# pylint: disable=W0718 """ 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, ImageOps from torchvision import transforms MEAN = 0.5 STD = 0.5 def _select_largest_deepface_bbox(faces): max_face = 0 best_bbox = (0, 0, 0, 0) for face in faces: facial_area = face.get("facial_area") or {} face_area = facial_area.get("w", 0) * facial_area.get("h", 0) if face.get("confidence", 0) > 0.5 and face_area > max_face: best_bbox = ( int(facial_area["x"]), int(facial_area["y"]), int(facial_area["x"] + facial_area["w"]), int(facial_area["y"] + facial_area["h"]), ) max_face = face_area return best_bbox def _detect_face_with_deepface(frame): try: from deepface import DeepFace except Exception: return None try: faces = DeepFace.extract_faces( img_path=frame, detector_backend="yolov8", align=False, enforce_detection=False, expand_percentage=0, ) except Exception as exc: print(f"DeepFace detection failed, falling back to OpenCV Haar detection: {exc}") return None if not faces: return (0, 0, 0, 0) return _select_largest_deepface_bbox(faces) def _detect_face_with_opencv(frame): cascade_path = cv2.data.haarcascades + "haarcascade_frontalface_default.xml" detector = cv2.CascadeClassifier(cascade_path) if detector.empty(): return (0, 0, 0, 0) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = detector.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(40, 40)) if len(faces) == 0: return (0, 0, 0, 0) x, y, w, h = max(faces, key=lambda item: item[2] * item[3]) return int(x), int(y), int(x + w), int(y + h) def detect_face_bbox(frame): bbox = _detect_face_with_deepface(frame) if bbox is None: bbox = _detect_face_with_opencv(frame) if bbox == (0, 0, 0, 0): print("No faces detected in the image. Using the entire image as the face region.") return bbox 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. 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) -> None: self.img_size = img_size self.pixel_transform = transforms.Compose( [ transforms.Resize(self.img_size), transforms.ToTensor(), transforms.Normalize([MEAN], [STD]), ] ) def preprocess_frames(self, frames): """ Apply preprocessing 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 """ state = torch.get_rng_state() def find_max_h(bbox_list): h_max = 0 for bbox in bbox_list: h = bbox[3] - bbox[1] if h>h_max: h_max = h return h_max pixel_values_ref_img_list, bbox_list, bbox_final_list, frame_crop_list = [], [], [], [] for frame in frames: bbox_list.append(detect_face_bbox(frame)) h_max = find_max_h(bbox_list) for frame, bbox in zip(frames, bbox_list): if bbox==(0,0,0,0): bbox_final_list.append((0,0,0,0)) frame_crop = frame frame_crop_list.append(cv2.resize(frame_crop, (512,512))) else: frame_h, frame_w, _ = frame.shape w, h = bbox[2] - bbox[0], bbox[3] - bbox[1] if h_max < 1.4*h: h = h_max bbox_final = (int(max(bbox[0] - 0.1*w, 0)), int(bbox[1]), int(min(bbox[2] + 0.1*w, frame_w)), int(min(bbox[1] + 1.1*h, frame_h))) # bbox_final = (0,0,512,512) bbox_final_list.append(bbox_final) frame_face = frame[bbox_final[1]:bbox_final[3], bbox_final[0]:bbox_final[2]] frame_crop = cv2.resize(frame_face, (512,512)) frame_crop_list.append(frame_crop) pixel_values_ref_img_list.append(self._augmentation(Image.fromarray(cv2.cvtColor(frame_crop, cv2.COLOR_BGR2RGB)).convert("RGB"), self.pixel_transform, state).unsqueeze(0)) return pixel_values_ref_img_list, bbox_final_list, frame_crop_list def preprocess_frames_jump(self, frames): """ Apply preprocessing 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 """ frame_h, frame_w, _ = frames[0].shape state = torch.get_rng_state() def find_max_h(bbox_list): h_max = 0 h_min = 100000 for bbox in bbox_list: h = bbox[3] - bbox[1] if h>h_max: h_max = h if h 0.2: jumps.append((count_prev, count-1)) count_prev = count jumps.append((count_prev, count)) return jumps bbox_list = [] for frame in frames: bbox_list.append(detect_face_bbox(frame)) pixel_values_ref_img_all, bbox_final_all, frames_list_chunk = [], [], [] jumps = differ(bbox_list) print( f"[HighSync] Face tracking: split {len(frames)} frame(s) into " f"{len(jumps)} stable face segment(s).", flush=True, ) for segment_index, jump in enumerate(jumps, start=1): pixel_values_ref_img_list, bbox_final_list = [], [] bbox_chunk = bbox_list[jump[0]:jump[1]+1] frames_chunk = frames[jump[0]:jump[1]+1] h_max, h_min = find_max_h(bbox_chunk) use_h = h_max < 1.4*h_min print( f"[HighSync] Face segment {segment_index}/{len(jumps)}: " f"local frames {jump[0]}-{jump[1]} " f"({len(frames_chunk)} frame(s)), stable_crop_height={use_h}.", flush=True, ) for frame, bbox in zip(frames_chunk, bbox_chunk): if bbox==(0,0,0,0): bbox_final_list.append((0,0,0,0)) frame_crop = frame else: w, h = bbox[2] - bbox[0], bbox[3] - bbox[1] if use_h: h = h_max bbox_final = (int(max(bbox[0] - 0.1*w, 0)), int(bbox[1]), int(min(bbox[2] + 0.1*w, frame_w)), int(min(bbox[1] + 1.1*h, frame_h))) bbox_final_list.append(bbox_final) frame_face = frame[bbox_final[1]:bbox_final[3], bbox_final[0]:bbox_final[2]] frame_crop = cv2.resize(frame_face, (512,512)) pixel_values_ref_img_list.append(self._augmentation(Image.fromarray(cv2.cvtColor(frame_crop, cv2.COLOR_BGR2RGB)).convert("RGB"), self.pixel_transform, state).unsqueeze(0)) pixel_values_ref_img_all.append(pixel_values_ref_img_list) bbox_final_all.append(bbox_final_list) frames_list_chunk.append(frames_chunk) return pixel_values_ref_img_all, bbox_final_all, frames_list_chunk def preprocess_frames_deepface_no_detect(self, frames): """ Apply preprocessing to the 14 source images 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 """ pixel_values_ref_img_list = [] for frame in frames: pixel_values_ref_img_list.append(self._augmentation(Image.fromarray(frame).convert("RGB"), self.pixel_transform).unsqueeze(0)) return pixel_values_ref_img_list 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 = None #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: frame = sorted(os.listdir(source_image_path))[0] source_image = Image.open( os.path.join(source_image_path, frame)) ref_image_pil = source_image.convert("RGB") padding = 250 ref_image_pil = ImageOps.expand(ref_image_pil, border=padding, fill=0) # 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 None: print(f"error in image embedding : {source_image_path}") raise 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()