Mixing
Parameters of dlib shape predictor model
I trained dlib shape predictor model on my custom data (using train_shape_predictor.py file). As a result I got .dat file. Now I have an image containing an object on which the dlib prediction model has been trained. How I will use that prediction model, to predict a shape in the input image?
I am seeing Dlib shape prediction documentation, there is mentioned that dlib shape predictor accepts two arguments :
- An image
- A box (Dlib Rectangle)
Now what will be these parameters, in my case, as I have just one image (Containing an object, which will be predicted through trained model)?
Any sort of help in that regard will be highly appreciated.
python computer-vision training-data dlib
asked Jan 1 at 13:08
user8611018user8611018
307
add a comment |
I trained dlib shape predictor model on my custom data (using train_shape_predictor.py file). As a result I got .dat file. Now I have an image containing an object on which the dlib prediction model has been trained. How I will use that prediction model, to predict a shape in the input image?
I am seeing Dlib shape prediction documentation, there is mentioned that dlib shape predictor accepts two arguments :
- An image
- A box (Dlib Rectangle)
Now what will be these parameters, in my case, as I have just one image (Containing an object, which will be predicted through trained model)?
Any sort of help in that regard will be highly appreciated.
python computer-vision training-data dlib
asked Jan 1 at 13:08
user8611018user8611018
307
add a comment |
I trained dlib shape predictor model on my custom data (using train_shape_predictor.py file). As a result I got .dat file. Now I have an image containing an object on which the dlib prediction model has been trained. How I will use that prediction model, to predict a shape in the input image?
I am seeing Dlib shape prediction documentation, there is mentioned that dlib shape predictor accepts two arguments :
- An image
- A box (Dlib Rectangle)
Now what will be these parameters, in my case, as I have just one image (Containing an object, which will be predicted through trained model)?
Any sort of help in that regard will be highly appreciated.
python computer-vision training-data dlib
asked Jan 1 at 13:08
user8611018user8611018
307
I trained dlib shape predictor model on my custom data (using train_shape_predictor.py file). As a result I got .dat file. Now I have an image containing an object on which the dlib prediction model has been trained. How I will use that prediction model, to predict a shape in the input image?
I am seeing Dlib shape prediction documentation, there is mentioned that dlib shape predictor accepts two arguments :
- An image
- A box (Dlib Rectangle)
Now what will be these parameters, in my case, as I have just one image (Containing an object, which will be predicted through trained model)?
Any sort of help in that regard will be highly appreciated.
python computer-vision training-data dlib
python computer-vision training-data dlib
asked Jan 1 at 13:08
user8611018user8611018
307
asked Jan 1 at 13:08
user8611018user8611018
307
asked Jan 1 at 13:08
user8611018user8611018
307
asked Jan 1 at 13:08
user8611018user8611018
307
asked Jan 1 at 13:08
user8611018user8611018
307
307
add a comment |
add a comment |
1 Answer
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As the document says:
- image is a numpy ndarray containing either an 8bit grayscale or RGB
image. --> Pass your image here - box is the bounding box to begin the shape prediction inside. --> if you already have the bounding box of your object (e.g. where about a face is in the image), pass it here.
A typical application would be:
rects = dlib.simple_object_detector(image)
for rect in rects:
shape = dlib.shape_predictor(image, rect)
answered Jan 1 at 13:36
Quang HoangQuang Hoang
2,1571915
Is it necessary to use dlib object detector. Is there any other way, in which I wouldn't have to use dlib object detector? Can you please shed some light on that?
– user8611018
Jan 1 at 14:03
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
As the document says:
- image is a numpy ndarray containing either an 8bit grayscale or RGB
image. --> Pass your image here - box is the bounding box to begin the shape prediction inside. --> if you already have the bounding box of your object (e.g. where about a face is in the image), pass it here.
A typical application would be:
rects = dlib.simple_object_detector(image)
for rect in rects:
shape = dlib.shape_predictor(image, rect)
answered Jan 1 at 13:36
Quang HoangQuang Hoang
2,1571915
Is it necessary to use dlib object detector. Is there any other way, in which I wouldn't have to use dlib object detector? Can you please shed some light on that?
– user8611018
Jan 1 at 14:03
add a comment |
As the document says:
- image is a numpy ndarray containing either an 8bit grayscale or RGB
image. --> Pass your image here - box is the bounding box to begin the shape prediction inside. --> if you already have the bounding box of your object (e.g. where about a face is in the image), pass it here.
A typical application would be:
rects = dlib.simple_object_detector(image)
for rect in rects:
shape = dlib.shape_predictor(image, rect)
answered Jan 1 at 13:36
Quang HoangQuang Hoang
2,1571915
Is it necessary to use dlib object detector. Is there any other way, in which I wouldn't have to use dlib object detector? Can you please shed some light on that?
– user8611018
Jan 1 at 14:03
add a comment |
As the document says:
- image is a numpy ndarray containing either an 8bit grayscale or RGB
image. --> Pass your image here - box is the bounding box to begin the shape prediction inside. --> if you already have the bounding box of your object (e.g. where about a face is in the image), pass it here.
A typical application would be:
rects = dlib.simple_object_detector(image)
for rect in rects:
shape = dlib.shape_predictor(image, rect)
answered Jan 1 at 13:36
Quang HoangQuang Hoang
2,1571915
As the document says:
- image is a numpy ndarray containing either an 8bit grayscale or RGB
image. --> Pass your image here - box is the bounding box to begin the shape prediction inside. --> if you already have the bounding box of your object (e.g. where about a face is in the image), pass it here.
A typical application would be:
rects = dlib.simple_object_detector(image)
for rect in rects:
shape = dlib.shape_predictor(image, rect)
answered Jan 1 at 13:36
Quang HoangQuang Hoang
2,1571915
answered Jan 1 at 13:36
Quang HoangQuang Hoang
2,1571915
answered Jan 1 at 13:36
Quang HoangQuang Hoang
2,1571915
answered Jan 1 at 13:36
Quang HoangQuang Hoang
2,1571915
2,1571915
Is it necessary to use dlib object detector. Is there any other way, in which I wouldn't have to use dlib object detector? Can you please shed some light on that?
– user8611018
Jan 1 at 14:03
add a comment |
Is it necessary to use dlib object detector. Is there any other way, in which I wouldn't have to use dlib object detector? Can you please shed some light on that?
– user8611018
Jan 1 at 14:03
Is it necessary to use dlib object detector. Is there any other way, in which I wouldn't have to use dlib object detector? Can you please shed some light on that?
– user8611018
Jan 1 at 14:03
Is it necessary to use dlib object detector. Is there any other way, in which I wouldn't have to use dlib object detector? Can you please shed some light on that?
– user8611018
Jan 1 at 14:03
add a comment |
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