Loading repository data…
Loading repository data…
Ornella-Gigante / repository
En este pequeño proyecto, utilizaré una imagen para hacer algunas transformaciones que se podrían usar para aumentar un conjunto de datos y mejorar el modelo utilizando varias capas y una red convolucional.
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
Advanced Image Processing and Visualization using PyTorch and TorchVision
This project demonstrates advanced image transformations using PyTorch and TorchVision. It includes a Jupyter Notebook (Ornella_Gigante_Lab4_Sprint2.ipynb) that applies various photometric and geometric adjustments to images, such as brightness, contrast, gamma correction, and hue/saturation shifts. The toolkit also visualizes the effects of these transformations, enabling users to compare original and modified images side-by-side.
Key Use Cases:
| Transformation | Description | Example Use Case |
|---|---|---|
| Brightness Adjustment | Modifies image illumination (adjust_brightness). | Simulate low-light conditions. |
| Contrast Enhancement | Adjusts difference between light and dark areas (adjust_contrast). | Improve feature visibility. |
| Gamma Correction 🌓 | Non-linear adjustment for perceptual brightness (adjust_gamma). | Correct underexposed images. |
| Hue/Saturation 🎨 | Alters color tones and intensity (adjust_hue, adjust_saturation). | Artistic style transfer. |
| Sharpness Optimization 🔍 | Enhances edge clarity (adjust_sharpness). | Improve OCR readability. |
| Visual Comparison | Side-by-side display of original vs. transformed images. | Quality assessment. |
Clone Repository:
git clone https://github.com/yourusername/image-transformation-toolkit.git
cd image-transformation-toolkit
Install Dependencies:
pip install torch torchvision pillow matplotlib
Launch Jupyter Notebook:
jupyter notebook Ornella_Gigante_Lab4_Sprint2.ipynb
from PIL import Image
from torchvision.transforms import functional as F
img = Image.open('path/to/image.jpg')
img_tensor = F.to_tensor(img) # Convert to PyTorch tensor
# Adjust brightness (50% reduction)
brightness_adjusted = F.adjust_brightness(img_tensor, brightness_factor=0.5)
# Increase contrast by 150%
contrast_enhanced = F.adjust_contrast(img_tensor, contrast_factor=1.5)
import matplotlib.pyplot as plt
fig, axes = plt.subplots(1, 2, figsize=(12, 6))
axes[0].imshow(F.to_pil_image(img_tensor))
axes[0].set_title("Original Image")
axes[1].imshow(F.to_pil_image(brightness_adjusted))
axes[1].set_title("Brightness Adjusted")
plt.show()
| Parameter | Typical Range | Effect Visualization |
|---|---|---|
brightness_factor | 0.0 (black) to 1.0+ | Brightness |
contrast_factor | 0.0 (gray) to 1.0+ | Contrast |
hue_factor | -0.5 to 0.5 | Hue |
MIT License
👩💻 Author: Ornella Gigante
"Transform images like a pro with PyTorch's powerful vision toolkit!" 🚀