mango.py

# -*- coding: utf-8 -*-
"""mango.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1qnvh0PqsE_sSRjFO9i-B2uXYg5xZqstN
"""

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
IMG_SIZE=224
BATCH_SIZE=326

# Define data generators for train, validation and test sets
train_datagen = ImageDataGenerator(
    rescale=1./255,
    validation_split=0.2
)

train_generator = train_datagen.flow_from_directory(
    '/content/drive/MyDrive/Mangos/Mangos/train',
    target_size=(IMG_SIZE, IMG_SIZE),
    batch_size=BATCH_SIZE,
    class_mode='binary',
    subset='training'
)

val_generator = train_datagen.flow_from_directory(
    '/content/drive/MyDrive/Mangos/Mangos/train',
    target_size=(IMG_SIZE, IMG_SIZE),
    batch_size=BATCH_SIZE,
    class_mode='binary',
    subset='validation'
)

test_datagen = ImageDataGenerator(rescale=1./255)

test_generator = test_datagen.flow_from_directory(
    '/content/drive/MyDrive/Mangos/Mangos/test',
    target_size=(IMG_SIZE, IMG_SIZE),
    batch_size=BATCH_SIZE,
    class_mode='binary'
)

from google.colab import drive
drive.mount('/content/drive')

#Define the model
model = keras.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(128, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dense(1, activation='sigmoid')
])

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

history = model.fit(train_generator,validation_data=val_generator,epochs=5)

model.save("Model.h5","label.txt")

#test your image
from keras.models import load_model  # TensorFlow is required for Keras to work
from PIL import Image, ImageOps  # Install pillow instead of PIL
import numpy as np

#load the model
model = load_model('/content/Model.h5')
#classes
class_names = ['raw mango ','ripe mango','neutral mango']
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)

image = Image.open("/content/drive/MyDrive/Mangos/Mangos/train/Ripe/WhatsApp Image 2024-02-15 at 11.45.21 AM.jpeg").convert("RGB")

# resizing the image to be at least 224x224 and then cropping from the center
size = (224, 224)
image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)

# turn the image into a numpy array
image_array = np.asarray(image)

# Normalize the image
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1

# Load the image into the array
data[0] = normalized_image_array

# Predicts the model
prediction = model.predict(data)
index = np.argmax(prediction)
confidence_score = prediction[0][index]
# Print prediction and confidence score
print("Result: ", class_names[index], end="")
print("\n")
print("Accuracy: ", confidence_score)