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import time
import torch
import onnx
import cv2
import onnxruntime
import numpy as np
from tqdm import tqdm
import torch.nn as nn
from onnx import numpy_helper
from skimage import transform as trans
import torchvision.transforms.functional as F
import torch.nn.functional as F
from utils import mask_crop, laplacian_blending
arcface_dst = np.array(
[[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366],
[41.5493, 92.3655], [70.7299, 92.2041]],
dtype=np.float32)
def estimate_norm(lmk, image_size=112, mode='arcface'):
assert lmk.shape == (5, 2)
assert image_size % 112 == 0 or image_size % 128 == 0
if image_size % 112 == 0:
ratio = float(image_size) / 112.0
diff_x = 0
else:
ratio = float(image_size) / 128.0
diff_x = 8.0 * ratio
dst = arcface_dst * ratio
dst[:, 0] += diff_x
tform = trans.SimilarityTransform()
tform.estimate(lmk, dst)
M = tform.params[0:2, :]
return M
def norm_crop2(img, landmark, image_size=112, mode='arcface'):
M = estimate_norm(landmark, image_size, mode)
warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
return warped, M
class Inswapper():
def __init__(self, model_file=None, batch_size=32, providers=['CPUExecutionProvider']):
self.model_file = model_file
self.batch_size = batch_size
model = onnx.load(self.model_file)
graph = model.graph
self.emap = numpy_helper.to_array(graph.initializer[-1])
self.session_options = onnxruntime.SessionOptions()
self.session = onnxruntime.InferenceSession(self.model_file, sess_options=self.session_options, providers=providers)
def forward(self, imgs, latents):
preds = []
for img, latent in zip(imgs, latents):
img = img / 255
pred = self.session.run(['output'], {'target': img, 'source': latent})[0]
preds.append(pred)
def get(self, imgs, target_faces, source_faces):
imgs = list(imgs)
preds = [None] * len(imgs)
matrs = [None] * len(imgs)
for idx, (img, target_face, source_face) in enumerate(zip(imgs, target_faces, source_faces)):
matrix, blob, latent = self.prepare_data(img, target_face, source_face)
pred = self.session.run(['output'], {'target': blob, 'source': latent})[0]
pred = pred.transpose((0, 2, 3, 1))[0]
pred = np.clip(255 * pred, 0, 255).astype(np.uint8)[:, :, ::-1]
preds[idx] = pred
matrs[idx] = matrix
return (preds, matrs)
def prepare_data(self, img, target_face, source_face):
if isinstance(img, str):
img = cv2.imread(img)
aligned_img, matrix = norm_crop2(img, target_face.kps, 128)
blob = cv2.dnn.blobFromImage(aligned_img, 1.0 / 255, (128, 128), (0., 0., 0.), swapRB=True)
latent = source_face.normed_embedding.reshape((1, -1))
latent = np.dot(latent, self.emap)
latent /= np.linalg.norm(latent)
return (matrix, blob, latent)
def batch_forward(self, img_list, target_f_list, source_f_list):
num_samples = len(img_list)
num_batches = (num_samples + self.batch_size - 1) // self.batch_size
for i in tqdm(range(num_batches), desc="Generating face"):
start_idx = i * self.batch_size
end_idx = min((i + 1) * self.batch_size, num_samples)
batch_img = img_list[start_idx:end_idx]
batch_target_f = target_f_list[start_idx:end_idx]
batch_source_f = source_f_list[start_idx:end_idx]
batch_pred, batch_matr = self.get(batch_img, batch_target_f, batch_source_f)
yield batch_pred, batch_matr
def paste_to_whole(foreground, background, matrix, mask=None, crop_mask=(0,0,0,0), blur_amount=0.1, erode_amount = 0.15, blend_method='linear'):
inv_matrix = cv2.invertAffineTransform(matrix)
fg_shape = foreground.shape[:2]
bg_shape = (background.shape[1], background.shape[0])
foreground = cv2.warpAffine(foreground, inv_matrix, bg_shape, borderValue=0.0)
if mask is None:
mask = np.full(fg_shape, 1., dtype=np.float32)
mask = mask_crop(mask, crop_mask)
mask = cv2.warpAffine(mask, inv_matrix, bg_shape, borderValue=0.0)
else:
assert fg_shape == mask.shape[:2], "foreground & mask shape mismatch!"
mask = mask_crop(mask, crop_mask).astype('float32')
mask = cv2.warpAffine(mask, inv_matrix, (background.shape[1], background.shape[0]), borderValue=0.0)
_mask = mask.copy()
_mask[_mask > 0.05] = 1.
non_zero_points = cv2.findNonZero(_mask)
_, _, w, h = cv2.boundingRect(non_zero_points)
mask_size = int(np.sqrt(w * h))
if erode_amount > 0:
kernel_size = max(int(mask_size * erode_amount), 1)
structuring_element = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size))
mask = cv2.erode(mask, structuring_element)
if blur_amount > 0:
kernel_size = max(int(mask_size * blur_amount), 3)
if kernel_size % 2 == 0:
kernel_size += 1
mask = cv2.GaussianBlur(mask, (kernel_size, kernel_size), 0)
mask = np.tile(np.expand_dims(mask, axis=-1), (1, 1, 3))
if blend_method == 'laplacian':
composite_image = laplacian_blending(foreground, background, mask.clip(0,1), num_levels=4)
else:
composite_image = mask * foreground + (1 - mask) * background
return composite_image.astype("uint8").clip(0, 255)
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