Mixing
Removing Presenter From Slides
Clash Royale CLAN TAG#URR8PPP
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Often you can find slide presentation where the presenter is also shown on the same slide:
When no pdf slides are available, I sometimes take scree shots of the video. However, I would like to remove the presenter, if possible.
In Mathematica there is the FindFaces function. It could be used to identify the person, but then I don't know how to cut it out accurately... ?
The result should look like this:
Do you know how I could do that ? ... and if not in Mathematica, do you know any other software?
image-processing
asked 1 hour ago
james
716418
add a comment |Â
up vote
4
down vote
favorite
Often you can find slide presentation where the presenter is also shown on the same slide:
When no pdf slides are available, I sometimes take scree shots of the video. However, I would like to remove the presenter, if possible.
In Mathematica there is the FindFaces function. It could be used to identify the person, but then I don't know how to cut it out accurately... ?
The result should look like this:
Do you know how I could do that ? ... and if not in Mathematica, do you know any other software?
image-processing
asked 1 hour ago
james
716418
add a comment |Â
up vote
4
down vote
favorite
up vote
4
down vote
favorite
Often you can find slide presentation where the presenter is also shown on the same slide:
When no pdf slides are available, I sometimes take scree shots of the video. However, I would like to remove the presenter, if possible.
In Mathematica there is the FindFaces function. It could be used to identify the person, but then I don't know how to cut it out accurately... ?
The result should look like this:
Do you know how I could do that ? ... and if not in Mathematica, do you know any other software?
image-processing
asked 1 hour ago
james
716418
Often you can find slide presentation where the presenter is also shown on the same slide:
When no pdf slides are available, I sometimes take scree shots of the video. However, I would like to remove the presenter, if possible.
In Mathematica there is the FindFaces function. It could be used to identify the person, but then I don't know how to cut it out accurately... ?
The result should look like this:
Do you know how I could do that ? ... and if not in Mathematica, do you know any other software?
image-processing
image-processing
asked 1 hour ago
james
716418
asked 1 hour ago
james
716418
asked 1 hour ago
james
716418
asked 1 hour ago
james
716418
asked 1 hour ago
james
716418
716418
add a comment |Â
add a comment |Â
1 Answer
1
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oldest
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up vote
4
down vote
This is an entertaining question. We'll use some neural networks from the neural network repository to attempt to solve it.
We'll use Ademxapp model, so here's a function to evaluate the net and give us back masks for each type of object it detects.
netevaluate[img_, device_: "CPU"] :=
Block[net, resized, encData, dec, mean, var, prob,
net = NetModel["Ademxapp Model A1 Trained on ADE20K Data"];
resized = ImageResize[img, 504];
encData = Normal@NetExtract[net, "Input"];
dec = NetExtract[net, "Output"];
mean, var = Lookup[encData, "MeanImage", "VarianceImage"];
prob = NetReplacePart[
net, "Input" ->
NetEncoder["Image", ImageDimensions@resized,
"MeanImage" -> mean, "VarianceImage" -> var],
"Output" -> Automatic][resized, TargetDevice -> device];
prob = ArrayResample[prob, Append[Reverse@ImageDimensions@img, 150]];
dec[prob]]
Now we'll write a function to only get data about people in the image. Now, from the documentation in the repository, I know that the label for the "person" mask is 13, and that's the only mask that we care about.
getPeople[i_] := Map[ReplaceAll[13 -> 1, _ -> 0], netevaluate[i], 2] // Image
Now we can simply get the largest item in that mask and remove it. We could try Inpaint but it didn't work very well on this image.
removePresenter[i_] := ImageAdd[i, Dilation[SelectComponents[getPeople[i], "Count", -1], 3]]
Try playing with the argument to Dilation if it doesn't take enough of the presenter. I would also consider changing the neural network for others in the "Semantic Segmentation" section if this one isn't accurate enough for you.
answered 1 hour ago
Carl Lange
1,113216
add a comment |Â
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
4
down vote
This is an entertaining question. We'll use some neural networks from the neural network repository to attempt to solve it.
We'll use Ademxapp model, so here's a function to evaluate the net and give us back masks for each type of object it detects.
netevaluate[img_, device_: "CPU"] :=
Block[net, resized, encData, dec, mean, var, prob,
net = NetModel["Ademxapp Model A1 Trained on ADE20K Data"];
resized = ImageResize[img, 504];
encData = Normal@NetExtract[net, "Input"];
dec = NetExtract[net, "Output"];
mean, var = Lookup[encData, "MeanImage", "VarianceImage"];
prob = NetReplacePart[
net, "Input" ->
NetEncoder["Image", ImageDimensions@resized,
"MeanImage" -> mean, "VarianceImage" -> var],
"Output" -> Automatic][resized, TargetDevice -> device];
prob = ArrayResample[prob, Append[Reverse@ImageDimensions@img, 150]];
dec[prob]]
Now we'll write a function to only get data about people in the image. Now, from the documentation in the repository, I know that the label for the "person" mask is 13, and that's the only mask that we care about.
getPeople[i_] := Map[ReplaceAll[13 -> 1, _ -> 0], netevaluate[i], 2] // Image
Now we can simply get the largest item in that mask and remove it. We could try Inpaint but it didn't work very well on this image.
removePresenter[i_] := ImageAdd[i, Dilation[SelectComponents[getPeople[i], "Count", -1], 3]]
Try playing with the argument to Dilation if it doesn't take enough of the presenter. I would also consider changing the neural network for others in the "Semantic Segmentation" section if this one isn't accurate enough for you.
answered 1 hour ago
Carl Lange
1,113216
add a comment |Â
up vote
4
down vote
This is an entertaining question. We'll use some neural networks from the neural network repository to attempt to solve it.
We'll use Ademxapp model, so here's a function to evaluate the net and give us back masks for each type of object it detects.
netevaluate[img_, device_: "CPU"] :=
Block[net, resized, encData, dec, mean, var, prob,
net = NetModel["Ademxapp Model A1 Trained on ADE20K Data"];
resized = ImageResize[img, 504];
encData = Normal@NetExtract[net, "Input"];
dec = NetExtract[net, "Output"];
mean, var = Lookup[encData, "MeanImage", "VarianceImage"];
prob = NetReplacePart[
net, "Input" ->
NetEncoder["Image", ImageDimensions@resized,
"MeanImage" -> mean, "VarianceImage" -> var],
"Output" -> Automatic][resized, TargetDevice -> device];
prob = ArrayResample[prob, Append[Reverse@ImageDimensions@img, 150]];
dec[prob]]
Now we'll write a function to only get data about people in the image. Now, from the documentation in the repository, I know that the label for the "person" mask is 13, and that's the only mask that we care about.
getPeople[i_] := Map[ReplaceAll[13 -> 1, _ -> 0], netevaluate[i], 2] // Image
Now we can simply get the largest item in that mask and remove it. We could try Inpaint but it didn't work very well on this image.
removePresenter[i_] := ImageAdd[i, Dilation[SelectComponents[getPeople[i], "Count", -1], 3]]
Try playing with the argument to Dilation if it doesn't take enough of the presenter. I would also consider changing the neural network for others in the "Semantic Segmentation" section if this one isn't accurate enough for you.
answered 1 hour ago
Carl Lange
1,113216
add a comment |Â
up vote
4
down vote
up vote
4
down vote
This is an entertaining question. We'll use some neural networks from the neural network repository to attempt to solve it.
We'll use Ademxapp model, so here's a function to evaluate the net and give us back masks for each type of object it detects.
netevaluate[img_, device_: "CPU"] :=
Block[net, resized, encData, dec, mean, var, prob,
net = NetModel["Ademxapp Model A1 Trained on ADE20K Data"];
resized = ImageResize[img, 504];
encData = Normal@NetExtract[net, "Input"];
dec = NetExtract[net, "Output"];
mean, var = Lookup[encData, "MeanImage", "VarianceImage"];
prob = NetReplacePart[
net, "Input" ->
NetEncoder["Image", ImageDimensions@resized,
"MeanImage" -> mean, "VarianceImage" -> var],
"Output" -> Automatic][resized, TargetDevice -> device];
prob = ArrayResample[prob, Append[Reverse@ImageDimensions@img, 150]];
dec[prob]]
Now we'll write a function to only get data about people in the image. Now, from the documentation in the repository, I know that the label for the "person" mask is 13, and that's the only mask that we care about.
getPeople[i_] := Map[ReplaceAll[13 -> 1, _ -> 0], netevaluate[i], 2] // Image
Now we can simply get the largest item in that mask and remove it. We could try Inpaint but it didn't work very well on this image.
removePresenter[i_] := ImageAdd[i, Dilation[SelectComponents[getPeople[i], "Count", -1], 3]]
Try playing with the argument to Dilation if it doesn't take enough of the presenter. I would also consider changing the neural network for others in the "Semantic Segmentation" section if this one isn't accurate enough for you.
answered 1 hour ago
Carl Lange
1,113216
This is an entertaining question. We'll use some neural networks from the neural network repository to attempt to solve it.
We'll use Ademxapp model, so here's a function to evaluate the net and give us back masks for each type of object it detects.
netevaluate[img_, device_: "CPU"] :=
Block[net, resized, encData, dec, mean, var, prob,
net = NetModel["Ademxapp Model A1 Trained on ADE20K Data"];
resized = ImageResize[img, 504];
encData = Normal@NetExtract[net, "Input"];
dec = NetExtract[net, "Output"];
mean, var = Lookup[encData, "MeanImage", "VarianceImage"];
prob = NetReplacePart[
net, "Input" ->
NetEncoder["Image", ImageDimensions@resized,
"MeanImage" -> mean, "VarianceImage" -> var],
"Output" -> Automatic][resized, TargetDevice -> device];
prob = ArrayResample[prob, Append[Reverse@ImageDimensions@img, 150]];
dec[prob]]
Now we'll write a function to only get data about people in the image. Now, from the documentation in the repository, I know that the label for the "person" mask is 13, and that's the only mask that we care about.
getPeople[i_] := Map[ReplaceAll[13 -> 1, _ -> 0], netevaluate[i], 2] // Image
Now we can simply get the largest item in that mask and remove it. We could try Inpaint but it didn't work very well on this image.
removePresenter[i_] := ImageAdd[i, Dilation[SelectComponents[getPeople[i], "Count", -1], 3]]
Try playing with the argument to Dilation if it doesn't take enough of the presenter. I would also consider changing the neural network for others in the "Semantic Segmentation" section if this one isn't accurate enough for you.
answered 1 hour ago
Carl Lange
1,113216
answered 1 hour ago
Carl Lange
1,113216
answered 1 hour ago
Carl Lange
1,113216
answered 1 hour ago
Carl Lange
1,113216
1,113216
add a comment |Â
add a comment |Â
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