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The biggest change I noticed was a diminishment in the number of false positives in the Android app due to objects that look like a yellow blob; of course, this could be because the threshold was increased.
Now, with a newest and hopefully improved model, I was ready to use it to detect Pikachu in videos. Performing an object detection from a video is not as hard or fancy as it sounds.
In layman terms, we can say that a video is a collection of images that follow a sequence, therefore the detection process is fairly similar to that of detecting from a normal image.
Why fairly similar? Well, due to the nature of a video, there are several steps regarding the preprocessing and the preparation of the frame that has to be solved before feeding it to the detection model.
In the next lines, I will explain this, plus the procedure I followed to perform the detections and how I created a new video to display them.
Most of my code is based on a Python notebook provided in the TensorFlow Object Detection repo; this code does most of the hard work as it includes many functions that ease the detection process.
From a high-level perspective, the code I wrote has three main tasks:. To begin with, the frozen model, data labels, and video have to be loaded.
For simplicity reasons, I recommend a short, and medium-sized video since it can take a bit of time to process the whole movie.
The principal functionality of the script is based on a loop that iterates over every frame of the video. At each iteration, a frame is read and its color space is changed.
Next, the actual detection process is made to find all those nice and yellow Pikachus. As a result, the coordinates of the bounding box where the Pikachu is located if any was found and the confidence value of the detection, are returned.
Subsequently, a copy of the frame will be created, containing the bounding box where the Pikachu should be as long as the confidence score is above the given threshold.
All the newly created copies of the frames carrying the detection boxes discussed in the preceding step are used to construct a new video.
To build this video, a VideoWriter object is needed, and at each iteration of the previously discussed loop, the copy of the frame will be written into this object without any sound.
These two videos showcase how the model performs. The detections from the first video were pretty good. Even though Pikachu kept holding a ketchup bottle through the whole video, the model was able to detect it in most scenes.
On the other hand, there is an instance at where it was not detected, moreover, the shot where Scyther the green mantis looking thing broke the ketchup bottle to was a false positive.
The performance of the model on the second video was not as good as the first one, with the central problem being during the scenes where there are two Pikachus on the frame.
In this case, the model seems to detect as a Pikachu the two of them, instead of having one detection for each — a clear example is at where both Pikachus are slapping each other sad scene : , I know.
In this article, I talked about how we can use the TensorFlow Object Detection package to detect Pikachus in videos. In the beginning, I discussed a bit of my previous work in which I used an earlier version of a model to do the detections on an Android device.
Said model, even though it was doing its job, had some problems that I wished to work on; those improvements led me to do this project and to build a detection model for videos.
The new model works as intended. Sure, there are some hiccups here and there — that are related to the false positives, and Pikachus not detected — but it does what it has to do.
Thanks for reading. I really hope this guide helped some of you. If you have any questions, comments, doubt, or just want to chat, leave a comment and I will be happy to help.
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Get started. Open in app. Sign in. Editors' Picks Features Explore Contribute. Detecting Pikachu in videos using Tensorflow Object Detection.
Juan De Dios Santos. Detecting Pikachu on Android using Tensorflow Object Detection This guide explain how to train a object detection model, using Pikachu as the target object, with the purpose of….
Improvements to model As mentioned before, in a previous work, I did the original training of a Pikachu detection model with the goal of using it on Android devices as well as a Python notebook.
Detection from videos Performing an object detection from a video is not as hard or fancy as it sounds. From a high-level perspective, the code I wrote has three main tasks: Loading the resources To begin with, the frozen model, data labels, and video have to be loaded.
Iterating over video The principal functionality of the script is based on a loop that iterates over every frame of the video. Price, differences, gameplay, and more.
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The 42 Most Anticipated Films of Akhil Arora Wednesday January 2, Avengers: Endgame, Star Wars: Episode IX, Toy Story 4, The Lion King, Captain Marvel, Spider-Man: Far From Home, Kesari, Panipat, Brahmastra, and more — these are the most anticipated and highly-awaited Hollywood and Bollywood films of Surprised Pikachu Is Twitter's Latest Favourite Meme Offbeat NDTV Offbeat Desk Wednesday November 14, The latest meme to take over Twitter is known as 'Surprised Pikachu', and it is as funny as it sounds.
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