#Downy Mildew Disease detection TensorFlow object detection + Downy Mildew Disease detection

1. Install labelImg: pip3 install labelImg
2. Directory structure: Object-Detection->data ->training ->images ->train ->test

3.change code xml_to_csv.py def main(): for directory in ['train','test']: image_path = os.path.join(os.getcwd(), 'images/{}'.format(directory)) xml_df = xml_to_csv(image_path) xml_df.to_csv('data/{}_labels.csv'.format(directory), index=None) print('Successfully converted xml to csv.')

4. Run python3 xml_to_csv.py

5. Change code in generate_tfrecord.py

Add all the classes

def class_text_to_int(row_label): if row_label == 'downy': return 1 else: return 0

6. Run python3 generate_tfrecord.py --csv_input=data/train_labels.csv --output_path=data/train.record --image_dir=images/

python3 generate_tfrecord.py --csv_input=data/test_labels.csv --output_path=data/test.record --image_dir=images/

Now, in your data directory, you should have train.record and test.record.

7. Clone fresh tensorflow models git clone https://github.com/tensorflow/models.git

Then, follow the installation instructions:

sudo apt-get install protobuf-compiler python-pil python-lxml sudo pip install jupyter sudo pip install matplotlib

8. Go to research foilder and open terminal

From tensorflow/models/research

protoc object_detection/protos/*.proto --python_out=.

9.set python path

From tensorflow/models/research

export PYTHONPATH=$PYTHONPATH:pwd:pwd/slim

10. Install in to python sudo python3 setup.py install

11. wget http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz

Get Mobilnet configuration files

12. Get the sample config files from models/research/object_detection/samples/configs/ssd_mobilenet_v1_pets.config edit the config file with all the path #change num of classes num_classes: 1 #change the batch size batch_size: 24 #change if you want initial_learning_rate: 0.004

    fine_tune_checkpoint: "ssd_mobilenet_v1_coco_11_06_2017/model.ckpt"

    input_path: "data/train.record"

    label_map_path: "data/object-detection.pbtxt"

    input_path: "data/test.record"

    label_map_path: "training/object-detection.pbtxt"

    After Editing above items, Put the config in the training directory.

13. Inside training dir, add object-detection.pbtxt: Add all your classes.

item { id: 1 name: 'downy' }

14. Add Object-Detection.pbtxt file in into data directory

15. Copy data, image, training, ssd_mobilenet_v1_coco_11_06_2017 (unzipped) in to object-detection directory) merge the data directory with existing directory

16. Run the command: python3 model_main.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config

17. If any error wrt 'nets' then set the python Path again on research directory # From tensorflow/models/research export PYTHONPATH=$PYTHONPATH:pwd:pwd/slim

18. find the temp directory where the event files are generated and run #Try-->

tensorboard --logdir='/tmp/tmp8ecnodas'

in browser open--> http://127.0.0.1:6006/

19. run python3 export_inference_graph.py
    --input_type image_tensor
    --pipeline_config_path training/ssd_mobilenet_v1_pets.config
    --trained_checkpoint_prefix training/model.ckpt-23361
    --output_directory invoice_inference_graph_23361

20. Run--> jupyter notebook

21. Goto object_detection_tutorial.ipynb directry -->In Variables

What model to download.

MODEL_NAME = 'invoice_graph_2'

Path to frozen detection graph. This is the actual model that is used for the object detection.

PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph.pb'

List of the strings that is used to add correct label for each box.

PATH_TO_LABELS = os.path.join('training', 'object-detection.pbtxt')

NUM_CLASSES=1

-->delete Download Model content(optional)

-->In Detection

For the sake of simplicity we will use only 2 images:

image1.jpg

image2.jpg

If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.

PATH_TO_TEST_IMAGES_DIR = 'test_images' TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 10) ]#range() as per the number of images

Size, in inches, of the output images.

IMAGE_SIZE = (12, 8)

22. goto Cell -->Run All Take a look at test results