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improved face align,

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More stable and precise version of the face transformation matrix.
Now full_faces are aligned with the upper and lateral boundaries of the frame,
result: fix of cutted mouth, increase area of the cheeks of side faces
before/after https://i.imgur.com/t9IyGZv.jpg
therefore, additional training is required for existing models.
Optionally, you can re-extract dst faces of your project, if they have problems with cutted mouth or cheeks.
This commit is contained in:
Colombo 2019-12-19 18:33:04 +04:00
parent 9e9dc364c9
commit dd1d5e8909
4 changed files with 66 additions and 54 deletions
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18
samplelib/SampleProcessor.py
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@ -177,8 +177,8 @@ class SampleProcessor(object):
if len(mask.shape) == 2:
mask = mask[...,np.newaxis]
img = np.concatenate( (img, mask ), -1 )
return img
return img, mask
img = sample_bgr
@ -222,14 +222,20 @@ class SampleProcessor(object):
img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], target_ft), (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
mask = cv2.warpAffine( mask, LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], target_ft), (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
#then apply transforms
img = do_transform (img, mask)
img, mask = do_transform (img, mask)
img = np.concatenate( (img, mask ), -1 )
img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
else:
img = do_transform (img, mask)
img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, target_ft), (resolution,resolution), borderMode=(cv2.BORDER_REPLICATE if border_replicate else cv2.BORDER_CONSTANT), flags=cv2.INTER_CUBIC )
img, mask = do_transform (img, mask)
mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, target_ft)
img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=(cv2.BORDER_REPLICATE if border_replicate else cv2.BORDER_CONSTANT), flags=cv2.INTER_CUBIC )
mask = cv2.warpAffine( mask, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_CUBIC )
img = np.concatenate( (img, mask[...,None] ), -1 )
else:
img = do_transform (img, mask)
img, mask = do_transform (img, mask)
img = np.concatenate( (img, mask ), -1 )
img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
if random_sub_res != 0: