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fix cv2.resize interpolation

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This commit is contained in:
Colombo 2020-07-03 18:40:35 +04:00
parent 55c51578e5
commit 770da74a9b
6 changed files with 25 additions and 25 deletions
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4
core/imagelib/filters.py
View file

@ -79,8 +79,8 @@ def apply_random_bilinear_resize( img, chance, max_size_per, mask=None, rnd_stat
rw = w - int( trg * int(w*(max_size_per/100.0)) ) rw = w - int( trg * int(w*(max_size_per/100.0)) )
rh = h - int( trg * int(h*(max_size_per/100.0)) ) rh = h - int( trg * int(h*(max_size_per/100.0)) )
result = cv2.resize (result, (rw,rh), cv2.INTER_LINEAR ) result = cv2.resize (result, (rw,rh), interpolation=cv2.INTER_LINEAR )
result = cv2.resize (result, (w,h), cv2.INTER_LINEAR ) result = cv2.resize (result, (w,h), interpolation=cv2.INTER_LINEAR )
if mask is not None: if mask is not None:
result = img*(1-mask) + result*mask result = img*(1-mask) + result*mask

4
core/imagelib/warp.py
View file

@ -51,7 +51,7 @@ def warp_by_params (params, img, can_warp, can_transform, can_flip, border_repli
rw = params['rw'] rw = params['rw']
if (can_warp or can_transform) and rw is not None: if (can_warp or can_transform) and rw is not None:
img = cv2.resize(img, (16,16), cv2_inter) img = cv2.resize(img, (16,16), interpolation=cv2_inter)
if can_warp: if can_warp:
img = cv2.remap(img, params['mapx'], params['mapy'], cv2_inter ) img = cv2.remap(img, params['mapx'], params['mapy'], cv2_inter )
@ -60,7 +60,7 @@ def warp_by_params (params, img, can_warp, can_transform, can_flip, border_repli
if (can_warp or can_transform) and rw is not None: if (can_warp or can_transform) and rw is not None:
img = cv2.resize(img, (rw,rw), cv2_inter) img = cv2.resize(img, (rw,rw), interpolation=cv2_inter)
if len(img.shape) == 2: if len(img.shape) == 2:
img = img[...,None] img = img[...,None]

8
facelib/FaceEnhancer.py
View file

@ -248,7 +248,7 @@ class FaceEnhancer(object):
final_img = final_img [t_padding*up_res:(h-b_padding)*up_res, l_padding*up_res:(w-r_padding)*up_res,:] final_img = final_img [t_padding*up_res:(h-b_padding)*up_res, l_padding*up_res:(w-r_padding)*up_res,:]
if preserve_size: if preserve_size:
final_img = cv2.resize (final_img, (iw,ih), cv2.INTER_LANCZOS4) final_img = cv2.resize (final_img, (iw,ih), interpolation=cv2.INTER_LANCZOS4)
if not is_tanh: if not is_tanh:
final_img = np.clip( final_img/2+0.5, 0, 1 ) final_img = np.clip( final_img/2+0.5, 0, 1 )
@ -278,7 +278,7 @@ class FaceEnhancer(object):
preupscale_rate = 1.0 / ( max(h,w) / patch_size ) preupscale_rate = 1.0 / ( max(h,w) / patch_size )
if preupscale_rate != 1.0: if preupscale_rate != 1.0:
inp_img = cv2.resize (inp_img, ( int(w*preupscale_rate), int(h*preupscale_rate) ), cv2.INTER_LANCZOS4) inp_img = cv2.resize (inp_img, ( int(w*preupscale_rate), int(h*preupscale_rate) ), interpolation=cv2.INTER_LANCZOS4)
h,w,c = inp_img.shape h,w,c = inp_img.shape
i_max = w-patch_size+1 i_max = w-patch_size+1
@ -310,10 +310,10 @@ class FaceEnhancer(object):
final_img /= final_img_div final_img /= final_img_div
if preserve_size: if preserve_size:
final_img = cv2.resize (final_img, (w,h), cv2.INTER_LANCZOS4) final_img = cv2.resize (final_img, (w,h), interpolation=cv2.INTER_LANCZOS4)
else: else:
if preupscale_rate != 1.0: if preupscale_rate != 1.0:
final_img = cv2.resize (final_img, (tw,th), cv2.INTER_LANCZOS4) final_img = cv2.resize (final_img, (tw,th), interpolation=cv2.INTER_LANCZOS4)
if not is_tanh: if not is_tanh:
final_img = np.clip( final_img/2+0.5, 0, 1 ) final_img = np.clip( final_img/2+0.5, 0, 1 )

20
merger/MergeMasked.py
View file

@ -54,11 +54,11 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
prd_face_bgr = np.clip(prd_face_bgr, 0, 1) prd_face_bgr = np.clip(prd_face_bgr, 0, 1)
if cfg.super_resolution_power != 0: if cfg.super_resolution_power != 0:
prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size, output_size), interpolation=cv2.INTER_CUBIC)
prd_face_dst_mask_a_0 = cv2.resize (prd_face_dst_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) prd_face_dst_mask_a_0 = cv2.resize (prd_face_dst_mask_a_0, (output_size, output_size), interpolation=cv2.INTER_CUBIC)
if cfg.mask_mode == 1: #dst if cfg.mask_mode == 1: #dst
wrk_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (output_size,output_size), cv2.INTER_CUBIC) wrk_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (output_size,output_size), interpolation=cv2.INTER_CUBIC)
elif cfg.mask_mode == 2: #learned-prd elif cfg.mask_mode == 2: #learned-prd
wrk_face_mask_a_0 = prd_face_mask_a_0 wrk_face_mask_a_0 = prd_face_mask_a_0
elif cfg.mask_mode == 3: #learned-dst elif cfg.mask_mode == 3: #learned-dst
@ -70,16 +70,16 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
elif cfg.mask_mode >= 6 and cfg.mask_mode <= 9: #XSeg modes elif cfg.mask_mode >= 6 and cfg.mask_mode <= 9: #XSeg modes
if cfg.mask_mode == 6 or cfg.mask_mode == 8 or cfg.mask_mode == 9: if cfg.mask_mode == 6 or cfg.mask_mode == 8 or cfg.mask_mode == 9:
# obtain XSeg-prd # obtain XSeg-prd
prd_face_xseg_bgr = cv2.resize (prd_face_bgr, (xseg_input_size,)*2, cv2.INTER_CUBIC) prd_face_xseg_bgr = cv2.resize (prd_face_bgr, (xseg_input_size,)*2, interpolation=cv2.INTER_CUBIC)
prd_face_xseg_mask = xseg_256_extract_func(prd_face_xseg_bgr) prd_face_xseg_mask = xseg_256_extract_func(prd_face_xseg_bgr)
X_prd_face_mask_a_0 = cv2.resize ( prd_face_xseg_mask, (output_size, output_size), cv2.INTER_CUBIC) X_prd_face_mask_a_0 = cv2.resize ( prd_face_xseg_mask, (output_size, output_size), interpolation=cv2.INTER_CUBIC)
if cfg.mask_mode >= 7 and cfg.mask_mode <= 9: if cfg.mask_mode >= 7 and cfg.mask_mode <= 9:
# obtain XSeg-dst # obtain XSeg-dst
xseg_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, xseg_input_size, face_type=cfg.face_type) xseg_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, xseg_input_size, face_type=cfg.face_type)
dst_face_xseg_bgr = cv2.warpAffine(img_bgr, xseg_mat, (xseg_input_size,)*2, flags=cv2.INTER_CUBIC ) dst_face_xseg_bgr = cv2.warpAffine(img_bgr, xseg_mat, (xseg_input_size,)*2, flags=cv2.INTER_CUBIC )
dst_face_xseg_mask = xseg_256_extract_func(dst_face_xseg_bgr) dst_face_xseg_mask = xseg_256_extract_func(dst_face_xseg_bgr)
X_dst_face_mask_a_0 = cv2.resize (dst_face_xseg_mask, (output_size,output_size), cv2.INTER_CUBIC) X_dst_face_mask_a_0 = cv2.resize (dst_face_xseg_mask, (output_size,output_size), interpolation=cv2.INTER_CUBIC)
if cfg.mask_mode == 6: #'XSeg-prd' if cfg.mask_mode == 6: #'XSeg-prd'
wrk_face_mask_a_0 = X_prd_face_mask_a_0 wrk_face_mask_a_0 = X_prd_face_mask_a_0
@ -94,7 +94,7 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
# resize to mask_subres_size # resize to mask_subres_size
if wrk_face_mask_a_0.shape[0] != mask_subres_size: if wrk_face_mask_a_0.shape[0] != mask_subres_size:
wrk_face_mask_a_0 = cv2.resize (wrk_face_mask_a_0, (mask_subres_size, mask_subres_size), cv2.INTER_CUBIC) wrk_face_mask_a_0 = cv2.resize (wrk_face_mask_a_0, (mask_subres_size, mask_subres_size), interpolation=cv2.INTER_CUBIC)
# process mask in local predicted space # process mask in local predicted space
if 'raw' not in cfg.mode: if 'raw' not in cfg.mode:
@ -131,7 +131,7 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
img_face_mask_a [ img_face_mask_a < (1.0/255.0) ] = 0.0 # get rid of noise img_face_mask_a [ img_face_mask_a < (1.0/255.0) ] = 0.0 # get rid of noise
if wrk_face_mask_a_0.shape[0] != output_size: if wrk_face_mask_a_0.shape[0] != output_size:
wrk_face_mask_a_0 = cv2.resize (wrk_face_mask_a_0, (output_size,output_size), cv2.INTER_CUBIC) wrk_face_mask_a_0 = cv2.resize (wrk_face_mask_a_0, (output_size,output_size), interpolation=cv2.INTER_CUBIC)
wrk_face_mask_a = wrk_face_mask_a_0[...,None] wrk_face_mask_a = wrk_face_mask_a_0[...,None]
@ -293,8 +293,8 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
if cfg.bicubic_degrade_power != 0: if cfg.bicubic_degrade_power != 0:
p = 1.0 - cfg.bicubic_degrade_power / 101.0 p = 1.0 - cfg.bicubic_degrade_power / 101.0
img_bgr_downscaled = cv2.resize (img_bgr, ( int(img_size[0]*p), int(img_size[1]*p ) ), cv2.INTER_CUBIC) img_bgr_downscaled = cv2.resize (img_bgr, ( int(img_size[0]*p), int(img_size[1]*p ) ), interpolation=cv2.INTER_CUBIC)
img_bgr = cv2.resize (img_bgr_downscaled, img_size, cv2.INTER_CUBIC) img_bgr = cv2.resize (img_bgr_downscaled, img_size, interpolation=cv2.INTER_CUBIC)
new_out = cv2.warpAffine( out_face_bgr, face_mat, img_size, np.empty_like(img_bgr), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ) new_out = cv2.warpAffine( out_face_bgr, face_mat, img_size, np.empty_like(img_bgr), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )

4
samplelib/SampleGeneratorFaceXSeg.py
View file

@ -85,8 +85,8 @@ class SampleGeneratorFaceXSeg(SampleGeneratorBase):
if face_type == sample.face_type: if face_type == sample.face_type:
if w != resolution: if w != resolution:
img = cv2.resize( img, (resolution, resolution), cv2.INTER_LANCZOS4 ) img = cv2.resize( img, (resolution, resolution), interpolation=cv2.INTER_LANCZOS4 )
mask = cv2.resize( mask, (resolution, resolution), cv2.INTER_LANCZOS4 ) mask = cv2.resize( mask, (resolution, resolution), interpolation=cv2.INTER_LANCZOS4 )
else: else:
mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, face_type) mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, face_type)
img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LANCZOS4 ) img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LANCZOS4 )

10
samplelib/SampleProcessor.py
View file

@ -144,14 +144,14 @@ class SampleProcessor(object):
img = cv2.warpAffine( img, mat, (warp_resolution, warp_resolution), flags=cv2.INTER_LINEAR ) img = cv2.warpAffine( img, mat, (warp_resolution, warp_resolution), flags=cv2.INTER_LINEAR )
img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR) img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR)
img = cv2.resize( img, (resolution,resolution), cv2.INTER_LINEAR ) img = cv2.resize( img, (resolution,resolution), interpolation=cv2.INTER_LINEAR )
else: else:
if face_type != sample_face_type: if face_type != sample_face_type:
mat = LandmarksProcessor.get_transform_mat (sample_landmarks, resolution, face_type) mat = LandmarksProcessor.get_transform_mat (sample_landmarks, resolution, face_type)
img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_LINEAR ) img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_LINEAR )
else: else:
if w != resolution: if w != resolution:
img = cv2.resize( img, (resolution, resolution), cv2.INTER_LINEAR ) img = cv2.resize( img, (resolution, resolution), interpolation=cv2.INTER_LINEAR )
img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR) img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR)
@ -180,13 +180,13 @@ class SampleProcessor(object):
img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_CUBIC ) img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_CUBIC )
else: else:
if w != resolution: if w != resolution:
img = cv2.resize( img, (resolution, resolution), cv2.INTER_CUBIC ) img = cv2.resize( img, (resolution, resolution), interpolation=cv2.INTER_CUBIC )
# Apply random color transfer # Apply random color transfer
if ct_mode is not None and ct_sample is not None: if ct_mode is not None and ct_sample is not None:
if ct_sample_bgr is None: if ct_sample_bgr is None:
ct_sample_bgr = ct_sample.load_bgr() ct_sample_bgr = ct_sample.load_bgr()
img = imagelib.color_transfer (ct_mode, img, cv2.resize( ct_sample_bgr, (resolution,resolution), cv2.INTER_LINEAR ) ) img = imagelib.color_transfer (ct_mode, img, cv2.resize( ct_sample_bgr, (resolution,resolution), interpolation=cv2.INTER_LINEAR ) )
img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate) img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate)
@ -227,7 +227,7 @@ class SampleProcessor(object):
elif sample_type == SPST.IMAGE: elif sample_type == SPST.IMAGE:
img = sample_bgr img = sample_bgr
img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=True) img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=True)
img = cv2.resize( img, (resolution, resolution), cv2.INTER_CUBIC ) img = cv2.resize( img, (resolution, resolution), interpolation=cv2.INTER_CUBIC )
out_sample = img out_sample = img
if data_format == "NCHW": if data_format == "NCHW":