#PI wants me to collect data we have . already collected so its not even showing me anything. interesting

[caninecowboy]

feeling increasingly discouraged about my research YEEHAW

[cancerbiophd]

hey julia, im really sad because after a year or so of writing for a published paper, the prof analyzing the data found an inconsistency in my database and when he fixed it the new results he got changed the outline of our paper in a big way. i'm still getting primary authorship because i did all the data collecting and initial writing, but pretty much all i wrote is gonna have to be scrapped :( i was really proud of what i wrote and having it go through editing because it was mine (1/2)

(2/2)I feel so disappointed with everything though and i know it’s my first ever real writing experience but i was hoping it would work out a bit better :c im still gonna have access to see how the paper evolves and be invited to the talks about it, but it just isn’t mine anymore and i kind of feel like i failed, i was banking on this paper to apply for grad school because being published gives me an edge but it doesn’t feel like its my work anymore :( what do u do when u have a sad science day?

Oh maria /biiiig hug/ i’m so sorry to hear you’re going through a rough time. it’s ok to feel sad and disappointed because i know it’s tough to watch something you’ve worked so hard on get scrapped like that. but you know what? you’re tougher! and i know you’ll get through this. 

Firstly, give yourself a warm hug for completing the monumental task that is writing a manuscript, regardless of what ended up happening. Finishing something so challenging is not trivial! And you know what, all those experience points you gained by going through this journey are yours to keep. So when it comes to writing another manuscript in grad school, you’ll know exactly what to do! And that’s pretty great, yeah?

Secondly, I know you feel bad, and that’s ok, but I want to slip a note into your thoughts that you have not failed, my friend. You have not failed. Mistakes happen, even to seasoned professionals, because mistakes are a part of life. I guarantee you that every single research lab in existence right now (and have existed) has had to go back to square one or two and start over at some point. And they did, and fixed what needed to be fixed, and then carried on to do great things. Sometimes you gotta erase what’s already on the paper to make room for something more beautiful. 

If you feel this paper doesn’t feel like your baby anymore, that’s ok. I totally understand why you would feel that way, and if squishing and squeezing your emotions into accepting the paper like it was before is adding to the bad feels, then you don’t have to. You can just focus on and own the black and white facts of the paper: the experimental design, the science behind the data, the major conclusions, the impact on society, etc. There doesn’t have to be an emotional attachment to go with it if you don’t want to. 

And you know what, the changes to this paper doesn’t change who you are. Maria: you are still the smart, funny, caring, kind, and passionate person that I’ve gotten to know you as. You’re still the awesome person who took the time to put together the most in-depth and hilaaarious powerpoint of fish and fauna to see while snorkeling in the Bahamas for me. You’re still the brilliant marine biologist who has a (really impressive!) CV that 100% reflects your passion and commitment to your work. You’re still a person who will leave the world a better place, and I know this because you’ve already made my world a better place. I feel so lucky to have you in my life. 

I hope you’re starting to feel better now :) If not, here are some things I do if I have a Bad Science Day:

Cry it out. There’s an odd sort of comfort that comes after a good cry. So sometimes if I feel like I’ve been holding back, I exhale softly and let it all out. It doesn’t solve anything, I know, but I do feel a teeny bit better and a teeny bit more whole. 

Write it out. Something I’ve learned about myself is that my brain tends to over-exaggerate things when it’s just swirling thoughts. But when I write it out and everything gets organized, it turns out things aren’t so bad after all. So give it a go, either in a word doc, an actual journal, or even a tumblr post (that you don’t have to publish, of course). 

Talk it out. Related, going on a verbal rant (or even written rant to someone) helps in a similar way. Even if it’s in private to my favorite stuffed animal or a pet. Just anything to get the thoughts out!

Listen to “comfort” music. I have a playlist of my all-time favorite songs and I listen to it on the drive home and I always feel a lil better. It’s hard to not feel a little happy and carefree when your favorite jam comes on. 

Sleep it off. Sometimes all I need is a good night’s sleep (or even a nap) to clear my head. Also I love sleeping, so it always feels good no matter what. 

Do something comforting. Anything to release those sweet sweet endorphins. My ideas of comforting routines are: eating whatever I’m craving at the moment, watching a favorite TV show or movie (usually something I’ve already seen), curling up with a good book or magazine, scrolling through Tumblr, doing my nails, hugging my dog and/or husband until I feel better, and walking around Homegoods, my favorite store (I’d honestly live there if I could). Doing these things also helps in that it takes my mind off whatever’s bothering me, even temporarily. 

Give myself a pep talk. Ok, oddly enough, the pep-talk-voice in my head is Gordon Ramsay. I don’t know how it manifested as him, but when he’s not yelling at chefs to get their shit together, he has a really encouraging and soothing voice! Anyway, sometimes he sits me down and tells me that everything is going to be ok, and here’s what we do next alright? Just one step at a time ok? That’s it. Good job. Good job. 

Just keep working. Sometimes my Bad Science Day starts at 9 AM in the morning, or it’s just a continuous Bad Science Week/Month/Year. So I put those feelings on hold and just stick to my schedule and try to be as productive as I can. Because even if Experiment 1 didn’t work, Experiment 2 might, and if it does, I’ll feel a little better! And if Experiment 2 doesn’t work, well, at least I finished it, and I’ll still feel a little better! And in any case, my projects aren’t gonna do themselves, no matter how I feel. So in the wise words of Dory: “Just keep swimming, just keep swimming!”

I talk to my PI. I’m super lucky to have an understanding PI, and I’ve talked to her about my frustrations on more than one occasion and she’s worked with me to come up with good solutions, or have said things to make me feel better. PIs are full of wisdom from experiencing their own fair share of Bad Science Days so they have lots of advice on how to feel better, such as looking at my results a different way so it goes form :( to :)

I let the passing of time lessen the hurt. Time doesn’t always heal, but it does make things that were terrible at the moment not so bad anymore. So if nothing else makes me feel better, at least I know “this too shall pass”. 

I hope this helps. I know things are ugh right now, but you’re going to be ok. And I’m here for you, ok? Feel free to reach out via chat or email. I would very much like to help you feel better

[blackgoddessofsassiness]

Valentines Day Wallpaper

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[chaj]

via www.pyimagesearch.com

Earlier this week I shared a tutorial on using OpenCV to stream live video over a network via ImageZMQ — and today I’m pleased to share an interview with Jeff Bass, the creator of ImageZMQ!

Jeff has over 40 years experience hacking with computers and electronics — and now he’s applying computer vision + Raspberry Pis to his permaculture farm for:

Data collection

Wildlife monitoring

Water meter and temperature reading

Jeff is truly one of my favorite people that I’ve ever had the honor to meet. Over his 40 year career he’s amassed an incredible amount of knowledge over computer science, electronics, statistics, and more.

He also spent 20 years doing statistics and data analysis at a large biotech company. The advice he gives is practical, to the point, and always very well said. It’s a privilege to have him here today.

On a personal note, Jeff was also one of the original PyImageSearch Gurus course members. He’s been a long-time reader and supporter — and he’s truly helped make this blog possible.

It’s a wonderful pleasure to have Jeff here today, and whether you’re looking for unique, practical applications of computer vision and OpenCV, or simply looking for advice on how to build your portfolio for a career in computer science, look no further than this interview!

An interview with Jeff Bass, creator of ImageZMQ

Adrian: Hey Jeff! Thank you for being here today. It’s wonderful to have you here on the PyImageSearch blog. For people who do not know you, who are you and what do you do?

Jeff: I’m a lifelong learner who’s been playing with electronics and computers for over 40 years. I studied econometrics, statistics and computer science in grad school. I developed a statistical software package for PCs back when PCs were a new thing. I spent 20 years doing statistics and data analysis at a big biotech company.

Now I’m retired from income producing endeavors and building a small permaculture farm in Southern California. I’m using computer vision, sensors and Raspberry Pis as tools to observe and manage the farm. I speak at garden clubs and conferences occasionally. I really enjoyed being a speaker at PyImageConf 2018.

Adrian: How did you first become interested in computer vision and deep learning?

Jeff: I got a Raspberry Pi and a Pi Camera module in 2013 when they first became available. I wanted to use them to observe and catalog wildlife activity as I got started with the farm. I was already very familiar with Linux and C, but the best Pi Camera interface was the “Picamera” module in Python. I started “web wandering” to learn more about Python programming and computer vision. I ran across your tutorial blogs and bought your Practical Python and OpenCV book. Once I’d worked through the examples in your book, I was hooked.

Figure 1: Jeff Bass runs Ying Yang Ranch, a permaculture farm designed to grow food with long term sustainability.

Adrian: Can you tell us a bit more about your farm? What is permaculture farming? Why is it important and how is it different than “traditional” farming?

Jeff: I call the farm Yin Yang Ranch. It is a small 2 acre “science project” in a suburban area. I started learning about permaculture at the same time I started learning about Raspberry Pis.

Permaculture is a collection of practices and design principles to grow food with long term sustainability as the primary goal. It starts with creating deep living soil with diverse microbiology, emulating an old growth forest. Permaculture design choices prioritize sustainability over efficiency. It is science based, emphasizing a cycle of careful observation, repeatable experiments and open sharing of best practices.

Permaculture farms are generally small and include many different kinds of plants growing together rather than rows of similar crops. Food plants are grown in the same space with native plants. I am growing figs, pears, pomegranates, plums, grapes, avocados, oranges, mulberries, blackberries and other edibles. But they are interplanted with native California Coast Live Oaks and Sycamore Trees. It doesn’t look much like a traditional farm. Traditional agriculture is efficient, but degrades soil and water resources. Permaculture is trying to change that.

Figure 2: Jeff uses Raspberry Pis + computer vision around his farm. An example of such is automatic water meter reading using OpenCV.

Adrian: How can Raspberry Pis and computer vision be helpful on your farm?

Jeff: We are in Southern California about 10 miles from the Malibu coast. Drought and limited rainfall are the toughest climate issues. Monitoring and observation are important, so I built a Raspberry Pi Camera system to read the water meter and monitor temperatures to optimize irrigation.

That led more questions and lots of fun ways to gather and analyze data:

How many gallons did it take to water the mulberries today?

When did the coyotes last run behind the barn?

What is the temperature and soil moisture under the avocado trees?

What is the temperature and moisture content of the 5 compost piles?

How is it changing over time?

How much solar electricity did we generate today?

How full are the rain barrels?

How do the birds, butterflies and other critter movements change with the seasons and with whatever is blooming or putting on fruit?

The Raspberry Pis are also keeping track of stuff like the opening and closing of garage and barn doors. And they can let me know when a package is delivered.

Adrian: You created a library called imagezmq. What is it and what does it do?

Jeff: The imagezmq library implements a simple and fast network of Raspberry Pis (clients) and servers.

Early on, I decided on a distributed design using Raspberry Pis for capturing images and using Macs for analyzing the images. The goal was to have Raspberry Pis do image capture and motion detection (is the water meter spinning?) and programmatically decide on a small subset of images to pass along to the Mac.

I spent a year trying different ways of sending images from multiple Raspberry Pis to a Mac. I settled on the open source ZMQ library and its PyZMQ python wrappers. My imagezmq library uses ZMQ to send images and event messages from a dozen Raspberry Pis to a Mac hub. ZMQ is fast, small, simple to use and doesn’t need a message broker.

Here are a pair of code snippets showing how to use imagezmq to continuously send images from a Raspberry Pi to a Mac. First the code running on each Raspberry Pi:

# run this program on each RPi to send a labelled image stream import socket import time from imutils.video import VideoStream import imagezmq sender = imagezmq.ImageSender(connect_to='tcp://jeff-macbook:5555') rpi_name = socket.gethostname() # send RPi hostname with each image picam = VideoStream(usePiCamera=True).start() time.sleep(2.0) # allow camera sensor to warm up while True: # send images as stream until Ctrl-C image = picam.read() sender.send_image(rpi_name, image)

Then the code running on the Mac (server):

# run this program on the Mac to display image streams from multiple RPis import cv2 import imagezmq image_hub = imagezmq.ImageHub() while True: # show streamed images until Ctrl-C rpi_name, image = image_hub.recv_image() cv2.imshow(rpi_name, image) # 1 window for each RPi cv2.waitKey(1) image_hub.send_reply(b'OK')

The hostname of each Raspberry Pi allows the Mac to put the image stream from that Raspberry Pi in a separate, labelled

cv2.imshow()

  window. I have a photo in my imagezmq github repository showing 8 Raspberry Pi camera feeds being displayed simultaneously on a single Mac:

Figure 3: Live streaming video from 8 Raspberry Pis to a central hub using Python, OpenCV, and ImageZMQ.

It uses 12 lines of Python code on each Raspberry Pi and 8 lines of Python on the Mac. One Mac can keep up with 8 to 10 Raspberry Pis at 10 FPS. ZMQ is fast.

imagezmq enables a computer vision pipeline to be easily distributed over multiple Raspberry Pis and Macs. The Raspberry Pi captures images at 16 FPS. It detects motion caused by the spinning of the water meter needle. It sends only the images where the needle starts moving or stops moving, which is only a small fraction of the images it captures. Then, the Mac uses more advanced computer vision techniques to read the “digital digits” portion of the water meter images and determine how much water is being used. Each computer is able to do the portion of the computer vision pipeline for which it is best suited. imagezmq enables that.

Adrian: What is your favorite computer vision + Raspberry Pi project that you’ve deployed on Yin Yang Ranch?

Jeff: I’ve mounted a Raspberry Pi on the back wall of my barn with an infrared floodlight. It tracks motion and sends images when the motion is “critter like”. I’ve captured images of coyotes, raccoons, possums, bats, hawks, squirrels and rabbits. I’m still working on the deep learning models to classify them correctly. It’s been a lot of fun for me and my neighbors to learn more about the wildlife around us.

Figure 4: A selection of Raspberry Pi components and cameras, including (left to right) Waveshare Combo IR lens and dual IR floodlights, PiNoir IR Camera with IR “Ring Light” floodlight, RPi Zero with PiCamera in white light “Ring Light” with DS18B20 temperature probe, RPi Zero with “Spy Cam” and longer range WiFi, and RPi Zero with older model (and half price) PiCamera.

Adrian: Raspberry Pis, while cheap, are by no means as powerful as a standard laptop/desktop. What are some of the lessons learned while using Raspberry Pis around the farm?

Jeff: Raspberry Pis are great at image capture. The Pi Camera module is very controllable in Python for changing things like exposure mode. A USB or laptop webcam is usually not controllable at all. Controlling exposure and other camera settings can be very helpful in wildlife tracking and even reading a water meter.

The Raspberry Pi GPIO pins can gather readings from temperature sensors, moisture sensors and others. The GPIO pins can be used to control lights like the lights that illuminate my water meter and my barn area. Laptops and desktops don’t do those things easily.

On the other hand, Raspberry Pis lack a high speed disk drive — the SD card is not suitable for writing lots of binary image files. In my system, the Raspberry Pis send the image files over the network rather than storing them locally. Laptops and desktops have fast disk storage and lots of RAM memory allowing more elaborate image processing. I try to have my Raspberry Pis do what they excel at and have the Macs do what they are good at.

Most of my Raspberry Pis are Raspberry Pi 3’s, but I also use the cheaper, smaller Raspberry Pi Zero for Pi Cameras that only need to do simple motion detection, like my driveway cam. When there is no need for additional image processing, even the smaller memory and less powerful processor of the Pi Zero is adequate.

Adrian: What types of hardware, cameras, and Raspberry Pi accessories do you use around the farm? How do you keep your Pis safe from getting wet and destroyed?

Jeff: I use Raspberry Pis with Pi Cameras at many places around the farm. They often have temperature, moisture and other sensors. I build multiple kinds of enclosures to protect the Raspberry Pis.

One of my favorites is to convert a existing outdoor light fixture into a waterproof Raspberry Pi container. You remove the light bulb, screw in a simple AC socket adapter and you have an easy rainproof enclosure that holds the Raspberry Pi and the Pi Camera module. A GPIO temperature probe fits as well. It is easy to add a Raspberry Pi controlled LED light so that the light fixture still provides light like it did before:

Figure 5: Converting an outdoor light fixture into a waterproof Raspberry Pi container.

Another enclosure is a simple glass mason jar with a plastic lid. It fits a Raspberry Pi and is waterproof. Power and camera cables can be routed through holes in the plastic lid. That’s how my water meter Pi Camera is built and it has been working well through all kinds of weather for over 2 years:

Figure 6: A simple Raspberry Pi enclosure using a simple mason jar.

For infrared applications like the night time critter camera behind my barn, I put the Raspberry Pi inside the barn. The camera and temperature sensor cables run through small holes in the barn wall. The Pi NoIR camera module is protected under a simple overhang of old shingles. Infrared light doesn’t pass through glass, so the Pi NoIR camera module cannot be in a glass enclosure. The shingle overhang over an otherwise unprotected PiNoIR module has been very effective:

Figure 7: A simple wooden overhang to protect the Raspberry Pi.

A closer view can be seen below:

Figure 8: A closer up view of the overhang (you can see the red light from the Raspberry Pi camera if you look closely).

I have also found that inexpensive (about $5) “Fake Security Cam” enclosures work really well as waterproof Raspberry Pi and Pi Camera enclosures. They easily hold a Raspberry Pi and they have a tripod-like angle adjuster:

Figure 9: The shell of a fake security security camera can easily house a Raspberry Pi + camera module.

Once put together the “fake” security camera becomes a real security camera:

Figure 10: Adding the Raspberry Pi + camera components to the security camera shell.

For power, I tend to run longer power lines (over 20 feet long) at 12 volts, then convert to 5 volts at the Raspberry Pi. I use 12 volt power adapters like those that are used to charge cell phones in cars. Cheap and effective. Most of my Raspberry Pis are networked with WiFi, but I have ethernet in my barn and at various places around the house, so some of my Raspberry Pis are using ethernet to send images.

Adrian: During your PyImageConf 2018 talk you discussed how projects like these can actually help people build their computer vision and deep learning resumes. Can you elaborate on what you mean there?

Jeff: During my 30 years of managing programming and data analysis teams, I found it very helpful when job candidates came to the interview with a specific project in their portfolio that showed their strengths. A computer vision project — even a hobby project like my Raspberry Pi water meter cam — can really help demonstrate practical skills and ability.

A well documented project demonstrates practical experience and problem solving. It shows the ability to complete large projects fully (80% solutions are good but 100% solutions demonstrate an ability to finish). A portfolio project can demonstrate other specific skills, such as using multiple computer vision libraries, the ability to write effective documentation, facility with Git / GitHub as a collaboration tool and technical communication skills. It is important to be able to tell a short, compelling project story — the “elevator speech” about your portfolio project.

Adrian: How as the PyImageSearch blog, the PyImageSearch Gurus course, and the books/courses helped you make this project a success?

Jeff: When I started learning about computer vision, I found that a lot of the material on the web was either too theoretical or gave simplistic code snippets without any fleshed out examples or complete code.

When I found your PyImageSearch blog, I found your tutorial projects to be very complete and helpful. You provided an understandable story line for each problem being solved. Your explanations were clear and complete with fully functioning code. Running some of the programs in your blog led me to buy your Practical Python and OpenCV book.

I took your PyImageSearch Gurus course and learned to code many specific computer vision techniques. I had previously read about many of these techniques, but your specific code examples provided the “how-to” I needed to write computer vision code for my own projects.

The license plate number reading section of your Gurus course was the basis for the first draft of my water meter digits reading program. Your deep learning book is helping me to write the next version of my object recognition software for tagging critters around the farm (racoon or possum?).

Adrian: Would you recommend PyImageSearch and the books/courses to other developers, researchers, and students trying to learn computer vision + deep learning?

Jeff: I would definitely recommend your PyImageSearch blog, books and courses to those trying to learn about this rapidly evolving field. You are very good at explaining complex techniques with a helpful mix of code and narrative discussion. Your books and courses provided a jump start to my understanding of the theory and practice of modern computer vision algorithms.

I had not programmed in Python before, and some of its idioms felt a bit odd to my C conditioned brain. Your practical examples helped me use the best features of Python in “Pythonic” ways. Your Gurus course is well structured with a good flow that builds on simple examples that are expanded lesson by lesson into fully developed programs that solve complex problems. Your coverage of the many different computer vision and deep learning techniques is broad and comprehensive. Your books and courses are a good value for what you charge for them. Highly recommended.

Adrian: What are your next steps for Yin Yang Ranch and your current computer vision/deep learning projects?

Jeff: I would like to do more wildlife identification and counting. The farm is next to an open space that is a wildlife corridor through a suburban area. I’m going to use more advanced deep learning techniques to identify different animals by species. When do specific animals come and go? What birds and butterflies show up in which part of the season? How are their numbers related to seasonal rainfall? I would like to use deep learning to identify specific individuals in a coyote pack. How long is a specific coyote staying in our area? I can recognize specific individuals when I look at captured images. I’d like to use deep learning to do that with software.

Adrian: Is there anything else you would like to share?

Jeff: Building stuff with software and hardware can be a lot of fun. If someone is reading this and wondering how to get started, I would suggest they start with something they are passionate about. There is likely to be some way that computer vision and deep learning can help with a project in their area of interest. I wanted to do some permaculture farm science and my computer vision projects are helping me do that. I’m learning a lot, giving some talks and farm tours to help others learn…and I’m having a lot of fun.

Adrian: If a PyImageSearch reader wants to chat, what is the best place to connect with you?

Jeff: Folks can read more and see more project pictures at my Yin Yang Ranch repository on GitHub:

https://github.com/jeffbass/yin-yang-ranch

Or they can send me an email at jeff [at] yin-yang-ranch.com.

Summary

In this blog post, we interviewed Jeff Bass, the creator of the ImageZMQ library (which we used in last weeks’s tutorial), used to facilitate live video streaming from a Raspberry Pi to a central server/hub using Python + OpenCV.

Jeff was motivated to create the ImageZMQ library to help around his permaculture farm. Using ImageZMQ and a set of Raspberry Pis, Jeff can apply computer vision and data science techniques to gather and analyze temperature readings, water meter flow, electricity usage, and more!

Please take a second to thank Jeff for taking the time to do the interview.

To be notified when future blog posts and interviews are published here on PyImageSearch, just be sure to enter your email address in the form below, and I’ll be sure to keep you in the loop.

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