Instructions to use fahimahamed1/NeoNude with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fahimahamed1/NeoNude with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fahimahamed1/NeoNude", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| """ | |
| Phase 2: Mask refinement. | |
| Creates maskref by isolating the green clothing mask from the GAN output | |
| and compositing it onto the corrected image. | |
| """ | |
| import cv2 | |
| import numpy as np | |
| def create_maskref(cv_mask, cv_correct): | |
| """Create the refined mask (maskref) from the raw GAN mask. | |
| Extracts the green clothing region from the GAN mask and overlays it | |
| onto the color-corrected input image. | |
| Args: | |
| cv_mask: Raw mask image from the correct-to-mask GAN phase. | |
| cv_correct: Color-corrected input image. | |
| Returns: | |
| maskref image (512x512 BGR). | |
| """ | |
| # Solid green background | |
| green = np.zeros((512, 512, 3), np.uint8) | |
| green[:, :, :] = (0, 255, 0) # BGR | |
| # Filter for green pixels from the GAN mask | |
| f1 = np.asarray([0, 250, 0]) | |
| f2 = np.asarray([10, 255, 10]) | |
| green_mask = cv2.inRange(cv_mask, f1, f2) | |
| # Dilate the mask slightly | |
| kernel = np.ones((5, 5), np.uint8) | |
| green_mask = cv2.dilate(green_mask, kernel, iterations=1) | |
| # Composite: keep corrected image outside green, green inside | |
| green_mask_inv = cv2.bitwise_not(green_mask) | |
| res1 = cv2.bitwise_and(cv_correct, cv_correct, mask=green_mask_inv) | |
| res2 = cv2.bitwise_and(green, green, mask=green_mask) | |
| return cv2.add(res1, res2) | |