Hugging Face's logo

Spaces:
tree3po
/
supervision
Sleeping

App Files Community
supervision
File size: 2,888 Bytes 
e751200
 
 
 
 
 
 
 
 
2d673aa
c269db1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e751200
2d673aa
e751200
becce92
e751200
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
500075a
e751200
 
 
 
 
 
 
2d673aa
 
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
 
import cv2
import gradio as gr
import supervision as sv
from ultralytics import YOLO
from PIL import Image
import torch
import time
import numpy as np
import uuid
import spaces

print(YOLO)

"""| YOLOv8      | `yolov8n.pt` `yolov8s.pt` `yolov8m.pt` `yolov8l.pt` `yolov8x.pt`                                       …
| YOLOv8-seg  | `yolov8n-seg.pt` `yolov8s-seg.pt` `yolov8m-seg.pt` `yolov8l-seg.pt` `yolov8x-seg.pt`                   …
| YOLOv8-pose | `yolov8n-pose.pt` `yolov8s-pose.pt` `yolov8m-pose.pt` `yolov8l-pose.pt` `yolov8x-pose.pt` `yolov8x-pose…
| YOLOv8-obb  | `yolov8n-obb.pt` `yolov8s-obb.pt` `yolov8m-obb.pt` `yolov8l-obb.pt` `yolov8x-obb.pt`                   …
| YOLOv8-cls  | `yolov8n-cls.pt` `yolov8s-cls.pt` `yolov8m-cls.pt` `yolov8l-cls.pt` `yolov8x-cls.pt`  """

ver=[6:11]
ltr=["n","s","m","1","x"]
tsk=["","seg","pose","obb","cls"]
#yolov8s.pt
modin=f"yolov{ver[2]{ltr[1]}-{tsk[0]}.pt"
print(modin)
model = YOLO(modin)

@spaces.GPU
def stream_object_detection(video):
    SUBSAMPLE=1
    cap = cv2.VideoCapture(video)
    # This means we will output mp4 videos
    video_codec = cv2.VideoWriter_fourcc(*"mp4v") # type: ignore
    fps = int(cap.get(cv2.CAP_PROP_FPS))
    desired_fps = fps // SUBSAMPLE
    width  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) // 2
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) // 2
    iterating, frame = cap.read()
    n_frames = 0
    output_video_name = f"output_{uuid.uuid4()}.mp4"
    output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height)) # type: ignore

    while iterating:
        frame = cv2.resize( frame, (0,0), fx=0.5, fy=0.5)
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        result = model(Image.fromarray(frame))[0]
        detections = sv.Detections.from_ultralytics(result)
        outp = draw_box(frame,detections)
        frame = np.array(outp)
        # Convert RGB to BGR
        frame = frame[:, :, ::-1].copy()
        output_video.write(frame)
        batch = []
        output_video.release()
        yield output_video_name
        output_video_name = f"output_{uuid.uuid4()}.mp4"
        output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height)) # type: ignore
        iterating, frame = cap.read()
        n_frames += 1

with gr.Blocks() as app:
    gr.HTML("<div style='font-size: 50px;font-weight: 800;'>Supervision</div><div style='font-size: 30px;'>Video Object Detection</div><div>Github:<a href='https://github.com/roboflow/supervision' target='_blank'>https://github.com/roboflow/supervision</a></div>")
    #inp = gr.Image(type="filepath")
    with gr.Row():
        with gr.Column():
            inp = gr.Video()
            btn = gr.Button()
        outp_v = gr.Video(label="Processed Video", streaming=True, autoplay=True)
    btn.click(stream_object_detection,inp,[outp_v])
app.queue().launch()