quacknet.dataAugmentation 

 1import numpy as np
 2from PIL import Image
 3import os
 4
 5class Augementation:
 6    def hotEncodeLabels(self, allLabels, numClasses):
 7        """
 8        Converts a list of integer labels into one hot encoded format
 9
10        Args:
11            allLabels (list of lists): All of the labels of the input data.
12            numClasses (int): The total number of classes.
13        
14        Returns:
15            list of ndarray: a 2D array of hot encoded labels.
16        """
17        labels = np.zeros((len(allLabels), numClasses))
18        for i in range(len(allLabels)):
19            labels[i][allLabels[i]] = 1
20        return labels
21
22    def getImagePaths(self, folderPath, extensions = ['.jpg', '.png', '.jpeg']):
23        """
24        Retrieves paths of all image files in a directory and its subdirectories.
25
26        Args:
27            folderPath (str): The path to the directory containing images.
28            extensions (list of str, optional): A list of file extensions to get as images. Default is ['.jpg', '.png', '.jpeg'].
29
30        Returns:
31            list of str: A list of full paths to image files.
32        """
33        imagePaths = []
34        for root, dirs, files in os.walk(folderPath):
35            for file in files:
36                if(any(file.lower().endswith(ext) for ext in extensions)):
37                    fullPath = os.path.join(root, file)
38                    imagePaths.append(fullPath)
39        return imagePaths
40
41    def preprocessImages(self, imagePaths, targetSize = (128, 128)):
42        """
43        Loads and preprocesses images by resising and normalising them.
44
45        Args:
46            imagePaths (list of str): A list of full paths to image files.
47            targetSize (tuple of int, optional): The desired size of the output images (width, height). Default is (128, 128).
48        
49        Returns:
50            ndarray: A list of preprocessed images with values normalised between 0 to 1.
51        """
52        images = []
53        for path in imagePaths:
54            img = Image.open(path).convert('RGB')
55            resized = img.resize(targetSize)
56            normalised = np.array(resized) / 255.0
57            images.append(normalised)
58        return np.array(images)
59
60    def dataAugmentation(self, images):
61        """
62        Performs basic data augmentation by flipping horizontally and vertically.
63
64        Args:
65            images (ndarray): A list of images to augment
66        
67        Returns:
68            ndarray: A list containing the augmented and original images.
69        """
70        allImages = []
71        for img in images:
72            allImages.append(img)
73            allImages.append(np.fliplr(img))
74            allImages.append(np.flipud(img))
75            allImages.append(np.flipud(np.fliplr(img)))
76        return np.array(allImages)
class Augementation: 
 6class Augementation:
 7    def hotEncodeLabels(self, allLabels, numClasses):
 8        """
 9        Converts a list of integer labels into one hot encoded format
10
11        Args:
12            allLabels (list of lists): All of the labels of the input data.
13            numClasses (int): The total number of classes.
14        
15        Returns:
16            list of ndarray: a 2D array of hot encoded labels.
17        """
18        labels = np.zeros((len(allLabels), numClasses))
19        for i in range(len(allLabels)):
20            labels[i][allLabels[i]] = 1
21        return labels
22
23    def getImagePaths(self, folderPath, extensions = ['.jpg', '.png', '.jpeg']):
24        """
25        Retrieves paths of all image files in a directory and its subdirectories.
26
27        Args:
28            folderPath (str): The path to the directory containing images.
29            extensions (list of str, optional): A list of file extensions to get as images. Default is ['.jpg', '.png', '.jpeg'].
30
31        Returns:
32            list of str: A list of full paths to image files.
33        """
34        imagePaths = []
35        for root, dirs, files in os.walk(folderPath):
36            for file in files:
37                if(any(file.lower().endswith(ext) for ext in extensions)):
38                    fullPath = os.path.join(root, file)
39                    imagePaths.append(fullPath)
40        return imagePaths
41
42    def preprocessImages(self, imagePaths, targetSize = (128, 128)):
43        """
44        Loads and preprocesses images by resising and normalising them.
45
46        Args:
47            imagePaths (list of str): A list of full paths to image files.
48            targetSize (tuple of int, optional): The desired size of the output images (width, height). Default is (128, 128).
49        
50        Returns:
51            ndarray: A list of preprocessed images with values normalised between 0 to 1.
52        """
53        images = []
54        for path in imagePaths:
55            img = Image.open(path).convert('RGB')
56            resized = img.resize(targetSize)
57            normalised = np.array(resized) / 255.0
58            images.append(normalised)
59        return np.array(images)
60
61    def dataAugmentation(self, images):
62        """
63        Performs basic data augmentation by flipping horizontally and vertically.
64
65        Args:
66            images (ndarray): A list of images to augment
67        
68        Returns:
69            ndarray: A list containing the augmented and original images.
70        """
71        allImages = []
72        for img in images:
73            allImages.append(img)
74            allImages.append(np.fliplr(img))
75            allImages.append(np.flipud(img))
76            allImages.append(np.flipud(np.fliplr(img)))
77        return np.array(allImages)
def hotEncodeLabels(self, allLabels, numClasses): 
 7    def hotEncodeLabels(self, allLabels, numClasses):
 8        """
 9        Converts a list of integer labels into one hot encoded format
10
11        Args:
12            allLabels (list of lists): All of the labels of the input data.
13            numClasses (int): The total number of classes.
14        
15        Returns:
16            list of ndarray: a 2D array of hot encoded labels.
17        """
18        labels = np.zeros((len(allLabels), numClasses))
19        for i in range(len(allLabels)):
20            labels[i][allLabels[i]] = 1
21        return labels

Converts a list of integer labels into one hot encoded format

Args: allLabels (list of lists): All of the labels of the input data. numClasses (int): The total number of classes.

Returns: list of ndarray: a 2D array of hot encoded labels.

def getImagePaths(self, folderPath, extensions=['.jpg', '.png', '.jpeg']): 
23    def getImagePaths(self, folderPath, extensions = ['.jpg', '.png', '.jpeg']):
24        """
25        Retrieves paths of all image files in a directory and its subdirectories.
26
27        Args:
28            folderPath (str): The path to the directory containing images.
29            extensions (list of str, optional): A list of file extensions to get as images. Default is ['.jpg', '.png', '.jpeg'].
30
31        Returns:
32            list of str: A list of full paths to image files.
33        """
34        imagePaths = []
35        for root, dirs, files in os.walk(folderPath):
36            for file in files:
37                if(any(file.lower().endswith(ext) for ext in extensions)):
38                    fullPath = os.path.join(root, file)
39                    imagePaths.append(fullPath)
40        return imagePaths

Retrieves paths of all image files in a directory and its subdirectories.

Args: folderPath (str): The path to the directory containing images. extensions (list of str, optional): A list of file extensions to get as images. Default is ['.jpg', '.png', '.jpeg'].

Returns: list of str: A list of full paths to image files.

def preprocessImages(self, imagePaths, targetSize=(128, 128)): 
42    def preprocessImages(self, imagePaths, targetSize = (128, 128)):
43        """
44        Loads and preprocesses images by resising and normalising them.
45
46        Args:
47            imagePaths (list of str): A list of full paths to image files.
48            targetSize (tuple of int, optional): The desired size of the output images (width, height). Default is (128, 128).
49        
50        Returns:
51            ndarray: A list of preprocessed images with values normalised between 0 to 1.
52        """
53        images = []
54        for path in imagePaths:
55            img = Image.open(path).convert('RGB')
56            resized = img.resize(targetSize)
57            normalised = np.array(resized) / 255.0
58            images.append(normalised)
59        return np.array(images)

Loads and preprocesses images by resising and normalising them.

Args: imagePaths (list of str): A list of full paths to image files. targetSize (tuple of int, optional): The desired size of the output images (width, height). Default is (128, 128).

Returns: ndarray: A list of preprocessed images with values normalised between 0 to 1.

def dataAugmentation(self, images): 
61    def dataAugmentation(self, images):
62        """
63        Performs basic data augmentation by flipping horizontally and vertically.
64
65        Args:
66            images (ndarray): A list of images to augment
67        
68        Returns:
69            ndarray: A list containing the augmented and original images.
70        """
71        allImages = []
72        for img in images:
73            allImages.append(img)
74            allImages.append(np.fliplr(img))
75            allImages.append(np.flipud(img))
76            allImages.append(np.flipud(np.fliplr(img)))
77        return np.array(allImages)

Performs basic data augmentation by flipping horizontally and vertically.

Args: images (ndarray): A list of images to augment

Returns: ndarray: A list containing the augmented and original images.