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remove gfpgan dep

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Qing
2024-08-12 11:02:55 +08:00
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iopaint/plugins/facexlib/utils/face_restoration_helper.py Normal file
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import cv2
import numpy as np
import os
import torch
from torchvision.transforms.functional import normalize
from ..detection import init_detection_model
from ..parsing import init_parsing_model
from ..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,
model_rootpath=None,
):
self.template_3points = template_3points # improve robustness
self.upscale_factor = 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
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],
]
)
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, model_rootpath=model_rootpath
)
# init face parsing model
self.use_parse = use_parse
self.face_parse = init_parsing_model(
model_name="parsenet", device=self.device, model_rootpath=model_rootpath
)
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: # RGBA image with alpha channel
img = img[:, :, 0:3]
self.input_img = img
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 = min(h, w) / resize
h, w = int(h / scale), int(w / scale)
input_img = cv2.resize(
self.input_img, (w, h), interpolation=cv2.INTER_LANCZOS4
)
with torch.no_grad():
bboxes = self.face_det.detect_faces(input_img, 0.97) * scale
for bbox in bboxes:
# remove faces with too small eye distance: side faces or too small faces
eye_dist = np.linalg.norm([bbox[5] - bbox[7], bbox[6] - bbox[8]])
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):
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
)
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."
for restored_face, inverse_affine in zip(
self.restored_faces, self.inverse_affine_matrices
):
# 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
inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up))
if self.use_parse:
# inference
face_input = cv2.resize(
restored_face, (512, 512), interpolation=cv2.INTER_LINEAR
)
face_input = img2tensor(
face_input.astype("float32") / 255.0, 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()
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):
mask[out == idx] = color
# blur the mask
mask = cv2.GaussianBlur(mask, (101, 101), 11)
mask = cv2.GaussianBlur(mask, (101, 101), 11)
# remove the black borders
thres = 10
mask[:thres, :] = 0
mask[-thres:, :] = 0
mask[:, :thres] = 0
mask[:, -thres:] = 0
mask = mask / 255.0
mask = cv2.resize(mask, restored_face.shape[:2])
mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up), flags=3)
inv_soft_mask = mask[:, :, None]
pasted_face = inv_restored
else: # use square parse maps
mask = np.ones(self.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
# 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]
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)
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 = []