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import os
import time
from typing import Literal, Union, List, Tuple
from tqdm import tqdm
from PIL import Image
from transformers import (
CLIPVisionModelWithProjection,
CLIPImageProcessor,
)
import h5py
import torch
import numpy as np
from ...data.extract_feature.base_extract_feature import BaseFeatureExtractor
from ...data.emb.h5py_emb import save_value_with_h5py
from ..process.image_process import dynamic_crop_resize_image
from ..utils.data_type_util import convert_images
from .clip_vision_extractor import ImageClipVisionFeatureExtractorV2
class InsightFaceExtractor(BaseFeatureExtractor):
"""选择clip的image_embeds，一张图像的输出特征是N，根据模型的选择可能是512、768、1024
Args:
BaseFeatureExtractor (_type_): _description_
"""
def __init__(
self,
pretrained_model_name_or_path: str,
name: str = None,
device: str = "cpu",
dtype=torch.float32,
model_name: str = "buffalo_l",
allowed_modules: List[str] = ["detection", "recognition"],
providers: List[str] = ["CUDAExecutionProvider", "CPUExecutionProvider"],
need_align_face: bool = False,
):
from insightface.app import FaceAnalysis
super().__init__(device, dtype, name)
self.pretrained_model_name_or_path = pretrained_model_name_or_path
self.extractor = FaceAnalysis(
name=model_name,
root=pretrained_model_name_or_path,
allowed_modules=allowed_modules,
providers=providers,
)
self.extractor.prepare(ctx_id=0, det_size=(640, 640))
self.need_align_face = need_align_face
def extract_images(
self,
data: Union[str, List[str], Image.Image, List[Image.Image], np.ndarray],
target_width: int = None,
target_height: int = None,
return_type: str = "numpy",
input_rgb_order: str = "rgb",
) -> Union[np.ndarray, torch.Tensor]:
data = convert_images(
data,
return_type="pil",
input_rgb_order=input_rgb_order,
return_rgb_order="bgr",
)
if target_height is not None and target_width is not None:
data = [
dynamic_crop_resize_image(
image,
target_height=target_height,
target_width=target_width,
)
for image in data
]
data = [np.array(x.convert("RGB"))[:, :, ::-1] for x in data]
with torch.no_grad():
faces = [self.extractor.get(x) for x in data]
emb = [self.get_target_emb(x) for x in faces]
if self.need_align_face:
from insightface.utils import face_align
align_face_image = [
face_align.norm_crop(x, landmark=faces[i][0].kps, image_size=224)
for i, x in enumerate(data)
]
else:
align_face_image = None
emb = np.concatenate(np.expand_dims(emb, axis=0), axis=0)
if return_type == "torch":
emb = torch.from_numpy(emb).to(device=self.device)
return emb, align_face_image
def get_target_emb(self, data):
outputs = data[0]["embedding"]
return outputs
def extract_video(
self,
video_dataset,
target_width: int = None,
target_height: int = None,
return_type: str = "numpy",
track_performance: bool = False,
input_rgb_order: str = "rgb",
) -> Union[np.ndarray, torch.Tensor]:
embs = []
sample_indexs = []
if track_performance:
performance = {}
if self.need_align_face:
align_face_images = []
else:
align_face_images = None
with torch.no_grad():
for i, (batch, batch_index) in enumerate(video_dataset):
# TODO: 现阶段复用hugging face diffusers img2img pipeline中的抽取代码，
# 由于该代码目前只支持Image的预处理，故先将numpy.ndarray转换成PIL.Image
batch = [Image.fromarray(batch[b_i]) for b_i in range(len(batch))]
emb, align_face_image = self.extract_images(
data=batch,
target_width=target_width,
target_height=target_height,
return_type=return_type,
input_rgb_order=input_rgb_order,
)
embs.append(emb)
sample_indexs.extend(batch_index)
if self.need_align_face:
align_face_images.append(align_face_image)
sample_indexs = np.array(sample_indexs)
if return_type == "numpy":
embs = np.concatenate(embs, axis=0)
elif return_type == "torch":
embs = torch.concat(embs)
sample_indexs = torch.from_numpy(sample_indexs)
return sample_indexs, embs, align_face_images
def extract(
self,
data: Union[str, List[str]],
data_type: Literal["image", "video"],
return_type: str = "numpy",
save_emb_path: str = None,
save_type: str = "h5py",
emb_key: str = "image_embeds",
sample_index_key: str = "sample_indexs",
insert_name_to_key: bool = False,
overwrite: bool = False,
input_rgb_order: str = "rgb",
save_sample_index: bool = True,
**kwargs,
) -> Union[np.ndarray, torch.tensor]:
if self.name is not None and insert_name_to_key:
emb_key = f"{self.name}_{emb_key}"
sample_index_key = f"{self.name}_{sample_index_key}"
if save_emb_path is not None and os.path.exists(save_emb_path):
with h5py.File(save_emb_path, "r") as f:
if not overwrite and emb_key in f and sample_index_key in f:
return None
if data_type == "image":
emb = self.extract_images(
data=data,
return_type=return_type,
input_rgb_order=input_rgb_order,
**kwargs,
)
if save_emb_path is None:
return emb
else:
raise NotImplementedError("save images emb")
elif data_type == "video":
sample_indexs, emb = self.extract_video(
video_dataset=data,
return_type=return_type,
input_rgb_order=input_rgb_order,
**kwargs,
)
if save_emb_path is None:
return sample_indexs, emb
else:
if save_type == "h5py":
self.save_video_emb_with_h5py(
save_emb_path=save_emb_path,
emb=emb,
emb_key=emb_key,
sample_indexs=sample_indexs,
sample_index_key=sample_index_key,
overwrite=overwrite,
save_sample_index=save_sample_index,
)
return sample_indexs, emb
else:
raise ValueError(f"only support save_type={save_type}")
@staticmethod
def save_images_emb_with_h5py(
save_emb_path: str,
emb: np.ndarray = None,
emb_key: str = "image_embeds",
) -> h5py.File:
save_value_with_h5py(save_emb_path, value=emb, key=emb_key)
@staticmethod
def save_video_emb_with_h5py(
save_emb_path: str,
emb: np.ndarray = None,
emb_key: str = "image_embeds",
sample_indexs: np.ndarray = None,
sample_index_key: str = "sample_indexs",
overwrite: bool = False,
save_sample_index: bool = True,
) -> h5py.File:
save_value_with_h5py(
save_emb_path,
value=emb,
key=emb_key,
overwrite=overwrite,
dtype=np.float16,
)
if save_sample_index:
save_value_with_h5py(
save_emb_path,
value=sample_indexs,
key=sample_index_key,
overwrite=overwrite,
dtype=np.uint32,
)
class InsightFaceExtractorNormEmb(InsightFaceExtractor):
def __init__(
self,
pretrained_model_name_or_path: str,
name: str = None,
device: str = "cpu",
dtype=torch.float32,
model_name: str = "buffalo_l",
allowed_modules: List[str] = ["detection", "recognition"],
providers: List[str] = ["CUDAExecutionProvider", "CPUExecutionProvider"],
):
super().__init__(
pretrained_model_name_or_path,
name,
device,
dtype,
model_name,
allowed_modules,
providers,
)
def get_target_emb(self, data):
outputs = data[0].normed_embedding
return outputs
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