Added option to mask only faces to eliminate blurring artifacts around faces

scripts/entities/face.py

@@ -0,0 +1,147 @@
+import traceback
+
+import cv2
+import numpy as np
+from modules import images
+from PIL import Image
+
+
+from scripts.entities.rect import Point, Rect
+
+
+class Face:
+    def __init__(self, entire_image: np.ndarray, face_area: Rect, face_margin: float, face_size: int, upscaler: str):
+        self.face_area = face_area
+        self.center = face_area.center
+        left, top, right, bottom = face_area.to_square()
+
+        self.left, self.top, self.right, self.bottom = self.__ensure_margin(
+            left, top, right, bottom, entire_image, face_margin
+        )
+
+        self.width = self.right - self.left
+        self.height = self.bottom - self.top
+
+        self.image = self.__crop_face_image(entire_image, face_size, upscaler)
+        self.face_size = face_size
+        self.scale_factor = face_size / self.width
+        self.face_area_on_image = self.__get_face_area_on_image()
+        self.landmarks_on_image = self.__get_landmarks_on_image()
+
+    def __get_face_area_on_image(self):
+        left = int((self.face_area.left - self.left) * self.scale_factor)
+        top = int((self.face_area.top - self.top) * self.scale_factor)
+        right = int((self.face_area.right - self.left) * self.scale_factor)
+        bottom = int((self.face_area.bottom - self.top) * self.scale_factor)
+        return self.__clip_values(left, top, right, bottom)
+
+    def __get_landmarks_on_image(self):
+        landmarks = []
+        if self.face_area.landmarks is not None:
+            for landmark in self.face_area.landmarks:
+                landmarks.append(
+                    Point(
+                        int((landmark.x - self.left) * self.scale_factor),
+                        int((landmark.y - self.top) * self.scale_factor),
+                    )
+                )
+        return landmarks
+
+    def __crop_face_image(self, entire_image: np.ndarray, face_size: int, upscaler: str):
+        cropped = entire_image[self.top : self.bottom, self.left : self.right, :]
+        if upscaler:
+            return images.resize_image(0, Image.fromarray(cropped), face_size, face_size, upscaler)
+        else:
+            return Image.fromarray(cv2.resize(cropped, dsize=(face_size, face_size)))
+
+    def __ensure_margin(self, left: int, top: int, right: int, bottom: int, entire_image: np.ndarray, margin: float):
+        entire_height, entire_width = entire_image.shape[:2]
+
+        side_length = right - left
+        margin = min(min(entire_height, entire_width) / side_length, margin)
+        diff = int((side_length * margin - side_length) / 2)
+
+        top = top - diff
+        bottom = bottom + diff
+        left = left - diff
+        right = right + diff
+
+        if top < 0:
+            bottom = bottom - top
+            top = 0
+        if left < 0:
+            right = right - left
+            left = 0
+
+        if bottom > entire_height:
+            top = top - (bottom - entire_height)
+            bottom = entire_height
+        if right > entire_width:
+            left = left - (right - entire_width)
+            right = entire_width
+
+        return left, top, right, bottom
+
+    def get_angle(self) -> float:
+        landmarks = getattr(self.face_area, "landmarks", None)
+        if landmarks is None:
+            return 0
+
+        eye1 = getattr(landmarks, "eye1", None)
+        eye2 = getattr(landmarks, "eye2", None)
+        if eye2 is None or eye1 is None:
+            return 0
+
+        try:
+            dx = eye2.x - eye1.x
+            dy = eye2.y - eye1.y
+            if dx == 0:
+                dx = 1
+            angle = np.arctan(dy / dx) * 180 / np.pi
+
+            if dx < 0:
+                angle = (angle + 180) % 360
+            return angle
+        except Exception:
+            print(traceback.format_exc())
+            return 0
+
+    def rotate_face_area_on_image(self, angle: float):
+        center = [
+            (self.face_area_on_image[0] + self.face_area_on_image[2]) / 2,
+            (self.face_area_on_image[1] + self.face_area_on_image[3]) / 2,
+        ]
+
+        points = [
+            [self.face_area_on_image[0], self.face_area_on_image[1]],
+            [self.face_area_on_image[2], self.face_area_on_image[3]],
+        ]
+
+        angle = np.radians(angle)
+        rot_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])
+
+        points = np.array(points) - center
+        points = np.dot(points, rot_matrix.T)
+        points += center
+        left, top, right, bottom = (int(points[0][0]), int(points[0][1]), int(points[1][0]), int(points[1][1]))
+
+        left, right = (right, left) if left > right else (left, right)
+        top, bottom = (bottom, top) if top > bottom else (top, bottom)
+
+        width, height = right - left, bottom - top
+        if width < height:
+            left, right = left - (height - width) // 2, right + (height - width) // 2
+        elif height < width:
+            top, bottom = top - (width - height) // 2, bottom + (width - height) // 2
+        return self.__clip_values(left, top, right, bottom)
+
+    def __clip_values(self, *args):
+        result = []
+        for val in args:
+            if val < 0:
+                result.append(0)
+            elif val > self.face_size:
+                result.append(self.face_size)
+            else:
+                result.append(val)
+        return tuple(result)

scripts/entities/rect.py

@@ -0,0 +1,78 @@
+from typing import Dict, NamedTuple, Tuple
+
+import numpy as np
+
+
+class Point(NamedTuple):
+    x: int
+    y: int
+
+
+class Landmarks(NamedTuple):
+    eye1: Point
+    eye2: Point
+    nose: Point
+    mouth1: Point
+    mouth2: Point
+
+
+class Rect:
+    def __init__(
+        self,
+        left: int,
+        top: int,
+        right: int,
+        bottom: int,
+        tag: str = "face",
+        landmarks: Landmarks = None,
+        attributes: Dict[str, str] = {},
+    ) -> None:
+        self.tag = tag
+        self.left = left
+        self.top = top
+        self.right = right
+        self.bottom = bottom
+        self.center = int((right + left) / 2)
+        self.middle = int((top + bottom) / 2)
+        self.width = right - left
+        self.height = bottom - top
+        self.size = self.width * self.height
+        self.landmarks = landmarks
+        self.attributes = attributes
+
+    @classmethod
+    def from_ndarray(
+        cls,
+        face_box: np.ndarray,
+        tag: str = "face",
+        landmarks: Landmarks = None,
+        attributes: Dict[str, str] = {},
+    ) -> "Rect":
+        left, top, right, bottom, *_ = list(map(int, face_box))
+        return cls(left, top, right, bottom, tag, landmarks, attributes)
+
+    def to_tuple(self) -> Tuple[int, int, int, int]:
+        return self.left, self.top, self.right, self.bottom
+
+    def to_square(self):
+        left, top, right, bottom = self.to_tuple()
+
+        width = right - left
+        height = bottom - top
+
+        if width % 2 == 1:
+            right = right + 1
+            width = width + 1
+        if height % 2 == 1:
+            bottom = bottom + 1
+            height = height + 1
+
+        diff = int(abs(width - height) / 2)
+        if width > height:
+            top = top - diff
+            bottom = bottom + diff
+        else:
+            left = left - diff
+            right = right + diff
+
+        return left, top, right, bottom

scripts/inferencers/bisenet_mask_generator.py

@@ -0,0 +1,88 @@
+from typing import List, Tuple
+
+import cv2
+import modules.shared as shared
+import numpy as np
+import torch
+from facexlib.parsing import init_parsing_model
+from facexlib.utils.misc import img2tensor
+from torchvision.transforms.functional import normalize
+from PIL import Image
+from scripts.inferencers.mask_generator import MaskGenerator
+from scripts.reactor_logger import logger
+
+class BiSeNetMaskGenerator(MaskGenerator):
+    def __init__(self) -> None:
+        self.mask_model = init_parsing_model(device=shared.device)
+
+    def name(self):
+        return "BiSeNet"
+
+    def generate_mask(
+        self,
+        face_image: np.ndarray,
+        face_area_on_image: Tuple[int, int, int, int],
+        affected_areas: List[str],
+        mask_size: int,
+        use_minimal_area: bool,
+        fallback_ratio: float = 0.25,
+        **kwargs,
+    ) -> np.ndarray:
+        original_face_image = face_image
+        face_image = face_image.copy()
+        face_image = face_image[:, :, ::-1]
+
+        if use_minimal_area:
+            face_image = MaskGenerator.mask_non_face_areas(face_image, face_area_on_image)
+
+        h, w, _ = face_image.shape
+
+        if w != 512 or h != 512:
+            rw = (int(w * (512 / w)) // 8) * 8
+            rh = (int(h * (512 / h)) // 8) * 8
+            face_image = cv2.resize(face_image, dsize=(rw, rh))
+
+        face_tensor = img2tensor(face_image.astype("float32") / 255.0, float32=True)
+        normalize(face_tensor, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+        face_tensor = torch.unsqueeze(face_tensor, 0).to(shared.device)
+
+        with torch.no_grad():
+            face = self.mask_model(face_tensor)[0]
+        face = face.squeeze(0).cpu().numpy().argmax(0)
+        face = face.copy().astype(np.uint8)
+
+        mask = self.__to_mask(face, affected_areas)
+        
+        if mask_size > 0:
+            mask = cv2.dilate(mask, np.ones((5, 5), np.uint8), iterations=mask_size)
+        
+        if w != 512 or h != 512:
+            mask = cv2.resize(mask, dsize=(w, h))
+        
+        """if MaskGenerator.calculate_mask_coverage(mask) < fallback_ratio:
+            logger.info("Use fallback mask generator")
+            mask = self.fallback_mask_generator.generate_mask(
+                original_face_image, face_area_on_image, use_minimal_area=True
+            )"""
+       
+        return mask
+
+    def __to_mask(self, face: np.ndarray, affected_areas: List[str]) -> np.ndarray:
+        keep_face = "Face" in affected_areas
+        keep_neck = "Neck" in affected_areas
+        keep_hair = "Hair" in affected_areas
+        keep_hat = "Hat" in affected_areas
+
+        mask = np.zeros((face.shape[0], face.shape[1], 3), dtype=np.uint8)
+        num_of_class = np.max(face)
+        for i in range(1, num_of_class + 1):
+            index = np.where(face == i)
+            if i < 14 and keep_face:
+                mask[index[0], index[1], :] = [255, 255, 255]
+            elif i == 14 and keep_neck:
+                mask[index[0], index[1], :] = [255, 255, 255]
+            elif i == 17 and keep_hair:
+                mask[index[0], index[1], :] = [255, 255, 255]
+            elif i == 18 and keep_hat:
+                mask[index[0], index[1], :] = [255, 255, 255]
+        return mask

scripts/inferencers/mask_generator.py

@@ -0,0 +1,37 @@
+from abc import ABC, abstractmethod
+from typing import Tuple
+
+import cv2
+import numpy as np
+
+
+class MaskGenerator(ABC):
+    @abstractmethod
+    def name(self) -> str:
+        pass
+
+    @abstractmethod
+    def generate_mask(
+        self,
+        face_image: np.ndarray,
+        face_area_on_image: Tuple[int, int, int, int],
+        **kwargs,
+    ) -> np.ndarray:
+        pass
+
+    @staticmethod
+    def mask_non_face_areas(image: np.ndarray, face_area_on_image: Tuple[int, int, int, int]) -> np.ndarray:
+        left, top, right, bottom = face_area_on_image
+        image = image.copy()
+        image[:top, :] = 0
+        image[bottom:, :] = 0
+        image[:, :left] = 0
+        image[:, right:] = 0
+        return image
+
+    @staticmethod
+    def calculate_mask_coverage(mask: np.ndarray):
+        gray_mask = cv2.cvtColor(mask, cv2.COLOR_RGB2GRAY)
+        non_black_pixels = np.count_nonzero(gray_mask)
+        total_pixels = gray_mask.size
+        return non_black_pixels / total_pixels

scripts/reactor_faceswap.py

@@ -66,6 +66,8 @@ class FaceSwapScript(scripts.Script):
                     img = gr.Image(type="pil")
                     enable = gr.Checkbox(False, label="Enable", info=f"The Fast and Simple FaceSwap Extension - {version_flag}")
                     save_original = gr.Checkbox(False, label="Save Original", info="Save the original image(s) made before swapping; If you use \"img2img\" - this option will affect with \"Swap in generated\" only")
+                    mask_face = gr.Checkbox(False, label="Mask Faces", info="Attempt to mask only the faces and eliminate pixelation of the image around the contours.")
+
                     gr.Markdown("<br>")
                     gr.Markdown("Source Image (above):")
                     with gr.Row():
@@ -211,6 +213,7 @@ class FaceSwapScript(scripts.Script):
             source_hash_check,
             target_hash_check,
             device,
+            mask_face
         ]
 
 
@@ -264,6 +267,7 @@ class FaceSwapScript(scripts.Script):
         source_hash_check,
         target_hash_check,
         device,
+        mask_face
     ):
         self.enable = enable
         if self.enable:
@@ -291,6 +295,7 @@ class FaceSwapScript(scripts.Script):
             self.source_hash_check = source_hash_check
             self.target_hash_check = target_hash_check
             self.device = device
+            self.mask_face = mask_face
             if self.gender_source is None or self.gender_source == "No":
                 self.gender_source = 0
             if self.gender_target is None or self.gender_target == "No":
@@ -334,6 +339,7 @@ class FaceSwapScript(scripts.Script):
                             source_hash_check=self.source_hash_check,
                             target_hash_check=self.target_hash_check,
                             device=self.device,
+                            mask_face=mask_face
                         )
                         p.init_images[i] = result
                         # result_path = get_image_path(p.init_images[i], p.outpath_samples, "", p.all_seeds[i], p.all_prompts[i], "txt", p=p, suffix="-swapped")
@@ -385,6 +391,7 @@ class FaceSwapScript(scripts.Script):
                                 source_hash_check=self.source_hash_check,
                                 target_hash_check=self.target_hash_check,
                                 device=self.device,
+                                mask_face=self.mask_face
                             )
                             if result is not None and swapped > 0:
                                 result_images.append(result)
@@ -442,6 +449,7 @@ class FaceSwapScript(scripts.Script):
                     source_hash_check=self.source_hash_check,
                     target_hash_check=self.target_hash_check,
                     device=self.device,
+                    mask_face=self.mask_face
                 )
                 try:
                     pp = scripts_postprocessing.PostprocessedImage(result)
@@ -468,6 +476,8 @@ class FaceSwapScriptExtras(scripts_postprocessing.ScriptPostprocessing):
                 with gr.Column():
                     img = gr.Image(type="pil")
                     enable = gr.Checkbox(False, label="Enable", info=f"The Fast and Simple FaceSwap Extension - {version_flag}")
+                    mask_face = gr.Checkbox(False, label="Mask Faces", info="Attempt to mask only the faces and eliminate pixelation of the image around the contours.")
+                    
                     gr.Markdown("Source Image (above):")
                     with gr.Row():
                         source_faces_index = gr.Textbox(
@@ -582,6 +592,7 @@ class FaceSwapScriptExtras(scripts_postprocessing.ScriptPostprocessing):
             'gender_target': gender_target,
             'codeformer_weight': codeformer_weight,
             'device': device,
+            'mask_face':mask_face
         }
         return args
 
@@ -631,6 +642,7 @@ class FaceSwapScriptExtras(scripts_postprocessing.ScriptPostprocessing):
             self.gender_target = args['gender_target']
             self.codeformer_weight = args['codeformer_weight']
             self.device = args['device']
+            self.mask_face = args['mask_face']
             if self.gender_source is None or self.gender_source == "No":
                 self.gender_source = 0
             if self.gender_target is None or self.gender_target == "No":
@@ -669,6 +681,7 @@ class FaceSwapScriptExtras(scripts_postprocessing.ScriptPostprocessing):
                     source_hash_check=True,
                     target_hash_check=True,
                     device=self.device,
+                    mask_face=self.mask_face
                 )
                 try:
                     pp.info["ReActor"] = True

scripts/reactor_swapper.py

@@ -2,13 +2,16 @@ import copy
 import os
 from dataclasses import dataclass
 from typing import List, Union
-from typing import Tuple
+
 import cv2
 import numpy as np
-from PIL import Image
+from numpy import uint8
-from insightface.app.common import Face as IFace
+from PIL import Image, ImageDraw
+from scripts.inferencers.bisenet_mask_generator import BiSeNetMaskGenerator
+from scripts.entities.face import Face
+from scripts.entities.rect import Rect
 import insightface
-
+from torchvision.transforms.functional import to_pil_image
 from scripts.reactor_helpers import get_image_md5hash, get_Device
 from modules.face_restoration import FaceRestoration
 try: # A1111
@@ -18,6 +21,7 @@ except: # SD.Next
 from modules.upscaler import UpscalerData
 from modules.shared import state
 from scripts.reactor_logger import logger
+
 try:
     from modules.paths_internal import models_path
 except:
@@ -28,16 +32,6 @@ except:
 
 import warnings
 
-PILImage = Image.Image
-CV2ImgU8 = np.ndarray[int, np.dtype[uint8]]
-Face = IFace
-BoxCoords = Tuple[int, int, int, int]
-
-
-class Gender(Enum):
-    AUTO = -1
-    FEMALE = 0
-    MALE = 1
 np.warnings = warnings
 np.warnings.filterwarnings('ignore')
 
@@ -86,8 +80,9 @@ def check_process_halt(msgforced: bool = False):
 
 
 FS_MODEL = None
+MASK_MODEL = None
 CURRENT_FS_MODEL_PATH = None
-
+CURRENT_MASK_MODEL_PATH = None
 ANALYSIS_MODEL = None
 
 SOURCE_FACES = None
@@ -115,6 +110,8 @@ def getFaceSwapModel(model_path: str):
     return FS_MODEL
 
 
+
+
 def restore_face(image: Image, enhancement_options: EnhancementOptions):
     result_image = image
 
@@ -178,7 +175,28 @@ def enhance_image(image: Image, enhancement_options: EnhancementOptions):
         result_image = restore_face(result_image, enhancement_options)
 
     return result_image
+def enhance_image_and_mask(image: Image.Image, enhancement_options: EnhancementOptions,target_img_orig:Image.Image,entire_mask_image:Image.Image)->Image.Image:
+    result_image = image
+    
+    if check_process_halt(msgforced=True):
+        return result_image
+    
+    if enhancement_options.do_restore_first:
+        
+        result_image = restore_face(result_image, enhancement_options)
+        result_image = Image.composite(result_image,target_img_orig,entire_mask_image)
+        result_image = upscale_image(result_image, enhancement_options)
 
+    else:
+
+        result_image = upscale_image(result_image, enhancement_options)
+        entire_mask_image = Image.fromarray(cv2.resize(np.array(entire_mask_image),result_image.size, interpolation=cv2.INTER_AREA)).convert("L")
+        result_image = Image.composite(result_image,target_img_orig,entire_mask_image)
+        result_image = restore_face(result_image, enhancement_options)
+
+    return result_image
+
+    
 def get_gender(face, face_index):
     gender = [
         x.sex
@@ -292,6 +310,7 @@ def swap_face(
     source_hash_check: bool = True,
     target_hash_check: bool = False,
     device: str = "CPU",
+    mask_face:bool = False
 ):
     global SOURCE_FACES, SOURCE_IMAGE_HASH, TARGET_FACES, TARGET_IMAGE_HASH, PROVIDERS
     result_image = target_img
@@ -318,7 +337,8 @@ def swap_face(
 
         source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
         target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
-
+        target_img_orig = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
+        entire_mask_image = np.zeros_like(np.array(target_img))
         output: List = []
         output_info: str = ""
         swapped = 0
@@ -421,7 +441,12 @@ def swap_face(
                         
                         if target_face is not None and wrong_gender == 0:
                             logger.status("Swapping Source into Target")
-                            result = face_swapper.get(result, target_face, source_face)
+                            swapped_image = face_swapper.get(result, target_face, source_face)
+                                                    
+                            if mask_face:
+                                result = apply_face_mask(swapped_image=swapped_image,target_image=result,target_face=target_face,entire_mask_image=entire_mask_image)
+                            else:
+                                result = swapped_image
                             swapped += 1
                         
                         elif wrong_gender == 1:
@@ -455,8 +480,13 @@ def swap_face(
                 result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
                 
                 if enhancement_options is not None and swapped > 0:
-                    result_image = enhance_image(result_image, enhancement_options)
+                    if mask_face and entire_mask_image is not None:
-
+                        enhance_image_and_mask(result_image, enhancement_options,Image.fromarray(target_img_orig),Image.fromarray(entire_mask_image).convert("L"))    
+                    else:    
+                        result_image = enhance_image(result_image, enhancement_options)
+                elif mask_face and entire_mask_image is not None and swapped > 0:
+                    result_image = Image.composite(result_image,Image.fromarray(target_img_orig),Image.fromarray(entire_mask_image).convert("L"))
+               
             else:
                 logger.status("No source face(s) in the provided Index")
         else:
@@ -465,99 +495,158 @@ def swap_face(
     return result_image, output, swapped
 
 
-def merge_images_with_mask(
+
-    image1: CV2ImgU8, image2: CV2ImgU8, mask: CV2ImgU8
+def apply_face_mask(swapped_image:np.ndarray,target_image:np.ndarray,target_face,entire_mask_image:np.array)->np.ndarray:
-) -> CV2ImgU8:
+    logger.status("Masking Face")
+    mask_generator =  BiSeNetMaskGenerator()
+    face = Face(target_image,Rect.from_ndarray(np.array(target_face.bbox)),1.6,512,"")
+    face_image = np.array(face.image)
+    process_face_image(face)
+    face_area_on_image = face.face_area_on_image
+    mask = mask_generator.generate_mask(face_image,face_area_on_image=face_area_on_image,affected_areas=["Face"],mask_size=0,use_minimal_area=True)
+    mask = cv2.blur(mask, (12, 12))
+    """entire_mask_image = np.zeros_like(target_image)"""
+    larger_mask = cv2.resize(mask, dsize=(face.width, face.height))
+    entire_mask_image[
+        face.top : face.bottom,
+        face.left : face.right,
+    ] = larger_mask
+   
+   
+    result = Image.composite(Image.fromarray(swapped_image),Image.fromarray(target_image), Image.fromarray(entire_mask_image).convert("L"))
+    return np.array(result)
+    
+
+def correct_face_tilt(angle: float) -> bool:
+        
+        angle = abs(angle)
+        if angle > 180:
+            angle = 360 - angle
+        return angle > 40
+def _dilate(arr: np.ndarray, value: int) -> np.ndarray:
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (value, value))
+    return cv2.dilate(arr, kernel, iterations=1)
+
+
+def _erode(arr: np.ndarray, value: int) -> np.ndarray:
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (value, value))
+    return cv2.erode(arr, kernel, iterations=1)
+colors = [
+    (255, 0, 0),
+    (0, 255, 0),
+    (0, 0, 255),
+    (255, 255, 0),
+    (255, 0, 255),
+    (0, 255, 255),
+    (255, 255, 255),
+    (128, 0, 0),
+    (0, 128, 0),
+    (128, 128, 0),
+    (0, 0, 128),
+    (0, 128, 128),
+]
+
+
+def color_generator(colors):
+    while True:
+        for color in colors:
+            yield color
+
+
+color_iter = color_generator(colors)
+def process_face_image(
+        face: Face,
+        **kwargs,
+    ) -> Image:
+        image = np.array(face.image)
+        overlay = image.copy()
+        cv2.rectangle(overlay, (0, 0), (image.shape[1], image.shape[0]), next(color_iter), -1)
+        l, t, r, b = face.face_area_on_image
+        cv2.rectangle(overlay, (l, t), (r, b), (0, 0, 0), 10)
+        if face.landmarks_on_image is not None:
+            for landmark in face.landmarks_on_image:
+                cv2.circle(overlay, (int(landmark.x), int(landmark.y)), 6, (0, 0, 0), 10)
+        alpha = 0.3
+        output = cv2.addWeighted(image, 1 - alpha, overlay, alpha, 0)
+        
+        return Image.fromarray(output)
+def dilate_erode(img: Image.Image, value: int) -> Image.Image:
     """
-    Merges two images using a given mask. The regions where the mask is set will be replaced with the corresponding
+    The dilate_erode function takes an image and a value.
-    areas of the second image.
+    If the value is positive, it dilates the image by that amount.
+    If the value is negative, it erodes the image by that amount.
 
-    Args:
+    Parameters
-        image1 (CV2Img): The base image, which must have the same shape as image2.
+    ----------
-        image2 (CV2Img): The image to be merged, which must have the same shape as image1.
+        img: PIL.Image.Image
-        mask (CV2Img): A binary mask specifying the regions to be merged. The mask shape should match image1's first two dimensions.
+            the image to be processed
+        value: int
+            kernel size of dilation or erosion
 
-    Returns:
+    Returns
-        CV2Img: The merged image.
+    -------
-
+        PIL.Image.Image
-    Raises:
+            The image that has been dilated or eroded
-        ValueError: If the shapes of the images and mask do not match.
     """
+    if value == 0:
+        return img
 
-    if image1.shape != image2.shape or image1.shape[:2] != mask.shape:
+    arr = np.array(img)
-        raise ValueError("Img should have the same shape")
+    arr = _dilate(arr, value) if value > 0 else _erode(arr, -value)
-    mask = mask.astype(np.uint8)
-    masked_region = cv2.bitwise_and(image2, image2, mask=mask)
-    inverse_mask = cv2.bitwise_not(mask)
-    empty_region = cv2.bitwise_and(image1, image1, mask=inverse_mask)
-    merged_image = cv2.add(empty_region, masked_region)
-    return merged_image
 
+    return Image.fromarray(arr)
 
-def erode_mask(mask: CV2ImgU8, kernel_size: int = 3, iterations: int = 1) -> CV2ImgU8:
+def mask_to_pil(masks, shape: tuple[int, int]) -> list[Image.Image]:
     """
-    Erodes a binary mask using a given kernel size and number of iterations.
+    Parameters
+    ----------
+    masks: torch.Tensor, dtype=torch.float32, shape=(N, H, W).
+        The device can be CUDA, but `to_pil_image` takes care of that.
 
-    Args:
+    shape: tuple[int, int]
-        mask (CV2Img): The binary mask to erode.
+        (width, height) of the original image
-        kernel_size (int, optional): The size of the kernel. Default is 3.
-        iterations (int, optional): The number of erosion iterations. Default is 1.
-
-    Returns:
-        CV2Img: The eroded mask.
     """
-    kernel = np.ones((kernel_size, kernel_size), np.uint8)
+    n = masks.shape[0]
-    eroded_mask = cv2.erode(mask, kernel, iterations=iterations)
+    return [to_pil_image(masks[i], mode="L").resize(shape) for i in range(n)]
-    return eroded_mask
 
-
+def create_mask_from_bbox(
-def apply_gaussian_blur(
+    bboxes: list[list[float]], shape: tuple[int, int]
-    mask: CV2ImgU8, kernel_size: Tuple[int, int] = (5, 5), sigma_x: int = 0
+) -> list[Image.Image]:
-) -> CV2ImgU8:
     """
-    Applies a Gaussian blur to a mask.
+    Parameters
+    ----------
+        bboxes: list[list[float]]
+            list of [x1, y1, x2, y2]
+            bounding boxes
+        shape: tuple[int, int]
+            shape of the image (width, height)
 
-    Args:
+    Returns
-        mask (CV2Img): The mask to blur.
+    -------
-        kernel_size (tuple, optional): The size of the kernel, e.g. (5, 5). Default is (5, 5).
+        masks: list[Image.Image]
-        sigma_x (int, optional): The standard deviation in the X direction. Default is 0.
+        A list of masks
 
-    Returns:
-        CV2Img: The blurred mask.
     """
-    blurred_mask = cv2.GaussianBlur(mask, kernel_size, sigma_x)
+    masks = []
-    return blurred_mask
+    for bbox in bboxes:
+        mask = Image.new("L", shape, 0)
+        mask_draw = ImageDraw.Draw(mask)
+        mask_draw.rectangle(bbox, fill=255)
+        masks.append(mask)
+    return masks
+
+def rotate_image(image: Image, angle: float) -> Image:
+    if angle == 0:
+        return image
+    return Image.fromarray(rotate_array(np.array(image), angle))
 
 
-def dilate_mask(mask: CV2ImgU8, kernel_size: int = 5, iterations: int = 1) -> CV2ImgU8:
+def rotate_array(image: np.ndarray, angle: float) -> np.ndarray:
-    """
+    if angle == 0:
-    Dilates a binary mask using a given kernel size and number of iterations.
+        return image
 
-    Args:
+    h, w = image.shape[:2]
-        mask (CV2Img): The binary mask to dilate.
+    center = (w // 2, h // 2)
-        kernel_size (int, optional): The size of the kernel. Default is 5.
-        iterations (int, optional): The number of dilation iterations. Default is 1.
 
-    Returns:
+    M = cv2.getRotationMatrix2D(center, angle, 1.0)
-        CV2Img: The dilated mask.
+    return cv2.warpAffine(image, M, (w, h))
-    """
-    kernel = np.ones((kernel_size, kernel_size), np.uint8)
-    dilated_mask = cv2.dilate(mask, kernel, iterations=iterations)
-    return dilated_mask
-
-
-def get_face_mask(aimg: CV2ImgU8, bgr_fake: CV2ImgU8) -> CV2ImgU8:
-    """
-    Generates a face mask by performing bitwise OR on two face masks and then dilating the result.
-
-    Args:
-        aimg (CV2Img): Input image for generating the first face mask.
-        bgr_fake (CV2Img): Input image for generating the second face mask.
-
-    Returns:
-        CV2Img: The combined and dilated face mask.
-    """
-    mask1 = generate_face_mask(aimg, device=shared.device)
-    mask2 = generate_face_mask(bgr_fake, device=shared.device)
-    mask = dilate_mask(cv2.bitwise_or(mask1, mask2))
-    return mask