Added data files

cycleGAN/data/customdataset.py

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+import random 
+import sys
+import torch 
+from torch.utils.data import Dataset, DataLoader
+from PIL import Image, ImageFilter
+import pandas as pd 
+import numpy as np 
+import os
+
+# sys.path.append("/iitjhome/m23csa016/DLASS4")
+sys.path.append("data")
+TRAIN_LABELS = "data/Assignment_4/Train/Train_labels.csv"
+TEST_LABELS = "data/Assignment_4/Test/Test_Labels.csv"
+
+TRAIN_DATA_DIR = "data/Assignment_4/Train/Train_data"
+TEST_DATA_DIR = "data/Assignment_4/Test/Test"
+
+TRAIN_SKETCH_DIR = "data/Assignment_4/Train/Contours"
+TEST_SKETCH_DIR = "data/Assignment_4/Test/Test_contours"
+
+# Create Dataset
+class ISICDataset(Dataset):
+    def __init__(self, datadir, csvpath, sketchdir, transform=None):
+        self.datadir = datadir
+        self.csv = pd.read_csv(csvpath)
+        self.sketchdir = sketchdir
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.csv[:300])
+
+    def __getitem__(self, index):
+        img_path = os.path.join(self.datadir, self.csv.iloc[index, 0] + ".jpg")
+        image = Image.open(img_path)
+
+        # Apply Gaussian blur
+        blurred_image = image.filter(ImageFilter.GaussianBlur(radius=2))
+
+        # Apply sharpening
+        sharpened_image = image.filter(ImageFilter.UnsharpMask)
+        
+        labels = self.csv.iloc[index, 1:].values
+
+        sketch_name = random.choice(os.listdir(self.sketchdir))
+        sketch_path = os.path.join(self.sketchdir, sketch_name)
+        fs, ext = os.path.splitext(sketch_path)
+
+        while ext not in ['.jpg', '.jpeg', '.png']:
+          sketch_name = random.choice(os.listdir(self.sketchdir))
+          sketch_path = os.path.join(self.sketchdir, sketch_name)
+          fs, ext = os.path.splitext(sketch_path)
+
+        sketch = Image.open(sketch_path)
+
+        image = self.transform['img'](sharpened_image)
+        sketch = self.transform['sketch'](sketch)
+        
+        true_label = np.argmax(labels)
+        # Create a numpy array of zeros with shape (num_classes, 256, 256)
+        encoded_label = np.zeros((7, image.size(1), image.size(1)), dtype=np.float32)
+
+        # Set all elements in the channel corresponding to the true label to 1
+        encoded_label[true_label, :, :] = 1  # Use broadcasting to set all elements
+
+        label = torch.tensor(encoded_label, dtype=torch.float32)
+
+        return label, image, sketch
+    
+
+def prepdata(config, transform=None):
+    # Train Dataset and Dataloader
+    train_dataset = ISICDataset(TRAIN_DATA_DIR, TRAIN_LABELS, TRAIN_SKETCH_DIR, transform=transform)
+    train_dataloader = DataLoader(train_dataset, batch_size=config['batch_size'], shuffle=True, num_workers=2)
+
+    # Train Dataset and Dataloader
+    test_dataset = ISICDataset(TEST_DATA_DIR, TEST_LABELS, TEST_SKETCH_DIR, transform=transform)
+    test_dataloader = DataLoader(test_dataset, batch_size=config['batch_size'], shuffle=True, num_workers=2)
+
+    return train_dataloader, test_dataloader
+
+
+

cycleGAN/data/make_dataset.py

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+from zipfile import ZipFile
+import os
+
+# Extract the data
+DATA_DIR = "data"
+EXTRACT_PATH = "data"
+
+TRAIN_DIR = os.path.join(DATA_DIR, "Assignment_4/Train")
+TRAIN_FILES = os.listdir(TRAIN_DIR)
+
+TEST_DIR = os.path.join(DATA_DIR, "Assignment_4/Test")
+TEST_FILES = os.listdir(os.path.join(DATA_DIR, "Assignment_4/Test"))
+
+def extract_zipfile(file_path, extract_path):
+    with ZipFile(file_path, 'r') as asz:
+        asz.extractall(extract_path)
+
+extract_zipfile(os.path.join(DATA_DIR, "Assignment_4-20240408T115837Z-002.zip"), EXTRACT_PATH)
+
+for file in TRAIN_FILES:
+    if file.endswith(".zip"):
+        file_path = os.path.join(TRAIN_DIR, file)
+        extract_zipfile(file_path, TRAIN_DIR)
+
+for file in TEST_FILES:
+    if file.endswith(".zip"):
+        file_path = os.path.join(TEST_DIR, file)
+        extract_zipfile(file_path, TEST_DIR)