Ajuste tensor

app.py

@@ -1,18 +1,24 @@
 import gradio as gr
 import torch
+import json
 import yolov7
 
 
 # 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/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
+#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/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
     
+model_path = "kadirnar/yolov7-v0.1" #"kadirnar/yolov7-tiny-v0.1"
+image_size = 640
+conf_threshold = 0.25
+iou_threshold = 0.45
+
 def yolov7_inference(
     image: gr.inputs.Image = None,
-    model_path: gr.inputs.Dropdown = None,
-    image_size: gr.inputs.Slider = 640,
-    conf_threshold: gr.inputs.Slider = 0.25,
-    iou_threshold: gr.inputs.Slider = 0.45,
+    #model_path: gr.inputs.Dropdown = None,
+    #image_size: gr.inputs.Slider = 640,
+    #conf_threshold: gr.inputs.Slider = 0.25,
+    #iou_threshold: gr.inputs.Slider = 0.45,
 ):
     """
     YOLOv7 inference function
@@ -30,35 +36,48 @@ def yolov7_inference(
     model.conf = conf_threshold
     model.iou = iou_threshold
     results = model([image], size=image_size)
-    return results.render()[0]
+    tensor = {
+      "tensorflow": [ 
+      ]
+    }
+
+    if results.pred is not None:
+        for i, element in enumerate(results.pred[0]):
+            object = {}
+            #print (element[0])
+            itemclass = round(element[5].item())
+            object["classe"] = itemclass
+            object["nome"] = results.names[itemclass]
+            object["score"] = element[4].item()
+            object["x"] = element[0].item()
+            object["y"] = element[1].item()
+            object["w"] = element[2].item()
+            object["h"] = element[3].item()
+            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.Dropdown(
-        choices=[
-            "kadirnar/yolov7-tiny-v0.1",
-            "kadirnar/yolov7-v0.1",
-        ],
-        default="kadirnar/yolov7-tiny-v0.1",
-        label="Model",
-    ),
-    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
 ]
 
-outputs = gr.outputs.Image(type="filepath", label="Output Image")
+#outputs = gr.outputs.Image(type="filepath", label="Output Image")
 title = "Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors"
 
-examples = [['small-vehicles1.jpeg', 'kadirnar/yolov7-tiny-v0.1', 640, 0.25, 0.45], ['zidane.jpg', 'kadirnar/yolov7-v0.1', 640, 0.25, 0.45]]
+examples = [['small-vehicles1.jpeg'], ['zidane.jpg']]
 demo_app = gr.Interface(
     fn=yolov7_inference,
     inputs=inputs,
-    outputs=outputs,
+    outputs=["text"],
     title=title,
     examples=examples,
-    cache_examples=True,
-    theme='huggingface',
+    #cache_examples=True,
+    #theme='huggingface',
 )
-demo_app.launch(debug=True, enable_queue=True)
+#demo_app.launch(debug=True, server_name="192.168.0.153", server_port=8080, enable_queue=True)
+demo_app.launch(debug=True, server_port=8083, enable_queue=True)