Delete CellCTC

CellCTC/.gitattributes

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CellCTC/chanage_names.py

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-import os
-
-# Base directory for the annotations
-annotations_dir = "/l/users/sahal.mullappilly/Komal/documents/Cell/CTCdataset/Annotations"
-
-# Function to rename the files in the annotations folder
-def rename_annotation_files():
-    # Walk through the directories and files in the annotations folder
-    for root, dirs, files in os.walk(annotations_dir):
-        for file in files:
-            # Check if the file is a PNG (annotation files)
-            if file.endswith(".png"):
-                # Create a new name based on the current naming pattern
-                base_filename = file.replace("man_track", "t").replace(".png", ".png")  # Rename pattern
-                new_file_path = os.path.join(root, base_filename)
-                
-                # Get the current file path
-                old_file_path = os.path.join(root, file)
-                
-                # Rename the file
-                os.rename(old_file_path, new_file_path)
-                print(f"Renamed {old_file_path} to {new_file_path}")
-
-# Call the function to rename the annotation files
-# rename_annotation_files()
-
-print("All annotation files have been renamed successfully!")
-
-# Directories to process
-jpeg_images_dir = "/l/users/sahal.mullappilly/Komal/documents/Cell/CTCdataset/JPEGImages"
-annotations_dir = "/l/users/sahal.mullappilly/Komal/documents/Cell/CTCdataset/Annotations"
-
-# Function to rename files in a directory
-def rename_files_in_directory(directory):
-    for root, dirs, files in os.walk(directory):
-        for file in files:
-            # Skip if file doesn't have the expected format (adjust this based on your requirements)
-            if file.endswith(".png") or file.endswith(".jpg"):
-                try:
-                    # Extract the numeric part from the filename
-                    num_str = file.lstrip('t').split('.')[0]  # Remove 't' and split on the dot
-                    new_name = f"{num_str}.png" if file.endswith(".png") else f"{num_str}.jpg"
-
-                    # Define the old and new file paths
-                    old_file_path = os.path.join(root, file)
-                    new_file_path = os.path.join(root, new_name)
-
-                    # Rename the file
-                    os.rename(old_file_path, new_file_path)
-                    print(f"Renamed: {old_file_path} -> {new_file_path}")
-
-                except Exception as e:
-                    print(f"Error renaming {file}: {e}")
-
-# Rename files in JPEGImages directory
-rename_files_in_directory(jpeg_images_dir)
-
-# Rename files in Annotations directory
-rename_files_in_directory(annotations_dir)
-
-print("File renaming completed!")

CellCTC/convert_to_jpg.py

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-import os
-from PIL import Image
-import numpy as np
-
-# Base directory for the datasets
-base_dir = "/l/users/sahal.mullappilly/Komal/documents/Cell/CTCdataset"
-
-# List of datasets and their video folder names
-datasets = [
-    {"dataset_name": "BF-C2DL-HSC", "subfolders": ["01", "02"]},
-    {"dataset_name": "BF-C2DL-MuSC", "subfolders": ["01", "02"]},
-    {"dataset_name": "DIC-C2DH-HeLa", "subfolders": ["01", "02"]},
-    {"dataset_name": "Fluo-C2DL-Huh7", "subfolders": ["01", "02"]},
-    {"dataset_name": "Fluo-C2DL-MSC", "subfolders": ["01", "02"]},
-    {"dataset_name": "Fluo-N2DH-GOWT1", "subfolders": ["01", "02"]},
-    {"dataset_name": "Fluo-N2DL-HeLa", "subfolders": ["01", "02"]},
-    {"dataset_name": "PhC-C2DH-U373", "subfolders": ["01", "02"]},
-    {"dataset_name": "PhC-C2DL-PSC", "subfolders": ["01", "02"]},
-    {"dataset_name": "Fluo-N2DH-SIM+", "subfolders": ["01", "02"]}
-]
-
-# Function to convert TIFF to JPG
-def convert_tiff_to_jpg(tiff_path, jpg_path):
-    try:
-        # Open the tiff image using PIL
-        img = Image.open(tiff_path)
-        
-        # Check if image is empty
-        if img.size == (0, 0):
-            print(f"Warning: {tiff_path} is empty. Skipping this file.")
-            return
-        
-        # Check for bit depth and convert to 8-bit if necessary (normalize if > 8-bit)
-        if img.mode != 'RGB':
-            img = img.convert('RGB')  # Convert to RGB if not already in that mode
-
-        # Convert the image to a NumPy array to normalize pixel values if necessary
-        img_array = np.array(img)
-        
-        # Normalize if pixel values are outside the range of [0, 255] (for 16-bit or higher)
-        if img_array.max() > 255:
-            img_array = (img_array / img_array.max()) * 255  # Normalize to 0-255 range
-            img_array = img_array.astype(np.uint8)
-
-        # Save the image as JPG
-        img = Image.fromarray(img_array)
-        img.save(jpg_path, 'JPEG')
-        print(f"Converted {tiff_path} to {jpg_path}")
-    except Exception as e:
-        print(f"Error converting {tiff_path}: {e}")
-
-# Function to process dataset
-def process_dataset(dataset_name, subfolders):
-    # Path for the main "JPEGImages" folder where all images will be stored
-    main_jpeg_dir = os.path.join(base_dir, "JPEGImages")
-    os.makedirs(main_jpeg_dir, exist_ok=True)
-
-    for subfolder in subfolders:
-        # Paths for the subfolder
-        input_folder = os.path.join(base_dir, "datasets", dataset_name, subfolder)
-        output_folder = os.path.join(main_jpeg_dir, f"{dataset_name}-{subfolder}")
-        
-        # Create output directory for each subfolder inside "JPEGImages"
-        os.makedirs(output_folder, exist_ok=True)
-
-        # Process each .tif file in the subfolder
-        for root, dirs, files in os.walk(input_folder):
-            for file in files:
-                if file.endswith(".tif"):
-                    tiff_path = os.path.join(root, file)
-                    # Define the path for the new jpg file
-                    jpg_filename = file.replace(".tif", ".jpg")
-                    jpg_path = os.path.join(output_folder, jpg_filename)
-                    
-                    # Convert .tif to .jpg
-                    convert_tiff_to_jpg(tiff_path, jpg_path)
-
-# Process each dataset
-for dataset in datasets:
-    process_dataset(dataset["dataset_name"], dataset["subfolders"])
-
-print("All TIFF images converted to JPG and saved in the 'JPEGImages' folder!")

CellCTC/download.py

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-import os
-import requests
-from tqdm import tqdm
-
-# Define the dataset details
-datasets = [
-    {
-        "name": "BF-C2DL-HSC",
-        "url": "http://data.celltrackingchallenge.net/training-datasets/BF-C2DL-HSC.zip",
-        "size": "1.6 GB"
-    },
-    {
-        "name": "BF-C2DL-MuSC",
-        "url": "http://data.celltrackingchallenge.net/training-datasets/BF-C2DL-MuSC.zip",
-        "size": "1.2 GB"
-    },
-    {
-        "name": "DIC-C2DH-HeLa",
-        "url": "http://data.celltrackingchallenge.net/training-datasets/DIC-C2DH-HeLa.zip",
-        "size": "37 MB"
-    },
-    {
-        "name": "Fluo-C2DL-Huh7",
-        "url": "http://data.celltrackingchallenge.net/training-datasets/Fluo-C2DL-Huh7.zip",
-        "size": "36 MB"
-    },
-    {
-        "name": "Fluo-C2DL-MSC",
-        "url": "http://data.celltrackingchallenge.net/training-datasets/Fluo-C2DL-MSC.zip",
-        "size": "72 MB"
-    },
-    {
-        "name": "Fluo-N2DH-GOWT1",
-        "url": "http://data.celltrackingchallenge.net/training-datasets/Fluo-N2DH-GOWT1.zip",
-        "size": "53 MB"
-    },
-    {
-        "name": "Fluo-N2DL-HeLa",
-        "url": "http://data.celltrackingchallenge.net/training-datasets/Fluo-N2DL-HeLa.zip",
-        "size": "182 MB"
-    },
-    {
-        "name": "PhC-C2DH-U373",
-        "url": "http://data.celltrackingchallenge.net/training-datasets/PhC-C2DH-U373.zip",
-        "size": "40 MB"
-    },
-    {
-        "name": "PhC-C2DL-PSC",
-        "url": "http://data.celltrackingchallenge.net/training-datasets/PhC-C2DL-PSC.zip",
-        "size": "124 MB"
-    },
-    {
-        "name": "Fluo-N2DH-SIM+",
-        "url": "http://data.celltrackingchallenge.net/training-datasets/Fluo-N2DH-SIM+.zip",
-        "size": "91 MB"
-    }
-]
-
-# Directory where you want to save the datasets
-base_dir = "./CTCdataset"
-
-# Function to download dataset
-def download_dataset(url, save_path):
-    print(f"Downloading {url} to {save_path}")
-    response = requests.get(url, stream=True)
-    total_size = int(response.headers.get('Content-Length', 0))
-    with open(save_path, 'wb') as f:
-        with tqdm(total=total_size, unit='B', unit_scale=True) as pbar:
-            for chunk in response.iter_content(chunk_size=1024):
-                if chunk:
-                    f.write(chunk)
-                    pbar.update(len(chunk))
-
-# Create the base directory if it doesn't exist
-os.makedirs(base_dir, exist_ok=True)
-
-# Download all datasets
-for dataset in datasets:
-    dataset_dir = os.path.join(base_dir, dataset["name"])
-    os.makedirs(dataset_dir, exist_ok=True)
-    
-    # Define the path where the zip file will be saved
-    zip_file_path = os.path.join(dataset_dir, f"{dataset['name']}.zip")
-    
-    # Download the dataset
-    download_dataset(dataset["url"], zip_file_path)
-
-    print(f"Dataset {dataset['name']} downloaded successfully!")
-
-print("All datasets downloaded successfully!")

CellCTC/extract.py

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-import os
-import zipfile
-
-# Define the dataset names
-datasets = [
-    "BF-C2DL-HSC",
-    "BF-C2DL-MuSC",
-    "DIC-C2DH-HeLa",
-    "Fluo-C2DL-Huh7",
-    "Fluo-C2DL-MSC",
-    "Fluo-N2DH-GOWT1",
-    "Fluo-N2DL-HeLa",
-    "PhC-C2DH-U373",
-    "PhC-C2DL-PSC",
-    "Fluo-N2DH-SIM+"
-]
-
-# Directory where the datasets are stored
-base_dir = "/l/users/sahal.mullappilly/Komal/documents/Cell/CTCdataset"
-
-# Function to extract dataset
-def extract_zip(zip_path, extract_to):
-    print(f"Extracting {zip_path} to {extract_to}")
-    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
-        zip_ref.extractall(extract_to)
-    print(f"Extraction complete for {zip_path}!")
-
-# Extract all datasets
-for dataset in datasets:
-    dataset_dir = os.path.join(base_dir, dataset)
-    zip_file_path = os.path.join(dataset_dir, f"{dataset}.zip")
-    
-    # Extract the dataset
-    if os.path.exists(zip_file_path):
-        extract_zip(zip_file_path, dataset_dir)
-    else:
-        print(f"Zip file for {dataset} not found!")
-
-print("All datasets extracted successfully!")