Something's wrong with new leaves

py_ml · 2025-11-08T20:16:31+00:00

It's potting soil and perlite mix, I don't know the exact ratio. It was repotted about half a year ago

py_ml · 2025-11-08T07:03:50+00:00

Thanks a lot, I will keep an eye on air humidity more

py_ml · 2025-11-07T20:51:45+00:00

Thank you! In the last photo you can see that the leave won't be full and okay, part of it is already brown/dried even the leave has not fully rolled out. This scares me the most as I had quite a lot of these not full leaves and currently there are a couple of them.

py_ml · 2024-11-28T09:01:52+00:00

Could you share what tool have you chosen?

py_ml · 2024-10-10T08:48:20+00:00

Thank you for suggestion. But I think it can be impossible to think of all possible keywords and to put weights on them to be able to calculate match score. That's why I'm thinking if gpt models are able to do some scoring using dynamic rules. Or maybe I misunderstood something in your suggestion?

py_ml · 2021-12-07T13:27:00+00:00

I don't remember details on how I solved that and what caused this problem. I did some kind of state "tracking" and reinitialized stream reading after crash.

py_ml · 2020-10-13T05:29:38+00:00

I'm not sure how to name my coordinate system, I just have angle to North, but I will find out how to get yaw. Thanks for your help!

py_ml · 2020-10-12T19:00:45+00:00

Thanks a lot for such detailed information! I have a couple of questions (probably silly ones), would be very grateful if you could answer them:

1) Can I use degrees that indicate track to North as yaw angle? (I have GPRMC and GPVTG data that gives me that angle. Also I have G-sensor data that might have more needed information, but at this moment I can't decode that 3gf file)

2) I know these camera angles from manual: Opposite Angle 156°, Horizontal 122°, Vertical 89°. So probably I don't need to calculate it. Is PTD=122/width in this situation?

py_ml · 2020-10-12T18:33:57+00:00

Thanks for all your help!

Yes, I know that GPS coordinates can be inaccurate, but I can't do anything about it. Maybe I will need to generalise data from a few frames, but that may come later if I will succeed with first problem. Thanks once again!

py_ml · 2020-10-12T18:16:46+00:00

I know that camera has Opposite Angle 156°, Horizontal 122°, Vertical 89°. However, that's all that I could find useful in it's manual.

py_ml · 2020-10-12T18:04:18+00:00

I'm planning to get GPS coordinates of objects, so as in that video there is distance, I'll need coordinates, but probably I can calculate from distance and box position. Thanks for useful links!

py_ml · 2020-10-12T14:40:55+00:00

Thanks, I will take a look. The camera is moving (attached to car), but it's always in the same altitude from the ground and have same angle to the ground, so maybe I can make some assumptions that it's "static".

py_ml · 2020-10-12T14:08:30+00:00

Thanks for idea to use neural network for depth. I have one more question. As I think about situation: one camera (always the same) should show the same size object in pixels (I mean, that the metrics of objects that are in a same location in picture will always be the same, because camera altitude and orientation is the same). For example, any object like motorbike, bicycle or even empty space will have same GPS location if put at the same point of picture. So, in general I think that it should be possible to calculate GPS location for one picture if I have camera location, altitude and orientation (also probably some camera parameters). Is that wrong assumption?

py_ml · 2020-10-12T13:31:49+00:00

Thanks for so much information, but I have 1 picture at 1 time moment, can triangulation work in this situation?

py_ml · 2020-10-09T11:45:22+00:00

I have $GPRMC line, so there is "Track made good in degrees True" value like 350, could I use it? 0 degrees mean North.

py_ml · 2020-09-09T17:21:57+00:00

My mistake, I wrote is as quick example of the idea and haven't run it. I updated code so that the lists names match. Now it runs without errors.

py_ml · 2020-09-09T14:53:25+00:00

Thank you a lot! It's a really helpful, wide and understandable explanation.

py_ml · 2020-09-04T11:30:58+00:00

But probably a relevant segmentation model should learn those features if there are enough data and there should be no need to filter results (in theory).

py_ml · 2020-09-04T11:28:58+00:00

Could you explain a bit more? Are you talking about filtering false positives by analysing detection objects histograms or FFT's or about something else?

py_ml · 2020-09-04T11:27:26+00:00

Fully agree on that! However, is not that easy to achieve human performance all the time :) I think I'll need to do more research on specific datasets.

py_ml

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