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| import gradio as gr | |
| import numpy as np | |
| import cv2 | |
| from PIL import Image | |
| from ultralytics import YOLO | |
| import torch | |
| from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor | |
| # Define available YOLO models | |
| available_models = { | |
| "X-ray": YOLO("xray.pt"), | |
| "CT scan": YOLO("CT.pt"), | |
| "Ultrasound": YOLO("ultrasound.pt"), | |
| # Add more models as needed | |
| } | |
| def segment_image(input_image, selected_model): | |
| # Resize the input image to 255x255 | |
| img = np.array(input_image) | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
| model = available_models[selected_model] | |
| # Perform object detection and segmentation | |
| results = model(img) | |
| mask = results[0].masks.data.numpy() | |
| target_height = img.shape[0] | |
| target_width = img.shape[1] | |
| # Resize the mask using OpenCV | |
| resized_mask = cv2.resize(mask[0], (target_width, target_height)) | |
| resized_mask = (resized_mask * 255).astype(np.uint8) | |
| # Create a copy of the original image | |
| overlay_image = img.copy() | |
| # Apply the resized mask to the overlay image | |
| overlay_image[resized_mask > 0] = [0, 255, 255] # Overlay in green | |
| # Convert the overlay image to PIL format | |
| overlay_pil = Image.fromarray(overlay_image) | |
| # Convert the resized mask to PIL format | |
| mask_pil = Image.fromarray(resized_mask) | |
| for result in results: | |
| boxes = result.boxes | |
| bbox = boxes.xyxy.tolist()[0] | |
| sam_checkpoint = "sam_vit_h_4b8939.pth" | |
| model_type = "vit_h" | |
| sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) | |
| sam.to(device='cpu') | |
| predictor = SamPredictor(sam) | |
| predictor.set_image(img) | |
| input_box = np.array(bbox) | |
| masks_, _, _ = predictor.predict( | |
| point_coords=None, | |
| point_labels=None, | |
| box=input_box, | |
| multimask_output=False) | |
| fmask = masks_[0].astype(int) | |
| resized_mask1 = cv2.resize(fmask, (target_width, target_height)) | |
| resized_mask1 = (resized_mask1 * 255).astype(np.uint8) | |
| overlay_image1 = img.copy() | |
| # Apply the resized mask to the overlay image | |
| overlay_image1[resized_mask1 > 0] = [255, 255, 0] # Overlay in green | |
| # Convert the overlay image to PIL format | |
| overlay_pil1 = Image.fromarray(overlay_image1) | |
| return overlay_pil, overlay_pil1 # Return both overlay image and mask | |
| # Create the Gradio interface with a dropdown for model selection | |
| iface = gr.Interface( | |
| fn=segment_image, | |
| inputs=[ | |
| gr.components.Image(type="pil", label="Upload an image"), | |
| gr.components.Dropdown( | |
| choices=list(available_models.keys()), | |
| label="Select YOLO Model", | |
| default="X-ray" | |
| ) | |
| ], | |
| outputs=[ | |
| gr.components.Image(type="pil", label="YOLO predicted mask and images"), | |
| gr.components.Image(type="pil", label="YOLO and SAM predicted mask and images ") | |
| ], | |
| title="YOLOv8 with SAM 😃", | |
| description='This software generates the segmentation mask Medical images' | |
| ) | |
| iface.launch() | |