admin/LC_NeoRefacer
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Add codeformer and update license

Browse Source
This commit is contained in:
Felipe Daragon
2025-04-09 23:03:26 +01:00
parent f40ffde905
commit 3549b7e50c
84 changed files with 12545 additions and 19 deletions
Download Patch File Download Diff File Expand all files Collapse all files

455
facelib/utils/face_restoration_helper.py Normal file
View File

@@ -0,0 +1,455 @@
import cv2
import numpy as np
import os
import torch
from torchvision.transforms.functional import normalize
from facelib.detection import init_detection_model
from facelib.parsing import init_parsing_model
from facelib.utils.misc import img2tensor, imwrite
def get_largest_face(det_faces, h, w):
def get_location(val, length):
if val < 0:
return 0
elif val > length:
return length
else:
return val
face_areas = []
for det_face in det_faces:
left = get_location(det_face[0], w)
right = get_location(det_face[2], w)
top = get_location(det_face[1], h)
bottom = get_location(det_face[3], h)
face_area = (right - left) * (bottom - top)
face_areas.append(face_area)
largest_idx = face_areas.index(max(face_areas))
return det_faces[largest_idx], largest_idx
def get_center_face(det_faces, h=0, w=0, center=None):
if center is not None:
center = np.array(center)
else:
center = np.array([w / 2, h / 2])
center_dist = []
for det_face in det_faces:
face_center = np.array([(det_face[0] + det_face[2]) / 2, (det_face[1] + det_face[3]) / 2])
dist = np.linalg.norm(face_center - center)
center_dist.append(dist)
center_idx = center_dist.index(min(center_dist))
return det_faces[center_idx], center_idx
class FaceRestoreHelper(object):
"""Helper for the face restoration pipeline (base class)."""
def __init__(self,
upscale_factor,
face_size=512,
crop_ratio=(1, 1),
det_model='retinaface_resnet50',
save_ext='png',
template_3points=False,
pad_blur=False,
use_parse=False,
device=None):
self.template_3points = template_3points # improve robustness
self.upscale_factor = int(upscale_factor)
# the cropped face ratio based on the square face
self.crop_ratio = crop_ratio # (h, w)
assert (self.crop_ratio[0] >= 1 and self.crop_ratio[1] >= 1), 'crop ration only supports >=1'
self.face_size = (int(face_size * self.crop_ratio[1]), int(face_size * self.crop_ratio[0]))
if self.template_3points:
self.face_template = np.array([[192, 240], [319, 240], [257, 371]])
else:
# standard 5 landmarks for FFHQ faces with 512 x 512
# facexlib
self.face_template = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935],
[201.26117, 371.41043], [313.08905, 371.15118]])
# dlib: left_eye: 36:41 right_eye: 42:47 nose: 30,32,33,34 left mouth corner: 48 right mouth corner: 54
# self.face_template = np.array([[193.65928, 242.98541], [318.32558, 243.06108], [255.67984, 328.82894],
# [198.22603, 372.82502], [313.91018, 372.75659]])
self.face_template = self.face_template * (face_size / 512.0)
if self.crop_ratio[0] > 1:
self.face_template[:, 1] += face_size * (self.crop_ratio[0] - 1) / 2
if self.crop_ratio[1] > 1:
self.face_template[:, 0] += face_size * (self.crop_ratio[1] - 1) / 2
self.save_ext = save_ext
self.pad_blur = pad_blur
if self.pad_blur is True:
self.template_3points = False
self.all_landmarks_5 = []
self.det_faces = []
self.affine_matrices = []
self.inverse_affine_matrices = []
self.cropped_faces = []
self.restored_faces = []
self.pad_input_imgs = []
if device is None:
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
else:
self.device = device
# init face detection model
self.face_det = init_detection_model(det_model, half=False, device=self.device)
# init face parsing model
self.use_parse = use_parse
self.face_parse = init_parsing_model(model_name='parsenet', device=self.device)
def set_upscale_factor(self, upscale_factor):
self.upscale_factor = upscale_factor
def read_image(self, img):
"""img can be image path or cv2 loaded image."""
# self.input_img is Numpy array, (h, w, c), BGR, uint8, [0, 255]
if isinstance(img, str):
img = cv2.imread(img)
if np.max(img) > 256: # 16-bit image
img = img / 65535 * 255
if len(img.shape) == 2: # gray image
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
elif img.shape[2] == 4: # BGRA image with alpha channel
img = img[:, :, 0:3]
self.input_img = img
if min(self.input_img.shape[:2])<512:
f = 512.0/min(self.input_img.shape[:2])
self.input_img = cv2.resize(self.input_img, (0,0), fx=f, fy=f, interpolation=cv2.INTER_LINEAR)
def get_face_landmarks_5(self,
only_keep_largest=False,
only_center_face=False,
resize=None,
blur_ratio=0.01,
eye_dist_threshold=None):
if resize is None:
scale = 1
input_img = self.input_img
else:
h, w = self.input_img.shape[0:2]
scale = resize / min(h, w)
scale = max(1, scale) # always scale up
h, w = int(h * scale), int(w * scale)
interp = cv2.INTER_AREA if scale < 1 else cv2.INTER_LINEAR
input_img = cv2.resize(self.input_img, (w, h), interpolation=interp)
with torch.no_grad():
bboxes = self.face_det.detect_faces(input_img)
if bboxes is None or bboxes.shape[0] == 0:
return 0
else:
bboxes = bboxes / scale
for bbox in bboxes:
# remove faces with too small eye distance: side faces or too small faces
eye_dist = np.linalg.norm([bbox[6] - bbox[8], bbox[7] - bbox[9]])
if eye_dist_threshold is not None and (eye_dist < eye_dist_threshold):
continue
if self.template_3points:
landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 11, 2)])
else:
landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 15, 2)])
self.all_landmarks_5.append(landmark)
self.det_faces.append(bbox[0:5])
if len(self.det_faces) == 0:
return 0
if only_keep_largest:
h, w, _ = self.input_img.shape
self.det_faces, largest_idx = get_largest_face(self.det_faces, h, w)
self.all_landmarks_5 = [self.all_landmarks_5[largest_idx]]
elif only_center_face:
h, w, _ = self.input_img.shape
self.det_faces, center_idx = get_center_face(self.det_faces, h, w)
self.all_landmarks_5 = [self.all_landmarks_5[center_idx]]
# pad blurry images
if self.pad_blur:
self.pad_input_imgs = []
for landmarks in self.all_landmarks_5:
# get landmarks
eye_left = landmarks[0, :]
eye_right = landmarks[1, :]
eye_avg = (eye_left + eye_right) * 0.5
mouth_avg = (landmarks[3, :] + landmarks[4, :]) * 0.5
eye_to_eye = eye_right - eye_left
eye_to_mouth = mouth_avg - eye_avg
# Get the oriented crop rectangle
# x: half width of the oriented crop rectangle
x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
# - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise
# norm with the hypotenuse: get the direction
x /= np.hypot(*x) # get the hypotenuse of a right triangle
rect_scale = 1.5
x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale)
# y: half height of the oriented crop rectangle
y = np.flipud(x) * [-1, 1]
# c: center
c = eye_avg + eye_to_mouth * 0.1
# quad: (left_top, left_bottom, right_bottom, right_top)
quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
# qsize: side length of the square
qsize = np.hypot(*x) * 2
border = max(int(np.rint(qsize * 0.1)), 3)
# get pad
# pad: (width_left, height_top, width_right, height_bottom)
pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
int(np.ceil(max(quad[:, 1]))))
pad = [
max(-pad[0] + border, 1),
max(-pad[1] + border, 1),
max(pad[2] - self.input_img.shape[0] + border, 1),
max(pad[3] - self.input_img.shape[1] + border, 1)
]
if max(pad) > 1:
# pad image
pad_img = np.pad(self.input_img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
# modify landmark coords
landmarks[:, 0] += pad[0]
landmarks[:, 1] += pad[1]
# blur pad images
h, w, _ = pad_img.shape
y, x, _ = np.ogrid[:h, :w, :1]
mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0],
np.float32(w - 1 - x) / pad[2]),
1.0 - np.minimum(np.float32(y) / pad[1],
np.float32(h - 1 - y) / pad[3]))
blur = int(qsize * blur_ratio)
if blur % 2 == 0:
blur += 1
blur_img = cv2.boxFilter(pad_img, 0, ksize=(blur, blur))
# blur_img = cv2.GaussianBlur(pad_img, (blur, blur), 0)
pad_img = pad_img.astype('float32')
pad_img += (blur_img - pad_img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
pad_img += (np.median(pad_img, axis=(0, 1)) - pad_img) * np.clip(mask, 0.0, 1.0)
pad_img = np.clip(pad_img, 0, 255) # float32, [0, 255]
self.pad_input_imgs.append(pad_img)
else:
self.pad_input_imgs.append(np.copy(self.input_img))
return len(self.all_landmarks_5)
def align_warp_face(self, save_cropped_path=None, border_mode='constant'):
"""Align and warp faces with face template.
"""
if self.pad_blur:
assert len(self.pad_input_imgs) == len(
self.all_landmarks_5), f'Mismatched samples: {len(self.pad_input_imgs)} and {len(self.all_landmarks_5)}'
for idx, landmark in enumerate(self.all_landmarks_5):
# use 5 landmarks to get affine matrix
# use cv2.LMEDS method for the equivalence to skimage transform
# ref: https://blog.csdn.net/yichxi/article/details/115827338
affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0]
self.affine_matrices.append(affine_matrix)
# warp and crop faces
if border_mode == 'constant':
border_mode = cv2.BORDER_CONSTANT
elif border_mode == 'reflect101':
border_mode = cv2.BORDER_REFLECT101
elif border_mode == 'reflect':
border_mode = cv2.BORDER_REFLECT
if self.pad_blur:
input_img = self.pad_input_imgs[idx]
else:
input_img = self.input_img
cropped_face = cv2.warpAffine(
input_img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132)) # gray
self.cropped_faces.append(cropped_face)
# save the cropped face
if save_cropped_path is not None:
path = os.path.splitext(save_cropped_path)[0]
save_path = f'{path}_{idx:02d}.{self.save_ext}'
imwrite(cropped_face, save_path)
def get_inverse_affine(self, save_inverse_affine_path=None):
"""Get inverse affine matrix."""
for idx, affine_matrix in enumerate(self.affine_matrices):
inverse_affine = cv2.invertAffineTransform(affine_matrix)
inverse_affine *= self.upscale_factor
self.inverse_affine_matrices.append(inverse_affine)
# save inverse affine matrices
if save_inverse_affine_path is not None:
path, _ = os.path.splitext(save_inverse_affine_path)
save_path = f'{path}_{idx:02d}.pth'
torch.save(inverse_affine, save_path)
def add_restored_face(self, face):
self.restored_faces.append(face)
def paste_faces_to_input_image(self, save_path=None, upsample_img=None, draw_box=False, face_upsampler=None):
h, w, _ = self.input_img.shape
h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor)
if upsample_img is None:
# simply resize the background
# upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4)
upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LINEAR)
else:
upsample_img = cv2.resize(upsample_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4)
assert len(self.restored_faces) == len(
self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.')
inv_mask_borders = []
for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices):
if face_upsampler is not None:
restored_face = face_upsampler.enhance(restored_face, outscale=self.upscale_factor)[0]
inverse_affine /= self.upscale_factor
inverse_affine[:, 2] *= self.upscale_factor
face_size = (self.face_size[0]*self.upscale_factor, self.face_size[1]*self.upscale_factor)
else:
# Add an offset to inverse affine matrix, for more precise back alignment
if self.upscale_factor > 1:
extra_offset = 0.5 * self.upscale_factor
else:
extra_offset = 0
inverse_affine[:, 2] += extra_offset
face_size = self.face_size
inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up))
# if draw_box or not self.use_parse: # use square parse maps
# mask = np.ones(face_size, dtype=np.float32)
# inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))
# # remove the black borders
# inv_mask_erosion = cv2.erode(
# inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8))
# pasted_face = inv_mask_erosion[:, :, None] * inv_restored
# total_face_area = np.sum(inv_mask_erosion) # // 3
# # add border
# if draw_box:
# h, w = face_size
# mask_border = np.ones((h, w, 3), dtype=np.float32)
# border = int(1400/np.sqrt(total_face_area))
# mask_border[border:h-border, border:w-border,:] = 0
# inv_mask_border = cv2.warpAffine(mask_border, inverse_affine, (w_up, h_up))
# inv_mask_borders.append(inv_mask_border)
# if not self.use_parse:
# # compute the fusion edge based on the area of face
# w_edge = int(total_face_area**0.5) // 20
# erosion_radius = w_edge * 2
# inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
# blur_size = w_edge * 2
# inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
# if len(upsample_img.shape) == 2: # upsample_img is gray image
# upsample_img = upsample_img[:, :, None]
# inv_soft_mask = inv_soft_mask[:, :, None]
# always use square mask
mask = np.ones(face_size, dtype=np.float32)
inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))
# remove the black borders
inv_mask_erosion = cv2.erode(
inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8))
pasted_face = inv_mask_erosion[:, :, None] * inv_restored
total_face_area = np.sum(inv_mask_erosion) # // 3
# add border
if draw_box:
h, w = face_size
mask_border = np.ones((h, w, 3), dtype=np.float32)
border = int(1400/np.sqrt(total_face_area))
mask_border[border:h-border, border:w-border,:] = 0
inv_mask_border = cv2.warpAffine(mask_border, inverse_affine, (w_up, h_up))
inv_mask_borders.append(inv_mask_border)
# compute the fusion edge based on the area of face
w_edge = int(total_face_area**0.5) // 20
erosion_radius = w_edge * 2
inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
blur_size = w_edge * 2
inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
if len(upsample_img.shape) == 2: # upsample_img is gray image
upsample_img = upsample_img[:, :, None]
inv_soft_mask = inv_soft_mask[:, :, None]
# parse mask
if self.use_parse:
# inference
face_input = cv2.resize(restored_face, (512, 512), interpolation=cv2.INTER_LINEAR)
face_input = img2tensor(face_input.astype('float32') / 255., bgr2rgb=True, float32=True)
normalize(face_input, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
face_input = torch.unsqueeze(face_input, 0).to(self.device)
with torch.no_grad():
out = self.face_parse(face_input)[0]
out = out.argmax(dim=1).squeeze().cpu().numpy()
parse_mask = np.zeros(out.shape)
MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0]
for idx, color in enumerate(MASK_COLORMAP):
parse_mask[out == idx] = color
# blur the mask
parse_mask = cv2.GaussianBlur(parse_mask, (101, 101), 11)
parse_mask = cv2.GaussianBlur(parse_mask, (101, 101), 11)
# remove the black borders
thres = 10
parse_mask[:thres, :] = 0
parse_mask[-thres:, :] = 0
parse_mask[:, :thres] = 0
parse_mask[:, -thres:] = 0
parse_mask = parse_mask / 255.
parse_mask = cv2.resize(parse_mask, face_size)
parse_mask = cv2.warpAffine(parse_mask, inverse_affine, (w_up, h_up), flags=3)
inv_soft_parse_mask = parse_mask[:, :, None]
# pasted_face = inv_restored
fuse_mask = (inv_soft_parse_mask<inv_soft_mask).astype('int')
inv_soft_mask = inv_soft_parse_mask*fuse_mask + inv_soft_mask*(1-fuse_mask)
if len(upsample_img.shape) == 3 and upsample_img.shape[2] == 4: # alpha channel
alpha = upsample_img[:, :, 3:]
upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img[:, :, 0:3]
upsample_img = np.concatenate((upsample_img, alpha), axis=2)
else:
upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img
if np.max(upsample_img) > 256: # 16-bit image
upsample_img = upsample_img.astype(np.uint16)
else:
upsample_img = upsample_img.astype(np.uint8)
# draw bounding box
if draw_box:
# upsample_input_img = cv2.resize(input_img, (w_up, h_up))
img_color = np.ones([*upsample_img.shape], dtype=np.float32)
img_color[:,:,0] = 0
img_color[:,:,1] = 255
img_color[:,:,2] = 0
for inv_mask_border in inv_mask_borders:
upsample_img = inv_mask_border * img_color + (1 - inv_mask_border) * upsample_img
# upsample_input_img = inv_mask_border * img_color + (1 - inv_mask_border) * upsample_input_img
if save_path is not None:
path = os.path.splitext(save_path)[0]
save_path = f'{path}.{self.save_ext}'
imwrite(upsample_img, save_path)
return upsample_img
def clean_all(self):
self.all_landmarks_5 = []
self.restored_faces = []
self.affine_matrices = []
self.cropped_faces = []
self.inverse_affine_matrices = []
self.det_faces = []
self.pad_input_imgs = []