app.py

app.py

@@ -0,0 +1,32 @@
+from ultralytics import YOLO
+import matplotlib.pyplot as plt
+import gradio as gr
+import cv2
+
+import seaborn as sns
+
+def predict(path:str):
+    model = YOLO("yolov8n.yaml")
+    model = YOLO("best.pt")
+    image = cv2.imread(path)
+    
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    results = model.predict(source=path) 
+    paleta= sns.color_palette("bright", 17)
+    fig = plt.figure()
+    vectObjs = results[0].masks.xy
+    resNumClase = results[0].boxes.cls.numpy().astype(int)
+    conf = results[0].boxes.conf.numpy()
+    for i in range(len(vectObjs)):
+        objDet = vectObjs[i].astype(int)
+        color = (paleta[i][0]*255, paleta[i][1]*255, paleta[i][2]*255)
+        image = cv2.polylines(image, [objDet], True, color, 4)
+        plt.text(objDet[0][0], objDet[0][1], results[0].names[resNumClase[i]]+" "+ str(conf[i]), bbox=dict(facecolor=paleta[i], alpha=0.5))
+    
+    plt.imshow(image)
+    plt.axis('off')
+    return plt
+gr.Interface(fn=predict, 
+             inputs=gr.components.Image(type="filepath", label="Input"),
+             outputs=gr.Plot(label="Resultado de detección de objetos con regularizacion")).launch()
+             #outputs=gr.components.Image(type="pil", label="Output")).launch()