StarMirror/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2024-03-22 13:10:55 +08:00
Code Issues Projects Releases Wiki Activity

new fix of "fixed bug when the same face could be detected twice"

Browse Source
This commit is contained in:
Colombo 2019-10-14 20:26:53 +04:00
parent 85cbdcd290
commit e18b07549b
1 changed files with 29 additions and 15 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
facelib/FANExtractor.py
View File

@ -4,6 +4,7 @@ from pathlib import Path
import cv2
import numpy as np
from numpy import linalg as npla
from facelib import FaceType, LandmarksProcessor
from nnlib import nnlib
@ -76,24 +77,37 @@ class FANExtractor(object):
landmarks.append (None)
if second_pass_extractor is not None:
for i in range(len(landmarks)):
new_landmarks = []
for lmrks in landmarks:
new_landmarks += [None]
try:
lmrks = landmarks[i]
if lmrks is None:
continue
if lmrks is not None:
image_to_face_mat = LandmarksProcessor.get_transform_mat (lmrks, 256, FaceType.FULL)
face_image = cv2.warpAffine(input_image, image_to_face_mat, (256, 256), cv2.INTER_CUBIC )
image_to_face_mat = LandmarksProcessor.get_transform_mat (lmrks, 256, FaceType.FULL)
face_image = cv2.warpAffine(input_image, image_to_face_mat, (256, 256), cv2.INTER_CUBIC )
rects2 = second_pass_extractor.extract(face_image, is_bgr=is_bgr)
if len(rects2) != 1: #dont do second pass if faces != 1 detected in cropped image
continue
lmrks2 = self.extract (face_image, [ rects2[0] ], is_bgr=is_bgr, multi_sample=True)[0]
source_lmrks2 = LandmarksProcessor.transform_points (lmrks2, image_to_face_mat, True)
landmarks[i] = source_lmrks2
rects2 = second_pass_extractor.extract(face_image, is_bgr=is_bgr)
if len(rects2) == 1: #dont do second pass if faces != 1 detected in cropped image
lmrks2 = self.extract (face_image, [ rects2[0] ], is_bgr=is_bgr, multi_sample=True)[0]
new_landmarks[-1] = LandmarksProcessor.transform_points (lmrks2, image_to_face_mat, True)
except:
continue
pass
for i, new_lmrks in enumerate(new_landmarks):
mat = LandmarksProcessor.get_transform_mat (new_lmrks, 256, FaceType.FULL)
center, p1,p2 = LandmarksProcessor.transform_points ([ [127,127], [0,0], [0,255] ], mat, True)
p_dist = npla.norm(p2-p1)
for j, other_lmrks in enumerate(landmarks):
if i != j:
other_mat = LandmarksProcessor.get_transform_mat (new_lmrks, 256, FaceType.FULL)
other_center = LandmarksProcessor.transform_points ([ [127,127] ], other_mat, True)
dist = npla.norm (other_center - center)
if dist < p_dist*0.25:
break
else:
landmarks[i] = new_lmrks
return landmarks