About bounding box localization


#21

I just finished this week an implementation of SSD with Keras.
I can send you the wiki page I made explaining how it is working in details (I need to review it one time to make sure there are no mistakes).


(Edwin) #22

Thanks Ben!

Just found this as well, did a search bbox in the new repo and found this as well.


(Edwin) #23

@jeremy a little bit stuck. How do I look at the bounding predicted bounded boxes after using learn.fit? Also, how do I pass in an individual image using this method.

Tried this method that worked on the classification task, but didn’t seem to work here.

trn_tfms, val_tfms = tfms_from_model(arch, sz)

img = Image.open(PATH+fn)
im = trn_tfms(np.array(img))
preds = learn.predict_array(im[None])
y = np.argmax(preds)
data.classes[y]


(Edwin) #24
  • Update
  1. The from the learn.predict() method are bounding box positions that come out.
  2. This worked for me as from previous answer on forum to get individual prediction.
io_img = io.imread(img_url)
im = self.trn_tfms(np.array(io_img)/255.0)
preds = to_np(self.learn.models.model(V(T(im[None]))))
  • using it an api so mt look a little different.

#25

Here is the document SSD-Description.pdf (2.4 MB).

Please note that it is not a tutorial on how to implement SSD but a summary of the information I collected while studying the model. I hope it can be still helpful.

Regarding the implementation, I encourage everyone to look at the following repo:

It is extremely useful to understand the details.


(Ching June Hao) #26

Hi there, I would like to ask, as I have implemented SSD too to detect custom object, but how should I crop the image out that detected by the box? Is there a way?


#27

@chingjunehao: The output of the SSD detector is a tensor containing N_box “box tensors”. Each “box tensor” contains the data relative to one box generated by the model, the data being (your data order might be different) [x_center, y_center, width, height, x_center_variance, y_center_variance, width_variance, height_variance, class1_score, …, classN_score, x_center_offset, y_center_offset, width_offset, height_offset].
The scores and offsets are the parameters that SSD predicts and the other parameters are fixed.
So in order to crop your image, you need to follow the steps below:

  1. Have a decoder function to compute the predicted position of the boxes in pixels.
  2. Filter the boxes to keep only the ones with the highest score (non-max suppression)
  3. Finally use the positions of the remaining boxes to crop your image.

(Divya Sivasankaran) #30

Interesting thread!

I’m using a combination of inception + ssd to train my own custom dataset. After weeks of painful annotations, I finally got to a good level of accuracy on the object categories.

Strangely though, I can see a consistent pattern in the bounding box predictions among all classes. It’s like they are all expanded to the right side alone to include extra space, yet a tight bound in the left (esp left -bottom). check out the images below… and I’m not exaggerating - but EVERY prediction comes out this way… so I’m pretty sure it has something to do with my data/ a possible bug.

Capture

Just wondering if anyone has faced a similar issue or has any ideas on why this might be happening? Any pointers would be great!


(Alex) #31

Yes, this implementation is amazing. Reading the documentation and the code for the ground truth encoding process helped my understanding a lot. It’s actually a lot better (more comprehensive, better documentation, all original models provided) than the one linked to further up in this thread:


(Pawan) #32

hey @arnaud can you please provide the labelImg and RectLabel functions.


(Morten Punnerud Engelstad) #33

Something like this you are looking for to save part of the image?

from PIL import Image
img = Image.open('saved_image.png')
print(img.size)
croppedIm = img.crop((490, 980, 1310, 1217))
croppedIm.save('crop.png')

(late answer)