update core.imagelib

core/imagelib/filters.py

@@ -2,7 +2,7 @@ import numpy as np
 from .blursharpen import LinearMotionBlur
 import cv2
 
-def apply_random_hsv_shift(img, rnd_state=None):
+def apply_random_hsv_shift(img, mask=None, rnd_state=None):
     if rnd_state is None:
         rnd_state = np.random
     
@@ -10,44 +10,57 @@ def apply_random_hsv_shift(img, rnd_state=None):
     h = ( h + rnd_state.randint(360) ) % 360
     s = np.clip ( s + rnd_state.random()-0.5, 0, 1 )
     v = np.clip ( v + rnd_state.random()/2-0.25, 0, 1 )                    
-    img = np.clip( cv2.cvtColor(cv2.merge([h, s, v]), cv2.COLOR_HSV2BGR) , 0, 1 )
-    return img
     
-def apply_random_motion_blur( img, chance, mb_max_size, rnd_state=None ):
+    result = np.clip( cv2.cvtColor(cv2.merge([h, s, v]), cv2.COLOR_HSV2BGR) , 0, 1 )
+    if mask is not None:
+        result = img*(1-mask) + result*mask
+        
+    return result
+    
+def apply_random_motion_blur( img, chance, mb_max_size, mask=None, rnd_state=None ):
     if rnd_state is None:
         rnd_state = np.random
     
     mblur_rnd_kernel = rnd_state.randint(mb_max_size)+1
     mblur_rnd_deg    = rnd_state.randint(360)
 
+    result = img
     if rnd_state.randint(100) < np.clip(chance, 0, 100):
-        img = LinearMotionBlur (img, mblur_rnd_kernel, mblur_rnd_deg )
+        result = LinearMotionBlur (result, mblur_rnd_kernel, mblur_rnd_deg )
+        if mask is not None:
+            result = img*(1-mask) + result*mask
         
-    return img
+    return result
     
-def apply_random_gaussian_blur( img, chance, kernel_max_size, rnd_state=None ):
+def apply_random_gaussian_blur( img, chance, kernel_max_size, mask=None, rnd_state=None ):
     if rnd_state is None:
         rnd_state = np.random
         
+    result = img
     if rnd_state.randint(100) < np.clip(chance, 0, 100):
         gblur_rnd_kernel = rnd_state.randint(kernel_max_size)*2+1
-        img = cv2.GaussianBlur(img, (gblur_rnd_kernel,)*2 , 0)
+        result = cv2.GaussianBlur(result, (gblur_rnd_kernel,)*2 , 0)
-    
+        if mask is not None:
-    return img
+            result = img*(1-mask) + result*mask
+            
+    return result
     
     
-def apply_random_bilinear_resize( img, chance, max_size_per, rnd_state=None ):
+def apply_random_bilinear_resize( img, chance, max_size_per, mask=None, rnd_state=None ):
     if rnd_state is None:
         rnd_state = np.random
 
+    result = img
     if rnd_state.randint(100) < np.clip(chance, 0, 100):
-        h,w,c = img.shape
+        h,w,c = result.shape
         
         trg = rnd_state.rand()
         rw = w - int( trg * int(w*(max_size_per/100.0)) )                        
         rh = h - int( trg * int(h*(max_size_per/100.0)) )   
              
-        img = cv2.resize (img, (rw,rh), cv2.INTER_LINEAR )
+        result = cv2.resize (result, (rw,rh), cv2.INTER_LINEAR )
-        img = cv2.resize (img, (w,h), cv2.INTER_LINEAR )
+        result = cv2.resize (result, (w,h), cv2.INTER_LINEAR )
-        
+        if mask is not None:
-    return img
+            result = img*(1-mask) + result*mask
+            
+    return result

core/imagelib/sd/__init__.py

@@ -0,0 +1 @@
+from .draw import *

core/imagelib/sd/draw.py

@@ -0,0 +1,40 @@
+"""
+Signed distance drawing functions using numpy.
+"""
+
+import numpy as np
+from numpy import linalg as npla
+
+def circle_faded( hw, center, fade_dists ):
+    """
+    returns drawn circle in [h,w,1] output range [0..1.0] float32
+    
+    hw         = [h,w]                      resolution
+    center     = [y,x]                      center of circle
+    fade_dists = [fade_start, fade_end]     fade values
+    """
+    h,w = hw      
+    
+    pts = np.empty( (h,w,2), dtype=np.float32 )
+    pts[...,1] = np.arange(h)[None,:]
+    pts[...,0] = np.arange(w)[:,None]
+    pts = pts.reshape ( (h*w, -1) )
+
+    pts_dists = np.abs ( npla.norm(pts-center, axis=-1) )
+    
+    pts_dists = ( pts_dists - fade_dists[0] ) / fade_dists[1]
+    pts_dists = np.clip( 1-pts_dists, 0, 1)
+    
+    return pts_dists.reshape ( (h,w,1) ).astype(np.float32)
+    
+def random_circle_faded ( hw, rnd_state=None ):
+    if rnd_state is None:
+        rnd_state = np.random
+        
+    h,w = hw
+    hw_max = max(h,w)
+    fade_start = rnd_state.randint(hw_max)
+    fade_end = fade_start + rnd_state.randint(hw_max- fade_start)
+    
+    return circle_faded (hw, [ rnd_state.randint(h), rnd_state.randint(w) ], 
+                             [fade_start, fade_end] )