness = 4 # 0 、4、8
# for coor in pts_3d[frame_index]:
# # coor = (coor +1 )/2.
# cv2.circle(frame, (int(coor[0]), int(coor[1])), point_size, point_color, thickness)
# cv2.imshow("a", frame)
# cv2.waitKey(30)
# 保存关键点
with open(output_pkl_path, "wb") as f:
pickle.dump(pts_3d, f)
finally:
cap.release() # 释放视频对象
return pts_3d
def prepare_video(
input_path: str,
output_path: str,
resize_option: bool = False
) -> int:
# 1 视频转换为25FPS
if resize_option:
cap = cv2.VideoCapture(input_path)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
scale = min(720 / width, 1280 / height)
new_width = int(width * scale)
new_height = int(height * scale)
# 确保新的宽高为偶数
new_width = new_width //2*2
new_height = new_height //2*2
cap.release()
vf_arg = f"scale={new_width}:{new_height}"
cmd = [
"ffmpeg", "-i", input_path,
"-vf", vf_arg,
"-r", "25", "-an", "-y", output_path
]
else:
cmd = [
"ffmpeg", "-i", input_path,
"-r", "25", "-an", "-y", output_path
]
try:
result = subprocess.run(
cmd,
check=True,
stderr=subprocess.PIPE,
stdout=subprocess.DEVNULL,
text=True
)
return 0
except subprocess.CalledProcessError as e:
raise FFmpegError(f"FFmpeg处理失败: {e.stderr}") from e
def data_preparation_mini(input_video, video_dir_path, resize_option = False):
# 检测系统环境是否有ffmpeg
if not shutil.which("ffmpeg"):
raise EnvironmentError("FFmpeg未安装或不在PATH中，请安装ffmpeg并设置为环境变量")
# 创建输出目录
data_dir = os.path.join(video_dir_path, "data")
os.makedirs(data_dir, exist_ok=True)
# 预处理视频
output_video = os.path.join(data_dir, "processed.mp4")
prepare_video(input_video, output_video, resize_option = resize_option)
# 提取关键点
output_pkl = output_video.replace(".mp4", ".pkl")
extract_from_video(output_video, output_pkl)
result = {
"status": "success",
"output_video": output_video,
"output_pkl": output_pkl
}
return result
def main():
# 检查命令行参数的数量
if len(sys.argv) != 3:
print("Usage: python data_preparation_mini.py <静默视频> <输出文件夹位置>")
sys.exit(1) # 参数数量不正确时退出程序
# 获取video_name参数
video = sys.argv[1]
video_dir_path = sys.argv[2]
print(f"Video dir path is set to: {video_dir_path}")
data_preparation_mini(video, video_dir_path)
print("Done!")
if __name__ == "__main__":
main()