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# Rentadrone_MachineLearning
Photovoltaic fault detector
# Rentadrone_MachineLearning Photovoltaic fault detector
## To do list:
- [x] Import model detection (SSD & YOLO3)
- [x] Model Panel Detection
- [ ] Model Soiling Fault Detection
- [ ] Model Diode Fault Detection
- [ ] Model Other Fault Detection
### Dependencies
* Python 3.x
* Numpy
* TensorFlow 1.x
* Keras 2.x
* OpenCV
* Beautiful Soup 4.x
## Detection
Grab the pretrained weights of SSD and YOLO3 from https://drive.google.com/drive/folders/1FuhIJFxuzB9CLuRNwbKWFFsM6Nyweorf?usp=sharing
## Training
### 1. Data preparation
View folder Train&Test_A/ and Train&Test_S/, example of panel anns and soiling fault anns.
Organize the dataset into 4 folders:
+ train_image_folder <= the folder that contains the train images.
+ train_annot_folder <= the folder that contains the train annotations in VOC format.
+ valid_image_folder <= the folder that contains the validation images.
+ valid_annot_folder <= the folder that contains the validation annotations in VOC format.
There is a one-to-one correspondence by file name between images and annotations.
For create own data set use LabelImg code from :
[https://github.com/tzutalin/labelImg](https://github.com/tzutalin/labelImg)
### 2. Edit the configuration file
The configuration file for YOLO3 is a json file, which looks like this (example soiling fault ):
```python
{
"model" : {
"min_input_size": 400,
"max_input_size": 400,
"anchors": [5,7, 10,14, 15, 15, 26,32, 45,119, 54,18, 94,59, 109,183, 200,21],
"labels": ["1"],
"backend": "full_yolo_backend.h5"
},
"train": {
"train_image_folder": "../Train&Test_S/Train/images/",
"train_annot_folder": "../Train&Test_S/Train/anns/",
"cache_name": "../Experimento_fault_1/Resultados_yolo3/full_yolo/experimento_fault_1_gpu.pkl",
"train_times": 1,
"batch_size": 2,
"learning_rate": 1e-4,
"nb_epochs": 200,
"warmup_epochs": 15,
"ignore_thresh": 0.5,
"gpus": "0,1",
"grid_scales": [1,1,1],
"obj_scale": 5,
"noobj_scale": 1,
"xywh_scale": 1,
"class_scale": 1,
"tensorboard_dir": "log_experimento_fault_gpu",
"saved_weights_name": "../Experimento_fault_1/Resultados_yolo3/full_yolo/experimento_yolo3_full_fault.h5",
"debug": true
},
"valid": {
"valid_image_folder": "../Train&Test_S/Test/images/",
"valid_annot_folder": "../Train&Test_S/Test/anns/",
"cache_name": "../Experimento_fault_1/Resultados_yolo3/full_yolo/val_fault_1.pkl",
"valid_times": 1
},
"test": {
"test_image_folder": "../Train&Test_S/Test/images/",
"test_annot_folder": "../Train&Test_S/Test/anns/",
"cache_name": "../Experimento_fault_1/Resultados_yolo3/full_yolo/test_fault_1.pkl",
"test_times": 1
}
}
```
The configuration file for SSD300 is a json file, which looks like this (example soiling fault ):
```
{
"model" : {
"backend": "ssd300",
"input": 400,
"labels": ["1"]
},
"train": {
"train_image_folder": "Train&Test_S/Train/images",
"train_annot_folder": "Train&Test_S/Train/anns",
"train_image_set_filename": "Train&Test_S/Train/train.txt",
"train_times": 1,
"batch_size": 12,
"learning_rate": 1e-4,
"warmup_epochs": 3,
"saved_weights_name": "Result_ssd300_fault_1/experimento_ssd300_fault_1.h5",
"debug": true
},
"test": {
"test_image_folder": "Train&Test_S/Test/images",
"test_annot_folder": "Train&Test_S/Test/anns",
"test_image_set_filename": "Train&Test_S/Test/test.txt"
}
}
```
### 3. Start the training process
`python train_ssd.py -c config.json -o /path/to/result`
or
`python train_ssd.py -c config.json -o /path/to/result`
By the end of this process, the code will write the weights of the best model to file best_weights.h5 (or whatever name specified in the setting "saved_weights_name" in the config.json file). The training process stops when the loss on the validation set is not improved in 20 consecutive epoches.
### 4. Perform detection using trained weights on image, set of images
`python predict_ssd.py -c config.json -i /path/to/image/or/video -o /path/output/result`
or
`python predict_yolo.py -c config.json -i /path/to/image/or/video -o /path/output/result`
It carries out detection on the image and write the image with detected bounding boxes to the same folder.
## Evaluation
The evaluation is integrated into the training process, if you want to do the independent evaluation you must go to the folder ssd_keras-master or keras-yolo3-master and use the following code
`python evaluate.py -c config.json`
Compute the mAP performance of the model defined in `saved_weights_name` on the validation dataset defined in `valid_image_folder` and `valid_annot_folder`.
# Result
All of weights of this trained model grab from https://drive.google.com/drive/folders/1FuhIJFxuzB9CLuRNwbKWFFsM6Nyweorf?usp=sharing
## Panel Detector
### SDD7
On folder Result_ssd7_panel show code (jupyter notebook), weight and result of this model (mAP 89.8%).
.. image:: /Result_ssd_panel/result_ssd7_panel/DJI_0020.jpg
:width: 200px
:align: center
/Result_ssd7_panel/result_ssd7_panel/DJI_0110.jpg
:width: 200px
:align: center
## Soiling Fault Detector
### SSD300
On folder Result_ssd300_fault_1 show code (jupyter notebook), weight and result of this model (mAP 79.5%).
/Result_ssd300_fault_1/result_ssd300_fault_1/Mision 11_DJI_0011.jpg
:width: 200px
:align: center
### YOLO3
On folder Result_ssd300_fault_1 show history train (yolo3_full_yolo.output), weight and result of this model (mAP 73.02%).
/Result_yolo3_fault_1/result_yolo3_fault_1/Mision 11_DJI_0011.jpg
:width: 200px
:align: center
## Diode Fault Detector