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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()
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