Talking to a bunch of friends yesterday about some data projects, and one of them involved scanning aerial videos of grasslands for endangered animals. So here I am today, trying out the Google TensorFlow Object Detection API - one year late, but better late than never right? LOL
I’m beginning first with images, but I’d love to eventually progress to videos, and training it to identify custom objects.
Installation
I am installing the below on a Macbook (aka I have no GPU).
1) Install Tensorflow
Thankfully I already have Tensorflow installed. If you don’t, follow the instructions here: https://www.tensorflow.org/install/
2) Install dependencies
Follow this: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md
Some additional notes/ troubleshooting:
-
I skipped downloading the COCO API as I was not interested in using the COCO evaluation metrics. All still good.
- When I was at the Protobuf Compilation step, it said:
'protoc' is not recognized as an internal or external command -
To solve that, follow the steps here: http://tech.yipp.ca/linux/install-google-protocol-buffers-linux/
- When testing the installation, it said
No module named 'object_detection'. - To solve that, run the below:
$ python setup.py build
$ python setup.py install
Check out this thread if you have more issues: https://github.com/tensorflow/models/issues/2031
- When testing the installation, it said
ImportError: No module named 'tensorflow'. -
To solve that, I made a copy of the entire research folder, and pasted it in another location, entirely separate/away from the tensorflow directory. (I was operating within the tensorflow directory before this.)
-
Separately, my tensorflow version was NOT at 1.4.0. I tried to use
pip install tensorflow==1.4.0(I use pip3 usually though but some complications about main and stuff) - couldn’t resolve it (said successfully installed but it still wasn’t 1.4.0. Thought I had too many versions of python/tensorflow in my computer but don’t think any of them converted to 1.4.0). Long story short, I checkedtf.__version__, found that I was on 1.4.1 and decided to ignore the warning. - After this I kind of skipped running the test - instead I went to access the jupyter notebook directly. All good (thankfully!)
Run the notebook!
Being the impatient me, I ran all the cells, skipped to the end and checked that the output was there. Hurrah! It was. Haha
Then I decided to change up the images (second last cell - under the Detection heading. Change the TEST_IMAGE_PATHS to a list of your full image paths).
To save the photos instead of displaying it, instead of plt.imsave(image_np), use plt.imsave('output.jpg', image_np)
Who knew, poodles (if they are sufficiently cute) can be classified as teddy bears!!!!!
MORE TEDDY BEARS!!! LOL
This is my dog! I need to level him up to teddy bear cuteness level LOL
Still my dog - still a dog LOL
Hypothesis: does it know it’s a dog because of its position (lying down)? Teddy bears don’t lie down. Let’s use a dog and a soft toy, both lying down on the floor, to try.
Result: soft toy detected as a CAT, dog detected as a BEAR!!!!!! Love this HAHAHAHAHA
Next time, I will move towards real time object recognition! AKA videos!! https://towardsdatascience.com/building-a-real-time-object-recognition-app-with-tensorflow-and-opencv-b7a2b4ebdc32
Or maybe first, building an app using AWS lambda: https://medium.com/tooso/serving-tensorflow-predictions-with-python-and-aws-lambda-facb4ab87ddd
Exciting times ahead!!!!