File size: 3,077 Bytes
4479f79 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
import os
import face_recognition
from PIL import Image, ImageOps
import numpy as np
def detect_and_crop_faces(input_dir, output_dir):
# 确保输出目录存在
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# 遍历输入目录中的所有文件
for filename in os.listdir(input_dir):
if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
input_path = os.path.join(input_dir, filename)
output_path = os.path.join(output_dir, filename.replace('.png', '.jpg'))
# 加载图像并处理透明背景
image = Image.open(input_path).convert("RGBA")
background = Image.new("RGBA", image.size, "WHITE")
alpha_composite = Image.alpha_composite(background, image).convert("RGB")
# 添加白色边缘，这里 padding 设为 10 像素，可按需调整
padded_image = ImageOps.expand(alpha_composite, border=10, fill='white')
# 尝试不同尺度的图像检测
scales = [0.6, 0.4, 0.2]
face_locations = []
for scale in scales:
resized_image = padded_image.resize((int(padded_image.width * scale), int(padded_image.height * scale)), Image.LANCZOS)
image_np = np.array(resized_image)
# Use the cnn model for detection
face_locations = face_recognition.face_locations(image_np, model="cnn")
if face_locations:
# Adjust the detected face positions to the original image size
face_locations = [(int(top / scale), int(right / scale), int(bottom / scale), int(left / scale)) for top, right, bottom, left in face_locations]
break
if face_locations:
# 假设第一个检测到的人脸是需要裁剪的
top, right, bottom, left = face_locations[0]
height = bottom - top
width = right - left
# 计算扩充后的区域
new_top = max(0, int(top - height * 0.3))
new_bottom = min(np.array(padded_image).shape[0], int(bottom + height * 0.3))
new_left = max(0, int(left - width * 0.3))
new_right = min(np.array(padded_image).shape[1], int(right + width * 0.3))
face_image = np.array(padded_image)[new_top:new_bottom, new_left:new_right]
# 将 NumPy 数组转换为 PIL 图像
face_pil = Image.fromarray(face_image)
# 保存裁剪后的人脸图像
face_pil.save(output_path)
print(f"已裁剪并保存: {output_path}")
else:
print(f"未在 {input_path} 中检测到人脸")
if __name__ == "__main__":
input_directory = "/mnt/bn/yg-butterfly-algo/personal/sunhm/code/XVerse/assets/XVerseBench_seg/human_seg"
output_directory = "/mnt/bn/yg-butterfly-algo/personal/sunhm/code/XVerse/assets/XVerseBench_seg/human"
detect_and_crop_faces(input_directory, output_directory)
|