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Merge branch 'main' into feature/basic-model-setup

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s495727 2024-05-05 19:46:13 +02:00
commit 81dc0f8771
11 changed files with 291 additions and 58 deletions
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54
.gitignore vendored
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data
archive.zip
.ipynb_checkpoints .ipynb_checkpoints
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__ __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/

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DataManager.py
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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")

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Makefile Normal file
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.PHONY: download-dataset sobel-dataset
download-dataset:
python3 ./file_manager/data_manager.py --download
sobel-dataset:
python3 ./file_manager/data_manager.py --sobel

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dataset/__init__.py Normal file
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dataset/consts.py Normal file
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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",
]

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dataset/dataset.py Normal file
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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)

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file_manager/__init__.py Normal file
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file_manager/data_manager.py Normal file
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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")

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file_manager/shard_files.py Normal file
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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

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requirements.txt Normal file
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tensorflow==2.16.1
numpy==1.26.4
opencv-python==4.9.0.80
wget==3.2

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test.py Normal file
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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)