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| import gradio as gr | |
| import cv2 | |
| from PIL import Image | |
| import numpy as np | |
| import torch | |
| from .detection import * | |
| from .masking import * | |
| from .synthesis import * | |
| def forward(image_cam, image_upload, prompt="", n_prompt=None, num_steps=20, seed=0, original_resolution=False): | |
| if image_cam is None: | |
| image = image_upload | |
| else: | |
| image = image_cam | |
| if not original_resolution: | |
| w,h = image.size | |
| ratio = 512/h | |
| new_size = int(w*ratio), int(h*ratio) | |
| image = image.resize(new_size) | |
| # detect face | |
| dets = detect_face(image) | |
| # segment hair and face | |
| faces, hairs = process_face(dets) | |
| # build mask | |
| mask = build_mask_multi(image, faces, hairs) | |
| # synthesise | |
| new_image = synthesis(image,mask, prompt, n_prompt, num_steps=num_steps, seed=seed) | |
| return new_image |