Google Summer of Code Update – July 2017

I’ve been working for Red Hen Lab and CCExtractor as part of GSoC 2017. This year, my project was divided into three major components:-

1. TV Commercial Classification – Details and code here (Done during June)
2. Improving the visual recognition pipeline
3. Optimizing the performance of the visual recognition pipeline and deploying it

Apart from this, I have also been working on analyzing and fixing issues with the CCExtractor code in order to better cater to some of Red Hen’s new international use cases.

In this post, I will describe what I have been up to this month, what things I have been spending my time working on, and some interesting decisions that I have had to make based on what I have observed in my experiments.

My Current Setup

Red Hen’s GSOC students have been working extensively on solving machine and deep learning problems which typically require a GPU for computational tractability. The Case HPC is where most of the important number crunching happens. However, being a shared resource, the HPC (and especially it’s GPU nodes) were not always readily available. I recently purchased a new laptop with a 4GB Nvidia GTX 1050 Ti GPU, which I have been using to work locally (as well as on my home institute cluster with more GPUs). Most of the benchmarking work that I have done have been on my computer, however the linearity of the observations and the computational power of other GPUs should hold for our particular use case of image classification (ie a more powerful GPU should take a linearly shorter time and a less powerful GPU a linearly longer time for the same use case).

Benchmarking ResNet for News Shot Classification

AlexNet (CaffeNet in our implementation) is the deep neural network architecture used for the shot characterization into 5 classes in the pipeline. This is a 7 layer deep architecture.

ResNet is a newer, deeper and more accurate model developed by Microsoft Research. It achieves state-of-the-art results on large scale visual recognition and is in essence a modern day upgrade to the 2014 CaffeNet architecture that we use.

Upon training ResNet on the same task with the same training/testing data split to identify news shot categories, the saturation accuracy of ResNet was superior. It achieves a peak accuracy of 93.7% whereas the AlexNet model achieves a peak accuracy of 88.3%. The ‘real’ accuracy (on exhaustive testing) for AlexNet is close to 86.5% and that for ResNet is close to 91%.

The training graph for the two architectures (done on a K40 GPU and a 1050 Ti GPU respectively) looks like this:-

ResNet takes a slightly larger time to converge, likely because of the deeper architecture requiring more time for the gradients to flow across the network. But it achieves an overall accuracy of roughly 5-6% higher than the AlexNet model after training is complete. This is to be expected because the ResNet model is newer and more state-of-the-art. However, it’s extreme depth in terms of layers leads to a much, much higher time taken to train and test (predict new samples using) the model.

While running 100 iterations 10 times and averaging the time values for a batch size of 10 at test time, the benchmarks for my Nvidia GTX 1050 Ti 4GB GPU, and that for an Intel Xeon CPU node look something like this:-

Model	Avg GPU Mode time (s)	Avg CPU Mode time (s)
AlexNet	7.34	283.66
ResNet	49.54	1812.31

My GPU seems to be around 3 times slower than a GTX 1080 judging by these benchmarks.

The final decision that I took on the basis of these observations was to stick to the existing AlexNet framework for the sake of speed on CPU nodes. An accuracy increase of 5-6% for the shot type categorization from an existing ~86% was good, but the tradeoff of 6-7 times the runtime was perhaps not ideal for our use case of processing over 300,000 hours worth of video.

Brazilian Timestamps and Deduplication

There was an issue with the Brazilian ISDB subtitle decoder which caused broken timestamps (Timestamps are the primary key for the Red Hen dataset, and without them, the data can’t be part of the searchable archive). There was also an issue with duplication
https://github.com/CCExtractor/ccextractor/issues/739

French OCR – Fixing an Issue with Image Transparency

https://github.com/CCExtractor/ccextractor/pull/759

This one was one of those problems that happened to be a one line fix, but involved a huge amount of analysis, reading and experimentation to get done right. It took me the good part of a week to get this to work as it is now, and hopefully this yields near perfect OCR results for all use cases, including those with transparent DVB subtitles.

Adding GPU specific usage to the Visual Pipeline

Earlier, the pipeline was supposed to be run as one single job on a CPU compute node, and even if the requested node was a GPU node, the capabilities of the GPU would not be used by it. I added the capability to use a GPU if available. If no GPU is available (or detected by the code), we fall back to default CPU execution.

The changes to make this happen can be seen at:-
https://github.com/gshruti95/news-shot-classification/pull/2

This is especially useful in speeding up the runtime of the feature extraction and classification steps that involve deep neural networks (namely anything that invovles running a Caffe model). GPU execution of these steps speeds up the runtime by an exponential factor which could be anything between 20 to 200 times depending on the individual computational power of the CPU/GPU in question. On my computer, a nearly 40 times speedup can be observed (benchmarks above).

Upgrading the YOLO person detector in the Visual Pipeline to YOLOv2

The current pipeline has the YOLO object detector specifically for person detection. However, YOLO was upgraded to YOLOv2 this year and has been accompanied by significant accuracy gains. I upgraded the version of YOLO used in the pipeline to the latest one, while retaining the same output format in the SHT and the JSON files.

I integrated the original C code based on Darknet for YOLOv2 to the pipeline, and person detection results are slightly better than before.

Singularity Container for Portable HPC Execution of the Visual Pipeline

Singularity is an HPC friendly alternative to the popular Docker framework. Red Hen has been heavily using Singularity this GSoC. One particularly useful use case is portable usage of the Singularity image on multiple HPC clusters (e.g. Case HPC, one of the many clusters at Erlangen HPC etc)

I have written a basic singularity image for the state of the code at this moment, which creates a container and then downloads all required models and dependencies, and sets up the container for usage of the visual recognition pipeline.

Upcoming Work

In the final third of GSOC, I will work on reducing the overall runtime of the pipeline by as much as possible. I will also work on writing an HPC job manager script which logically segments the different parts of the pipeline and submits and tracks different jobs for each based on available resources (GPU/CPU). Another thing to do is set up the pipeline with CPU optimized Intel Caffe which will allow automatic parallel processing on CPU nodes on HPC. After doing this and testing for sanity, the pipeline should be ready to be put into production on the entire NewsScape dataset, perhaps on multiple HPCs.

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Google Summer of Code, Week 13 – Wrapping Up

This was the final week of Google Summer of Code 2016.

This post will contain example outputs of the system I built in the last three months, along with any other work that I do till the official end of the summer.

Google Summer of Code, Week 12 – GUI Integration and DVB Languages

My work for this week focused on integrating the new features that I developed throughout GSOC into the GUI, and also adding support for multiple OCR languages to DVB subtitles.

GUI Integration

The most common users of CCExtractor use the GUI, and may not necessarily be experienced with the command line. Hence, it was important to integrate the new features into the GUI so that they may be available to a much wider audience.

The GUI is essentially a program which provides a graphical interface to the user who can supply the required options with ease, and the program in turn calls the ccextractor executable after parsing the options that the user supplied and converting them into a command string. The GUI interacts with the executable and works just like the normal executable would from the command line, but it gives easy to interpret visual feedback to the user, bundled in an easy to use application.

In addition to the SourceForge Windows GUI, CCExtractor also has a cross-platform Qt GUI which was developed by Oleg Kisselef in GSOC 2015 (https://github.com/kisselef/ccextractor-gui-qt). I needed to add parsing support for the options which I had added to the main program and create an interface which would allow those options to be sent to the executable.

It was fairly easy to add the options to the ‘Options’ window. A main checkbox needs to be checked in order to access the other parameters (equivalent to how -hardsubx needs to be specified before any related options on the command line). After I had created the UI, I had to map the elements in the GUI (radio buttons, sliders, checkboxes) to events in the application which would pass on the appropriate commands to the executable. I also verified that the options worked as intended, and constrained them to take only valid values, and also be initialized to the default and recommended values.

The resultant additions to the Qt GUI on Linux look like this:-

DVB Languages

Another thing which I did this week was to add support for potentially 99 different languages using Tesseract’s .traineddata files. Before this point,  only English was supported.

Adding this feature was like solving a partially solved jigsaw puzzle. I just had to complete some of the existing code to search for Tesseract language packs and make sure that it looked for the necessary files in the correct locations.

Initially, I had also added special cases for certain languages like Chinese (simplified) which seemed to come with non standard language codes in the video stream. However, instead of hard-coding a particular case like this, it was deemed better to let the user specify the non standard names, if at all necessary.

I added the -ocrlang and -dvblang parameters. -dvblang allows the user to select which language’s caption stream will be processed. In the event that there were multiple caption streams in the video, only the one specified by the parameter would be processed. -ocrlang allows the user to manually select the name of the Tesseract .traineddata file. This option is helpful if you want to OCR a caption stream of one language with the data of another language. e.g. -dvblang chs -ocrlang chi_tra will decode the Chinese (simplified) caption stream but perform OCR using the Chinese (Traditional) trained data. This option is also useful when the Tesseract .traineddata files don’t come with standard ISO names.

Google Summer of Code, Weeks 10 and 11 – Different Types of Subtitle Detection

In some cases, the detected text may be filled with noise and unwanted artifacts. Hence, there was a need to improve the text classifier in order to try and improve the quality of the detected captions. I set up three different levels of Tesseract subtitle line classifiers, and added confidence based thresholds as possible parameters which could potentially improve the quality of the OCR results.

The Three Modes – Frame, Word and Character

There are three different modes at which I used Tesseract to process a particular frame:-

• Frame: In this mode, the entire frame is processed at once, and the entire UTF8 text detected by Tesseract is written to the caption file.
• Word: In this mode, every word detected in the frame is individually processed. It can be later thresholded based on confidence or whether it is a dictionary word, or an expletive word etc.
• Letter: In this mode, every letter detected in the frame is individually processed. It can also be later thresholded. This mode is just present because of the possibility to make it so. For any practical purposes, the first two should serve fine.

I created a parameter called -ocr_mode which allows the user to specify the level at which the OCR will be performed and interpreted. The default is the ‘frame’ mode.

Tesseract Confidence Ratings

The Tesseract Engine supplies confidence ratings along with its OCR predictions. I chose to use these confidence values to improve the quality of text recognition performed by the system. I created an optional parameter called -conf_thresh which allows the user to put a threshold on the confidence rating of the text classification by Tesseract (having a default value of 0, ie all classifications accepted). Only classification results which had a confidence above the threshold were processed and written as captions.

The confidence thresholding works for each of the three OCR modes as described above. For the ‘frame’ mode, the confidence used is the mean text confidence, for the ‘word’ mode, the per-word confidence, and for the ‘letter’ mode, the per character confidence. These results are then thresholded and only the good ones remain.

Italic Detection

Another small part of my proposal was to detect if the formatting of the subtitles was italic. I had originally intended to do this using an orientation estimation using the Fourier transform or instead looking at the average angle of the longest lines in the characters found in their Hough Transform.

However, none of that proved to be necessary since the Tesseract API had a call to detect word font attributes. So, whenever italic detection was to be done, I set the OCR mode to word-wise and called the Tesseract API which would then determine if the word was italic.

An excerpt from the video at https://www.facebook.com/uniladmag/videos/2282957648393948 (about British warship HMS Bulwark) is:-

1
00:00:00,000 –> 00:00:04,959
<i>The ship is an enormous machine </i>

2
00:00:04,961 –> 00:00:07,919
<i>one of the complicated machines that Britains ever built </i>

3
00:00:07,921 –> 00:00:09,879
<i>we make our own water from sea water </i>

4
00:00:09,881 –> 00:00:10,959
<i>we deal with our own sewage </i>

5
00:00:10,961 –> 00:00:12,959
<i>we cook our own food </i>

6
00:00:12,961 –> 00:00:15,879
<i>we’re a floating city. </i>

Google Summer of Code, Weeks 8 and 9 – Detecting DVB Subtitle Color

DVB Subtitles

DVB (Digital Video Broadcasting) is the standard for TV video in a large number of countries, and is especially prevalent in Europe. In a DVB video stream, subtitles are present as colored bitmap images, which are simply overlaid on the video if subtitles are turned on in the viewing system.

CCExtractor already had excellent support for DVB subtitle text recognition, using Tesseract. It was done by first binarizing the bitmap so that text and the background were separate. This resulted in accurate text recognition by cleaning up the image, but lost color information in cases where multiple colors of text were present in a single bitmap. An additional requirement was to detect the color of each word in the subtitle.

Why Color Is Important

Color changes in DVB subtitles refer to speaker changes in the program. Assigning a different color for a different speaker enriches the assistive capabilities of captions (e.g. for hearing impaired people). Speaker change detection also holds a very large importance for various text processing algorithms for which CCExtractor is a major data source.

Bitmaps and Color Histograms

Bitmaps are just 2-D arrays of numbers, along with an accompanying palette. A palette is like a dictionary which represents a mapping from the pixel value in the bitmap to the actual RGB value. For example, the bitmap may have values ranging from 1 to 8, and 1 may represent Black (0,0,0), 8 may represent White (255,255,255) and so on. DVB subtitles are also bitmaps with their corresponding palettes. Color Histograms are a way to represent the frequency of each color in the image. They are a frequency representation of every single pixel value in the bitmap. The more the amount of a particular pixel value in the image, the higher will its histogram value be.

Word-Wise Color Quantization

The color detection for every word is done by iterating over the bounding boxes of every word obtained in the original DVB OCR results. For every bounding box, a color quantization process is performed. Color quantization essentially means changing the pixel value to a nearby value which has a much higher frequency in the histogram. Using this information from a two bin color quantization, the background and foreground colors are determined, and the foreground (text) color is assigned as the detected color of the text.

Successive words with the same color are grouped together and the points at which the color changed are marked with <font> tags.

Output

A DVB frame with three colours along with the corresponding output is as shown:-

34
00:01:47,780 –> 00:01:51,339
<font color=”#00ff00″>So he spent last night in a cell?</font>
<font color=”#ececec”>It’s a ROOM. Not a cell. </font><font color=”#ffff00″>Ian!</font>

The color values are exactly what their pixel values are in the bitmap.

DVB Crash Fix

As a result of working on DVB Color Detection, I also noticed and fixed an important bug which was causing a lot of periodic crashes while continuously processing DVB subtitles. The bug was largely due to Tesseract OCR returning multiple newlines at the end of a line. I made a quick fix by increasing the memory allocated to the resulting string variable. It resulted in a large increase in the stability of the DVB processing pipeline.
https://github.com/CCExtractor/ccextractor/issues/401
Although there are still a few issues and bugs in the program, the DVB system is quite stable.

Google Summer of Code, Weeks 6 and 7 – Detecting Colored Subtitles

Till this point, I have a system which works well for burned-in white subtitles and generates a timed output file. The next step is to add the same support for colored subtitles too.

The HSV Color Space

The HSV color space, and the Hue component (H) in particular, is an excellent representation of the exact color value of a pixel. The normal RGB space requires 3 values to represent the color, whereas the H component takes a value in the range of 0-360 and gives the necessary color information.

You can read more about the color space here.

The chart below shows how the values of H vary for different types of colors.

I exploited a conversion from the RGB to the HSV space in order to detect colored subtitles. Just like there was a luminance threshold in order to detect white subtitles, there is a threshold around the range of the user-specified hue value in order to detect subtitles of a particular color.

This hue based thresholding, along with the existing vertical edge dilation was used to detect subtitles of a particular color.

Color options in the program

The program has 7 predefined color names. The first and most prevalent case is White, the detection of which is luminance based. The other 6 are equally spaced in the hue value range. The colors, along with their hue values, are:-

1. Yellow – 60
2. Green – 120
3. Cyan – 180
4. Blue – 240
5. Magenta – 300
6. Red – 0

Each of these colors can be specified along with the -subcolor option. For example:-

ccextractor video.mp4 -hardsubx -subcolor yellow

In addition to these preset values, there is also the possibility to supply a custom hue value. This value is a custom value between 0 and 360 (not included) which can be supplied to the subcolor option, and could be of help to users who want to extract subtitles of the precise hue value in their stream if it fails to meet one of the presets.

Local Adaptive Thresholding

In addition to detecting colored subtitles, I was also able to improve the detection of white subtitles using local adaptive thresholding algorithms, and Sauvola Binarization in particular. This was an additional step which marginally improved the quality of results for white subtitles (which always have a pixel value greater than their surroundings), however could not be applied to colored subtitles in all cases due to a wide variety of contrasting backgrounds.

Google Summer of Code, Weeks 4 and 5 – Determining Subtitle Appearance Time

So far, I have been able to successfully extract white colored subtitles at an interval of 25 frames, and the output looks decent. However, I need to now actually created a timed transcript (e.g. an SRT file).

Original Plan

I had originally intended on having two strategies to determine subtitle time, which I had described in my proposal as:-

1. A linear search across the video at a certain interval. Whenever a subtitle gets detected, a binary search will be performed in a window around that frame. Using this, we will detect the exact time of the beginning and the end of the particular subtitle line. This will be of benefit in sequentially processing a file (possible use case of processing a live stream as it is being recorded).
2. If the entire video is already available to us, instead of doing linear search which will involve a lot of processing overheads for frames in which there are no subtitles, we can directly do a binary search on the entire video to detect subtitle lines. We will get the exact timing of the line as described above, but the overall processing will be faster

However, neither of them were possible, due to constraints which I had not originally anticipated.

FFMpeg Constraints

It turns out that binary search was not a viable option at all, because I could not arbitrarily seek to a timestamp in a video using the FFMpeg library. The closest thing which I could do was seek the file to the nearest I-frame and then iterate through frames to the desired timestamp and then reconstruct the needed frame. However, in a binary search, the whole point of which was to optimize the search, this way would create a massive processing overhead and high redundancy in reconstructing frames during the search. Instead, a linear search with a specified step-size seemed a much better option.

The problem that I described is fairly well documented online:-
– http://stackoverflow.com/questions/17546073/how-can-i-seek-to-frame-no-x-with-ffmpeg
– http://www.mjbshaw.com/2012/04/seeking-in-ffmpeg-know-your-timestamp.html

New Plan – Efficient Linear Search

I decided to use a linear search across the video with a specified step-size, which was a parameter called the minimum subtitle duration. I set the default value for this as 0.5 seconds, which seems a reasonable assumption for most subtitles.

I also needed to convert times to a single format (milliseconds), from the various different time bases that various different video streams could have. From here, I iterated through the video and sampled frames at regular intervals. The decision that a subtitle line was the same as the last encountered one was when it’s Levenshtein distance was very low. This was necessary in order to combine successive detections which were off by a character or two, which happens quite often due to the natural noise present in the video stream. Whenever the detected subtitle line ended, I would encode it with the seen times.

Integrating with the CCExtractor Encoder

It was really easy to integrate the calculated time with the CCExtractor encoder structure (which with itself brought full output parameter functionality). All I had to do was call two functions at the appropriate times in my code:-

add_cc_sub_text(ctx->dec_sub, subtitle_text, begin_time, end_time, “”, “BURN”, CCX_ENC_UTF_8);
encode_sub(enc_ctx, ctx->dec_sub);

That says a lot about how well written and modularized the existing library is.

And oh, I chose the subtitle mode ‘BURN’ myself. It stands for burned-in subtitles xD.

Example Output

The SRT output for the video at https://www.facebook.com/uefachampionsleague/videos/1255606254485834/ (A Gerard Pique interview), looked as follows:-

1
00:00:00,000 –> 00:00:06,919
Well, they’re both spectacles,

2
00:00:06,921 –> 00:00:08,879
NBA basketball as well as football here.

3
00:00:08,881 –> 00:00:12,879
It’s a spectacle for the fans, they enjoy it and see

4
00:00:12,881 –> 00:00:13,919
something different.

5
00:00:13,921 –> 00:00:19,919
And I think a player like Messi could be compared to Curry

6
00:00:19,921 –> 00:00:21,919
in the USA,

7
00:00:21,921 –> 00:00:24,879
because they have created something special

8
00:00:24,881 –> 00:00:26,999
something not seen before,

9
00:00:27,001 –> 00:00:30,919
and something that makes people excited, ecstatic.

It looks pretty good, and the times are pretty close to perfect, with some variation at the extremes due to those edge frames not being processed. A lower value for the minimum subtitle duration will give even more accurately timed results, but will take a longer processing time.

Google Summer of Code, Weeks 2 and 3 – Recognizing White Subtitles

These last two weeks were slightly challenging owing to the fact that I had to learn a lot of new things in order to complete my tasks.

Setting up the HardsubX expansion in CCExtractor

Before I could get started on diving deep into writing code, I needed to organize and setup the workflow of all the new code which I am supposed to write throughout the summer into the original program. This included parsing input parameters for the new type of extraction process, creating and organizing new files in the source code, and changing the compilation settings and dependencies to match what I would need for my pipeline.

The pipeline essentially comprises of the following entities:-

1. The ‘main’ file
   Handles the parsing of parameters and initializing the required data structures.
2. The Decoder
   Gets the text of the burned in subtitle in the video by processing it
3. The Timer
   Gets the precise timing of each extracted subtitle
4. The Encoder
   Converts the output of the decoder and the timer into a standard output format such as a .srt(SubRip) file

I created separate files for each of these entities and their helper functions, along with one shared header file which would allow the internal librarization of the files (being able to use functions from one in another), as well as the potential external librarization (being able to be called from the main CCExtractor library).

You can view the project repository at https://github.com/Abhinav95/ccextractor. The new source code files are in the ‘src/lib_ccx’ directory and have the ‘hardsubx’ prefix in their names.

Processing a Video Stream in C

The very first step when trying to get subtitles from a video frame, is to actually get those video frames themselves and store them in a data structure in the context of the program. The FFMpeg library is the comprehensive open source media processing library in use today. I am using its C API to process the input video stream.

I needed to store the video stream format and codec information in the program context. Then, out of the many different kinds of streams present in the media file (video, audio, captions, others), I needed to find the ID of the video stream and then process only its packets. Every video stream packet is then decoded and the image content extracted and stored in a Leptonica PIX structure (for compatibility with Tesseract OCR). For the sake of efficiency and avoiding redundancy in frame extraction, I extract frames at an interval of 0.5 seconds, which I have assumed to be the minimum time that a subtitle line is present in the video. This number can be fine-tuned based on the real situation, but some threshold is required in order to avoid the massive processing overheads of reading every single frame in the video.

In a nutshell, the process goes like this. FFMpeg gives me the video frames at a certain interval, and then I further process them to detect subtitles.

Example frame:-

Detecting Subtitle Regions

The detection of white subtitle regions involved two steps:-

1. Luminance based thresholding
2. Vertical Edge detection and Dilation

The Luminance (L) of a particular pixel represents the ‘whiteness’ of the pixel. The closer it is to pure white, the higher is its luminance. When aiming to detect white subtitles, luminance based thresholding is useful because if we binarize the image in such a way that only regions of high luminance are retained, then all of the white subtitle regions will be retained (with possibly other white objects/artifacts too). This thresholding is done to narrow down the search for the candidate subtitle region.

Thresholded Luminance image:-

The second part of the subtitle detection pipeline is the detection of vertical edges in the image, which is done by a vertical Sobel filter. This method is effective because subtitles have a high density of strong vertical edges in their region, due to the alternating white foreground letters and the non-white background. The edge image is then dilated with a horizontal structuring element in order to get the rough region of the subtitles.

Vertical edges:-

After dilation and thresholding:-

The final subtitle region is determined by taking a bitwise AND of the two feature images described above, i.e. regions which are both wide and also have strong vertical edges. Both these features are typical of white letters in the subtitle line. In some cases, one step may not work well. For instance, if there is a white background, then the thresholded luminance image will not be an accurate representation of the subtitle region. Also, if there is an object with lots of vertical edges near the subtitle region, the edge image will not be an accurate representation. But using both of them together give us a high likelihood of accurately detecting the subtitle region.

Subtitle Recognition / OCR

Once the subtitle region of interest has been detected, the actual text needs to be recognized using OCR (Optical Character Recognition). The intuitive choice to perform this task was the Tesseract OCR library by Google, which has already been previously used by CCExtractor to recognize DVB subtitles (predominantly used in Europe) which essentially comprise of a subtitle bitmap being overlaid on the video frame. An OCR essentially works using character and word classification based on stored labels on trained data. In a layman’s terms, you show the OCR engine 1000 images of the letter ‘a’, and it learns to recognize the letter ‘a’ the next time it sees it.

For the output image of the previous steps:-

Tesseract’s Detected text : “Well, they’re both spectacles,”

All I need to do is pass the binarized image containing only the clean detected subtitle text to a Tesseract API handle and it returns the recognized text to me. Pretty cool, right?

Over the course of the summer, I will have to use the Tesseract API extensively, as compared to just directly making a call to get the recognized subtitle text. I will be using advanced Tesseract features such as the per character and the per word confidence ratings in order to refine and improve my text classification output. A common use case for this would be to root out simple misclassifications such as ‘giape’ instead of ‘grape’ in the recognized text, and to get the overall output to have the highest probability of being correct.

What’s Next?

The next thing that I need to work on is to accurately and optimally determine the time that each subtitle line was present in the video. This will involve seeking the video around the neighborhood of the frame of the originally detected subtitle, and then determining when that particular subtitle line appeared in the video for the first and the last time. A potential problem with optimizing this seems to be the fact that ffmpeg does not allow straightforward seeking to a given frame number or a timestamp, and I will have to manually seek to the desired location from the nearest I-frame (You can understand this problem better by understanding the GOP structure of video frames, explained here).

Here’s looking forward to weeks 4 and 5 and the mid-term evaluation which is on the near horizon. I’ll keep posting my progress, right here. Cheers!

Google Summer of Code, Week 1 – The Bug Hunt

I have just finished my first week of the Google Summer of Code (GSOC) program with the CCExtractor organization. This is the first of thirteen weeks during which I will be working on developing a system which will be able to extract hard (burned-in) subtitles from a given video, adding to the current functionality of CCExtractor which extracts soft subtitles, i.e. those which are part of the data structures of the video stream but are not part of the video itself. This process will involve subtitle text localization followed by optical character recognition (OCR). In a common man’s words, this will make the computer understand which letters are part of the subtitles as compared to the series of pixels it originally sees in the video frame. These recognized letters can then be written to one of many popular subtitle formats such as the SubRip format (with a .srt file).

You can read my project proposal here.

The Setup

I am working from my university accommodation, with my development environment being Linux (Ubuntu 14.04). My setup can be seen in the featured image on this post, with two OS’s running side by side. I write all my code in Ubuntu and have GitHub or any docs open on the left screen in Windows 10.

A great thing about GSOC is the complete freedom you are provided, as long as you work for the stipulated amount of time. It is like a full-time job, without the fixed hours or travel times. As long as I finish my assigned tasks and work for 40 hours in the week on my project, I can do whatever I want with my spare time. This week has been really fun in that aspect. I have had the perfect blend of working and enjoying my summer at the same time. In fact, if you look at the picture of my setup, you will see League of Legends open in the background (I was about to play a game at the moment of taking the picture). I would often update a pull request and run tests on it, and play a game while the tests ran, and then get back to work immediately after. I never felt short of time to enjoy my personal life while working on the project at the same time. I am working on a computer vision problem which interests me and is fun, while getting enough time to game or go out, and earning $5500 over the summer. What more could I ask for, really?

The Bug Hunt

The first week of the coding duration of the program was devoted to trying to fix existing bugs in the code. The rationale behind this is that all developers work together for a week to try and fix as many bugs as possible following which there is a new, improved release of CCExtractor (version 0.81 to be precise). This gives all developers a much more stable/bug-free version to work with for the remaining part of GSOC.

In addition to fixing bugs, this week also gives a lot of time to actually get acquainted with the code in depth, which was particularly useful for me because I need to seamlessly integrate the pipeline I will develop with the normal workflow of the existing program, for which I need to know what is happening in the code and what parts I need to use and edit. And fixing bugs is a fantastic way to achieve this higher level of acquaintance with the code base.

I was assigned 6 bugs to try and fix during this week. I successfully managed to fix 3 bugs. The bugs assigned to me were:-

1. Case fixing for teletext subtitles (Fixed)
2. Seeking DVD video using the IFO (information) metadata file
3. DVB subtitle extraction not working for a Spanish channel
4. Issues with timing is ISDB (Brazilian) subtitles (Partially Fixed)
5. Missing subtitles in a Korean broadcast
6. Very high RAM consumption by the program (Fixed)

I also managed to fix another bug which was reported by David Liontooth of the Red Hen Lab at UCLA and had originated due to a previous pull request of mine. This entire process educated me a bit about how the maintenance stage of the software development life cycle is the one which involves the most effort. It is easy to write hundreds of lines of code and make something work at first glance, but with usage over time, we inevitably discover problems which need to be fixed. And in the open source world, it is good form for the person who wrote the part of the code which is causing problems to be responsible for it and work on a fix.

The Major Fix – Reducing RAM Consumption by 180 MB

In my opinion, the most significant issue which I was able to solve this week was reducing the memory consumption of the program by 180 MB.

A fellow developer had reported very high memory consumption by every instance of CCExtractor in an application he is building. After a lot of analysis, I narrowed down the problem to the fact that around 180 MB of space was being allocated statically for EPG (Electronic Program Guide) data for two months in every instance, even if it was never being used. Once I had this pinpointed, I submitted a fix which changed the allocation to only when needed. Easy enough right? That’s what I thought too, and I ran tests on my pull request, which did reduce memory consumption from 200 MB to around 20 MB. Bizarrely, the fix was causing segmentation faults (accessing invalid parts of the memory) for a set of test cases. I had no idea why. I spent the whole next day on tracing variable initializations throughout the code which could be causing the issue, and finally saw that at one small line of code, the wrong context was being passed to a function causing a required value to not be set. You can take a look at the technical details here.

I managed to fix the issue by passing the correct context later, but the baffling question was that why did it work earlier even when the wrong context was passed? As it turned out, when 180 MB of memory was previously being allocated (and set to 0 entirely), the value which was later uninitialized due to having an invalid pointer actually pointed to one of these 0’s in this huge chunk of memory, thus getting a valid value, and not causing anything to break. This was a perfect example of how a bug may just sit there, hidden and not break anything and come up later in the development life cycle and completely mess with your mind. But anyway, it was fixed and the program was now running as expected, while being 180 MB lighter on RAM 😀

An excerpt of the conversation on the GitHub issue page is as follows:-

It made me really happy to be able to help out with a fellow developer’s application. After all, that is what open source is all about. We come together and use the cool things that each other have developed, and move ahead to create something even better. It may be something as small as writing a small ping pong game or something as big as writing a deep neural network API such as Caffe which drives cutting edge features of even giant companies like Facebook, every little bit counts, and makes the code that the world uses better.

Moving to Week 2

In week 2, I will actually commence work on my project. I need to work on a frame extraction and pre-processing module for white colored burned-in subtitles. I look forward to making it happen, and I will update my progress right here. Cheers!