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global refactoring and fixes,

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removed support of extracted(aligned) PNG faces. Use old builds to convert from PNG to JPG.

fanseg model file in facelib/ is renamed
This commit is contained in:
Colombo 2020-03-13 08:09:00 +04:00
parent 921b464d5b
commit 61472cdaf7
82 changed files with 3838 additions and 3812 deletions
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33
samplelib/SampleGeneratorFaceCelebAMaskHQ.py
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@ -152,15 +152,15 @@ class SampleGeneratorFaceCelebAMaskHQ(SampleGeneratorBase):
file_ids = list(mask_file_id_hash.keys())
shuffle_file_ids = []
shuffle_file_ids_random_ct = []
resolution = 256
random_flip = True
rotation_range=[-15,15]
scale_range=[-0.25, 0.75]
scale_range=[-0.10, 0.95]
tx_range=[-0.3, 0.3]
ty_range=[-0.3, 0.3]
random_bilinear_resize = (25,75)
motion_blur = (25, 5)
gaussian_blur = (25, 5)
@ -174,22 +174,15 @@ class SampleGeneratorFaceCelebAMaskHQ(SampleGeneratorBase):
if len(shuffle_file_ids) == 0:
shuffle_file_ids = file_ids.copy()
np.random.shuffle(shuffle_file_ids)
if len(shuffle_file_ids_random_ct) == 0:
shuffle_file_ids_random_ct = file_ids.copy()
np.random.shuffle(shuffle_file_ids_random_ct)
file_id = shuffle_file_ids.pop()
#file_id_random_ct = shuffle_file_ids_random_ct.pop()
masks = mask_file_id_hash[file_id]
image_path = images_path / f'{file_id}.jpg'
#image_random_ct_path = images_path / f'{file_id_random_ct}.jpg'
skin_path = masks.get(MaskType.skin, None)
hair_path = masks.get(MaskType.hair, None)
hat_path = masks.get(MaskType.hat, None)
neck_path = masks.get(MaskType.neck, None)
#neck_path = masks.get(MaskType.neck, None)
img = cv2_imread(image_path).astype(np.float32) / 255.0
mask = cv2_imread(masks_path / skin_path)[...,0:1].astype(np.float32) / 255.0
@ -205,7 +198,13 @@ class SampleGeneratorFaceCelebAMaskHQ(SampleGeneratorBase):
if hat_path.exists():
hat = cv2_imread(hat_path)[...,0:1].astype(np.float32) / 255.0
mask *= (1-hat)
#if neck_path is not None:
# neck_path = masks_path / neck_path
# if neck_path.exists():
# neck = cv2_imread(neck_path)[...,0:1].astype(np.float32) / 255.0
# mask = np.clip(mask+neck, 0, 1)
warp_params = imagelib.gen_warp_params(resolution, random_flip, rotation_range=rotation_range, scale_range=scale_range, tx_range=tx_range, ty_range=ty_range )
img = cv2.resize( img, (resolution,resolution), cv2.INTER_LANCZOS4 )
@ -215,9 +214,6 @@ class SampleGeneratorFaceCelebAMaskHQ(SampleGeneratorBase):
v = np.clip ( v + np.random.random()/2-0.25, 0, 1 )
img = np.clip( cv2.cvtColor(cv2.merge([h, s, v]), cv2.COLOR_HSV2BGR) , 0, 1 )
#img_random_ct = cv2.resize( cv2_imread(image_random_ct_path).astype(np.float32) / 255.0, (resolution,resolution), cv2.INTER_LANCZOS4 )
#img = imagelib.color_transfer ('idt', img, img_random_ct )
if motion_blur is not None:
chance, mb_max_size = motion_blur
chance = np.clip(chance, 0, 100)
@ -241,6 +237,15 @@ class SampleGeneratorFaceCelebAMaskHQ(SampleGeneratorBase):
if gblur_rnd_chance < chance:
img = cv2.GaussianBlur(img, (gblur_rnd_kernel,) *2 , 0)
if random_bilinear_resize is not None:
chance, max_size_per = random_bilinear_resize
chance = np.clip(chance, 0, 100)
pick_chance = np.random.randint(100)
resize_to = resolution - int( np.random.rand()* int(resolution*(max_size_per/100.0)) )
img = cv2.resize (img, (resize_to,resize_to), cv2.INTER_LINEAR )
img = cv2.resize (img, (resolution,resolution), cv2.INTER_LINEAR )
mask = cv2.resize( mask, (resolution,resolution), cv2.INTER_LANCZOS4 )[...,None]
mask = imagelib.warp_by_params (warp_params, mask, can_warp=True, can_transform=True, can_flip=True, border_replicate=False, cv2_inter=cv2.INTER_LANCZOS4)
mask[mask < 0.5] = 0.0