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import dlib
from skimage import io
from skimage import transform as sktransform
import numpy as np
from matplotlib import pyplot as plt
import json
import os
import random
from PIL import Image
from imgaug import augmenters as iaa
from .DeepFakeMask import dfl_full,facehull,components,extended
import cv2
import tqdm
def name_resolve(path):
name = os.path.splitext(os.path.basename(path))[0]
vid_id, frame_id = name.split('_')[0:2]
return vid_id, frame_id
def total_euclidean_distance(a,b):
assert len(a.shape) == 2
return np.sum(np.linalg.norm(a-b,axis=1))
def random_get_hull(landmark,img1,hull_type):
if hull_type == 0:
mask = dfl_full(landmarks=landmark.astype('int32'),face=img1, channels=3).mask
return mask/255
elif hull_type == 1:
mask = extended(landmarks=landmark.astype('int32'),face=img1, channels=3).mask
return mask/255
elif hull_type == 2:
mask = components(landmarks=landmark.astype('int32'),face=img1, channels=3).mask
return mask/255
elif hull_type == 3:
mask = facehull(landmarks=landmark.astype('int32'),face=img1, channels=3).mask
return mask/255
def random_erode_dilate(mask, ksize=None):
if random.random()>0.5:
if ksize is None:
ksize = random.randint(1,21)
if ksize % 2 == 0:
ksize += 1
mask = np.array(mask).astype(np.uint8)*255
kernel = np.ones((ksize,ksize),np.uint8)
mask = cv2.erode(mask,kernel,1)/255
else:
if ksize is None:
ksize = random.randint(1,5)
if ksize % 2 == 0:
ksize += 1
mask = np.array(mask).astype(np.uint8)*255
kernel = np.ones((ksize,ksize),np.uint8)
mask = cv2.dilate(mask,kernel,1)/255
return mask
# borrow from https://github.com/MarekKowalski/FaceSwap
def blendImages(src, dst, mask, featherAmount=0.2):
maskIndices = np.where(mask != 0)
src_mask = np.ones_like(mask)
dst_mask = np.zeros_like(mask)
maskPts = np.hstack((maskIndices[1][:, np.newaxis], maskIndices[0][:, np.newaxis]))
faceSize = np.max(maskPts, axis=0) - np.min(maskPts, axis=0)
featherAmount = featherAmount * np.max(faceSize)
hull = cv2.convexHull(maskPts)
dists = np.zeros(maskPts.shape[0])
for i in range(maskPts.shape[0]):
dists[i] = cv2.pointPolygonTest(hull, (maskPts[i, 0], maskPts[i, 1]), True)
weights = np.clip(dists / featherAmount, 0, 1)
composedImg = np.copy(dst)
composedImg[maskIndices[0], maskIndices[1]] = weights[:, np.newaxis] * src[maskIndices[0], maskIndices[1]] + (1 - weights[:, np.newaxis]) * dst[maskIndices[0], maskIndices[1]]
composedMask = np.copy(dst_mask)
composedMask[maskIndices[0], maskIndices[1]] = weights[:, np.newaxis] * src_mask[maskIndices[0], maskIndices[1]] + (
1 - weights[:, np.newaxis]) * dst_mask[maskIndices[0], maskIndices[1]]
return composedImg, composedMask
# borrow from https://github.com/MarekKowalski/FaceSwap
def colorTransfer(src, dst, mask):
transferredDst = np.copy(dst)
maskIndices = np.where(mask != 0)
maskedSrc = src[maskIndices[0], maskIndices[1]].astype(np.int32)
maskedDst = dst[maskIndices[0], maskIndices[1]].astype(np.int32)
meanSrc = np.mean(maskedSrc, axis=0)
meanDst = np.mean(maskedDst, axis=0)
maskedDst = maskedDst - meanDst
maskedDst = maskedDst + meanSrc
maskedDst = np.clip(maskedDst, 0, 255)
transferredDst[maskIndices[0], maskIndices[1]] = maskedDst
return transferredDst
class BIOnlineGeneration():
def __init__(self):
with open('precomuted_landmarks.json', 'r') as f:
self.landmarks_record = json.load(f)
for k,v in self.landmarks_record.items():
self.landmarks_record[k] = np.array(v)
# extract all frame from all video in the name of {videoid}_{frameid}
self.data_list = [
'000_0000.png',
'001_0000.png'
] * 10000
# predefine mask distortion
self.distortion = iaa.Sequential([iaa.PiecewiseAffine(scale=(0.01, 0.15))])
def gen_one_datapoint(self):
background_face_path = random.choice(self.data_list)
data_type = 'real' if random.randint(0,1) else 'fake'
if data_type == 'fake' :
face_img,mask = self.get_blended_face(background_face_path)
mask = ( 1 - mask ) * mask * 4
else:
face_img = io.imread(background_face_path)
mask = np.zeros((317, 317, 1))
# randomly downsample after BI pipeline
if random.randint(0,1):
aug_size = random.randint(64, 317)
face_img = Image.fromarray(face_img)
if random.randint(0,1):
face_img = face_img.resize((aug_size, aug_size), Image.BILINEAR)
else:
face_img = face_img.resize((aug_size, aug_size), Image.NEAREST)
face_img = face_img.resize((317, 317),Image.BILINEAR)
face_img = np.array(face_img)
# random jpeg compression after BI pipeline
if random.randint(0,1):
quality = random.randint(60, 100)
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality]
face_img_encode = cv2.imencode('.jpg', face_img, encode_param)[1]
face_img = cv2.imdecode(face_img_encode, cv2.IMREAD_COLOR)
face_img = face_img[60:317,30:287,:]
mask = mask[60:317,30:287,:]
# random flip
if random.randint(0,1):
face_img = np.flip(face_img,1)
mask = np.flip(mask,1)
return face_img,mask,data_type
def get_blended_face(self,background_face_path):
background_face = io.imread(background_face_path)
background_landmark = self.landmarks_record[background_face_path]
foreground_face_path = self.search_similar_face(background_landmark,background_face_path)
foreground_face = io.imread(foreground_face_path)
# down sample before blending
aug_size = random.randint(128,317)
background_landmark = background_landmark * (aug_size/317)
foreground_face = sktransform.resize(foreground_face,(aug_size,aug_size),preserve_range=True).astype(np.uint8)
background_face = sktransform.resize(background_face,(aug_size,aug_size),preserve_range=True).astype(np.uint8)
# get random type of initial blending mask
mask = random_get_hull(background_landmark, background_face)
# random deform mask
mask = self.distortion.augment_image(mask)
mask = random_erode_dilate(mask)
# filte empty mask after deformation
if np.sum(mask) == 0 :
raise NotImplementedError
# apply color transfer
foreground_face = colorTransfer(background_face, foreground_face, mask*255)
# blend two face
blended_face, mask = blendImages(foreground_face, background_face, mask*255)
blended_face = blended_face.astype(np.uint8)
# resize back to default resolution
blended_face = sktransform.resize(blended_face,(317,317),preserve_range=True).astype(np.uint8)
mask = sktransform.resize(mask,(317,317),preserve_range=True)
mask = mask[:,:,0:1]
return blended_face,mask
def search_similar_face(self,this_landmark,background_face_path):
vid_id, frame_id = name_resolve(background_face_path)
min_dist = 99999999
# random sample 5000 frame from all frams:
all_candidate_path = random.sample( self.data_list, k=5000)
# filter all frame that comes from the same video as background face
all_candidate_path = filter(lambda k:name_resolve(k)[0] != vid_id, all_candidate_path)
all_candidate_path = list(all_candidate_path)
# loop throungh all candidates frame to get best match
for candidate_path in all_candidate_path:
candidate_landmark = self.landmarks_record[candidate_path].astype(np.float32)
candidate_distance = total_euclidean_distance(candidate_landmark, this_landmark)
if candidate_distance < min_dist:
min_dist = candidate_distance
min_path = candidate_path
return min_path
if __name__ == '__main__':
ds = BIOnlineGeneration()
from tqdm import tqdm
all_imgs = []
for _ in tqdm(range(50)):
img,mask,label = ds.gen_one_datapoint()
mask = np.repeat(mask,3,2)
mask = (mask*255).astype(np.uint8)
img_cat = np.concatenate([img,mask],1)
all_imgs.append(img_cat)
all_in_one = Image.new('RGB', (2570,2570))
for x in range(5):
for y in range(10):
idx = x*10+y
im = Image.fromarray(all_imgs[idx])
dx = x*514
dy = y*257
all_in_one.paste(im, (dx,dy))
all_in_one.save("all_in_one.jpg") |