TL;DR. After reading this post, you will learn how to run state of the art object detection and segmentation on a video file Fast . Even on an old laptop with an integrated graphics card, old CPU, and only 2G of RAM. Run like Fast and Furious So here is the catch. This will only work if you have an internet connection and own a Google Gmail account. Since you are reading this, it is very likely you are already qualified. All the code in the post runs entirely in the cloud. Thanks to Google’s Colaboratory a.k.a. Google Colab! I am going to show you how to run our code on Colab with a server-grade CPU , > 10 GB of RAM and a powerful GPU for FREE! Yes, you hear me right. Using Google Colab with GPU enabled Colab was build to facilitate machine learning professionals collaborating with each other more seamlessly. I have shared my , click to open it. Python notebook for this post Log in to your Google Gmail account on the upper right corner if you haven’t done so. It will ask you to open it with Colab at the top of the screen. Then you are going to make a copy so you can edit it. Now you should be able to click on the “ ” menu button to choose the version of Python and whether to use GPU/CPU to accelerate the computation. Runtime The environment is all set. So easy isn’t it? No hassles like installing Cuda and cudnn to make GPU working on a local machine. Run this code to confirm TensorFlow can see the GPU. It outputs: Found GPU at: /device:GPU:0 Great, We are good to go! If you are curious about what the GPU model you are using. It is a Nvidia Tesla K80 with 24G of memory. Quite powerful. Run this code to find out yourself. You will see the 24G graphics memory does help later. It makes possible to process more frames at a time to accelerate the video processing. Mask R-CNN Demo The demo is based on the . It is an implementation of on Keras+TensorFlow. It not only generates the bounding box for a detected object but also generate a mask over the object area. Mask R-CNN GitHub repo Mask R-CNN Install Dependencies and run Demo Mask R-CNN has some dependencies to install before we can run the demo. Colab allows you to install Python packages through , and general Linux package/library through . pip apt-get In case you don’t know yet. Your current instance of Google Colab is running on an Ubuntu virtual machine. You can run almost every Linux command you normally do on a Linux machine. Mask R-CNN depends on , we are installing it with the following cell. pycocotools It clones the coco repository from GitHub. Install build dependencies. Finally, build and install the coco API library. All this happens in the cloud virtual machine, and quite fast. We are now ready to clone the Mask_RCNN repo from GitHub and cd into the directory. Notice how we change directory with Python script instead of running a shell ‘cd’ command. Since we are running Python in current notebook. Now you should be able to run the Mask R-CNN demo on colab like you would on a local machine. So go ahead and run it in your Colab notebook. So far those sample images came from the GitHub repo. But how do you predict with custom images? Predict with Custom Images In order to upload a image to Colab notebook, there are three options that I think of. 1. Use a Free image hosting provider like the . imgbb 2.Create a GitHub repo, then download the image link from colab. After uploading images by either of those two options, you will get a link to the image. Which can be downloaded to your colab VM with Linux command. It downloads one image to the ./images folder. wget !wget -P ./images https://preview.ibb.co/cubifS/sh_expo.jpg The first two options will be ideal if you just want to upload 1 or 2 images and don’t care other people on the internet also be able to see it given the link. 3. Use Google Drive The option is ideal if you have private images/videos/other files to be uploaded to colab. Run this block to authenticate the VM to connect to your Google Drive. It will ask for two verification code during the run. Then execute this cell to mount the Drive to the directory ‘ ’ drive You can now access your Google drive content in directory ./drive !ls drive/ Hope you are having fun so far, why not try this on a video file? Processing Videos Processing a video file will take three steps. 1. Video to images frames. 2. Process images 3. Turn processed images to output videos. In our previous demo, we ask the model to process just one image at a time. As configured in the . IMAGES_PER_GPU If we are going to process the whole video one frame at a time, it will take a long time. So instead we are going to leverage GPU to process multiple frames in parallel. The pipeline of Mask R-CNN is quite computationally intensive and takes a lot of GPU memory. I find the Tesla K80 GPU on Colab with 24G of memory can safely process 3 images at a time. If you go beyond that the notebook might crash in the middle of processing the video. So in the code below, we set the to 3 and use library to stage 3 images at a time before processing them with the model. batch_size cv2 After running this code, you should now have all processed image files in one folder . ./videos/save The next step is easy, we just need to generate the new video from those images. We are going to use cv2’s to accomplish this. VideoWriter But two things you want to make sure: 1. The frames need to be ordered in the same way as they are extracted from the original video. (Or backward if you prefer to watch the video that way) 2.The frame rate matches the original video. You can use the following code to check the frame rate of a video or just open the file property. Finally here is the code to generate the video from processed image frames. If you have gone this far, the processed video should now be ready to be downloaded to your local machine. Free free to try your favorite video clip. Maybe intentionally decrease the frame rate when reconstructing the video to watch it in slow motion. Summary and Further reading In the post, we walked through how to run your model on Google Colab with GPU acceleration. You have learned how to do object detection and segmentation on a video. Thanks to the powerful GPU on Colab, made it possible to process multiple frames in parallel to speed up the process. Further reading If you want to learn more about the technology behind the object detection and segmentation algorithm. Here is the original goes through the detail of the model. paper of Mask R-CNN Or if you just get started with objection detection, check out my goes through important basics shared between many models. object detection/localization guide series Here again , and for your convenience. the Python notebook for this post GitHub repo Like this article? Consider and share with more people. 👏 Share on Twitter Share on Facebook Originally published at www.dlology.com . For more deep learning experiences like this.
