fix sample processor

core/imagelib/warp.py

@@ -104,10 +104,11 @@ def gen_pts(W, H, rnd_state=None):
     return pts1, pts2
     
     
-def gen_warp_params (w, flip=False, rotation_range=[-10,10], scale_range=[-0.5, 0.5], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05], rnd_state=None  ):
+def gen_warp_params (w, flip=False, rotation_range=[-10,10], scale_range=[-0.5, 0.5], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05], rnd_state=None, warp_rnd_state=None  ):
     if rnd_state is None:
         rnd_state = np.random
-
+    if warp_rnd_state is None:
+        warp_rnd_state = np.random
     rw = None
     if w < 64:        
         rw = w
@@ -120,13 +121,13 @@ def gen_warp_params (w, flip=False, rotation_range=[-10,10], scale_range=[-0.5,
     p_flip = flip and rnd_state.randint(10) < 4
 
     #random warp V1
-    cell_size = [ w // (2**i) for i in range(1,4) ] [ rnd_state.randint(3) ]
+    cell_size = [ w // (2**i) for i in range(1,4) ] [ warp_rnd_state.randint(3) ]
     cell_count = w // cell_size + 1
     grid_points = np.linspace( 0, w, cell_count)
     mapx = np.broadcast_to(grid_points, (cell_count, cell_count)).copy()
     mapy = mapx.T
-    mapx[1:-1,1:-1] = mapx[1:-1,1:-1] + randomex.random_normal( size=(cell_count-2, cell_count-2) )*(cell_size*0.24)
+    mapx[1:-1,1:-1] = mapx[1:-1,1:-1] + randomex.random_normal( size=(cell_count-2, cell_count-2), rnd_state=warp_rnd_state )*(cell_size*0.24)
-    mapy[1:-1,1:-1] = mapy[1:-1,1:-1] + randomex.random_normal( size=(cell_count-2, cell_count-2) )*(cell_size*0.24)
+    mapy[1:-1,1:-1] = mapy[1:-1,1:-1] + randomex.random_normal( size=(cell_count-2, cell_count-2), rnd_state=warp_rnd_state )*(cell_size*0.24)
     half_cell_size = cell_size // 2
     mapx = cv2.resize(mapx, (w+cell_size,)*2 )[half_cell_size:-half_cell_size,half_cell_size:-half_cell_size].astype(np.float32)
     mapy = cv2.resize(mapy, (w+cell_size,)*2 )[half_cell_size:-half_cell_size,half_cell_size:-half_cell_size].astype(np.float32)

core/randomex.py

@@ -1,12 +1,14 @@
 import numpy as np
 
-def random_normal( size=(1,), trunc_val = 2.5 ):
+def random_normal( size=(1,), trunc_val = 2.5, rnd_state=None ):
+    if rnd_state is None:
+        rnd_state = np.random
     len = np.array(size).prod()
     result = np.empty ( (len,) , dtype=np.float32)
 
     for i in range (len):
         while True:
-            x = np.random.normal()
+            x = rnd_state.normal()
             if x >= -trunc_val and x <= trunc_val:
                 break
         result[i] = (x / trunc_val)

samplelib/SampleProcessor.py

@@ -47,10 +47,11 @@ class SampleProcessor(object):
         SPCT = SampleProcessor.ChannelType
         SPFMT = SampleProcessor.FaceMaskType
 
-        sample_rnd_seed = np.random.randint(0x80000000)
         
         outputs = []
         for sample in samples:
+            sample_rnd_seed = np.random.randint(0x80000000)
+            
             sample_face_type = sample.face_type
             sample_bgr = sample.load_bgr()
             sample_landmarks = sample.landmarks
@@ -83,40 +84,34 @@ class SampleProcessor(object):
             if debug and is_face_sample:
                 LandmarksProcessor.draw_landmarks (sample_bgr, sample_landmarks, (0, 1, 0))
 
-            params_per_resolution = {}            
-            warp_rnd_state = np.random.RandomState (sample_rnd_seed-1)          
-            for opts in output_sample_types:
-                resolution = opts.get('resolution', None)
-                if resolution is None:
-                    continue
-                if resolution not in params_per_resolution:
-                    params_per_resolution[resolution] = imagelib.gen_warp_params(resolution, 
-                                                                             sample_process_options.random_flip, 
-                                                                             rotation_range=sample_process_options.rotation_range, 
-                                                                             scale_range=sample_process_options.scale_range, 
-                                                                             tx_range=sample_process_options.tx_range, 
-                                                                             ty_range=sample_process_options.ty_range, 
-                                                                             rnd_state=warp_rnd_state)
-            
             outputs_sample = []
             for opts in output_sample_types:
+                resolution     = opts.get('resolution', 0)
                 sample_type    = opts.get('sample_type', SPST.NONE)
                 channel_type   = opts.get('channel_type', SPCT.NONE)                
-                resolution     = opts.get('resolution', 0)
                 nearest_resize_to = opts.get('nearest_resize_to', None)
                 warp           = opts.get('warp', False)
                 transform      = opts.get('transform', False)
-                motion_blur    = opts.get('motion_blur', None)
-                gaussian_blur  = opts.get('gaussian_blur', None)
-                denoise_filter = opts.get('denoise_filter', False)
-                random_bilinear_resize = opts.get('random_bilinear_resize', None)
-                random_rgb_levels = opts.get('random_rgb_levels', False)
-                random_hsv_shift = opts.get('random_hsv_shift', False)
-                random_circle_mask = opts.get('random_circle_mask', False)
                 normalize_tanh = opts.get('normalize_tanh', False)
                 ct_mode        = opts.get('ct_mode', None)
                 data_format    = opts.get('data_format', 'NHWC')
                 
+                rnd_seed_shift      = opts.get('rnd_seed_shift', 0)
+                warp_rnd_seed_shift = opts.get('warp_rnd_seed_shift', rnd_seed_shift)
+                
+                rnd_state      = np.random.RandomState (sample_rnd_seed+rnd_seed_shift)
+                warp_rnd_state = np.random.RandomState (sample_rnd_seed+warp_rnd_seed_shift)
+                
+                warp_params = imagelib.gen_warp_params(resolution, 
+                                                       sample_process_options.random_flip, 
+                                                       rotation_range=sample_process_options.rotation_range, 
+                                                       scale_range=sample_process_options.scale_range, 
+                                                       tx_range=sample_process_options.tx_range, 
+                                                       ty_range=sample_process_options.ty_range, 
+                                                       rnd_state=rnd_state,
+                                                       warp_rnd_state=warp_rnd_state,
+                                                       )
+                
                 if sample_type == SPST.FACE_MASK or sample_type == SPST.IMAGE: 
                     border_replicate = False
                 elif sample_type == SPST.FACE_IMAGE:
@@ -155,7 +150,7 @@ class SampleProcessor(object):
                             mat  = LandmarksProcessor.get_transform_mat (sample_landmarks, warp_resolution, face_type)
                             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 (warp_params, img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR)
                             img = cv2.resize( img, (resolution,resolution), interpolation=cv2.INTER_LINEAR )
                         else:
                             if face_type != sample_face_type:
@@ -165,7 +160,7 @@ class SampleProcessor(object):
                                 if w != resolution:
                                     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 (warp_params, img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR)
 
                         if face_mask_type == SPFMT.EYES_MOUTH:
                             div = img.max()
@@ -183,16 +178,6 @@ class SampleProcessor(object):
                     elif sample_type == SPST.FACE_IMAGE:
                         img = sample_bgr                      
                             
-                        
-                        if random_rgb_levels:
-                            random_mask = sd.random_circle_faded ([w,w], rnd_state=np.random.RandomState (sample_rnd_seed) ) if random_circle_mask else None
-                            img = imagelib.apply_random_rgb_levels(img, mask=random_mask, rnd_state=np.random.RandomState (sample_rnd_seed) )
-
-                        if random_hsv_shift:
-                            random_mask = sd.random_circle_faded ([w,w], rnd_state=np.random.RandomState (sample_rnd_seed+1) ) if random_circle_mask else None
-                            img = imagelib.apply_random_hsv_shift(img, mask=random_mask, rnd_state=np.random.RandomState (sample_rnd_seed+1) )
-
-                            
                         if face_type != sample_face_type:
                             mat = LandmarksProcessor.get_transform_mat (sample_landmarks, resolution, face_type)
                             img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_CUBIC )
@@ -206,27 +191,10 @@ class SampleProcessor(object):
                                ct_sample_bgr = ct_sample.load_bgr()
                             img = imagelib.color_transfer (ct_mode, img, cv2.resize( ct_sample_bgr, (resolution,resolution), interpolation=cv2.INTER_LINEAR ) )
 
-                        
+                        img  = imagelib.warp_by_params (warp_params, 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)
   
                         img = np.clip(img.astype(np.float32), 0, 1)
 
-                        if motion_blur is not None:                            
-                            random_mask = sd.random_circle_faded ([resolution,resolution], rnd_state=np.random.RandomState (sample_rnd_seed+2)) if random_circle_mask else None
-                            img = imagelib.apply_random_motion_blur(img, *motion_blur, mask=random_mask,rnd_state=np.random.RandomState (sample_rnd_seed+2) )
-
-                        if gaussian_blur is not None:
-                            random_mask = sd.random_circle_faded ([resolution,resolution], rnd_state=np.random.RandomState (sample_rnd_seed+3)) if random_circle_mask else None
-                            img = imagelib.apply_random_gaussian_blur(img, *gaussian_blur, mask=random_mask,rnd_state=np.random.RandomState (sample_rnd_seed+3) )
-                                
-                        if random_bilinear_resize is not None:
-                            random_mask = sd.random_circle_faded ([resolution,resolution], rnd_state=np.random.RandomState (sample_rnd_seed+4)) if random_circle_mask else None
-                            img = imagelib.apply_random_bilinear_resize(img, *random_bilinear_resize, mask=random_mask,rnd_state=np.random.RandomState (sample_rnd_seed+4) )
-
-                        if denoise_filter:
-                            d_size = ( (max(*img.shape[:2]) // 128) + 1 )*2 +1
-                            img = cv2.bilateralFilter( np.clip(img*255, 0,255).astype(np.uint8), d_size, 80, 80).astype(np.float32) / 255.0
-
                         # Transform from BGR to desired channel_type
                         if channel_type == SPCT.BGR:
                             out_sample = img
@@ -246,7 +214,7 @@ class SampleProcessor(object):
                         out_sample = np.transpose(out_sample, (2,0,1) )
                 elif sample_type == SPST.IMAGE:
                     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 (warp_params, img,  warp, transform, can_flip=True, border_replicate=True)
                     img  = cv2.resize( img,  (resolution, resolution), interpolation=cv2.INTER_CUBIC )
                     out_sample = img
                     
@@ -261,7 +229,7 @@ class SampleProcessor(object):
                     out_sample = l
                 elif sample_type == SPST.PITCH_YAW_ROLL or sample_type == SPST.PITCH_YAW_ROLL_SIGMOID:
                     pitch,yaw,roll = sample.get_pitch_yaw_roll()
-                    if params_per_resolution[resolution]['flip']:
+                    if warp_params['flip']:
                         yaw = -yaw
 
                     if sample_type == SPST.PITCH_YAW_ROLL_SIGMOID:
@@ -278,65 +246,3 @@ class SampleProcessor(object):
 
         return outputs
 
-"""
-
-        STRUCT        = 4  #mask structure as grayscale
-                           elif face_mask_type == SPFMT.STRUCT:
-                            if sample.eyebrows_expand_mod is not None:
-                                img = LandmarksProcessor.get_face_struct_mask (sample_bgr.shape, sample_landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
-                            else:
-                                img = LandmarksProcessor.get_face_struct_mask (sample_bgr.shape, sample_landmarks)
-     
-        
-        
-        close_sample = sample.close_target_list[ np.random.randint(0, len(sample.close_target_list)) ] if sample.close_target_list is not None else None
-        close_sample_bgr = close_sample.load_bgr() if close_sample is not None else None
-
-        if debug and close_sample_bgr is not None:
-            LandmarksProcessor.draw_landmarks (close_sample_bgr, close_sample.landmarks, (0, 1, 0))
-        RANDOM_CLOSE               = 0x00000040, #currently unused
-        MORPH_TO_RANDOM_CLOSE      = 0x00000080, #currently unused
-
-if f & SPTF.RANDOM_CLOSE != 0:
-                img_type += 10
-            elif f & SPTF.MORPH_TO_RANDOM_CLOSE != 0:
-                img_type += 20
-if img_type >= 10 and img_type <= 19: #RANDOM_CLOSE
-    img_type -= 10
-    img = close_sample_bgr
-    cur_sample = close_sample
-
-elif img_type >= 20 and img_type <= 29: #MORPH_TO_RANDOM_CLOSE
-    img_type -= 20
-    res = sample.shape[0]
-
-    s_landmarks = sample.landmarks.copy()
-    d_landmarks = close_sample.landmarks.copy()
-    idxs = list(range(len(s_landmarks)))
-    #remove landmarks near boundaries
-    for i in idxs[:]:
-        s_l = s_landmarks[i]
-        d_l = d_landmarks[i]
-        if s_l[0] < 5 or s_l[1] < 5 or s_l[0] >= res-5 or s_l[1] >= res-5 or \
-            d_l[0] < 5 or d_l[1] < 5 or d_l[0] >= res-5 or d_l[1] >= res-5:
-            idxs.remove(i)
-    #remove landmarks that close to each other in 5 dist
-    for landmarks in [s_landmarks, d_landmarks]:
-        for i in idxs[:]:
-            s_l = landmarks[i]
-            for j in idxs[:]:
-                if i == j:
-                    continue
-                s_l_2 = landmarks[j]
-                diff_l = np.abs(s_l - s_l_2)
-                if np.sqrt(diff_l.dot(diff_l)) < 5:
-                    idxs.remove(i)
-                    break
-    s_landmarks = s_landmarks[idxs]
-    d_landmarks = d_landmarks[idxs]
-    s_landmarks = np.concatenate ( [s_landmarks, [ [0,0], [ res // 2, 0], [ res-1, 0], [0, res//2], [res-1, res//2] ,[0,res-1] ,[res//2, res-1] ,[res-1,res-1] ] ] )
-    d_landmarks = np.concatenate ( [d_landmarks, [ [0,0], [ res // 2, 0], [ res-1, 0], [0, res//2], [res-1, res//2] ,[0,res-1] ,[res//2, res-1] ,[res-1,res-1] ] ] )
-    img = imagelib.morph_by_points (sample_bgr, s_landmarks, d_landmarks)
-    cur_sample = close_sample
-else:
-    """