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import cv2
import numpy as np
from PIL import Image, ImageDraw
from torchvision.transforms.functional import to_pil_image
from scripts.reactor_logger import logger
from scripts.reactor_inferencers.bisenet_mask_generator import BiSeNetMaskGenerator
from scripts.reactor_entities.face import FaceArea
from scripts.reactor_entities.rect import Rect
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
def process_face_image(
face: FaceArea,
**kwargs,
) -> Image:
image = np.array(face.image)
overlay = image.copy()
color_iter = color_generator(colors)
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 apply_face_mask(swapped_image:np.ndarray,target_image:np.ndarray,target_face,entire_mask_image:np.array)->np.ndarray:
logger.status("Correcting Face Mask")
mask_generator = BiSeNetMaskGenerator()
face = FaceArea(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 rotate_array(image: np.ndarray, angle: float) -> np.ndarray:
if angle == 0:
return image
h, w = image.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
return cv2.warpAffine(image, M, (w, h))
def rotate_image(image: Image, angle: float) -> Image:
if angle == 0:
return image
return Image.fromarray(rotate_array(np.array(image), angle))
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)
def dilate_erode(img: Image.Image, value: int) -> Image.Image:
"""
The dilate_erode function takes an image and a value.
If the value is positive, it dilates the image by that amount.
If the value is negative, it erodes the image by that amount.
Parameters
----------
img: PIL.Image.Image
the image to be processed
value: int
kernel size of dilation or erosion
Returns
-------
PIL.Image.Image
The image that has been dilated or eroded
"""
if value == 0:
return img
arr = np.array(img)
arr = _dilate(arr, value) if value > 0 else _erode(arr, -value)
return Image.fromarray(arr)
def mask_to_pil(masks, shape: tuple[int, int]) -> list[Image.Image]:
"""
Parameters
----------
masks: torch.Tensor, dtype=torch.float32, shape=(N, H, W).
The device can be CUDA, but `to_pil_image` takes care of that.
shape: tuple[int, int]
(width, height) of the original image
"""
n = masks.shape[0]
return [to_pil_image(masks[i], mode="L").resize(shape) for i in range(n)]
def create_mask_from_bbox(
bboxes: list[list[float]], shape: tuple[int, int]
) -> list[Image.Image]:
"""
Parameters
----------
bboxes: list[list[float]]
list of [x1, y1, x2, y2]
bounding boxes
shape: tuple[int, int]
shape of the image (width, height)
Returns
-------
masks: list[Image.Image]
A list of masks
"""
masks = []
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
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