[Solved] leaf disease detection using keras
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@arunksoman
i'll follow this proceedure, but would I be able to deploy this code in raspberry pi 4? -
@sreu13 As the @SuperGops says you have to use fit_transform. It can be implemented on Raspberry Pi 4. But if it slows down your pi you have use multiprocessing as well as threads to improve that.
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@arunksoman
by multiprocessing, do you mean to use multilabel binarizer, if yes , then I have already used it during the training process.
@SuperGops , fit_transform has also been used in the code prior to using label_binarizer. -
@sreu13 I didn't mean that. I said if you are trying to run your code on RasPi4 or any other version of RasPi, you have to do some optimization on the code for better performance. Then Multiprocessing and threading Module comes into the picture.
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@arunksoman
i followed your proceedure to and installed the requirements.txt file
after running the code, I got the following errorFile "C:\Users\admin\Anaconda3\envs\test2\lib\site-packages\scipy\special\basic.py", line 15, in <module>
from ._ufuncs import (ellipkm1, mathieu_a, mathieu_b, iv, jv, gamma,ImportError: cannot import name 'ellipkm1'
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@sreu13 I didn't tell you to install anything via anaconda package manager. You have to uninstall those things first and install python 3.6.5. It was the first step.You have to read things carefully before executing anything.
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@sreu13 how is the progress?
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@salmanfaris i know there was a delay in replies, i thought of finishing the project and replying here!....
i ddnt get the exact reason, but the problem was solvedwhat i did was , label binarized was working fine when i did created the training model. So i loaded my training model and deleted the script for the training procedure. i ended up with the code given below. and it worked up fine.
import numpy as np import pickle import cv2 from os import listdir from sklearn.preprocessing import LabelBinarizer from keras.models import Sequential from keras.layers.normalization import BatchNormalization from keras.layers.convolutional import Conv2D from keras.layers.convolutional import MaxPooling2D from keras.layers.core import Activation, Flatten, Dropout, Dense from keras import backend as K from keras.preprocessing.image import ImageDataGenerator from keras.optimizers import Adam 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 EPOCHS = 25 INIT_LR = 1e-3 BS = 32 default_image_size = tuple((256, 256)) image_size = 0 directory_root = 'PlantVillage' width=256 height=256 depth=3 #Function to convert images to array 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 listdir(directory_root) image_list, label_list = [], [] try: print("[INFO] Loading images ...") root_dir = listdir(directory_root) for directory in root_dir : # remove .DS_Store from list if directory == ".DS_Store" : root_dir.remove(directory) for plant_folder in root_dir : plant_disease_folder_list = listdir(f"{directory_root}/{plant_folder}") for disease_folder in plant_disease_folder_list : # remove .DS_Store from list if disease_folder == ".DS_Store" : plant_disease_folder_list.remove(disease_folder) for plant_disease_folder in plant_disease_folder_list: print(f"[INFO] Processing {plant_disease_folder} ...") plant_disease_image_list = listdir(f"{directory_root}/{plant_folder}/{plant_disease_folder}") for single_plant_disease_image in plant_disease_image_list : if single_plant_disease_image == ".DS_Store" : plant_disease_image_list.remove(single_plant_disease_image) for image in plant_disease_image_list[:200]: image_directory = f"{directory_root}/{plant_folder}/{plant_disease_folder}/{image}" if image_directory.endswith(".jpg") == True or image_directory.endswith(".JPG") == True: image_list.append(convert_image_to_array(image_directory)) label_list.append(plant_disease_folder) print("[INFO] Image loading completed") except Exception as e: print(f"Error : {e}") image_size = len(image_list) #Transform Image Labels uisng Scikit Learn's LabelBinarizer 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_) #Print the classes print(label_binarizer.classes_) #load saved pickle model loaded_model = pickle.load(open('cnn_model.pkl', 'rb')) model_disease=loaded_model #load plant leaf image image_dir="plantdisease/Validation_Set/Potato___Early_blight/1d301622-e359-49d5-b4ca-6837f254fd1b___RS_Early.B 6719.JPG" #convert leaf image to arrays im=convert_image_to_array(image_dir) np_image_li = np.array(im, dtype=np.float16) / 225.0 npp_image = np.expand_dims(np_image_li, axis=0) result=model_disease.predict(npp_image) print(result) #printing result itemindex = np.where(result==np.max(result)) print("probability:"+str(np.max(result))+"\n"+label_binarizer.classes_[itemindex[1][0]])
