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| import gradio as gr | |
| import os | |
| import torch | |
| import numpy as np | |
| import cv2 | |
| import matplotlib.pyplot as plt | |
| import base64 | |
| import json | |
| from typing import Tuple, Dict | |
| from timeit import default_timer as timer | |
| from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor | |
| from segment_anything.utils.onnx import SamOnnxModel | |
| import torch.nn.functional as F | |
| from model import create_sam_model | |
| # 1.Setup variables | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| checkpoint = "sam_vit_b_01ec64.pth" | |
| model_type = "vit_b" | |
| # 2.Model preparation and load save weights | |
| medsam_model = create_sam_model(model_type,checkpoint,device) | |
| mask_generator = SamAutomaticMaskGenerator( | |
| model=medsam_model, | |
| points_per_side=32, | |
| pred_iou_thresh=0.86, | |
| stability_score_thresh=0.92, | |
| crop_n_layers=1, | |
| crop_n_points_downscale_factor=2, | |
| min_mask_region_area=100, # Requires open-cv to run post-processing | |
| ) | |
| # 3.Predict fn | |
| def show_anns(anns): | |
| if len(anns) == 0: | |
| return | |
| sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True) | |
| ax = plt.gca() | |
| ax.set_autoscale_on(False) | |
| img = np.ones((sorted_anns[0]['segmentation'].shape[0], sorted_anns[0]['segmentation'].shape[1], 4)) | |
| img[:,:,3] = 0 | |
| for ann in sorted_anns: | |
| m = ann['segmentation'] | |
| color_mask = np.concatenate([np.random.random(3), [0.35]]) | |
| img[m] = color_mask | |
| ax.imshow(img) | |
| def predict(img) -> Tuple[Dict, float]: | |
| """Transforms and performs a prediction on img and returns prediction and time taken. | |
| """ | |
| # Start the timer | |
| start_time = timer() | |
| # Transform the target image and add a batch dimension | |
| img_np = np.array(img) | |
| # Convierte de BGR a RGB si es necesario | |
| image = cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB) | |
| masks = mask_generator.generate(image) | |
| # Calculate the prediction time | |
| pred_time = round(timer() - start_time, 5) | |
| fig = plt.figure(figsize=(20,20)) | |
| plt.imshow(image) | |
| show_anns(masks) | |
| plt.axis('off') | |
| # Return the prediction dictionary and prediction time | |
| return fig, pred_time | |
| # 4. Gradio app | |
| # Create title, description and article strings | |
| title = "MedSam" | |
| description = "a specialized SAM model finely tuned for the segmentation of medical images. With this app, effortlessly extract image embeddings using the model's advanced mask decoder." | |
| article = "Created at gradio-sam-predictor-image-embedding-generator.ipynb ." | |
| # Create examples list from "examples/" directory | |
| example_list = [["examples/" + example] for example in os.listdir("examples")] | |
| # Create the Gradio demo | |
| demo = gr.Interface(fn=predict, # mapping function from input to output | |
| inputs=gr.Image(type="pil"), # what are the inputs? | |
| outputs=[gr.Plot(label="Predictions"), # what are the outputs? | |
| gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs | |
| examples=example_list, | |
| title=title, | |
| description=description, | |
| article=article) | |
| # Launch the demo! | |
| demo.launch(debug=False, # print errors locally? | |
| share=True) # generate a publically shareable URL? | |