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import os
import pandas as pd
import torch
from PIL import Image
from ultralytics import YOLO
import gradio as gr
class YOLODetect():
def __init__(self, modelo):
self.modelo = modelo
def predecir(self, source, imgsz=640, conf=0.7, iou=0.50):
self.results = self.modelo.predict(source=source, save=True, imgsz=imgsz, conf=conf, iou=iou)
return self.results
def render(self):
result = self.results[0]
file_name = os.path.join(result.save_dir, result.path)
render = Image.open(file_name)
return render
# Inicializa el modelo YOLOv8
path_best_model = 'yolov8n-pose.pt'
modelo_yolo = YOLO(path_best_model)
def detect_objects(im, size, iou, conf):
'''Wrapper para Gradio'''
g = (int(size) / max(im.size)) # gain
im = im.resize(tuple([int(x * g) for x in im.size]), Image.LANCZOS) # resize with antialiasing
model = YOLODetect(modelo_yolo)
results = model.predecir(source=im, imgsz=int(size), conf=conf, iou=iou)
objects_detected = results[0].boxes.cls.tolist() # Clases detectadas.
objects_conf = results[0].boxes.conf.tolist() # Probabilidad de detecci贸n por clase detectada.
objects_nested_list = pd.DataFrame({'Clase': objects_detected, 'Probabilidad': objects_conf})
result_img = model.render()
return result_img, objects_nested_list
def save_feedback(size, iou, conf,
object_count_detected,
objects_list,
user_text, feedback_text, check_status):
try:
# Aqu铆 puede ir el c贸digo para almacenar los datos en una base de datos.
return "Se guard贸 el feedback exitosamente."
except Exception as err:
print(err)
return "Error al guardar el feedback."
# Configura la interfaz de Gradio
with gr.Blocks() as demo:
gr.Markdown("# YOLOv8 Detecci贸n de postura")
with gr.Row():
iou_threshold = gr.Slider(label="NMS IoU Threshold (0.0 - 1.0)", minimum=0.0, maximum=1.0, value=0.8)
conf_threshold = gr.Slider(label="Umbral o threshold (0.0 - 1.0)", minimum=0.0, maximum=1.0, value=0.9)
size = gr.Dropdown(label="Tama帽o de la imagen", choices=["640", "1280"], value="640")
with gr.Row():
uploaded_image = gr.Image(type="pil", label="Cargar imagen")
result_image = gr.Image(label="Resultado")
detect_btn = gr.Button("Detectar postura")
objects_table = gr.DataFrame(label="Objetos detectados")
feedback = gr.Textbox(label="Ingrese su feedback (m谩ximo 100 caracteres)", max_lines=2)
user_text = gr.Textbox(label="Ingrese el nombre del usuario (m谩ximo 50 caracteres)", max_lines=2)
check_status = gr.Checkbox(label="驴El resultado contiene la cantidad correcta de figuras detectadas?", value=False)
save_btn = gr.Button("Guardar feedback")
output_message = gr.Textbox(label="Mensaje de salida", interactive=False)
detect_btn.click(fn=detect_objects,
inputs=[uploaded_image, size, iou_threshold, conf_threshold],
outputs=[result_image, objects_table])
save_btn.click(fn=save_feedback,
inputs=[size, iou_threshold, conf_threshold,
objects_table, user_text, feedback, check_status],
outputs=[output_message])
# Ejecuta la interfaz de Gradio
if __name__ == "__main__":
demo.launch()
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