import matplotlib.pyplot as plt
import numpy as np
import cv2
import os
import PIL
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
dataset_url = “https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz”
data_dir = tf.keras.utils.get_file(‘flower_photos’, origin=dataset_url, cache_dir=‘.’, untar = True)
import pathlib
data_dir = pathlib.Path(data_dir)
data_dir
flowers_images_dict = {
‘roses’: list(data_dir.glob(r’roses/')),
‘daisy’: list(data_dir.glob(r’daisy/‘)),
‘dandelion’: list(data_dir.glob(r’dandelion/')),
‘sunflowers’: list(data_dir.glob(r’sunflowers/’)),
‘tulips’: list(data_dir.glob(r’tulips/*')),
}
flowers_labels_dict = {
‘roses’: 0,
‘daisy’: 1,
‘dandelion’: 2,
‘sunflowers’: 3,
‘tulips’: 4,
}
X, y = ,
for flower_name, images in flowers_images_dict.items():
for image in images:
img = cv2.imread(str(image))
resized_img = cv2.resize(img,(180,180))
X.append(resized_img)
y.append(flowers_labels_dict[flower_name])
