Pixel annomolies with scipy.ndimage.geometric_transform
0
I am attempting to implement a displacement map on a numpy image. Here is my implementation.
def displaceImage(img,displacementMap,distance=10,angle=45):
"""
NOTE: If displacementMap is smaller than img, then everthing out of bounds
is not displaced!
"""
angle=math.radians(angle)
img=numpyArray(img)
displacementMap=numpyArray(displacementMap)
if len(displacementMap.shape)>2:
# convert displacement map to grayscale
displacementMap=colorSpaces.grayscale(displacementMap)
def dissp(point):
"""
Called for each point in the array. Returns a same shaped tuple where it should go.
"""
if point[0]<displacementMap.shape[0] and point[1]<displacementMap.shape[1]:
delta=displacementMap[point[0],point[1]]*distance
toX=point[0]+delta*math.sin(angle)
toY=point[1]-delta*math.cos(angle)
if len(point)>2: # array is (w,h,rgb)
point=(toX,toY,point[2])
else: # array is (w,h) -- aka grayscale
point=(toX,toY)
return point
img=scipy.ndimage.geometric_transform(img,dissp,mode="nearest")
return img
And below I attempt to use a b&w perlin noise to displace to the top left quadrant of the image. As you can see from the central leaves/sky area, it appears to be doing what one would expect. The problem is, of course, the stray pixels everywhere. Thoughts?
python numpy scipy
asked Jan 1 at 1:35
TheHeadlessSourceManTheHeadlessSourceMan
105110
add a comment |
0
I am attempting to implement a displacement map on a numpy image. Here is my implementation.
def displaceImage(img,displacementMap,distance=10,angle=45):
"""
NOTE: If displacementMap is smaller than img, then everthing out of bounds
is not displaced!
"""
angle=math.radians(angle)
img=numpyArray(img)
displacementMap=numpyArray(displacementMap)
if len(displacementMap.shape)>2:
# convert displacement map to grayscale
displacementMap=colorSpaces.grayscale(displacementMap)
def dissp(point):
"""
Called for each point in the array. Returns a same shaped tuple where it should go.
"""
if point[0]<displacementMap.shape[0] and point[1]<displacementMap.shape[1]:
delta=displacementMap[point[0],point[1]]*distance
toX=point[0]+delta*math.sin(angle)
toY=point[1]-delta*math.cos(angle)
if len(point)>2: # array is (w,h,rgb)
point=(toX,toY,point[2])
else: # array is (w,h) -- aka grayscale
point=(toX,toY)
return point
img=scipy.ndimage.geometric_transform(img,dissp,mode="nearest")
return img
And below I attempt to use a b&w perlin noise to displace to the top left quadrant of the image. As you can see from the central leaves/sky area, it appears to be doing what one would expect. The problem is, of course, the stray pixels everywhere. Thoughts?
python numpy scipy
asked Jan 1 at 1:35
TheHeadlessSourceManTheHeadlessSourceMan
105110
Update: I thought that perhaps this was due to an uninitialized buffer, so I tried:bg=np.copy(img) img=scipy.ndimage.geometric_transform(img,dissp,output=bg,mode="nearest")but it did not fix the problem
– TheHeadlessSourceMan
Jan 1 at 16:11
Update 2: I noticed that setting the geometric_transform to order=1 seemed to work, so I thought that perhaps the curve was developing a "ring" and clipping. Yet np.clip(img,0.0,1.0) doesn't help.
– TheHeadlessSourceMan
Jan 1 at 23:00
add a comment |
0
0
0
I am attempting to implement a displacement map on a numpy image. Here is my implementation.
def displaceImage(img,displacementMap,distance=10,angle=45):
"""
NOTE: If displacementMap is smaller than img, then everthing out of bounds
is not displaced!
"""
angle=math.radians(angle)
img=numpyArray(img)
displacementMap=numpyArray(displacementMap)
if len(displacementMap.shape)>2:
# convert displacement map to grayscale
displacementMap=colorSpaces.grayscale(displacementMap)
def dissp(point):
"""
Called for each point in the array. Returns a same shaped tuple where it should go.
"""
if point[0]<displacementMap.shape[0] and point[1]<displacementMap.shape[1]:
delta=displacementMap[point[0],point[1]]*distance
toX=point[0]+delta*math.sin(angle)
toY=point[1]-delta*math.cos(angle)
if len(point)>2: # array is (w,h,rgb)
point=(toX,toY,point[2])
else: # array is (w,h) -- aka grayscale
point=(toX,toY)
return point
img=scipy.ndimage.geometric_transform(img,dissp,mode="nearest")
return img
And below I attempt to use a b&w perlin noise to displace to the top left quadrant of the image. As you can see from the central leaves/sky area, it appears to be doing what one would expect. The problem is, of course, the stray pixels everywhere. Thoughts?
python numpy scipy
asked Jan 1 at 1:35
TheHeadlessSourceManTheHeadlessSourceMan
105110
I am attempting to implement a displacement map on a numpy image. Here is my implementation.
def displaceImage(img,displacementMap,distance=10,angle=45):
"""
NOTE: If displacementMap is smaller than img, then everthing out of bounds
is not displaced!
"""
angle=math.radians(angle)
img=numpyArray(img)
displacementMap=numpyArray(displacementMap)
if len(displacementMap.shape)>2:
# convert displacement map to grayscale
displacementMap=colorSpaces.grayscale(displacementMap)
def dissp(point):
"""
Called for each point in the array. Returns a same shaped tuple where it should go.
"""
if point[0]<displacementMap.shape[0] and point[1]<displacementMap.shape[1]:
delta=displacementMap[point[0],point[1]]*distance
toX=point[0]+delta*math.sin(angle)
toY=point[1]-delta*math.cos(angle)
if len(point)>2: # array is (w,h,rgb)
point=(toX,toY,point[2])
else: # array is (w,h) -- aka grayscale
point=(toX,toY)
return point
img=scipy.ndimage.geometric_transform(img,dissp,mode="nearest")
return img
And below I attempt to use a b&w perlin noise to displace to the top left quadrant of the image. As you can see from the central leaves/sky area, it appears to be doing what one would expect. The problem is, of course, the stray pixels everywhere. Thoughts?
python numpy scipy
python numpy scipy
asked Jan 1 at 1:35
TheHeadlessSourceManTheHeadlessSourceMan
105110
asked Jan 1 at 1:35
TheHeadlessSourceManTheHeadlessSourceMan
105110
asked Jan 1 at 1:35
TheHeadlessSourceManTheHeadlessSourceMan
105110
asked Jan 1 at 1:35
TheHeadlessSourceManTheHeadlessSourceMan
105110
asked Jan 1 at 1:35
TheHeadlessSourceManTheHeadlessSourceMan
105110
105110
Update: I thought that perhaps this was due to an uninitialized buffer, so I tried:bg=np.copy(img) img=scipy.ndimage.geometric_transform(img,dissp,output=bg,mode="nearest")but it did not fix the problem
– TheHeadlessSourceMan
Jan 1 at 16:11
Update 2: I noticed that setting the geometric_transform to order=1 seemed to work, so I thought that perhaps the curve was developing a "ring" and clipping. Yet np.clip(img,0.0,1.0) doesn't help.
– TheHeadlessSourceMan
Jan 1 at 23:00
add a comment |
Update: I thought that perhaps this was due to an uninitialized buffer, so I tried:bg=np.copy(img) img=scipy.ndimage.geometric_transform(img,dissp,output=bg,mode="nearest")but it did not fix the problem
– TheHeadlessSourceMan
Jan 1 at 16:11
Update 2: I noticed that setting the geometric_transform to order=1 seemed to work, so I thought that perhaps the curve was developing a "ring" and clipping. Yet np.clip(img,0.0,1.0) doesn't help.
– TheHeadlessSourceMan
Jan 1 at 23:00
Update: I thought that perhaps this was due to an uninitialized buffer, so I tried:
bg=np.copy(img) img=scipy.ndimage.geometric_transform(img,dissp,output=bg,mode="nearest") but it did not fix the problem– TheHeadlessSourceMan
Jan 1 at 16:11
Update: I thought that perhaps this was due to an uninitialized buffer, so I tried:
bg=np.copy(img) img=scipy.ndimage.geometric_transform(img,dissp,output=bg,mode="nearest") but it did not fix the problem– TheHeadlessSourceMan
Jan 1 at 16:11
Update 2: I noticed that setting the geometric_transform to order=1 seemed to work, so I thought that perhaps the curve was developing a "ring" and clipping. Yet np.clip(img,0.0,1.0) doesn't help.
– TheHeadlessSourceMan
Jan 1 at 23:00
Update 2: I noticed that setting the geometric_transform to order=1 seemed to work, so I thought that perhaps the curve was developing a "ring" and clipping. Yet np.clip(img,0.0,1.0) doesn't help.
– TheHeadlessSourceMan
Jan 1 at 23:00
add a comment |
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Update: I thought that perhaps this was due to an uninitialized buffer, so I tried:
bg=np.copy(img) img=scipy.ndimage.geometric_transform(img,dissp,output=bg,mode="nearest")but it did not fix the problem– TheHeadlessSourceMan
Jan 1 at 16:11
Update 2: I noticed that setting the geometric_transform to order=1 seemed to work, so I thought that perhaps the curve was developing a "ring" and clipping. Yet np.clip(img,0.0,1.0) doesn't help.
– TheHeadlessSourceMan
Jan 1 at 23:00