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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
Converter:
Session is now saved to the model folder. blur and erode ranges are increased to -400+400 hist-match-bw is now replaced with seamless2 mode. Added 'ebs' color transfer mode (works only on Windows). FANSEG model (used in FAN-x mask modes) is retrained with new model configuration and now produces better precision and less jitter
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Colombo
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70dada42ea
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29 changed files with 768 additions and 314 deletions
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@ -249,52 +249,224 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
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return mat
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def get_image_hull_mask (image_shape, image_landmarks, eyebrows_expand_mod=1.0, ie_polys=None):
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if len(image_landmarks) != 68:
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raise Exception('get_image_hull_mask works only with 68 landmarks')
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int_lmrks = np.array(image_landmarks.copy(), dtype=np.int)
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hull_mask = np.zeros(image_shape[0:2]+(1,),dtype=np.float32)
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def expand_eyebrows(lmrks, eyebrows_expand_mod=1.0):
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if len(lmrks) != 68:
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raise Exception('works only with 68 landmarks')
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lmrks = np.array( lmrks.copy(), dtype=np.int )
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# #nose
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ml_pnt = (int_lmrks[36] + int_lmrks[0]) // 2
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mr_pnt = (int_lmrks[16] + int_lmrks[45]) // 2
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ml_pnt = (lmrks[36] + lmrks[0]) // 2
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mr_pnt = (lmrks[16] + lmrks[45]) // 2
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# mid points between the mid points and eye
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ql_pnt = (int_lmrks[36] + ml_pnt) // 2
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qr_pnt = (int_lmrks[45] + mr_pnt) // 2
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ql_pnt = (lmrks[36] + ml_pnt) // 2
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qr_pnt = (lmrks[45] + mr_pnt) // 2
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# Top of the eye arrays
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bot_l = np.array((ql_pnt, int_lmrks[36], int_lmrks[37], int_lmrks[38], int_lmrks[39]))
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bot_r = np.array((int_lmrks[42], int_lmrks[43], int_lmrks[44], int_lmrks[45], qr_pnt))
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bot_l = np.array((ql_pnt, lmrks[36], lmrks[37], lmrks[38], lmrks[39]))
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bot_r = np.array((lmrks[42], lmrks[43], lmrks[44], lmrks[45], qr_pnt))
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# Eyebrow arrays
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top_l = int_lmrks[17:22]
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top_r = int_lmrks[22:27]
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top_l = lmrks[17:22]
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top_r = lmrks[22:27]
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# Adjust eyebrow arrays
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int_lmrks[17:22] = top_l + eyebrows_expand_mod * 0.5 * (top_l - bot_l)
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int_lmrks[22:27] = top_r + eyebrows_expand_mod * 0.5 * (top_r - bot_r)
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lmrks[17:22] = top_l + eyebrows_expand_mod * 0.5 * (top_l - bot_l)
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lmrks[22:27] = top_r + eyebrows_expand_mod * 0.5 * (top_r - bot_r)
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return lmrks
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r_jaw = (int_lmrks[0:9], int_lmrks[17:18])
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l_jaw = (int_lmrks[8:17], int_lmrks[26:27])
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r_cheek = (int_lmrks[17:20], int_lmrks[8:9])
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l_cheek = (int_lmrks[24:27], int_lmrks[8:9])
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nose_ridge = (int_lmrks[19:25], int_lmrks[8:9],)
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r_eye = (int_lmrks[17:22], int_lmrks[27:28], int_lmrks[31:36], int_lmrks[8:9])
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l_eye = (int_lmrks[22:27], int_lmrks[27:28], int_lmrks[31:36], int_lmrks[8:9])
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nose = (int_lmrks[27:31], int_lmrks[31:36])
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def get_image_hull_mask (image_shape, image_landmarks, eyebrows_expand_mod=1.0, ie_polys=None, color=(1,) ):
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hull_mask = np.zeros(image_shape[0:2]+( len(color),),dtype=np.float32)
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lmrks = expand_eyebrows(image_landmarks, eyebrows_expand_mod)
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r_jaw = (lmrks[0:9], lmrks[17:18])
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l_jaw = (lmrks[8:17], lmrks[26:27])
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r_cheek = (lmrks[17:20], lmrks[8:9])
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l_cheek = (lmrks[24:27], lmrks[8:9])
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nose_ridge = (lmrks[19:25], lmrks[8:9],)
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r_eye = (lmrks[17:22], lmrks[27:28], lmrks[31:36], lmrks[8:9])
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l_eye = (lmrks[22:27], lmrks[27:28], lmrks[31:36], lmrks[8:9])
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nose = (lmrks[27:31], lmrks[31:36])
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parts = [r_jaw, l_jaw, r_cheek, l_cheek, nose_ridge, r_eye, l_eye, nose]
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for item in parts:
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merged = np.concatenate(item)
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cv2.fillConvexPoly(hull_mask, cv2.convexHull(merged), 1)
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cv2.fillConvexPoly(hull_mask, cv2.convexHull(merged), color )
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if ie_polys is not None:
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ie_polys.overlay_mask(hull_mask)
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return hull_mask
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def alpha_to_color (img_alpha, color):
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if len(img_alpha.shape) == 2:
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img_alpha = img_alpha[...,None]
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h,w,c = img_alpha.shape
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result = np.zeros( (h,w, len(color) ), dtype=np.float32 )
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result[:,:] = color
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return result * img_alpha
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def get_cmask (image_shape, lmrks, eyebrows_expand_mod=1.0):
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h,w,c = image_shape
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hull = get_image_hull_mask (image_shape, lmrks, eyebrows_expand_mod, color=(1,) )
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result = np.zeros( (h,w,3), dtype=np.float32 )
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def process(w,h, data ):
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d = {}
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cur_lc = 0
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all_lines = []
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for s, pts_loop_ar in data:
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lines = []
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for pts, loop in pts_loop_ar:
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pts_len = len(pts)
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lines.append ( [ [ pts[i], pts[(i+1) % pts_len ] ] for i in range(pts_len - (0 if loop else 1) ) ] )
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lines = np.concatenate (lines)
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lc = lines.shape[0]
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all_lines.append(lines)
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d[s] = cur_lc, cur_lc+lc
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cur_lc += lc
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all_lines = np.concatenate (all_lines, 0)
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#calculate signed distance for all points and lines
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line_count = all_lines.shape[0]
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pts_count = w*h
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all_lines = np.repeat ( all_lines[None,...], pts_count, axis=0 ).reshape ( (pts_count*line_count,2,2) )
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pts = np.empty( (h,w,line_count,2), dtype=np.float32 )
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pts[...,1] = np.arange(h)[:,None,None]
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pts[...,0] = np.arange(w)[:,None]
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pts = pts.reshape ( (h*w*line_count, -1) )
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a = all_lines[:,0,:]
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b = all_lines[:,1,:]
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pa = pts-a
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ba = b-a
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ph = np.clip ( np.einsum('ij,ij->i', pa, ba) / np.einsum('ij,ij->i', ba, ba), 0, 1 )
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dists = npla.norm ( pa - ba*ph[...,None], axis=1).reshape ( (h,w,line_count) )
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def get_dists(name, thickness=0):
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s,e = d[name]
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result = dists[...,s:e]
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if thickness != 0:
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result = np.abs(result)-thickness
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return np.min (result, axis=-1)
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return get_dists
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l_eye = lmrks[42:48]
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r_eye = lmrks[36:42]
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l_brow = lmrks[22:27]
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r_brow = lmrks[17:22]
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mouth = lmrks[48:60]
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up_nose = np.concatenate( (lmrks[27:31], lmrks[33:34]) )
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down_nose = lmrks[31:36]
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nose = np.concatenate ( (up_nose, down_nose) )
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gdf = process ( w,h,
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(
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('eyes', ((l_eye, True), (r_eye, True)) ),
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('brows', ((l_brow, False), (r_brow,False)) ),
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('up_nose', ((up_nose, False),) ),
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('down_nose', ((down_nose, False),) ),
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('mouth', ((mouth, True),) ),
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)
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)
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#import code
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#code.interact(local=dict(globals(), **locals()))
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eyes_fall_dist = w // 32
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eyes_thickness = max( w // 64, 1 )
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brows_fall_dist = w // 32
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brows_thickness = max( w // 256, 1 )
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nose_fall_dist = w / 12
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nose_thickness = max( w // 96, 1 )
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mouth_fall_dist = w // 32
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mouth_thickness = max( w // 64, 1 )
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eyes_mask = gdf('eyes',eyes_thickness)
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eyes_mask = 1-np.clip( eyes_mask/ eyes_fall_dist, 0, 1)
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#eyes_mask = np.clip ( 1- ( np.sqrt( np.maximum(eyes_mask,0) ) / eyes_fall_dist ), 0, 1)
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#eyes_mask = np.clip ( 1- ( np.cbrt( np.maximum(eyes_mask,0) ) / eyes_fall_dist ), 0, 1)
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brows_mask = gdf('brows', brows_thickness)
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brows_mask = 1-np.clip( brows_mask / brows_fall_dist, 0, 1)
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#brows_mask = np.clip ( 1- ( np.sqrt( np.maximum(brows_mask,0) ) / brows_fall_dist ), 0, 1)
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mouth_mask = gdf('mouth', mouth_thickness)
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mouth_mask = 1-np.clip( mouth_mask / mouth_fall_dist, 0, 1)
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#mouth_mask = np.clip ( 1- ( np.sqrt( np.maximum(mouth_mask,0) ) / mouth_fall_dist ), 0, 1)
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def blend(a,b,k):
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x = np.clip ( 0.5+0.5*(b-a)/k, 0.0, 1.0 )
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return (a-b)*x+b - k*x*(1.0-x)
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#nose_mask = (a-b)*x+b - k*x*(1.0-x)
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#nose_mask = np.minimum (up_nose_mask , down_nose_mask )
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#nose_mask = 1-np.clip( nose_mask / nose_fall_dist, 0, 1)
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nose_mask = blend ( gdf('up_nose', nose_thickness), gdf('down_nose', nose_thickness), nose_thickness*3 )
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nose_mask = 1-np.clip( nose_mask / nose_fall_dist, 0, 1)
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up_nose_mask = gdf('up_nose', nose_thickness)
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up_nose_mask = 1-np.clip( up_nose_mask / nose_fall_dist, 0, 1)
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#up_nose_mask = np.clip ( 1- ( np.cbrt( np.maximum(up_nose_mask,0) ) / nose_fall_dist ), 0, 1)
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down_nose_mask = gdf('down_nose', nose_thickness)
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down_nose_mask = 1-np.clip( down_nose_mask / nose_fall_dist, 0, 1)
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#down_nose_mask = np.clip ( 1- ( np.cbrt( np.maximum(down_nose_mask,0) ) / nose_fall_dist ), 0, 1)
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#nose_mask = np.clip( up_nose_mask + down_nose_mask, 0, 1 )
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#nose_mask /= np.max(nose_mask)
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#nose_mask = np.maximum (up_nose_mask , down_nose_mask )
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#nose_mask = down_nose_mask
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#nose_mask = np.zeros_like(nose_mask)
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eyes_mask = eyes_mask * (1-mouth_mask)
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nose_mask = nose_mask * (1-eyes_mask)
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hull_mask = hull[...,0].copy()
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hull_mask = hull_mask * (1-eyes_mask) * (1-brows_mask) * (1-nose_mask) * (1-mouth_mask)
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#eyes_mask = eyes_mask * (1-nose_mask)
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mouth_mask= mouth_mask * (1-nose_mask)
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brows_mask = brows_mask * (1-nose_mask)* (1-eyes_mask )
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hull_mask = alpha_to_color(hull_mask, (0,1,0) )
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eyes_mask = alpha_to_color(eyes_mask, (1,0,0) )
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brows_mask = alpha_to_color(brows_mask, (0,0,1) )
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nose_mask = alpha_to_color(nose_mask, (0,1,1) )
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mouth_mask = alpha_to_color(mouth_mask, (0,0,1) )
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#nose_mask = np.maximum( up_nose_mask, down_nose_mask )
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result = hull_mask + mouth_mask+ nose_mask + brows_mask + eyes_mask
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result *= hull
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#result = np.clip (result, 0, 1)
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return result
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def get_image_eye_mask (image_shape, image_landmarks):
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if len(image_landmarks) != 68:
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raise Exception('get_image_eye_mask works only with 68 landmarks')
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