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| import gradio as gr | |
| import torch | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| import io | |
| from ultralytics import FastSAM | |
| from ultralytics.models.fastsam import FastSAMPrompt | |
| # Set up device | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Load FastSAM model | |
| model = FastSAM("FastSAM-s.pt") # or FastSAM-x.pt | |
| def fig2img(fig): | |
| buf = io.BytesIO() | |
| fig.savefig(buf) | |
| buf.seek(0) | |
| img = Image.open(buf) | |
| return img | |
| def plot(annotations, prompt_process, mask_random_color=True, better_quality=True, retina=True, with_contours=True): | |
| for ann in annotations: | |
| image = ann.orig_img[..., ::-1] # BGR to RGB | |
| original_h, original_w = ann.orig_shape | |
| fig = plt.figure(figsize=(original_w / 100, original_h / 100)) | |
| plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0) | |
| plt.margins(0, 0) | |
| plt.gca().xaxis.set_major_locator(plt.NullLocator()) | |
| plt.gca().yaxis.set_major_locator(plt.NullLocator()) | |
| plt.imshow(image) | |
| if ann.masks is not None: | |
| masks = ann.masks.data | |
| if better_quality: | |
| if isinstance(masks[0], torch.Tensor): | |
| masks = np.array(masks.cpu()) | |
| for i, mask in enumerate(masks): | |
| mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)) | |
| masks[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8)) | |
| prompt_process.fast_show_mask( | |
| masks, | |
| plt.gca(), | |
| random_color=mask_random_color, | |
| bbox=None, | |
| points=None, | |
| pointlabel=None, | |
| retinamask=retina, | |
| target_height=original_h, | |
| target_width=original_w, | |
| ) | |
| if with_contours: | |
| contour_all = [] | |
| temp = np.zeros((original_h, original_w, 1)) | |
| for i, mask in enumerate(masks): | |
| mask = mask.astype(np.uint8) | |
| if not retina: | |
| mask = cv2.resize(mask, (original_w, original_h), interpolation=cv2.INTER_NEAREST) | |
| contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) | |
| contour_all.extend(iter(contours)) | |
| cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2) | |
| color = np.array([0 / 255, 0 / 255, 1.0, 0.8]) | |
| contour_mask = temp / 255 * color.reshape(1, 1, -1) | |
| plt.imshow(contour_mask) | |
| plt.axis("off") | |
| plt.close() | |
| return fig2img(fig) | |
| def segment_image(input_image, object_name): | |
| try: | |
| if input_image is None: | |
| return None, "Please upload an image before submitting." | |
| input_image = Image.fromarray(input_image).convert("RGB") | |
| # Run FastSAM model | |
| everything_results = model(input_image, retina_masks=True, imgsz=1024, conf=0.4, iou=0.9) | |
| # Prepare a Prompt Process object | |
| prompt_process = FastSAMPrompt(input_image, everything_results, device=device) | |
| # Use text prompt to segment the specified object | |
| results = prompt_process.text_prompt(text=object_name) | |
| if not results: | |
| return input_image, f"Could not find '{object_name}' in the image." | |
| # Plot the results | |
| result_image = plot(annotations=results, prompt_process=prompt_process) | |
| return result_image, f"Segmented '{object_name}' in the image." | |
| except Exception as e: | |
| return None, f"An error occurred: {str(e)}" | |
| # Create Gradio interface | |
| iface = gr.Interface( | |
| fn=segment_image, | |
| inputs=[ | |
| gr.Image(type="numpy", label="Upload an image"), | |
| gr.Textbox(label="Specify object to segment (e.g., dog, cat, grass)") | |
| ], | |
| outputs=[ | |
| gr.Image(type="pil", label="Segmented Image"), | |
| gr.Textbox(label="Status") | |
| ], | |
| title="FastSAM Segmentation with Object Specification", | |
| description="Upload an image and specify an object to segment using FastSAM." | |
| ) | |
| # Launch the interface | |
| iface.launch() |