Make the door small enough so that you can NFC tag them.
Have one reader on each side of the door, if the left one reads a chicken tag then it’s going in, if the right one does it it’s moving out.
Each movement sets a boolean.
Should work fine, unless they reverse out the door.
If you really want to over-engineer it then you could put a depth sensor above the indention where the eggs are, this would tell you if there’s a chicken and if there is an egg. I use an ultrasonic sensor like that on an Arduino to detect water levels, but it would work for any depth sensing.
Thanks for all suggestions - I used @robmarkcole 's deepstack object recognition installed through HACS.
Then I read up on custom models and followed the instructions and used a Google colab to train the model. For now I have the impression that due to memory and duration restrictions (I timed out on my first attempt) I got the best results from:
python3 train.py --epochs 150 --batch-size 32 --dataset-path "/content/drive/MyDrive/my-dataset"
Connecting Google drive was more straightforward than uploading and unzipping the training data.
Convincing the image processing to use your own trained model was a bit confusing:
image_processing:
- platform: deepstack_object
ip_address: localhost
port: 80
api_key: mysecretkey
custom_model: mask
confidence: 60
mask is not a file name mask, it was the name of the sample custom model for face mask recognition, so simply use the name of the model here. For some reason documentation mentions .pth files, the colab generates a .pt file and that works just fine.
Et voilà:
Rectangle is a bit fine, but I no longer need to look myself if there is an egg.
targets_found:
- egg: 77.362
summary:
egg: 1
last_target_detection: 2021-05-09_08-58-49
custom_model: best
all_objects:
- egg: 77.362
It somehow feels wrong I’m able to do this - pretty impressed with the whole thing.
Thx!
I love this application!
Robin,
2 applications really - yes, it shows if there is an egg which is nice as I prefer to get them rather than anyone else, but also I check if the happy hens are inside when the automation closed the door of the chicken coop. The automation is sunset+15mins but they have been surprised and chances of them surviving a night outside aren’t that high. So this potentially is a life saver.
Would be nice to have the server as an add-on but I’m not complaining, might look into it myself but that’s another learning track.
Thx!
Did you look into Frigate? That already has an addon and supports custom models I believe
Frigate looks very polished, but deepstack just works - their guide for training the model with a google colab is super simple. I started on tensorflow lite but then for training struggled with python version, dependencies and modules that either didn’t want to install or I couldn’t deinstall to start all over.
I’ll probably come back to it later, I have one non rtsp cam and then use motioneye to tilt another cam 90° so a few things to look into before I’m ready.
I have deepstack on the NUC, not on the PI so comparing performance, ease of setup against an addon on the PI is not really apples to apples. But then again getting a usb coral for the PI may solve a lot.
What a pleasant waste of time this all is… hehe. Thanks for sharing your work if I haven’t mentioned that before!
My wife has a hard time with me over-engineering things for the fun of it. When she asks “why” I always answer “because I can”. I mean, I don’t need a micro camera and sensors on my mailbox that transmit wifi from the ESP to Home Assistant so my house can speak to me to let me know I have mail, but I do it because “I can”.
hi @jhhbe, very nice example of using object recognition. I was looking for something very similar.
Question; where did you find the egg images to train the model? Would you be able to share these?
Thanks
Koen
Koen,
I created a set myself: I had a HA automation taking a picture in the chicken coop every 5 minutes. Then I used GitHub - tzutalin/labelImg to annotate the jpgs which didn’t take that long. Did that for a day or 3, made sure I had sunny and overcast pictures, as the lighting is very different.
I need to retrain my model as the hens decided to lay their eggs in the far end of their home now and for those it doesn’t work. So I doubt it’s going to be very effective in your setup.
Anyway, the original set I used is at jhhbe/dataset: dataset egg detection (github.com)
Have fun!
Hey do you still use it this way?
Possible to share some more info to set this all up?
i have an camera working on ha with rtsp i could install deepstack object to
but how do you get that image processed to check if its an egg or not?
Is it an automation to send it every x time to deepstack or how do you do it? tx on an advance
Yes, still use it that way, but as chickens decided to lay their eggs on the opposite side in the nest section, the camera can’t get a good view. So it only detects the chickens now.
Setup: install the deepstack docker container, put a deepstack section in configuration.yaml where you tell deepstack which camera to look at. Install HACS integration for deepstack - unsure what entity you get as part of the configuration setting and which from HACS, but I assume that: yaml entry gives a image_processing.scan service and HACS integration gives an entity that allows you to consume the result of that scan easily. I have an automation that calls the scan service every 5 minutes.
Optional: make a trained model available for use in the deepstack container.
I put some more effort in explaining the custom model part, as I thought the other bits and pieces were documented.
Good luck!
thank you for the info i have the deepstack part and the scanning allready working
i’m now trying to training the egg
The google colab is still online, and that was pretty easy. If you set-up a 5 min schedule saving pictures to your share folder to get training material specific to your setup, then the instructions in my earlier posts should have what you need.
Have fun!
i don’t know if its normal i think i have a error somewhere:
root@deepstack:~/deepstack-trainer# python3 train.py --dataset-path "/root/models/kipei"
Using torch 1.13.1+cu117 CPU
Namespace(adam=False, batch_size=16, bucket='', cache_images=False, cfg='./models/yolov5m.yaml', classes='', data={'train': '/root/models/kipei', 'val': '/root/models/kipei', 'nc': 2, 'names': ['egg', '']}, dataset_path='/root/models/kipei', device='', epochs=300, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_imgs=16, model='yolov5m', multi_scale=False, name='exp', noautoanchor=False, nosave=False, notest=False, project='train-runs/kipei', rect=False, resume=False, save_dir='train-runs/kipei/exp2', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5m.pt', workers=8, world_size=1)
Start Tensorboard with "tensorboard --logdir train-runs/kipei", view at http://localhost:6006/
Hyperparameters {'lr0': 0.01, 'lrf': 0.2, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, 'warmup_momentum': 0.8, 'warmup_bias_lr': 0.1, 'box': 0.05, 'cls': 0.5, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mosaic': 1.0, 'mixup': 0.0}
Overriding model.yaml nc=80 with nc=2
from n params module arguments
0 -1 1 5280 models.common.Focus [3, 48, 3]
1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
2 -1 1 67680 models.common.BottleneckCSP [96, 96, 2]
3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
4 -1 1 639168 models.common.BottleneckCSP [192, 192, 6]
5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
6 -1 1 2550144 models.common.BottleneckCSP [384, 384, 6]
7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
8 -1 1 1476864 models.common.SPP [768, 768, [5, 9, 13]]
9 -1 1 4283136 models.common.BottleneckCSP [768, 768, 2, False]
10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 1219968 models.common.BottleneckCSP [768, 384, 2, False]
14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 1 305856 models.common.BottleneckCSP [384, 192, 2, False]
18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 1072512 models.common.BottleneckCSP [384, 384, 2, False]
21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 4283136 models.common.BottleneckCSP [768, 768, 2, False]
24 [17, 20, 23] 1 28287 models.yolo.Detect [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
Traceback (most recent call last):
File "train.py", line 530, in <module>
train(hyp, opt, device, tb_writer, wandb)
File "train.py", line 90, in train
model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create
File "/root/deepstack-trainer/models/yolo.py", line 96, in __init__
self._initialize_biases() # only run once
File "/root/deepstack-trainer/models/yolo.py", line 151, in _initialize_biases
b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation.
root@deepstack:~/deepstack-trainer#
You can insert code in the colab - run ls -l /root/models/kipei to see if your training data is where you think it is. If not, use something like How to Connect Google Colab with Google Drive - MarkTechPost
For me it was just following cookbook style recipe - I have no idea what it does, which is why I commented - it feels wrong this is so easy. But I suppose if it isn’t, then it isn’t.
now the next part having it working on home assistant as telegram or other things
Very interested to get this working, but missing some information. Hope someone can help.
Maybe it is not possible with my setup. I have a RP4, 4GB with HA. Tried several image detection software like doods, that works. But the model used there can not detect eggs. So i came accros this post.
I am not very technical with deepstack and model learning. But i don’t know if it is hard. Some links mentioned above are not working anymore so i don´t know where to start.
I have deepstack installed inside my docker, i have a pictureset of a couple of days from the nest with and without chicken and egg (picture taken every five minutes).
Can someone tell me the steps what i must do now.
For example i see mentioned something in google colab:
/root/models/kipei (where can i find kipei script)
And i see someting like a command inside deepstack:
/deepstack-trainer# python3 train.py etc…etc…
So can’t see the forest for the trees anymore 
Hope someone can help me, because i really like to use this.
