| import gradio as gr | |
| from PIL import Image, ImageDraw | |
| from transparent_background import Remover | |
| import numpy as np | |
| from gradio_imageslider import ImageSlider | |
| def resize(img: Image, target_size_px: int): | |
| aspect_ratio = img.width / img.height | |
| if img.width > img.height: | |
| new_width = target_size_px | |
| new_height = int(target_size_px / aspect_ratio) | |
| else: | |
| new_height = target_size_px | |
| new_width = int(target_size_px * aspect_ratio) | |
| img = img.resize((new_width, new_height)) | |
| return img | |
| def infer(img: Image): | |
| img_resized = resize(img, 512) | |
| remover = Remover(mode="fast") | |
| masked_image = remover.process(img_resized, type="map") | |
| gray_image = masked_image.convert("L") | |
| binary_image = gray_image.point(lambda x: 0 if x < 128 else 255, "1") | |
| image_array = np.array(binary_image) | |
| white_pixels = np.where(image_array == True) | |
| x_coords = white_pixels[1] | |
| y_coords = white_pixels[0] | |
| min_x = np.min(x_coords) | |
| max_x = np.max(x_coords) | |
| min_y = np.min(y_coords) | |
| max_y = np.max(y_coords) | |
| draw = ImageDraw.Draw(img_resized) | |
| draw.rectangle([(min_x, min_y), (max_x, max_y)], outline="red", width=2) | |
| return img_resized, masked_image | |
| gr.Interface( | |
| fn=infer, | |
| description=""" | |
| This space performs salient object detection on an image uploaded by the user. I.e. it will detect the object(s) in the image foreground and draw a red rectangle around it. | |
| It uses the [transparent-background](https://github.com/plemeri/transparent-background) library, which is built on [InSPyReNet](https://github.com/plemeri/inspyrenet). | |
| """, | |
| inputs=gr.components.Image(type="pil", label="Input Image"), | |
| outputs=ImageSlider(label="Output", type="pil"), | |
| title="Salient Object Detection", | |
| ).launch() | |