diff --git a/.gitignore b/.gitignore index f47c613..3d25d2a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,54 @@ -data -archive.zip .ipynb_checkpoints -__pycache__ \ No newline at end of file +data/ +*.zip +# https://github.com/microsoft/vscode-python/blob/main/.gitignore +.DS_Store +.huskyrc.json +out +log.log +**/node_modules +*.pyc +*.vsix +envVars.txt +**/.vscode/.ropeproject/** +**/testFiles/**/.cache/** +*.noseids +.nyc_output +.vscode-test +__pycache__ +npm-debug.log +**/.mypy_cache/** +!yarn.lock +coverage/ +cucumber-report.json +**/.vscode-test/** +**/.vscode test/** +**/.vscode-smoke/** +**/.venv*/ +port.txt +precommit.hook +python_files/lib/** +python_files/get-pip.py +debug_coverage*/** +languageServer/** +languageServer.*/** +bin/** +obj/** +.pytest_cache +tmp/** +.python-version +.vs/ +test-results*.xml +xunit-test-results.xml +build/ci/performance/performance-results.json +!build/ +debug*.log +debugpy*.log +pydevd*.log +nodeLanguageServer/** +nodeLanguageServer.*/** +dist/** +# translation files +*.xlf +package.nls.*.json +l10n/ diff --git a/DataManager.py b/DataManager.py deleted file mode 100644 index d837ec8..0000000 --- a/DataManager.py +++ /dev/null @@ -1,55 +0,0 @@ -import glob -import shutil -import cv2 -from zipfile import ZipFile -import os -import wget - -mainPath="data/" -pathToTrainAndValidDate = mainPath + "%s/**/*.*" -pathToTestDataset = mainPath + "/test" -originalDatasetName = "original dataset" - -class DataManager: - - def downloadData(self): - if not os.path.isfile("archive.zip"): - wget.download("https://storage.googleapis.com/kaggle-data-sets/78313/182633/bundle/archive.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20240502%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240502T181500Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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") - - def unzipData(self, fileName, pathToExtract): - if not os.path.exists(mainPath): - os.makedirs("data") - ZipFile(fileName).extractall(mainPath + pathToExtract) - shutil.move("data/original dataset/test/test", "data", copy_function = shutil.copytree) - shutil.move("data/original dataset/New Plant Diseases Dataset(Augmented)/New Plant Diseases Dataset(Augmented)/train", "data/original dataset/train", copy_function = shutil.copytree) - shutil.move("data/original dataset/New Plant Diseases Dataset(Augmented)/New Plant Diseases Dataset(Augmented)/valid", "data/original dataset/valid", copy_function = shutil.copytree) - shutil.rmtree("data/original dataset/New Plant Diseases Dataset(Augmented)") - shutil.rmtree("data/Detection-of-plant-diseases/data/original dataset/test") - - def writeImageToGivenPath(self, image, path): - os.makedirs(path.rsplit('/', 1)[0], exist_ok=True) - cv2.imwrite(path, image) - - def resizeDataset(self, soruceDatasetName, width, height): - if not os.path.exists(mainPath + "resized dataset"): - for file in glob.glob(pathToTrainAndValidDate % soruceDatasetName, recursive=True): - pathToFile = file.replace("\\","/") - image = cv2.imread(pathToFile) - image = cv2.resize(image, (width, height)) - newPath = pathToFile.replace(soruceDatasetName,"resized dataset") - self.writeImageToGivenPath(image,newPath) - - def sobelx(self, soruceDatasetName): - if not os.path.exists(mainPath + "sobel dataset"): - for file in glob.glob(pathToTrainAndValidDate % soruceDatasetName, recursive=True): - pathToFile = file.replace("\\","/") - image = cv2.imread(pathToFile) - sobel = cv2.Sobel(image,cv2.CV_64F,1,0,ksize=5) - newPath = pathToFile.replace(soruceDatasetName,"sobel dataset") - self.writeImageToGivenPath(sobel,newPath) - -dataManager = DataManager() -dataManager.downloadData() -dataManager.unzipData("archive.zip","original dataset") -dataManager.resizeDataset("original dataset", 64, 64) -dataManager.sobelx("resized dataset") \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..5734438 --- /dev/null +++ b/Makefile @@ -0,0 +1,7 @@ +.PHONY: download-dataset sobel-dataset + +download-dataset: + python3 ./file_manager/data_manager.py --download + +sobel-dataset: + python3 ./file_manager/data_manager.py --sobel \ No newline at end of file diff --git a/dataset/__init__.py b/dataset/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/dataset/consts.py b/dataset/consts.py new file mode 100644 index 0000000..5f71df7 --- /dev/null +++ b/dataset/consts.py @@ -0,0 +1,40 @@ +PLANT_CLASSES = [ + "Tomato", + "Potato", + "Corn_(maize)", + "Apple", + "Blueberry", + "Soybean", + "Cherry_(including_sour)", + "Squash", + "Strawberry", + "Pepper,_bell", + "Peach", + "Grape", + "Orange", + "Raspberry", +] + +DISEASE_CLASSES = [ + "healthy", + "Northern_Leaf_Blight", + "Tomato_mosaic_virus", + "Early_blight", + "Leaf_scorch", + "Tomato_Yellow_Leaf_Curl_Virus", + "Cedar_apple_rust", + "Late_blight", + "Spider_mites Two-spotted_spider_mite", + "Black_rot", + "Bacterial_spot", + "Apple_scab", + "Powdery_mildew", + "Esca_(Black_Measles)", + "Haunglongbing_(Citrus_greening)", + "Leaf_Mold", + "Common_rust_", + "Target_Spot", + "Leaf_blight_(Isariopsis_Leaf_Spot)", + "Septoria_leaf_spot", + "Cercospora_leaf_spot Gray_leaf_spot", +] \ No newline at end of file diff --git a/dataset/dataset.py b/dataset/dataset.py new file mode 100644 index 0000000..e190384 --- /dev/null +++ b/dataset/dataset.py @@ -0,0 +1,75 @@ +import os +from pathlib import Path + +import tensorflow as tf + +from .consts import DISEASE_CLASSES, PLANT_CLASSES + + +class Dataset: + ''' Class to load and preprocess the dataset. + Loads images and labels from the given directory to tf.data.Dataset. + + + Args: + `data_dir (Path)`: Path to the dataset directory. + `seed (int)`: Seed for shuffling the dataset. + `repeat (int)`: Number of times to repeat the dataset. + `shuffle_buffer_size (int)`: Size of the buffer for shuffling the dataset. + `batch_size (int)`: Batch size for the dataset. + ''' + + def __init__(self, + data_dir: Path, + seed: int = 42, + repeat: int = 1, + shuffle_buffer_size: int = 10_000, + batch_size: int = 64) -> None: + self.data_dir = data_dir + self.seed = seed + self.repeat = repeat + self.shuffle_buffer_size = shuffle_buffer_size + self.batch_size = batch_size + + self.dataset = self.__load_dataset()\ + .shuffle(self.shuffle_buffer_size, seed=self.seed)\ + .repeat(self.repeat)\ + .batch(self.batch_size, drop_remainder=True)\ + .prefetch(tf.data.experimental.AUTOTUNE) + + def __load_dataset(self) -> tf.data.Dataset: + # check if path has 'test' word in it + dataset = tf.data.Dataset.list_files(str(self.data_dir / '*/*')) + if 'test' in str(self.data_dir).lower(): + # file names issue - labels have camel case (regex?) and differs from the train/valid sets + pass + else: + dataset = dataset.map( + self.__preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE) + + return dataset + + def __get_labels(self, image_path): + path = tf.strings.split(image_path, os.path.sep)[-2] + plant = tf.strings.split(path, '___')[0] + disease = tf.strings.split(path, '___')[1] + + one_hot_plant = plant == PLANT_CLASSES + one_hot_disease = disease == DISEASE_CLASSES + + return tf.cast(one_hot_plant, dtype=tf.uint8, name=None), tf.cast(one_hot_disease, dtype=tf.uint8, name=None) + + def __get_image(self, image_path): + img = tf.io.read_file(image_path) + img = tf.io.decode_jpeg(img, channels=3) + return tf.cast(img, dtype=tf.float32, name=None) / 255. + + def __preprocess(self, image_path): + labels = self.__get_labels(image_path) + image = self.__get_image(image_path) + + # returns X, Y1, Y2 + return image, labels[0], labels[1] + + def __getattr__(self, attr): + return getattr(self.dataset, attr) diff --git a/file_manager/__init__.py b/file_manager/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/file_manager/data_manager.py b/file_manager/data_manager.py new file mode 100644 index 0000000..9136161 --- /dev/null +++ b/file_manager/data_manager.py @@ -0,0 +1,83 @@ +import glob +import shutil +import cv2 +from zipfile import ZipFile +import os +import wget +import argparse +from pathlib import Path + +main_path = Path("data/") +path_to_train_and_valid = main_path / "%s/**/*.*" +path_to_test_dataset = main_path / "test" +original_dataset_name = "original_dataset" + +parser = argparse.ArgumentParser() +parser.add_argument("--download", action="store_true", + help="Download the data") +parser.add_argument("--sobel", action="store_true", + help="Apply Sobel filter to the dataset") + +args = parser.parse_args() + + +class DataManager: + + def download_data(self): + if not os.path.isfile("archive.zip"): + wget.download("https://storage.googleapis.com/kaggle-data-sets/78313/182633/bundle/archive.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20240502%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240502T181500Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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") + + def unzip_data(self, file_name, path_to_extract): + full_path_to_extract = main_path / path_to_extract + old_path = "New Plant Diseases Dataset(Augmented)/New Plant Diseases Dataset(Augmented)" + if not os.path.exists(main_path): + os.makedirs(main_path) + ZipFile(file_name).extractall(full_path_to_extract) + # shutil.move("data/test/test", + # full_path_to_extract, copy_function=shutil.copytree) + shutil.move(full_path_to_extract / old_path / "train", + full_path_to_extract / "train", copy_function=shutil.copytree) + shutil.move(full_path_to_extract / old_path / "valid", + full_path_to_extract / "valid", copy_function=shutil.copytree) + shutil.rmtree( + full_path_to_extract / "New Plant Diseases Dataset(Augmented)" + ) + shutil.rmtree( + full_path_to_extract / "new plant diseases dataset(augmented)" + ) + + def write_image(self, image, path): + os.makedirs(path.rsplit('/', 1)[0], exist_ok=True) + cv2.imwrite(path, image) + + def resize_dataset(self, source_dataset_name, width, height): + dataset_name = "resized_dataset" + if not os.path.exists(main_path / dataset_name): + for file in glob.glob(str(path_to_train_and_valid) % source_dataset_name, recursive=True): + path_to_file = file.replace("\\", "/") + image = cv2.imread(path_to_file) + image = cv2.resize(image, (width, height)) + new_path = path_to_file.replace( + source_dataset_name, dataset_name) + self.write_image(image, new_path) + + def sobelx(self, source_dataset_name): + dataset_name = "sobel_dataset" + if not os.path.exists(main_path / dataset_name): + for file in glob.glob(str(path_to_train_and_valid) % source_dataset_name, recursive=True): + path_to_file = file.replace("\\", "/") + image = cv2.imread(path_to_file) + sobel = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=5) + new_path = path_to_file.replace( + source_dataset_name, dataset_name) + self.write_image(sobel, new_path) + + +if __name__ == "__main__": + data_manager = DataManager() + if args.download: + data_manager.download_data() + data_manager.unzip_data("archive.zip", original_dataset_name) + data_manager.resize_dataset(original_dataset_name, 64, 64) + if args.sobel: + data_manager.sobelx("resized_dataset") diff --git a/file_manager/shard_files.py b/file_manager/shard_files.py new file mode 100644 index 0000000..d4236f7 --- /dev/null +++ b/file_manager/shard_files.py @@ -0,0 +1,19 @@ +from pathlib import Path + +# TODO: split the files into smaller dirs and make list of them +class FileSharder: + def __init__(self, + train_dir: Path = Path('./data/resized_dataset/train'), + valid_dir: Path = Path('./data/resized_dataset/valid'), + test_dir: Path = Path('./data/resized_dataset/test'), + shard_size = 5_000) -> None: + self.shard_size = shard_size + + self.train_dir = train_dir + self.valid_dir = valid_dir + self.test_dir = test_dir + + self.shard() + + def shard(self): + pass diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..cc6fe18 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +tensorflow==2.16.1 +numpy==1.26.4 +opencv-python==4.9.0.80 +wget==3.2 diff --git a/test.py b/test.py new file mode 100644 index 0000000..a75f18f --- /dev/null +++ b/test.py @@ -0,0 +1,10 @@ + +from pathlib import Path + +from dataset.dataset import Dataset + +train_dataset = Dataset(Path('data/resized_dataset/train')) +valid_dataset = Dataset(Path('data/resized_dataset/valid')) + +for i in train_dataset.take(1): + print(i)