""" This module is responsible for processing images, particularly for face-related tasks. It uses various libraries such as OpenCV, NumPy, and InsightFace to perform tasks like face detection, augmentation, and mask rendering. The ImageProcessor class encapsulates the functionality for these operations. """ import os from typing import List import cv2 import mediapipe as mp import numpy as np import torch from insightface.app import FaceAnalysis from PIL import Image from torchvision import transforms from ..utils.util import (blur_mask, get_landmark_overframes, get_mask, get_union_face_mask, get_union_lip_mask) MEAN = 0.5 STD = 0.5 class ImageProcessor: """ ImageProcessor is a class responsible for processing images, particularly for face-related tasks. It takes in an image and performs various operations such as augmentation, face detection, face embedding extraction, and rendering a face mask. The processed images are then used for further analysis or recognition purposes. Attributes: img_size (int): The size of the image to be processed. face_analysis_model_path (str): The path to the face analysis model. Methods: preprocess(source_image_path, cache_dir): Preprocesses the input image by performing augmentation, face detection, face embedding extraction, and rendering a face mask. close(): Closes the ImageProcessor and releases any resources being used. _augmentation(images, transform, state=None): Applies image augmentation to the input images using the given transform and state. __enter__(): Enters a runtime context and returns the ImageProcessor object. __exit__(_exc_type, _exc_val, _exc_tb): Exits a runtime context and handles any exceptions that occurred during the processing. """ def __init__(self, img_size, face_analysis_model_path) -> None: self.img_size = img_size self.pixel_transform = transforms.Compose( [ transforms.Resize(self.img_size), transforms.ToTensor(), transforms.Normalize([MEAN], [STD]), ] ) self.cond_transform = transforms.Compose( [ transforms.Resize(self.img_size), transforms.ToTensor(), ] ) self.attn_transform_64 = transforms.Compose( [ transforms.Resize( (self.img_size[0] // 8, self.img_size[0] // 8)), transforms.ToTensor(), ] ) self.attn_transform_32 = transforms.Compose( [ transforms.Resize( (self.img_size[0] // 16, self.img_size[0] // 16)), transforms.ToTensor(), ] ) self.attn_transform_16 = transforms.Compose( [ transforms.Resize( (self.img_size[0] // 32, self.img_size[0] // 32)), transforms.ToTensor(), ] ) self.attn_transform_8 = transforms.Compose( [ transforms.Resize( (self.img_size[0] // 64, self.img_size[0] // 64)), transforms.ToTensor(), ] ) self.face_analysis = FaceAnalysis( name="", root=face_analysis_model_path, providers=["CUDAExecutionProvider", "CPUExecutionProvider"], ) self.face_analysis.prepare(ctx_id=0, det_size=(640, 640)) def preprocess(self, source_image_path: str, cache_dir: str, face_region_ratio: float): """ Apply preprocessing to the source image to prepare for face analysis. Parameters: source_image_path (str): The path to the source image. cache_dir (str): The directory to cache intermediate results. Returns: None """ source_image = Image.open(source_image_path) ref_image_pil = source_image.convert("RGB") # 1. image augmentation pixel_values_ref_img = self._augmentation(ref_image_pil, self.pixel_transform) # 2.1 detect face faces = self.face_analysis.get(cv2.cvtColor(np.array(ref_image_pil.copy()), cv2.COLOR_RGB2BGR)) if not faces: print("No faces detected in the image. Using the entire image as the face region.") # Use the entire image as the face region face = { "bbox": [0, 0, ref_image_pil.width, ref_image_pil.height], "embedding": np.zeros(512) } else: # Sort faces by size and select the largest one faces_sorted = sorted(faces, key=lambda x: (x["bbox"][2] - x["bbox"][0]) * (x["bbox"][3] - x["bbox"][1]), reverse=True) face = faces_sorted[0] # Select the largest face # 2.2 face embedding face_emb = face["embedding"] # 2.3 render face mask get_mask(source_image_path, cache_dir, face_region_ratio) file_name = os.path.basename(source_image_path).split(".")[0] face_mask_pil = Image.open( os.path.join(cache_dir, f"{file_name}_face_mask.png")).convert("RGB") face_mask = self._augmentation(face_mask_pil, self.cond_transform) # 2.4 detect and expand lip, face mask sep_background_mask = Image.open( os.path.join(cache_dir, f"{file_name}_sep_background.png")) sep_face_mask = Image.open( os.path.join(cache_dir, f"{file_name}_sep_face.png")) sep_lip_mask = Image.open( os.path.join(cache_dir, f"{file_name}_sep_lip.png")) pixel_values_face_mask = [ self._augmentation(sep_face_mask, self.attn_transform_64), self._augmentation(sep_face_mask, self.attn_transform_32), self._augmentation(sep_face_mask, self.attn_transform_16), self._augmentation(sep_face_mask, self.attn_transform_8), ] pixel_values_lip_mask = [ self._augmentation(sep_lip_mask, self.attn_transform_64), self._augmentation(sep_lip_mask, self.attn_transform_32), self._augmentation(sep_lip_mask, self.attn_transform_16), self._augmentation(sep_lip_mask, self.attn_transform_8), ] pixel_values_full_mask = [ self._augmentation(sep_background_mask, self.attn_transform_64), self._augmentation(sep_background_mask, self.attn_transform_32), self._augmentation(sep_background_mask, self.attn_transform_16), self._augmentation(sep_background_mask, self.attn_transform_8), ] pixel_values_full_mask = [mask.view(1, -1) for mask in pixel_values_full_mask] pixel_values_face_mask = [mask.view(1, -1) for mask in pixel_values_face_mask] pixel_values_lip_mask = [mask.view(1, -1) for mask in pixel_values_lip_mask] return pixel_values_ref_img, face_mask, face_emb, pixel_values_full_mask, pixel_values_face_mask, pixel_values_lip_mask def close(self): """ Closes the ImageProcessor and releases any resources held by the FaceAnalysis instance. Args: self: The ImageProcessor instance. Returns: None. """ for _, model in self.face_analysis.models.items(): if hasattr(model, "Dispose"): model.Dispose() def _augmentation(self, images, transform, state=None): if state is not None: torch.set_rng_state(state) if isinstance(images, List): transformed_images = [transform(img) for img in images] ret_tensor = torch.stack(transformed_images, dim=0) # (f, c, h, w) else: ret_tensor = transform(images) # (c, h, w) return ret_tensor def __enter__(self): return self def __exit__(self, _exc_type, _exc_val, _exc_tb): self.close() class ImageProcessorForDataProcessing(): """ ImageProcessor is a class responsible for processing images, particularly for face-related tasks. It takes in an image and performs various operations such as augmentation, face detection, face embedding extraction, and rendering a face mask. The processed images are then used for further analysis or recognition purposes. Attributes: img_size (int): The size of the image to be processed. face_analysis_model_path (str): The path to the face analysis model. Methods: preprocess(source_image_path, cache_dir): Preprocesses the input image by performing augmentation, face detection, face embedding extraction, and rendering a face mask. close(): Closes the ImageProcessor and releases any resources being used. _augmentation(images, transform, state=None): Applies image augmentation to the input images using the given transform and state. __enter__(): Enters a runtime context and returns the ImageProcessor object. __exit__(_exc_type, _exc_val, _exc_tb): Exits a runtime context and handles any exceptions that occurred during the processing. """ def __init__(self, face_analysis_model_path, landmark_model_path, step) -> None: if step == 2: self.face_analysis = FaceAnalysis( name="", root=face_analysis_model_path, providers=["CUDAExecutionProvider", "CPUExecutionProvider"], ) self.face_analysis.prepare(ctx_id=0, det_size=(640, 640)) self.landmarker = None else: BaseOptions = mp.tasks.BaseOptions FaceLandmarker = mp.tasks.vision.FaceLandmarker FaceLandmarkerOptions = mp.tasks.vision.FaceLandmarkerOptions VisionRunningMode = mp.tasks.vision.RunningMode # Create a face landmarker instance with the video mode: options = FaceLandmarkerOptions( base_options=BaseOptions(model_asset_path=landmark_model_path), running_mode=VisionRunningMode.IMAGE, ) self.landmarker = FaceLandmarker.create_from_options(options) self.face_analysis = None def preprocess(self, source_image_path: str): """ Apply preprocessing to the source image to prepare for face analysis. Parameters: source_image_path (str): The path to the source image. cache_dir (str): The directory to cache intermediate results. Returns: None """ # 1. get face embdeding face_mask, face_emb, sep_pose_mask, sep_face_mask, sep_lip_mask = None, None, None, None, None if self.face_analysis: for frame in sorted(os.listdir(source_image_path)): try: source_image = Image.open( os.path.join(source_image_path, frame)) ref_image_pil = source_image.convert("RGB") # 2.1 detect face faces = self.face_analysis.get(cv2.cvtColor( np.array(ref_image_pil.copy()), cv2.COLOR_RGB2BGR)) # use max size face face = sorted(faces, key=lambda x: ( x["bbox"][2] - x["bbox"][0]) * (x["bbox"][3] - x["bbox"][1]))[-1] # 2.2 face embedding face_emb = face["embedding"] if face_emb is not None: break except Exception as _: continue if self.landmarker: # 3.1 get landmark landmarks, height, width = get_landmark_overframes( self.landmarker, source_image_path) assert len(landmarks) == len(os.listdir(source_image_path)) # 3 render face and lip mask face_mask = get_union_face_mask(landmarks, height, width) lip_mask = get_union_lip_mask(landmarks, height, width) # 4 gaussian blur blur_face_mask = blur_mask(face_mask, (64, 64), (51, 51)) blur_lip_mask = blur_mask(lip_mask, (64, 64), (31, 31)) # 5 seperate mask sep_face_mask = cv2.subtract(blur_face_mask, blur_lip_mask) sep_pose_mask = 255.0 - blur_face_mask sep_lip_mask = blur_lip_mask return face_mask, face_emb, sep_pose_mask, sep_face_mask, sep_lip_mask def close(self): """ Closes the ImageProcessor and releases any resources held by the FaceAnalysis instance. Args: self: The ImageProcessor instance. Returns: None. """ for _, model in self.face_analysis.models.items(): if hasattr(model, "Dispose"): model.Dispose() def _augmentation(self, images, transform, state=None): if state is not None: torch.set_rng_state(state) if isinstance(images, List): transformed_images = [transform(img) for img in images] ret_tensor = torch.stack(transformed_images, dim=0) # (f, c, h, w) else: ret_tensor = transform(images) # (c, h, w) return ret_tensor def __enter__(self): return self def __exit__(self, _exc_type, _exc_val, _exc_tb): self.close()