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image_augmentor.py
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import os
import random
import numpy as np
from PIL import Image
from keras.preprocessing.image import ImageDataGenerator
from skimage import io
from PyQt5.QtWidgets import (
QApplication,
QMainWindow,
QLabel,
QLineEdit,
QPushButton,
QCheckBox,
QVBoxLayout,
QWidget,
QFileDialog,
QMessageBox,
QHBoxLayout,
QComboBox,
QGridLayout
)
from PyQt5.QtGui import QFont, QIcon, QPalette, QColor
from PyQt5.QtCore import Qt
from imgaug import augmenters as iaa
import albumentations as A
import torchvision.transforms as transforms
import torch
import torchvision
import xml.etree.ElementTree as ET
import openpyxl
class ImageAugmentor(QMainWindow):
@staticmethod
def add_noise(img):
"""Add random noise to an image"""
VARIABILITY = 50
deviation = VARIABILITY * random.random()
noise = np.random.normal(0, deviation, img.shape)
img = np.clip(img + noise, 0, 255).astype(np.uint8)
return img
def add_noise_with_imgaug(self, img):
seq = iaa.SaltAndPepper(0.05)
noisy_img = seq.augment_image(img)
return noisy_img
def add_noise_with_albumentations(self, img):
transform = A.Compose([A.OneOf([
A.GaussNoise(p=1),
A.MultiplicativeNoise(p=1),
], p=1)])
noisy_img = transform(image=img)["image"]
return noisy_img
def add_noise_with_torchvision(self, img):
transform = torchvision.transforms.ToTensor()
img_tensor = transform(img)
noise_transform = torchvision.transforms.Compose([
torchvision.transforms.ToPILImage(),
torchvision.transforms.ToTensor(),
])
noise = torch.randn_like(img_tensor)
noisy_img = img_tensor + noise
noisy_img = noise_transform(noisy_img)
return noisy_img
def generate_annotation_file(self):
try:
image_directory = self.image_directory_edit.text()
annotations_directory = self.save_directory_edit.text()
for filename in os.listdir(image_directory):
image_path = os.path.join(image_directory, filename)
if os.path.isfile(image_path):
annotation_path = os.path.join(annotations_directory, f"{os.path.splitext(filename)[0]}.xml")
self.generate_voc_annotation(image_path, annotation_path)
QMessageBox.information(self,"Success", "Annotation files generated successfully.")
except Exception as e:
QMessageBox.warning(self,"Error", str(e))
def generate_voc_annotation(self, image_path, annotation_path):
try:
# Extract image dimensions
width, height = self.get_image_dimensions(image_path)
# Define the bounding box coordinates
xmin, ymin, xmax, ymax = 100, 100, 200, 200 # Example values
# Create the XML annotation tree
root = ET.Element("annotation")
folder = ET.SubElement(root, "folder")
folder.text = os.path.dirname(image_path)
filename = ET.SubElement(root, "filename")
filename.text = os.path.basename(image_path)
size = ET.SubElement(root, "size")
width_elem = ET.SubElement(size, "width")
width_elem.text = str(width)
height_elem = ET.SubElement(size, "height")
height_elem.text = str(height)
depth = ET.SubElement(size, "depth")
depth.text = "3" # Assuming RGB images
object_elem = ET.SubElement(root, "object")
name = ET.SubElement(object_elem, "name")
name.text = "object_class" # Replace with the actual class label
bndbox = ET.SubElement(object_elem, "bndbox")
xmin_elem = ET.SubElement(bndbox, "xmin")
xmin_elem.text = str(xmin)
ymin_elem = ET.SubElement(bndbox, "ymin")
ymin_elem.text = str(ymin)
xmax_elem = ET.SubElement(bndbox, "xmax")
xmax_elem.text = str(xmax)
ymax_elem = ET.SubElement(bndbox, "ymax")
ymax_elem.text = str(ymax)
# Create the annotation file
tree = ET.ElementTree(root)
tree.write(annotation_path)
except Exception as e:
raise Exception(self,"Error generating VOC annotation: " + str(e))
def get_image_dimensions(self, image_path):
try:
# Implement a function to get the image dimensions (width, height)
from PIL import Image
image = Image.open(image_path)
width, height = image.size
return width, height
except Exception as e:
raise Exception("Error getting image dimensions: " + str(e))
def generate_labeling_file(self):
try:
image_directory = self.image_directory_edit.text()
labeling_file_directory = self.save_directory_edit.text()
# Create the labeling file path
labeling_file_path = os.path.join(labeling_file_directory, "labeling_file.xlsx")
# Get the list of image files in the directory
image_files = os.listdir(image_directory)
# Create a new workbook
workbook = openpyxl.Workbook()
# Get the active sheet
sheet = workbook.active
# Write the header row
sheet['A1'] = 'Image File'
sheet['B1'] = 'Label'
# Iterate over each image file
for row, file in enumerate(image_files, start=2):
# Extract the label from the filename or provide a default label
label = self.extract_label_from_filename(file)
# Write the image file and label in the corresponding cells
sheet.cell(row=row, column=1).value = file
sheet.cell(row=row, column=2).value = label
# Save the workbook
workbook.save(labeling_file_path)
QMessageBox.information(self,"Success", "Labeling file generated successfully.")
except Exception as e:
QMessageBox.warning(self,"Error", str(e))
# Function to extract the label from the filename
def extract_label_from_filename(self, filename):
# For example, if the filename is "cat001.jpg", you can extract the label as "cat"
label = filename.split('.')[0] # Assumes the label is before the first dot
return label
def __init__(self):
super().__init__()
self.setWindowTitle("Image Augmentation")
self.setWindowIcon(QIcon("E:\Final_Project\puppy.jpg"))
self.setGeometry(100, 100, 800, 600)
# Central Widget
self.central_widget = QWidget(self)
self.setCentralWidget(self.central_widget)
self.layout = QVBoxLayout(self.central_widget)
self.layout.setAlignment(Qt.AlignTop)
self.layout.setContentsMargins(30, 30, 30, 30)
# Title Label
self.title_label = QLabel("Image Augmentation", self.central_widget)
self.title_label.setStyleSheet(
"font-size: 48px; font-weight: bold; color: #333333; margin-bottom: 30px;"
)
self.layout.addWidget(self.title_label, alignment=Qt.AlignCenter)
# Image and Save Directory Section
directory_layout = QHBoxLayout()
self.image_directory_label = QLabel("Image Directory:", self.central_widget)
self.save_directory_label = QLabel("Save Directory:", self.central_widget)
directory_layout.addWidget(self.image_directory_label)
directory_layout.addWidget(self.save_directory_label)
self.layout.addLayout(directory_layout)
directory_input_layout = QHBoxLayout()
self.image_directory_edit = QLineEdit(self.central_widget)
self.image_directory_edit.setReadOnly(True)
self.save_directory_edit = QLineEdit(self.central_widget)
self.save_directory_edit.setReadOnly(True)
directory_input_layout.addWidget(self.image_directory_edit)
directory_input_layout.addWidget(self.save_directory_edit)
self.layout.addLayout(directory_input_layout)
browse_layout = QHBoxLayout()
self.select_image_directory_button = QPushButton("Browse", self.central_widget)
self.select_image_directory_button.setObjectName("browse_button")
self.select_image_directory_button.clicked.connect(self.select_image_directory)
self.select_save_directory_button = QPushButton("Browse", self.central_widget)
self.select_save_directory_button.setObjectName("browse_button")
self.select_save_directory_button.clicked.connect(self.select_save_directory)
self.select_save_directory_button.setStyleSheet(
"""
QPushButton {
font-size: 16px;
font-weight: bold;
color: #ffffff;
background-color: #00a8e8;
border: none;
padding: 10px 20px;
border-radius: 5px;
}
QPushButton:hover {
background-color: #0091c9;
}
"""
)
self.select_image_directory_button.setStyleSheet(
"""
QPushButton {
font-size: 16px;
font-weight: bold;
color: #ffffff;
background-color: #00a8e8;
border: none;
padding: 10px 20px;
border-radius: 5px;
}
QPushButton:hover {
background-color: #0091c9;
}
"""
)
browse_layout.addWidget(self.select_image_directory_button)
browse_layout.addWidget(self.select_save_directory_button)
self.layout.addLayout(browse_layout)
# Augmentation Section
self.augmentation_label = QLabel("Select the augmentation library:", self.central_widget)
self.augmentation_combo_box = QComboBox(self.central_widget)
self.augmentation_combo_box.addItem("ImageDataGenerator (Keras)")
self.augmentation_combo_box.addItem("imgaug")
self.augmentation_combo_box.addItem("albumentations")
self.augmentation_combo_box.addItem("torchvision")
self.layout.addWidget(self.augmentation_label)
self.layout.addWidget(self.augmentation_combo_box)
# Apply Augmentation Button
self.apply_button = QPushButton("Apply Augmentation", self.central_widget)
self.apply_button.setObjectName("apply_button")
self.apply_button.clicked.connect(self.apply_augmentation)
self.layout.addWidget(self.apply_button, alignment=Qt.AlignCenter)
self.apply_button.setStyleSheet(
"""
QPushButton {
font-size: 20px;
font-weight: bold;
color: #ffffff;
background-color: #00a8e8;
border: none;
padding: 15px 40px;
border-radius: 5px;
}
QPushButton:hover {
background-color: #0091c9;
}
"""
)
# Augmentation Checkboxes
self.augmentation_checkboxes_layout = QGridLayout()
self.layout.addLayout(self.augmentation_checkboxes_layout)
self.augmentation_checkboxes = []
self.add_augmentation_checkbox("Rotate (7)", 0, 0)
self.add_augmentation_checkbox("Shift (4)", 0, 1)
self.add_augmentation_checkbox("Shear (3)", 0, 2)
self.add_augmentation_checkbox("Zoom (4)", 1, 0)
self.add_augmentation_checkbox("Flip (4)", 1, 1)
self.add_augmentation_checkbox("Change Brightness (5)", 1, 2)
self.add_augmentation_checkbox("Add Noise (5)", 2, 0)
self.add_augmentation_checkbox("Simulate cloud effect (5)", 2, 1)
self.add_augmentation_checkbox("Simulate fog effect (5)", 2, 2)
self.add_augmentation_checkbox("Simulate rain effect (5)", 3, 0)
self.add_augmentation_checkbox("Simulate snow effect (5)", 3, 1)
self.add_augmentation_checkbox("Simulate sun flare effect (5)", 3, 2)
# Select All Checkbox
self.select_all_checkbox = QCheckBox("Select All", self.central_widget)
self.select_all_checkbox.stateChanged.connect(self.toggle_all)
self.layout.addWidget(self.select_all_checkbox)
# Generate Annotation and Labeling Buttons
button_layout = QHBoxLayout()
self.generate_annotation_button = QPushButton("Generate Annotation File", self.central_widget)
self.generate_annotation_button.setObjectName("action_button")
self.generate_annotation_button.clicked.connect(self.generate_annotation_file)
self.generate_annotation_button.setStyleSheet(
"""
QPushButton {
font-size: 16px;
font-weight: bold;
color: #ffffff;
background-color: #00a8e8;
border: none;
padding: 10px 20px;
border-radius: 5px;
}
QPushButton:hover {
background-color: #0091c9;
}
"""
)
self.generate_labeling_button = QPushButton("Generate Labeling File", self.central_widget)
self.generate_labeling_button.setObjectName("action_button")
self.generate_labeling_button.clicked.connect(self.generate_labeling_file)
self.generate_labeling_button.setStyleSheet(
"""
QPushButton {
font-size: 16px;
font-weight: bold;
color: #ffffff;
background-color: #00a8e8;
border: none;
padding: 10px 20px;
border-radius: 5px;
}
QPushButton:hover {
background-color: #0091c9;
}
"""
)
button_layout.addWidget(self.generate_annotation_button)
button_layout.addWidget(self.generate_labeling_button)
self.layout.addLayout(button_layout)
# Apply the custom palette to the window
self.set_palette()
def add_augmentation_checkbox(self, label, row, col):
checkbox = QCheckBox(label, self.central_widget)
self.augmentation_checkboxes_layout.addWidget(checkbox, row, col)
self.augmentation_checkboxes.append(checkbox)
def toggle_all(self, state):
for checkbox in self.augmentation_checkboxes:
checkbox.setChecked(state == Qt.Checked)
def set_palette(self):
# Define a custom color palette
palette = QPalette()
palette.setColor(QPalette.Background, QColor("#f9f9f9"))
palette.setColor(QPalette.Base, QColor("#ffffff"))
palette.setColor(QPalette.Text, QColor("#333333"))
# Set the palette to the application
self.setPalette(palette)
def select_image_directory(self):
image_directory = QFileDialog.getExistingDirectory(self, "Select Image Directory")
self.image_directory_edit.setText(image_directory)
def select_save_directory(self):
save_directory = QFileDialog.getExistingDirectory(self, "Select Save Directory")
self.save_directory_edit.setText(save_directory)
def apply_augmentation(self):
try:
augmentation_folders = {
"Rotate (7)": "Rotated_Images",
"Shift (4)": "Shifted_Images",
"Shear (3)": "Sheared_Images",
"Zoom (4)": "Zoomed_Images",
"Flip (4)": "Flipped_Images",
"Change Brightness (5)": "Brightness_Images",
"Add Noise (5)": "Noisy_Images",
"Simulate cloud effect (5)": "Cloudy_Images",
"Simulate fog effect (5)": "fog _Images",
"Simulate rain effect (5)": "rainy_Images",
"Simulate snow effect (5)": "snowy_Images",
"Simulate sun flare effect (5)": "sun_flare_Images",
}
image_directory = self.image_directory_edit.text()
save_directory = self.save_directory_edit.text()
augmentation_library = self.augmentation_combo_box.currentText()
for checkbox in self.augmentation_checkboxes:
if checkbox.isChecked():
text = checkbox.text()
augmentation_folder = augmentation_folders.get(text, "Other")
# Create the folder for the augmentation method
augmentation_path = os.path.join(save_directory, augmentation_folder)
os.makedirs(augmentation_path, exist_ok=True)
if text.startswith("Rotate"):
if augmentation_library == "imgaug":
self.rotate_images_with_imgaug(image_directory, num_images=7, augmentation_path = augmentation_path)
elif augmentation_library == "albumentations":
self.rotate_images_with_albumentations(image_directory, num_images=7,augmentation_path = augmentation_path)
elif augmentation_library == "torchvision":
self.rotate_images_with_torchvision(image_directory, num_images=7,augmentation_path = augmentation_path)
else:
self.rotate_images(image_directory, save_directory, num_images=7)
elif text.startswith("Shift"):
if augmentation_library == "imgaug":
self.shift_images_with_imagau(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "albumentations":
self.shift_images_with_albumentations(image_directory,num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "torchvision":
self.shift_images_with_torchvision(image_directory, num_images=4, augmentation_path = augmentation_path)
else:
self.shift_images(image_directory, save_directory, num_images=4, )
elif text.startswith("Shear"):
if augmentation_library == "imgaug":
self.shear_images_with_imagau(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "albumentations":
self.shear_images_with_albumentations(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "torchvision":
self.shear_images_with_torchvision(image_directory, num_images=4, augmentation_path = augmentation_path)
else:
self.shear_images(image_directory, save_directory, num_images=3)
elif text.startswith("Zoom"):
if augmentation_library == "imgaug":
self.zoom_images_with_imagau(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "albumentations":
self.zoom_images_with_albumentations(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "torchvision":
self.zoom_images_with_torchvision(image_directory, num_images=4, augmentation_path = augmentation_path)
else:
self.zoom_images(image_directory, save_directory, num_images=4)
elif text.startswith("Flip"):
if augmentation_library == "imgaug":
self.flip_images_with_imagau(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "albumentations":
self.flip_images_with_albumentations(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "torchvision":
self.flip_images_with_torchvision(image_directory, num_images=4, augmentation_path = augmentation_path)
else:
self.flip_images(image_directory, save_directory, num_images=4)
elif text.startswith("Change Brightness"):
if augmentation_library == "imgaug":
self.change_brightness_images_with_imagau(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "albumentations":
self.change_brightness_images_with_albumentations(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "torchvision":
self.change_brightness_images_with_torchvision(image_directory, num_images=4, augmentation_path = augmentation_path)
else:
self.change_brightness(image_directory, save_directory, num_images=5)
elif text.startswith("Add Noise"):
if augmentation_library == "imgaug":
self.add_salt_pepper_noise_with_imagau(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "albumentations":
self.add_salt_pepper_noise_with_albumentations(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "torchvision":
self.add_salt_pepper_noise_images_with_torchvision()
else:
self.add_salt_pepper_noise(image_directory, num_images=5, augmentation_path = augmentation_path)
elif text.startswith("Simulate cloud effect"):
if augmentation_library == "imgaug":
self.add_cloud_effect_with_imagau(image_directory, num_images=5, augmentation_path = augmentation_path)
elif augmentation_library == "albumentations":
self.add_cloud_effect_with_albumentations()
elif augmentation_library == "torchvision":
self.add_cloud_effect_with_torchvision()
else:
self.add_cloud_effect()
elif text.startswith("Simulate fog effect"):
if augmentation_library == "imgaug":
self.add_fog_effect_with_imagau(image_directory, num_images=5, augmentation_path = augmentation_path)
elif augmentation_library == "albumentations":
self.add_fog_effect_with_albumentations(image_directory, num_images=5, augmentation_path = augmentation_path)
elif augmentation_library == "torchvision":
self.add_fog_effect_with_torchvision()
else:
self.add_fog_effect()
elif text.startswith("Simulate rain effect"):
if augmentation_library == "imgaug":
self.add_rain_effect_with_imagau(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "albumentations":
self.add_rain_effect_with_albumentations(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "torchvision":
self.add_rain_effect_with_torchvision()
else:
self.add_rain_effect()
elif text.startswith("Simulate snow effect"):
if augmentation_library == "imgaug":
self.add_snow_effect_with_imagau(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "albumentations":
self.add_snow_effect_with_albumentations(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "torchvision":
self.add_snow_effect_with_torchvision()
else:
self.add_snow_effect()
elif text.startswith("Simulate sun flare effect"):
if augmentation_library == "imgaug":
self.add_sun_flare_effect_with_imagau(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "albumentations":
self.add_sun_flare_effect_with_albumentations(image_directory, num_images=4, augmentation_path = augmentation_path)
elif augmentation_library == "torchvision":
self.add_sun_flare_effect_with_with_torchvision()
else:
self.add_sun_flare_effect()
except Exception as e:
QMessageBox.warning(self,"Error", str(e))
def rotate_images(self, image_directory, save_directory, num_images):
datagen = ImageDataGenerator(rotation_range=180)
self.augment_images(datagen, image_directory, save_directory, "Rotated_img", num_images)
def rotate_images_with_imgaug(self, image_directory, num_images, augmentation_path):
# Create the augmentation sequence
seq = iaa.Affine(rotate=(-180, 180))
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
img = Image.open(img_path)
img_arr = np.array(img)
# Apply augmentation multiple times
for i in range(num_images):
augmented_img_arr = seq.augment_image(img_arr)
augmented_img = Image.fromarray(augmented_img_arr)
# Save the augmented image
#save_path = os.path.join(save_directory, f"Rotated_{i + 1}_{file}")
#augmented_img.save(save_path)
new_file = f"Rotated_{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Image rotation augmentation completed!")
def rotate_images_with_albumentations(self, image_directory, num_images, augmentation_path):
# Create the augmentation transform
transform = A.Rotate(limit=(-180, 180), p=1.0)
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
img = Image.open(img_path)
# Apply augmentation multiple times
for i in range(num_images):
augmented = transform(image=np.array(img))
augmented_img = Image.fromarray(augmented["image"])
# Save the augmented image
#save_path = os.path.join(save_directory, f"Rotated_{i + 1}_{file}")
#augmented_img.save(save_path)
new_file = f"Rotated_{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Image rotation augmentation completed!")
def rotate_images_with_torchvision(self, image_directory, num_images, augmentation_path):
# Create the rotation transform
transform = transforms.RandomRotation(degrees=(-180, 180))
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
if os.path.isfile(img_path):
# Load the image
img = Image.open(img_path)
# Apply augmentation to generate new images
augmented_images = [transform(img) for _ in range(num_images)]
# Save the augmented images
for i, augmented_img in enumerate(augmented_images):
new_file = f"Rotated_{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Image rotation augmentation completed!")
#Shift .....................................
def shift_images(self, image_directory, save_directory, num_images):
datagen = ImageDataGenerator(width_shift_range=0.2, height_shift_range=0.2, fill_mode="reflect")
self.augment_images(datagen, image_directory, save_directory, "Shifted_img", num_images)
def shift_images_with_imagau(self, image_directory, num_images, augmentation_path):
shift_range = (-120, 120)
seq = iaa.Sequential([
iaa.Affine(translate_px={"x": shift_range, "y": shift_range}, mode= 'reflect')
])
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
img = Image.open(img_path)
img_arr = np.array(img)
# Apply augmentation multiple times
for i in range(num_images):
augmented_img_arr = seq.augment_image(img_arr)
augmented_img = Image.fromarray(augmented_img_arr)
# Save the augmented image
#save_path = os.path.join(save_directory, f"Rotated_{i + 1}_{file}")
#augmented_img.save(save_path)
new_file = f"Shifted{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Shift augmentation completed!")
def shift_images_with_albumentations(self, image_directory, num_images, augmentation_path):
# Create the augmentation transform
transform = A.ShiftScaleRotate(shift_limit=0.2, p=1.0)
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
img = Image.open(img_path)
img_arr = np.array(img)
# Apply augmentation multiple times
for i in range(num_images):
augmented = transform(image=img_arr)
augmented_img_arr = augmented["image"]
augmented_img = Image.fromarray(augmented_img_arr)
# Save the augmented image
new_file = f"Shifted_{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Shift augmentation completed!")
def shift_images_with_torchvision(self, image_directory, num_images, augmentation_path):
# Create the augmentation transform
transform = transforms.RandomAffine(degrees=0, translate=(0.2, 0.2), scale=None, shear=None)
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
if os.path.isfile(img_path):
# Load the image
img = Image.open(img_path)
# Apply augmentation to generate new images
augmented_images = [transform(img) for _ in range(num_images)]
# Save the augmented images
for i, augmented_img in enumerate(augmented_images):
new_file = f"Shifted_{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Shift augmentation completed!")
# Shear ................................................
def shear_images(self, image_directory, save_directory, num_images):
datagen = ImageDataGenerator(shear_range=45, fill_mode="reflect")
self.augment_images(datagen, image_directory, save_directory, "Sheared_img", num_images)
def shear_images_with_imagau(self, image_directory, num_images, augmentation_path):
seq = iaa.Sequential([
iaa.ShearX((-20, 20)), # Apply shear transformation along the X-axis
iaa.ShearY((-20, 20)) # Apply shear transformation along the Y-axis
])
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
img = Image.open(img_path)
img_arr = np.array(img)
# Apply augmentation multiple times
for i in range(num_images):
augmented_img_arr = seq.augment_image(img_arr)
augmented_img = Image.fromarray(augmented_img_arr)
# Save the augmented image
new_file = f"Sheared_{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Shear augmentation completed!")
def shear_images_with_albumentations(self, image_directory, num_images, augmentation_path):
# Create the augmentation transform
shear_angle = 20
transform = A.ElasticTransform(p=1.0, alpha=shear_angle, sigma=10)
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
img = Image.open(img_path)
img_arr = np.array(img)
# Apply augmentation multiple times
for i in range(num_images):
augmented = transform(image=img_arr)
augmented_img_arr = augmented["image"]
augmented_img = Image.fromarray(augmented_img_arr)
# Save the augmented image
new_file = f"Sheared_{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Shear augmentation completed!")
def shear_images_with_torchvision(self, image_directory, num_images, augmentation_path):
# Create the augmentation transform
shear_angle = 20
transform = transforms.RandomAffine(0, shear=(0, shear_angle))
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
if os.path.isfile(img_path):
# Load the image
img = Image.open(img_path)
# Apply augmentation to generate new images
augmented_images = [transform(img) for _ in range(num_images)]
# Save the augmented images
for i, augmented_img in enumerate(augmented_images):
new_file = f"Sheared_{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Shear augmentation completed!")
#Zoom .......................................
def zoom_images(self, image_directory, save_directory, num_images):
datagen = ImageDataGenerator(zoom_range=0.5, fill_mode="reflect")
self.augment_images(datagen, image_directory, save_directory, "Zoomed_img", num_images)
def zoom_images_with_imagau(self, image_directory, num_images, augmentation_path):
seq = iaa.Affine(scale=(0.5, 1.5))
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
img = Image.open(img_path)
img_arr = np.array(img)
# Apply augmentation multiple times
for i in range(num_images):
augmented_img_arr = seq.augment_image(img_arr)
augmented_img = Image.fromarray(augmented_img_arr)
# Save the augmented image
new_file = f"Zoomed_{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Zoom augmentation completed!")
def zoom_images_with_albumentations(self, image_directory, num_images, augmentation_path):
# Create the augmentation transform
transform = A.RandomResizedCrop(height=512, width=512)
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
img = Image.open(img_path)
img_arr = np.array(img)
# Apply augmentation multiple times
for i in range(num_images):
augmented = transform(image=img_arr)
augmented_img_arr = augmented["image"]
augmented_img = Image.fromarray(augmented_img_arr)
# Save the augmented image
new_file = f"Zoomed_{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Zoom augmentation completed!")
def zoom_images_with_torchvision(self, image_directory, num_images, augmentation_path):
# Create the augmentation transform
transform = transforms.RandomResizedCrop(size=(224, 224))
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
if os.path.isfile(img_path):
# Load the image
img = Image.open(img_path)
# Apply augmentation to generate new images
augmented_images = [transform(img) for _ in range(num_images)]
# Save the augmented images
for i, augmented_img in enumerate(augmented_images):
new_file = f"Zoomed_{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Zoom augmentation completed!")
#Flip...................................................
def flip_images(self, image_directory, save_directory, num_images):
datagen = ImageDataGenerator(horizontal_flip=True, vertical_flip=True, fill_mode="reflect")
self.augment_images(datagen, image_directory, save_directory, "Flipped_img", num_images)
def flip_images_with_imagau(self, image_directory, num_images, augmentation_path):
seq = iaa.Sequential([
iaa.Fliplr(0.5), # Apply horizontal flip with 50% probability
iaa.Flipud(0.5) # Apply vertical flip with 50% probability
])
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
img = Image.open(img_path)
img_arr = np.array(img)
# Apply augmentation multiple times
for i in range(num_images):
augmented_img_arr = seq.augment_image(img_arr)
augmented_img = Image.fromarray(augmented_img_arr)
# Save the augmented image
new_file = f"flipped_Img{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Flip augmentation completed!")
def flip_images_with_albumentations(self, image_directory, num_images, augmentation_path):
# Create the augmentation transform
transform = A.Compose([
A.Flip(p=1.0), # Flip the image horizontally or vertically with a probability of 1.0
])
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
img = Image.open(img_path)
img_arr = np.array(img)
# Apply augmentation multiple times
for i in range(num_images):
augmented = transform(image=img_arr)
augmented_img_arr = augmented["image"]
augmented_img = Image.fromarray(augmented_img_arr)
# Save the augmented image
new_file = f"flipped_Img{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Flip augmentation completed!")
def flip_images_with_torchvision(self, image_directory, num_images, augmentation_path):
# Create the augmentation transform
flip_transform = transforms.RandomChoice([
transforms.RandomHorizontalFlip(p=1.0),
transforms.RandomVerticalFlip(p=1.0)
])
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
if os.path.isfile(img_path):
# Load the image
img = Image.open(img_path)
# Apply augmentation to generate new images
augmented_images = [flip_transform(img) for _ in range(num_images)]
# Save the augmented images
for i, augmented_img in enumerate(augmented_images):
new_file = f"flipped_Img{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Flip augmentation completed!")
#Change Brightness
def change_brightness(self, image_directory, save_directory, num_images):
datagen = ImageDataGenerator(brightness_range=[0.2, 2.1])
self.augment_images(datagen, image_directory, save_directory, "Brightness_img", num_images)
def change_brightness_images_with_imagau(self, image_directory, num_images, augmentation_path):
seq = iaa.Multiply((0.4, 1.8))
# Iterate over each image in the directory
image_files = os.listdir(image_directory)
for file in image_files:
img_path = os.path.join(image_directory, file)
img = Image.open(img_path)
img_arr = np.array(img)
# Apply augmentation multiple times
for i in range(num_images):
augmented_img_arr = seq.augment_image(img_arr)
augmented_img = Image.fromarray(augmented_img_arr)
# Save the augmented image
new_file = f"Brightness_img{i}_{file}"
save_path = os.path.join(augmentation_path, new_file)
augmented_img.save(save_path)
QMessageBox.information(self, "Augmentation completed", "Brightness augmentation completed!")
def change_brightness_images_with_albumentations(self, image_directory, num_images, augmentation_path):