Load the dataset and preprocess

image_classification.py

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+import os
+import pathlib
+import requests
+import zipfile
+from concurrent.futures import ThreadPoolExecutor
+from PIL import Image
+
+"""
+Load the rice image dataset and preprocessing 
+"""
+
+
+def download_dataset(url, filename):
+    response = requests.get(url)
+    if response == 200:
+        with open(filename, "wb") as f:
+            f.write(response.content)
+            return True
+    else:
+        return False
+
+
+def unzip_file(file_name, location):
+    with zipfile.ZipFile(file_name, location) as f:
+        f.extractall(location)
+
+
+def check_file_exists(directory_path) -> bool:
+    return os.path.exists(directory_path)
+
+
+def print_number_of_files(data_dir):
+    Arborio = list(data_dir.glob("Arborio/*"))
+    Basmati = list(data_dir.glob("Basmati/*"))
+    Ipsala = list(data_dir.glob("Ipsala/*"))
+    Jasmine = list(data_dir.glob("Jasmine/*"))
+    Karacadag = list(data_dir.glob("Karacadag/*"))
+
+    print("The length of Arborio: %d" % len(Arborio))
+    print("The length of Jasmine: %d" % len(Jasmine))
+    print("The length of Basmati: %d" % len(Basmati))
+    print("The length of Ipsala: %d" % len(Ipsala))
+    print("The length of Karacadag: %d" % len(Karacadag))
+
+
+def get_image_files(dir_path):
+    all_image_dirs = [f.path for f in os.scandir(dir_path) if f.is_dir()]
+    for dir in all_image_dirs:
+        for img in os.listdir(dir):
+            yield os.path.join(dir, img)
+
+
+def get_image_size(image_path) -> tuple:
+    image = Image.open(image_path)
+    return image.size
+
+
+def check_image_size(image_path, actual_width, actual_height) -> bool:
+    width, height = get_image_size(image_path)
+    if actual_width is width and actual_height is height:
+        print("checked")
+        return True
+    else:
+        return False
+
+
+def resize_image(image_path, target_size=(250, 250)):
+    image = Image.open(image_path)
+    resized_image = image.resize(target_size)
+    resized_image.save(image_path)
+    print(f'Resized image "{image_path}" to {target_size}.')
+
+
+def resize_if_required(data_dir, actual_width, actual_height):
+    with ThreadPoolExecutor(max_workers=8) as executor:
+        for img in get_image_files(data_dir):
+            result = executor.submit(check_image_size, img, actual_width, actual_height)
+            if not result:
+                resize_image(img)
+
+
+directory_path = "Rice_Image_Dataset"
+if not check_file_exists(directory_path):
+    if not download_dataset(link, directory_path):
+        print("Download failed")
+        exit()
+
+data_dir = pathlib.Path(directory_path).absolute()
+print_number_of_files(data_dir)
+
+first_image = next(get_image_files(data_dir))
+actual_width, actual_height = get_image_size(first_image)
+resize_if_required(data_dir, actual_width, actual_height)

requirements.txt

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+absl-py==2.1.0
+astunparse==1.6.3
+cachetools==5.3.3
+certifi==2024.2.2
+charset-normalizer==3.3.2
+contourpy==1.1.1
+cycler==0.12.1
+flatbuffers==23.5.26
+fonttools==4.49.0
+gast==0.4.0
+google-auth==2.28.1
+google-auth-oauthlib==1.0.0
+google-pasta==0.2.0
+grpcio==1.62.0
+h5py==3.10.0
+idna==3.6
+importlib-metadata==7.0.1
+importlib-resources==6.1.3
+keras==2.13.1
+kiwisolver==1.4.5
+libclang==16.0.6
+Markdown==3.5.2
+MarkupSafe==2.1.5
+matplotlib==3.7.5
+numpy==1.24.3
+oauthlib==3.2.2
+opencv-python==4.9.0.80
+opt-einsum==3.3.0
+packaging==23.2
+pillow==10.2.0
+protobuf==4.25.3
+pyasn1==0.5.1
+pyasn1-modules==0.3.0
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+requests==2.31.0
+requests-oauthlib==1.3.1
+rsa==4.9
+six==1.16.0
+tensorboard==2.13.0
+tensorboard-data-server==0.7.2
+tensorflow==2.13.1
+tensorflow-estimator==2.13.0
+tensorflow-io-gcs-filesystem==0.34.0
+termcolor==2.4.0
+typing-extensions==4.5.0
+urllib3==2.2.1
+werkzeug==3.0.1
+wrapt==1.16.0
+zipp==3.17.0