SAMSEGMENT / app.py
fireedman's picture
Update app.py
f72a933 verified
from typing import List
import os
import cv2
import supervision as sv
import numpy as np
import gradio as gr
import torch
from transformers import pipeline
from PIL import Image
# Definici贸n de la clase SamAutomaticMaskGenerator
class SamAutomaticMaskGenerator:
def __init__(self, sam_pipeline):
self.sam_pipeline = sam_pipeline
def generate(self, image_rgb):
# Convertir el array de NumPy a PIL Image
image_pil = Image.fromarray(image_rgb)
outputs = self.sam_pipeline(image_pil, points_per_batch=32)
mask = np.array(outputs['masks'], dtype=np.uint8)
return mask
# Configuraci贸n del modelo SAM
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
sam_pipeline = pipeline(
task="mask-generation",
model="facebook/sam-vit-large",
device=DEVICE
)
EXAMPLES = [
["https://media.roboflow.com/notebooks/examples/dog.jpeg"],
["https://media.roboflow.com/notebooks/examples/dog-3.jpeg"]
]
mask_generator = SamAutomaticMaskGenerator(sam_pipeline)
# Funci贸n para procesar y anotar la imagen
def process_image(image_pil):
# Convertir PIL Image a numpy array para procesamiento
image_rgb = np.array(image_pil)
image_bgr = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR)
# Generar la m谩scara y anotar la imagen
sam_result = mask_generator.generate(image_rgb)
mask_annotator = sv.MaskAnnotator(color_lookup=sv.ColorLookup.INDEX)
detections = sv.Detections.from_sam(sam_result=sam_result)
annotated_image = mask_annotator.annotate(scene=image_bgr.copy(), detections=detections)
# Convertir de nuevo a formato RGB y luego a PIL Image para Gradio
annotated_image_rgb = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
annotated_image_pil = Image.fromarray(annotated_image_rgb)
return image_pil, annotated_image_pil
# Construcci贸n de la interfaz Gradio
with gr.Blocks() as demo:
gr.Markdown("# SAM - Segmentaci贸n de Im谩genes")
with gr.Row():
with gr.Column():
input_image = gr.Image(type="pil", label="Cargar Imagen")
submit_button = gr.Button("Segmentar")
with gr.Column():
original_image = gr.Image(type="pil", label="Imagen Original")
segmented_image = gr.Image(type="pil", label="Imagen Segmentada")
submit_button.click(
process_image,
inputs=input_image,
outputs=[original_image, segmented_image]
)
with gr.Row():
gr.Examples(
examples=EXAMPLES,
fn=process_image,
inputs=[input_image],
outputs=[original_image, segmented_image],
cache_examples=False,
run_on_click=True
)
demo.launch(debug=True)