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object detection on RPi using OpenCV DNN
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README.md

OpenCV Object Detection without Tensorflow on Raspberry Pi for Automated Inventory Management

Authors:

Daniel Karasek (primary) - Kennesaw State University

Linda Vu - Kennesaw State University

Rachel Wendel - Kennesaw State University

######################################################

-Updates-

4/25/2019 Sixth official logged update!

*Presented the project at C-Day at KSU. We did not win the poster competition. However, we did run a test from 5:28pm to 7:27pm on two dozen glazed donuts. All data from this test is in the new CDayDataSpring2019 folder. The extracted data is also in the foloder as the test had 10 sec intervals and ran for 382 iterations. For this test, we had 100% accuracy.

*Got a lot of good feedback from C-Day on changes to implement in the project. Aside from exploring using motion detection for counting objects, using a Pi Zero will be added to future directions. The goal here is to make object detection for the price of a meal.

*Progress on development will take a short break as the semester is wrapping up. It will continue over the summer and for the rest of the year.


4/19/2019 Fifth official logged update!

*The project is being presented at C-Day at Kennesaw State University on April 25th, 2019. This is a poster competition where both undergrad and grad students submit projects and research to be judged. We hope to dominate this competition. Find our poster added to the repo.

*The current objectDetection.py file still has toilets and sinks removed as detected objects. They are still listed in the list of detectable objects. The plan is to keep them removed as counting sinks and toilets is outside our general use scope.

*Updated the file structure of the actual repository. Now there is a SampleData folder that has all of the data from a sample test in the same file folder format as the actual test data. There is also an included console output file to show what prints to console. In the console output file it shows how user input errors are handled for calibration and at the start of the objectDetection portion. If you do not know what can be detected (which is unlikely) or do not know how to spell the object you wish to calibrate/detect, wrong input for object name prompts if you wish to print all of the detectable objects.

*Old sample files have been removed as they do not reflect the current updates made to their formatting. The new format and sample files can be found in the SampleData folder.


4/13/2019 Fourth official logged update!

*Here is a linked to a short video of some of the live testing we did today using donuts. Also found in the readme below. https://www.youtube.com/watch?v=eluiDro3Tak

*Updated how data is written to console and to data log file. Makes both more readable.

*Added in a section of code specifically for detecting donuts. We have been running a lot of donut tests as of late. There is an error where when one donut is left in a box, the donut can be detected as the drain for a toilet or sink. I removed those objects as detectable. It's in line 132 to 134. Line 134 has to be un-indented twice if not using calibration code. It is more obvious when looking at the code.

*Made the time between iterations a changeable object at the top of the objectDetection python file. It is now easier to make this time interval part of the user input but is remaining hardcoded to avoid mistakes when we are running tests.


4/9/2019 Third official logged update!

*Added link to tutorial video of how the code works in the readme. The link can also be found here. https://youtu.be/A0Lc6IlNJRM

*Added in Google AI blog link as a reference.

*Corrected the origin of Mobilenets as a Google project that was published at Cornell.

*Corrected some of the readme as well as added in some notes about some of the code including the algorithm that handles detection box overlap. The corrections were all on what portions of the code does.

*Updated sample of log file and console output to have the runtime displayed.

*Forgot to mention in the last update that the notifications sent out from objectDetection now only track objects that are calibrated. All objects are recorded in log files to keep all data.

*Added in user input error handling in calibration and objectDetection code. In the calibration code, if the user inputs the wrong object the user is asked if they want the list of detectable objects displayed. After displaying the list, the user can then try to input an object again.


4/8/2019 Second official logged update!

*The code now logs runtimes for calibration and object detection. Both are recorded in seconds. The runtimes are also written to console.

*The names of objects put in the boxes for the images that are labeled ...WithBoxes have been changed. The font size has been made smaller to better fit in the box for each respective object. Also, the rounded detected percentage is printed next to the name of the object. Before the text was too large and would be hard to read when multiple objects were being detected.

*Regarding the fixed boxes, the sample images have been updated as well.

*Fixed the issue with iterations not being logged when not objects were detected. Now all iterations are logged in log file and in console.

*Set object detection count threshold to 5 to better reflect what we have written for a paper on this project.

*We have submitted a draft of paper for this project. If we move to get it published, we will put a copy of it in this Github.


4/7/2019 First official logged update! Many changes made since last commit.

*The code now handles double counting and false second counting of objects. It looks at the bounding boxes of each detected object and checks for overlap.

*The code now sends proper notifications when objects all are depleted and skip the depletion threshold amount. For example, the objects go from 10 being counted to 0 being counted. Notification now reflects that said object is depleted.

*The log files now record all objects that are detected in each captured image. Before it would only list out the objects above threshold. Adding in a list of the output from openCV to log all things that are detected.

*Calibration now adjusts for a 35% variance in its threshold. Through some testing 30% was too low.

*Did some testing of calibration and object detection code. Found out that where the items are in the image affects its calculated percentage. It has to do with how SSD works for detecting objects. Best advice is to have objects close to the center of the image and have objects organized in some fashion. For example, detect donuts by using the box they came in. Found out some objects are harder to detect than others. Bananas are a challenge if they have spots. Also, the objects you detect must be similar to objects trained on in COCO dataset. For example, it does not count plastic forks only real metal forks.

*In testing found out multiple object calibration does work but has issues with placement within the image as mentioned above.

*Some changes above have been added into the readme below. Only really things relating to understanding the project itself.

######################################################

-General project description and info on OpenCV DNN-

The project runs on a Raspberry Pi 3b+.

This project is designed to be a solution for low-cost at-home inventory management. The project itself uses a Raspberry Pi, Pi Camera, and Python. The camera captures images of objects pre-trained in data models and counts how many objects there are. It sends notifications saved as text strings to a mobile phone. The notifications contain the objects detected and the quantities of said objects. The softwares within Python used for the actual object detection are OpenCV, MobileNet, and SSD. OpenCV is the Open Source Computer Vision Library that has a Python interface. MobileNet and SSD are combined to handle the object recognition and are discussed further in this readme. The combination is MobileNet-SSD and is the software behind how the data models are created and used.

This project captures images using the standard Pi Camera V2.1 and detects objects in the images using the OpenCV DNN. The entire project does object detection on the Pi. The files are stored in a directory with common root folder as the project itself, but the storage can be wherever you choose. The only real restriction for this particular code is you must have the .pb and .pbtxt models in the same folder as main.py. You can have these two model files in any director if you set the path in readNetFromTensorflow('Path/To/File/' + modelName.pb, 'Path/To/File/' + modelName.pbtxt).

The code is an implementation of the Object Detection Tensorflow API found at the bottom of this readme. There is some code taken from rdeepc (Saumya Shovan Roy) in his overview of how OpenCV DNN works with his repo and Heartbeat article found at the bottom of this readme.

This is a project using OpenCV to detect objects trained on models using the coco data set. OpenCV is a library that can be used in tandem with Tensorflow but also without Tensorflow. OpenCV has its own Deep Neural Network (DNN) that supplements the need for Tensorflow deep learning libraries that are hard for edge devices. This project does not use Tensorflow as doing so is too strenuous on the Pi. Tensorflow can be installed and used within Raspbian but takes a lot of computing. Just using OpenCV does analysis on images with much less computing, which translates to much less time (few seconds). OpenCV uses the Tensorflow Google Protocol Buffer (protobuf / .pb) system that defines data objects, writes the data to files, and also reads the data. Protobuf is the foundation for the TFRecord system. Protobuf files are how trained models are stored. Protobuf is a way to store massive data in a smaller file size. When using .pb files (used by Tensorflow), you need a more powerful CPU/GPU than the Pi. The way OpenCV uses .pb files is through a .pbtxt file created from the .pb file. The .pbtxt file is a text-based representation of the serialized graph stored within the .pb file. The .pbtxt file avoids the usual computational heavy process of creating and using a graph object from the .pb file.

For info about TFRecords and protobuf, see the article at the bottom of this readme.

For the model, MobileNet-SSD v2 was used. It is used in the Heartbeat article as the suggested model due to its popularity. MobileNet is a base network that handles the classification of objects within a model. SSD is the detection network used. MobileNet has its own classification capability however by using SSD it can do object detection. A brief discussion of MobileNet and SSD can be found in the stack overflow article MobileNet vs SSD found at the bottom of this readme. MobileNet was created by Google, and the summary of the work can be found at the bottom of this readme in the Google AI post and in the paper summary published at Cornell. SSD was created in a collaboration between UNC Chapel Hill, Zoox, Google, and University Michigan Ann-Arbor, and the paper about SSD can be found at the bottom of this readme.

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-Things in the code-

This is a link to a YouTube video that shows a tutorial on how the detection program is run.

https://youtu.be/A0Lc6IlNJRM

Here is a link to a YouTube video from some live testing we did on April 13th, 2019 using donuts on campus at Kennesaw State University.

https://www.youtube.com/watch?v=eluiDro3Tak

The code is broken into a very readable file structure. Originally the project was one lengthy .py file that was harder to follow. Now, there is a main, calibration, objectDetection, and objectIdToName file. Each file is modular for easy use. There are comments in each to inform how to use each file. Additionally, there is a file sampleOfConsoleOutput.txt that shows a sample output in console for this project. .

The calibration code starts off with taking a picture and uses that picture to calibrate the detection threshold. This works by prompting for user input from the console. It first asks for what object is being calibrated for in the picture. The objects accepted are listed in the objectIDToName file. You can also list out all of the detectable objects if the input is incorrect. The assumption is made that the user knows what input is to be used for calibration. Then the user is prompted to enter in the number of the object being used for calibration. After this, the user is asked if more objects are being used for calibration. The calibration code can take multiple objects as input for calibration. The suggestion is to use only one or two objects.

The calibration portion of the code creates a detection threshold through an algorithm. If that threshold is not used, a default threshold is then used instead. It is set to .2 in the objectDetection code as that is a reasonable minimum threshold for our purposes. The .2 eliminates many of the false positives that occur.

When running the object detection portion of the code found within the objectDetection.py file, it currently runs with a while loop for running continuously. There is commented out code for a for loop that can be set with a certain amount of iterations for tests. A two iteration for loop is the simplest example of use for our inventory management idea as it can track change in two different images.

Within the while loop is an algorithm that detects any bounding box overlap. False positives can show up even with high detection thresholds. These false positives are double counts of an already detected object, with the double count either being a second count of that object or a completely different object. For example, it may correctly detect a banana but double count the banana or count it as a banana and an apple. The algorithm looks for other bounding boxes that are similar in position and shape to any exisitng boxes and ignores them if so. This greatly reduced these false positive errors.

The classNames file lists out all of the objects pre-trained in the MobileNet-SSD v2 model found in the documentation for the TensorFlow Object Detection API. The model has the recognized objects stored by numbers from 1 to 90 but doesn’t have the names of the objects. Hence the listing out of the objects in a dictionary. The objects are from the COCO Data Set. Find a link to COCO data set at the bottom of this readme.

The following line of code is the model created using OpenCV from the pre-trained .pb and it’s associated text-based .pbtxt as discussed above.

model = cv2.dnn.readNetFromTensorflow('frozen_inference_graph.pb', 'ssd_mobilenet_v2_coco_2018_03_29.pbtxt')

Past calibration, each image is double saved as the raw image and as the image with bounding boxes. The images with bounding boxes have labels that say what is detected with a rounded detection percentage next to the label.

The code creates a log file for each run. At the top of each section is the iteration. The next line lists the detection threshold that used. The next line lists the time between each image taken. Next in the log is an array that shows all objects detected including false positives. The dimensions of the array are 100 rows by 7 columns. The rows are for each object detected. It is capped at 100. This is the from the output listed in the objectDetection code. The first entry for each row of the array is empty. The second entry is the object that is detected represented by its ID number. The third entry is the detection percentage. The last four entries are the bounding box X and Y coordinates for each object. From this array, only objects above the calibrated detection threshold and those objects selected for calibration will be used in the code. However, it is important to have a log of everything that is detected. Below the array is the runtime for detecting all objects found in the array in seconds. Then the next line lists out each picture taken (imag/imageWithBoxes) followed by a comma then a dictionary of each object detected with the number detected. If the number is below the threshold set (objectDict[key] < 5 in code) then after the number of objects is a message that says (Running Low) in parenthesis. Below this line is a listing of each object detected with its percentage. If nothing is detected it prints 'Nothing Detected'. Each log file captures all data per run of the script. The file itself is created at the start of the for/while loop that the main code is within. Each pass through the loop is recorded in the log file. If the program crashes between loop iterations it can be detected in the log file. Also because the image file names are timestamps, you can know exactly when the process crashed.

There is also a log file made for calibration. It first lists the image used for calibration. Then it lists out each object used for calibration with the inputed count of objects that are in the calibration image. Following this it runs through iterations of calibration starting with a threshold of 0.9. For each iteration if it is still missing objects that were inputed for calibration, the threshold is reduced by .05. This repeats until all objects are detected. There is no extra code here to handle box overlap because those false positives are only realized after all real objects are detected as discovered through testing. If an iteration over counts the number of objects the threshold is increased by .01 until the correct number is reached. At the end of this log is the runtime of calibration which is measured in seconds.

Notifications are sent out using Notify-Run. Info on Notify-Run can be found at the bottom of this readme. It is a library that is used to send notifications to devices registered on a channel. There is an issue with a channel not working after use. Best solution is to just register a new channel. You can do so from commandline or in Python script. Doing so in the script doesn't display the channel info so I don't recommend this method. Using the commandline shows the channel name web address, and a QR code for the channel name web address. Any devices registered on the channel can see messages sent over it.

The calibration info and most of the info saved to the log file are printed out in console. This helps keep track of what is happening during script runs so you don't have to look at the calibration log and general data log files.

######################################################

-References / Resources for more info-

COCO Data Set http://cocodataset.org/

Google AI Blog Post Introducing Mobilenets https://ai.googleblog.com/2017/06/mobilenets-open-source-models-for.html

OpenCV website and PyPI page https://opencv.org/ https://pypi.org/project/opencv-python/

TFRecords and Protobuf https://halfbyte.io/33/

TensorFlow Object Detection API Documentation (with pre-trained models and code to create .pbtxt for OpenCV) https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API

General Implementation based on TensorFlow API Documentation for OpenCV by Saumya Shovan Roy https://github.com/rdeepc/ExploreOpencvDnn

Heartbeat article discussing OpenCV DNN by Saumya Shovan Roy https://heartbeat.fritz.ai/real-time-object-detection-on-raspberry-pi-using-opencv-dnn-98827255fa60

MobileNet vs SSD stack overflow https://stackoverflow.com/questions/49789001/mobilenet-vs-ssd

Cornell MobileNets paper summary and info https://arxiv.org/abs/1704.04861

UNC Chapel Hill, Zoox, Google, University of Michigan Ann-Arbor paper on SSD https://www.cs.unc.edu/~wliu/papers/ssd.pdf

Notify-Run Website and PyPI page https://notify.run/ https://pypi.org/project/notify-run/

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