[Solved] leaf disease detection using keras

[sreu13]

The code given below is for image classification, it seems to be running well until the last iteration which gives the following error >
"C:/Users/admin/Desktop/plant_disease_classification/plant_disease_classification/untitled1.py", line 45, in <module>
print (label_binarizer.classes_[predict1])

AttributeError: 'LabelBinarizer' object has no attribute 'classes_'

import numpy as np
import pickle
import cv2
from os import listdir
from sklearn.preprocessing import LabelBinarizer
from keras.models import load_model
from keras.models import Sequential
from keras.preprocessing import image
from keras.preprocessing.image import img_to_array
from sklearn.preprocessing import MultiLabelBinarizer

from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import tensorflow

default_image_size = tuple((256, 256))

#load the pickle file 
file_object = open(r'C:\Users\admin\Desktop\pr3\cnn_model.pkl', 'rb')
model = pickle.load(file_object)

#load the leaf image that is to be classified
imgpath=r'C:\Users\admin\Desktop\pr1\PlantVillage\diseases\Pepper__bell___healthy\0a3f2927-4410-46a3-bfda-5f4769a5aaf8___JR_HL 8275.JPG'


def convert_image_to_array(image_dir):
    try:
        image = cv2.imread(image_dir)
        if image is not None :
            image = cv2.resize(image, default_image_size)   
            return img_to_array(image)
        else :
            return np.array([])
    except Exception as e:
        print(f"Error : {e}")
        return None

imar = convert_image_to_array(imgpath) 
np_image_list = np.array([imar], dtype=np.float16) / 225.0 

label_binarizer = LabelBinarizer()

predict1 = model.predict(np_image_list) 

print (label_binarizer.classes_[predict1])

the screenshot of the above complied code and error is uploaded for reference.

[salmanfaris]

@sreu13 said in leaf disease detection using keras:

AttributeError: 'LabelBinarizer' object has no attribute 'classes_'

Hi, Can you try downgrade the scikit by typing

pip install scikit-learn==0.15.2

and are you following any guide or something, if yes can you share that also?

[sreu13]

@salmanfaris said in leaf disease detection using keras:

pip install scikit-learn==0.15.2

tried downgrading, but came up with this error.

ERROR: Failed building wheel for scikit-learn

and I've been following Kaggle kernel,
link-https://www.kaggle.com/emmarex/plant-disease-detection-using-keras

[SuperGops]

I think you need to use the fit or fit_transform function before you predict the classes and use the Binarizer. Have a look at scikit's official documentation for the same.

[sreu13]

@SuperGops

i've entered this code,

def fit_transform(self, n_classes):

but as I run it, I get the indentation Error given below
File"C:/Users/admin/Desktop/plant_disease_classification/plant_disease_classification/untitled1.py", line 42
label_binarizer = LabelBinarizer()
^
IndentationError: expected an indented block

[SuperGops]

@sreu13
fit and fit_transform are actually inbuilt functions found in the scikit-learn library. So I'd suggest you fit your model with the available data using those functions whose application can be found on scikit-learn's documentation and then proceed with the Binarizer.

[sreu13]

@SuperGops
so basically ,i'll have to restart and retrain the model with fit_transform?

[SuperGops]

@sreu13 Yup

[arunksoman]

Could you please follow my steps:

1. Uninstall your current Python 3.7 version
2. Install Python 3.6.5
3. If you are using spyder editor make a change to vscode.
4. Go to integrated terminal of vscode and create a virtual environment

python -m venv venv

5. Activate your virtual environment

.\venv\Scripts\activate

6. Create a requirements.txt over your current working directory. Contents for requirements.txt given below:

h5py==2.8.0
imutils==0.5.1
Keras==2.2.4
Keras-Applications==1.0.6
Keras-Preprocessing==1.0.5
kiwisolver==1.0.1
matplotlib==3.0.2
numpy==1.15.3
opencv-contrib-python==3.4.3.18
Pillow==5.3.0
PyWavelets==1.0.1
scikit-image==0.14.1
scikit-learn==0.20.0
scipy==1.1.0
six==1.11.0
sklearn==0.0
tensorboard==1.12.0
tensorflow==1.12.0
termcolor==1.1.0
toolz==0.9.0

7. Then install necessary packages using requirements.txt file

pip install -r requirements.txt

8. Then run your code within this venv and say what happened as reply here.

[sreu13]

@SuperGops
i also have another pickle file "label_transform", which I got as an output from referring the initial code from gaggle, is there any use of this file?

label_binarizer = LabelBinarizer()
image_labels = label_binarizer.fit_transform(label_list)
pickle.dump(label_binarizer,open('label_transform.pkl', 'wb'))
n_classes = len(label_binarizer.classes_)

Kaggle >https://www.kaggle.com/emmarex/plant-disease-detection-using-keras/data