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Merger: added mask mode ‘learned-prd + learned-dst’ – produces largest area of both dst and predicted masks

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Colombo 2020-04-13 22:04:24 +04:00
parent d1af3b51cd
commit 2cc0e64572
2 changed files with 14 additions and 11 deletions
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16
merger/MergeMasked.py
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@ -66,27 +66,29 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
wrk_face_mask_a_0 = prd_face_dst_mask_a_0 wrk_face_mask_a_0 = prd_face_dst_mask_a_0
elif cfg.mask_mode == 4: #learned-prd*learned-dst elif cfg.mask_mode == 4: #learned-prd*learned-dst
wrk_face_mask_a_0 = prd_face_mask_a_0*prd_face_dst_mask_a_0 wrk_face_mask_a_0 = prd_face_mask_a_0*prd_face_dst_mask_a_0
elif cfg.mask_mode >= 5 and cfg.mask_mode <= 8: #XSeg modes elif cfg.mask_mode == 5: #learned-prd+learned-dst
if cfg.mask_mode == 5 or cfg.mask_mode == 7 or cfg.mask_mode == 8: wrk_face_mask_a_0 = np.clip( prd_face_mask_a_0+prd_face_dst_mask_a_0, 0, 1)
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:
# 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, 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), cv2.INTER_CUBIC)
if cfg.mask_mode >= 6 and cfg.mask_mode <= 8: 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), cv2.INTER_CUBIC)
if cfg.mask_mode == 5: #'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
elif cfg.mask_mode == 6: #'XSeg-dst' elif cfg.mask_mode == 7: #'XSeg-dst'
wrk_face_mask_a_0 = X_dst_face_mask_a_0 wrk_face_mask_a_0 = X_dst_face_mask_a_0
elif cfg.mask_mode == 7: #'XSeg-prd*XSeg-dst' elif cfg.mask_mode == 8: #'XSeg-prd*XSeg-dst'
wrk_face_mask_a_0 = X_prd_face_mask_a_0 * X_dst_face_mask_a_0 wrk_face_mask_a_0 = X_prd_face_mask_a_0 * X_dst_face_mask_a_0
elif cfg.mask_mode == 8: #learned-prd*learned-dst*XSeg-prd*XSeg-dst elif cfg.mask_mode == 9: #learned-prd*learned-dst*XSeg-prd*XSeg-dst
wrk_face_mask_a_0 = prd_face_mask_a_0 * prd_face_dst_mask_a_0 * X_prd_face_mask_a_0 * X_dst_face_mask_a_0 wrk_face_mask_a_0 = prd_face_mask_a_0 * prd_face_dst_mask_a_0 * X_prd_face_mask_a_0 * X_dst_face_mask_a_0
wrk_face_mask_a_0[ wrk_face_mask_a_0 < (1.0/255.0) ] = 0.0 # get rid of noise wrk_face_mask_a_0[ wrk_face_mask_a_0 < (1.0/255.0) ] = 0.0 # get rid of noise

9
merger/MergerConfig.py
View file

@ -85,10 +85,11 @@ mask_mode_dict = {1:'dst',
2:'learned-prd', 2:'learned-prd',
3:'learned-dst', 3:'learned-dst',
4:'learned-prd*learned-dst', 4:'learned-prd*learned-dst',
5:'XSeg-prd', 5:'learned-prd+learned-dst',
6:'XSeg-dst', 6:'XSeg-prd',
7:'XSeg-prd*XSeg-dst', 7:'XSeg-dst',
8:'learned-prd*learned-dst*XSeg-prd*XSeg-dst' 8:'XSeg-prd*XSeg-dst',
9:'learned-prd*learned-dst*XSeg-prd*XSeg-dst'
} }