Hugging Face's logo

Spaces:
Atualli
/
yolox6g
Sleeping

App Files
yolox6g
File size: 2,500 Bytes 
a236f10
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
 
import gradio as gr
import torch
import json
import yolov5

# 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')

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")

def yolov5_inference(
    image: gr.inputs.Image = None,
   
):
    """
    YOLOv5 inference function
    Args:
        image: Input image
        model_path: Path to the model
        image_size: Image size
        conf_threshold: Confidence threshold
        iou_threshold: IOU threshold
    Returns:
        Rendered image
    """
    
    results = model([image], size=image_size)
    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"),
]

outputs = gr.outputs.Image(type="filepath", label="Output Image")
title = "YOLOv5"
description = "YOLOv5 is a family of object detection models pretrained on COCO dataset. This model is a pip implementation of the original YOLOv5 model."

examples = [['zidane.jpg'], ['image3.jpg']]
demo_app = gr.Interface(
    fn=yolov5_inference,
    inputs=inputs,
    outputs=["text"],
    title=title,
    examples=examples,
    #cache_examples=True,
    #live=True,
    #theme='huggingface',
)
demo_app.launch(debug=True, server_name="192.168.0.153", server_port=8080, enable_queue=True)
demo_app.launch(debug=True, enable_queue=True)
#demo_app.launch(debug=True, server_port=8083, enable_queue=True)