Update app.py

app.py

@@ -1,22 +1,26 @@
 import gradio as gr
 import torch
 import json
-import yolov5
+#import yolov5
+from ultralytics import YOLO
 
 # Images
-torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/WongKinYiu/yolov7/main/inference/images/image3.jpg', 'image3.jpg')
-torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt','yolov5s.pt')
+#torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
+#torch.hub.download_url_to_file('https://raw.githubusercontent.com/WongKinYiu/yolov7/main/inference/images/image3.jpg', 'image3.jpg')
+#torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt','yolov5s.pt')
 
-model_path = "yolov5x.pt" #"yolov5s.pt" #"yolov5m.pt", "yolov5l.pt", "yolov5x.pt",
-image_size = 640,
-conf_threshold = 0.25,
-iou_threshold = 0.45,
-model = yolov5.load(model_path, device="cpu")
+#model_path = "yolov5x.pt" #"yolov5x.pt" "yolov5m.pt", "yolov5l.pt", "yolov5x.pt",
+image_size = 640 #640 #320,
+conf_threshold = 0.25 #0.30,
+iou_threshold = 0.15
+#model = yolov5.load(model_path, device="cpu")
+model = YOLO("yolo11x.pt") #YOLO("yolo11x.pt")
 
 def yolov5_inference(
-    image: gr.inputs.Image = None,
-   
+    image: gr.inputs.Image = None #,
+   # image_size: gr.inputs.Slider = 640,
+   # conf_threshold: gr.inputs.Slider = 0.25,
+   # iou_threshold: gr.inputs.Slider = 0.45
 ):
     """
     YOLOv5 inference function
@@ -29,14 +33,15 @@ def yolov5_inference(
     Returns:
         Rendered image
     """
-    
-    results = model([image], size=image_size)
+    model.conf = conf_threshold
+    model.iou = iou_threshold
+    results = model([image]) #, size=image_size)
     tensor = {
       "tensorflow": [ 
       ]
     }
 
-    if results.pred is not None:
+    ''' if results.pred is not None:
         for i, element in enumerate(results.pred[0]):
             object = {}
             #print (element[0])
@@ -49,16 +54,44 @@ def yolov5_inference(
             object["w"] = element[2].item()
             object["h"] = element[3].item()
             tensor["tensorflow"].append(object)
-          
-  
+     '''
+    for result in results:
+
+         # As caixas delimitadoras (bounding boxes) são acessíveis via r.boxes
+        boxes = result.boxes
+
+        # Cada detecção individual em boxes tem atributos como xyxy, conf, cls
+        for box in boxes:
+            object = {}
+            # Coordenadas da caixa (formato xyxy)
+            x1, y1, x2, y2 = box.xyxy[0].cpu().numpy().astype(int)
+
+            # Confiança da detecção
+            numpy_array = box.conf[0].cpu().numpy()
+            object["score"] = numpy_array.item() if numpy_array.size > 0 else 0.0
+
+            # ID da classe
+            class_id = int(box.cls[0].cpu().numpy()) 
+            object["classe"] = class_id
+            # Nome da classe
+            # r.names é um dicionário que mapeia IDs de classe para nomes
+            object["nome"] = result.names[class_id]
+
+            object["x"] = int (x1)
+            object["y"] = int (y1)
+            object["w"] = int (x2)
+            object["h"] = int (y2)
+            tensor["tensorflow"].append(object)
 
     text = json.dumps(tensor)
-    #print (text)
     return text #results.render()[0]
         
 
 inputs = [
     gr.inputs.Image(type="pil", label="Input Image"),
+   # gr.inputs.Slider(minimum=0, maximum=1280, default=640, step=8, label="Image Size"),
+   # gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.01, label="conf_threshold"),
+   # gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.01, label="iou_threshold"),
 ]
 
 outputs = gr.outputs.Image(type="filepath", label="Output Image")
@@ -70,10 +103,12 @@ demo_app = gr.Interface(
     fn=yolov5_inference,
     inputs=inputs,
     outputs=["text"],
+    #outputs=outputs,
     title=title,
-    examples=examples,
+    #examples=examples,
     #cache_examples=True,
     #live=True,
     #theme='huggingface',
 )
-demo_app.launch(debug=True, enable_queue=True)
+demo_app.launch( enable_queue=True, share=True)
+#demo_app.launch(debug=True,  server_port=8087, enable_queue=True)