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added fanseg for future WF segmentation model

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Colombo 2020-03-08 00:49:12 +04:00
parent 3b6ad4abf9
commit 143792fd31
6 changed files with 429 additions and 112 deletions
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43
samplelib/SampleProcessor.py
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@ -148,7 +148,28 @@ class SampleProcessor(object):
raise ValueError("only channel_type.G supported for the mask")
elif sample_type == SPST.FACE_IMAGE:
img = sample_bgr
img = sample_bgr
if sample_face_type == FaceType.MARK_ONLY:
mat = LandmarksProcessor.get_transform_mat (sample_landmarks, warp_resolution, face_type)
img = cv2.warpAffine( img, mat, (warp_resolution,warp_resolution), flags=cv2.INTER_CUBIC )
img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=border_replicate)
img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
else:
img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=border_replicate)
mat = LandmarksProcessor.get_transform_mat (sample_landmarks, resolution, face_type)
img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_CUBIC )
img = np.clip(img.astype(np.float32), 0, 1)
# Apply random color transfer
if ct_mode is not None and ct_sample is not None:
if ct_sample_bgr is None:
ct_sample_bgr = ct_sample.load_bgr()
img = imagelib.color_transfer (ct_mode, img, cv2.resize( ct_sample_bgr, (resolution,resolution), cv2.INTER_LINEAR ) )
if motion_blur is not None:
chance, mb_max_size = motion_blur
chance = np.clip(chance, 0, 100)
@ -171,25 +192,7 @@ class SampleProcessor(object):
if gblur_rnd_chance < chance:
img = cv2.GaussianBlur(img, (gblur_rnd_kernel,) *2 , 0)
if sample_face_type == FaceType.MARK_ONLY:
mat = LandmarksProcessor.get_transform_mat (sample_landmarks, warp_resolution, face_type)
img = cv2.warpAffine( img, mat, (warp_resolution,warp_resolution), flags=cv2.INTER_CUBIC )
img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=border_replicate)
img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
else:
img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=border_replicate)
mat = LandmarksProcessor.get_transform_mat (sample_landmarks, resolution, face_type)
img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_CUBIC )
img = np.clip(img.astype(np.float32), 0, 1)
# Apply random color transfer
if ct_mode is not None and ct_sample is not None:
if ct_sample_bgr is None:
ct_sample_bgr = ct_sample.load_bgr()
img = imagelib.color_transfer (ct_mode, img, cv2.resize( ct_sample_bgr, (resolution,resolution), cv2.INTER_LINEAR ) )
# Transform from BGR to desired channel_type
if channel_type == SPCT.BGR:
out_sample = img