Posts made by sreu13

@arunksoman , any idea on this

@salmanfaris actually not at all solved!!..the problem is that...i do not know how to send data from raspberry pi to Google vm and get the results back from google vm to RPI4. I've researched a lot online...but cant find a proper route

@arunksoman thankyou so much...your reply really busted the stress out of me...can you help with any documentations which i could possibly go through

@arunksoman hi,..
actually at this point of time, I'm not willing to risk RPI4,....i thought of connecting the rpi4 to google cloud (already have an account with $300 credit) and proceeding with the code.
but i do not know if the below process can be executed

1. sending image from rpi4 to cloud
2. using this cloud for running prediction
3. the above two processes should be automated and image should be sent to cloud when pi boots up(VM engine can be activated whenever nessesary)

is this possible?

@arunksoman i'll try this method, but while executing swap command, will the rasbian os and the files it contains be effected??

@arunksoman tensorflow 2.0.0 version had been installed

@arunksoman i installed tensorflow from the below given link

and i installed it without entering into virtual environment

but this 10% memory issue is a serious pain

@arunksoman hi, i've run this code using python 3.7 on anaconda and it worked fine.
but as i run this on RPI 4, it showed the Hadoop error. I then updated the libraries and Hadoop error was solved..
now there is a new error

also can you take a look in the 10% memory error?...like what can be possibly done there??

@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 solved

what 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]])

Hi,
I came by this error after installing TensorFlow in RPI 4
can you guys find a fix?
error on Raspberry PI 4

tensorflow/core/platform/hadoop/hadoop_file_system.cc:132] HadoopFileSystem load error: libhdfs.so: cannot open shared object file: No such file or directory)