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| import pandas as pd | |
| import os | |
| from PIL import Image | |
| import numpy as np | |
| import torch | |
| import matplotlib.pyplot as plt | |
| from IPython import get_ipython | |
| import sys | |
| import gc | |
| import streamlit as st | |
| def show_image(image): | |
| """ | |
| Display an image in various environments (Jupyter, PyCharm, Hugging Face Spaces). | |
| Handles different types of image inputs (file path, PIL Image, numpy array, OpenCV, PyTorch tensor). | |
| Args: | |
| image (str or PIL.Image or numpy.ndarray or torch.Tensor): The image to display. | |
| """ | |
| in_jupyter = is_jupyter_notebook() | |
| in_colab = is_google_colab() | |
| # Convert image to PIL Image if it's a file path, numpy array, or PyTorch tensor | |
| if isinstance(image, str): | |
| if os.path.isfile(image): | |
| image = Image.open(image) | |
| else: | |
| raise ValueError("File path provided does not exist.") | |
| elif isinstance(image, np.ndarray): | |
| if image.ndim == 3 and image.shape[2] in [3, 4]: | |
| image = Image.fromarray(image[..., ::-1] if image.shape[2] == 3 else image) | |
| else: | |
| image = Image.fromarray(image) | |
| elif torch.is_tensor(image): | |
| image = Image.fromarray(image.permute(1, 2, 0).numpy().astype(np.uint8)) | |
| # Display the image | |
| if in_jupyter or in_colab: | |
| from IPython.display import display | |
| display(image) | |
| else: | |
| image.show() | |
| def show_image_with_matplotlib(image): | |
| if isinstance(image, str): | |
| image = Image.open(image) | |
| elif isinstance(image, np.ndarray): | |
| image = Image.fromarray(image) | |
| elif torch.is_tensor(image): | |
| image = Image.fromarray(image.permute(1, 2, 0).numpy().astype(np.uint8)) | |
| plt.imshow(image) | |
| plt.axis('off') # Turn off axis numbers | |
| plt.show() | |
| def is_jupyter_notebook(): | |
| """ | |
| Check if the code is running in a Jupyter notebook. | |
| Returns: | |
| bool: True if running in a Jupyter notebook, False otherwise. | |
| """ | |
| try: | |
| from IPython import get_ipython | |
| if 'IPKernelApp' not in get_ipython().config: | |
| return False | |
| if 'ipykernel' in str(type(get_ipython())): | |
| return True # Running in Jupyter Notebook | |
| except (NameError, AttributeError): | |
| return False # Not running in Jupyter Notebook | |
| return False # Default to False if none of the above conditions are met | |
| def is_pycharm(): | |
| return 'PYCHARM_HOSTED' in os.environ | |
| def is_google_colab(): | |
| return 'COLAB_GPU' in os.environ or 'google.colab' in sys.modules | |
| def get_image_path(name, path_type): | |
| """ | |
| Generates a path for models, images, or data based on the specified type. | |
| Args: | |
| name (str): The name of the model, image, or data folder/file. | |
| path_type (str): The type of path needed ('models', 'images', or 'data'). | |
| Returns: | |
| str: The full path to the specified resource. | |
| """ | |
| # Get the current working directory (assumed to be inside 'code' folder) | |
| current_dir = os.getcwd() | |
| # Get the directory one level up (the parent directory) | |
| parent_dir = os.path.dirname(current_dir) | |
| # Construct the path to the specified folder | |
| folder_path = os.path.join(parent_dir, path_type) | |
| # Construct the full path to the specific resource | |
| full_path = os.path.join(folder_path, name) | |
| return full_path | |
| def get_model_path(model_name): | |
| """ | |
| Get the path to the specified model folder. | |
| Args: | |
| model_name (str): Name of the model folder. | |
| Returns: | |
| str: Absolute path to the specified model folder. | |
| """ | |
| # Directory of the current script | |
| current_script_dir = os.path.dirname(os.path.abspath(__file__)) | |
| # Directory of the 'app' folder (parent of the 'my_model' folder) | |
| app_dir = os.path.dirname(os.path.dirname(current_script_dir)) | |
| # Path to the 'models/{model_name}' folder | |
| model_path = os.path.join(app_dir, "models", model_name) | |
| return model_path | |
| def free_gpu_resources(): | |
| """ | |
| Clears GPU memory. | |
| """ | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| torch.cuda.empty_cache() | |
| gc.collect() | |
| gc.collect() | |