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import os
import cv2
import requests
import torch
import numpy as np
import PIL.Image as Image
import PIL.ImageOps
# from insightface.app import FaceAnalysis
# from facexlib.parsing import init_parsing_model
from torchvision.transforms.functional import normalize
from typing import Union, Optional
from models.hyvideo.data_kits.face_align import AlignImage
from shared.utils import files_locator as fl
def _img2tensor(img: np.ndarray, bgr2rgb: bool = True) -> torch.Tensor:
if bgr2rgb:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = img.astype(np.float32) / 255.0
img = np.transpose(img, (2, 0, 1))
return torch.from_numpy(img)
def _pad_to_square(img: np.ndarray, pad_color: int = 255) -> np.ndarray:
h, w, _ = img.shape
if h == w:
return img
if h > w:
pad_size = (h - w) // 2
padded_img = cv2.copyMakeBorder(
img,
0,
0,
pad_size,
h - w - pad_size,
cv2.BORDER_CONSTANT,
value=[pad_color] * 3,
)
else:
pad_size = (w - h) // 2
padded_img = cv2.copyMakeBorder(
img,
pad_size,
w - h - pad_size,
0,
0,
cv2.BORDER_CONSTANT,
value=[pad_color] * 3,
)
return padded_img
class FaceProcessor:
def __init__(self):
self.align_instance = AlignImage("cuda", det_path= fl.locate_file("det_align/detface.pt"))
self.align_instance.facedet.model.to("cpu")
def process(
self,
image: Union[str, PIL.Image.Image],
resize_to: int = 512,
border_thresh: int = 10,
face_crop_scale: float = 1.5,
remove_bg= False,
# area=1.25
) -> PIL.Image.Image:
image_pil = PIL.ImageOps.exif_transpose(image).convert("RGB")
w, h = image_pil.size
self.align_instance.facedet.model.to("cuda")
_, _, bboxes_list = self.align_instance(np.array(image_pil)[:,:,[2,1,0]], maxface=True)
self.align_instance.facedet.model.to("cpu")
try:
bboxSrc = bboxes_list[0]
except:
bboxSrc = [0, 0, w, h]
x1, y1, ww, hh = bboxSrc
x2, y2 = x1 + ww, y1 + hh
# ww, hh = (x2-x1) * area, (y2-y1) * area
# center = [(x2+x1)//2, (y2+y1)//2]
# x1 = max(center[0] - ww//2, 0)
# y1 = max(center[1] - hh//2, 0)
# x2 = min(center[0] + ww//2, w)
# y2 = min(center[1] + hh//2, h)
frame = cv2.cvtColor(np.array(image_pil), cv2.COLOR_RGB2BGR)
h, w, _ = frame.shape
image_to_process = None
is_close_to_border = (
x1 <= border_thresh
and y1 <= border_thresh
and x2 >= w - border_thresh
and y2 >= h - border_thresh
)
if is_close_to_border:
# print(
# "[Info] Face is close to border, padding original image to square."
# )
image_to_process = _pad_to_square(frame, pad_color=255)
else:
cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
side = int(max(x2 - x1, y2 - y1) * face_crop_scale)
half = side // 2
left = int(max(cx - half, 0))
top = int(max(cy - half, 0))
right = int(min(cx + half, w))
bottom = int(min(cy + half, h))
cropped_face = frame[top:bottom, left:right]
image_to_process = _pad_to_square(cropped_face, pad_color=255)
image_resized = cv2.resize(
image_to_process, (resize_to, resize_to), interpolation=cv2.INTER_LANCZOS4 # .INTER_AREA
)
face_tensor = _img2tensor(image_resized).to("cpu")
from shared.utils.utils import remove_background, convert_tensor_to_image
if remove_bg:
face_tensor = remove_background(face_tensor)
img_out = Image.fromarray(face_tensor.clone().mul_(255).permute(1,2,0).to(torch.uint8).cpu().numpy())
return img_out
# class FaceProcessor2:
# def __init__(self, antelopv2_path=".", device: Optional[torch.device] = None):
# if device is None:
# self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# else:
# self.device = device
# providers = (
# ["CUDAExecutionProvider"]
# if self.device.type == "cuda"
# else ["CPUExecutionProvider"]
# )
# self.app = FaceAnalysis(
# name="antelopev2", root=antelopv2_path, providers=providers
# )
# self.app.prepare(ctx_id=0, det_size=(640, 640))
# self.parsing_model = init_parsing_model(
# model_name="bisenet", device=self.device
# )
# self.parsing_model.eval()
# print("FaceProcessor initialized successfully.")
# def process(
# self,
# image: Union[str, PIL.Image.Image],
# resize_to: int = 512,
# border_thresh: int = 10,
# face_crop_scale: float = 1.5,
# extra_input: bool = False,
# ) -> PIL.Image.Image:
# if isinstance(image, str):
# if image.startswith("http://") or image.startswith("https://"):
# image = PIL.Image.open(requests.get(image, stream=True, timeout=10).raw)
# elif os.path.isfile(image):
# image = PIL.Image.open(image)
# else:
# raise ValueError(
# f"Input string is not a valid URL or file path: {image}"
# )
# elif not isinstance(image, PIL.Image.Image):
# raise TypeError(
# "Input must be a file path, a URL, or a PIL.Image.Image object."
# )
# image = PIL.ImageOps.exif_transpose(image).convert("RGB")
# frame = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
# faces = self.app.get(frame)
# h, w, _ = frame.shape
# image_to_process = None
# if not faces:
# print(
# "[Warning] No face detected. Using the whole image, padded to square."
# )
# image_to_process = _pad_to_square(frame, pad_color=255)
# else:
# largest_face = max(
# faces, key=lambda f: (f.bbox[2] - f.bbox[0]) * (f.bbox[3] - f.bbox[1])
# )
# x1, y1, x2, y2 = map(int, largest_face.bbox)
# is_close_to_border = (
# x1 <= border_thresh
# and y1 <= border_thresh
# and x2 >= w - border_thresh
# and y2 >= h - border_thresh
# )
# if is_close_to_border:
# print(
# "[Info] Face is close to border, padding original image to square."
# )
# image_to_process = _pad_to_square(frame, pad_color=255)
# else:
# cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
# side = int(max(x2 - x1, y2 - y1) * face_crop_scale)
# half = side // 2
# left = max(cx - half, 0)
# top = max(cy - half, 0)
# right = min(cx + half, w)
# bottom = min(cy + half, h)
# cropped_face = frame[top:bottom, left:right]
# image_to_process = _pad_to_square(cropped_face, pad_color=255)
# image_resized = cv2.resize(
# image_to_process, (resize_to, resize_to), interpolation=cv2.INTER_AREA
# )
# face_tensor = (
# _img2tensor(image_resized, bgr2rgb=True).unsqueeze(0).to(self.device)
# )
# with torch.no_grad():
# normalized_face = normalize(face_tensor, [0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
# parsing_out = self.parsing_model(normalized_face)[0]
# parsing_mask = parsing_out.argmax(dim=1, keepdim=True)
# background_mask_np = (parsing_mask.squeeze().cpu().numpy() == 0).astype(
# np.uint8
# )
# white_background = np.ones_like(image_resized, dtype=np.uint8) * 255
# mask_3channel = cv2.cvtColor(background_mask_np * 255, cv2.COLOR_GRAY2BGR)
# result_img_bgr = np.where(mask_3channel == 255, white_background, image_resized)
# result_img_rgb = cv2.cvtColor(result_img_bgr, cv2.COLOR_BGR2RGB)
# img_white_bg = PIL.Image.fromarray(result_img_rgb)
# if extra_input:
# # 2. Create image with transparent background (new logic)
# # Create an alpha channel: 255 for foreground (not background), 0 for background
# alpha_channel = (parsing_mask.squeeze().cpu().numpy() != 0).astype(
# np.uint8
# ) * 255
# # Convert the resized BGR image to RGB
# image_resized_rgb = cv2.cvtColor(image_resized, cv2.COLOR_BGR2RGB)
# # Stack RGB channels with the new alpha channel
# rgba_image = np.dstack((image_resized_rgb, alpha_channel))
# # Create PIL image from the RGBA numpy array
# img_transparent_bg = PIL.Image.fromarray(rgba_image, "RGBA")
# return img_white_bg, img_transparent_bg
# else:
# return img_white_bg
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