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  1. .gitattributes +0 -34
  2. .gitignore +2 -0
  3. README.md +26 -3
  4. app.py +61 -0
  5. models/rtdetr_r101vd.onnx +3 -0
  6. models/rtdetr_r18vd.onnx +3 -0
  7. models/rtdetr_r50vd.onnx +3 -0
  8. poetry.lock +0 -0
  9. pyproject.toml +20 -0
  10. requirements.txt +1097 -0
  11. samples/1.jpg +0 -0
  12. utils.py +105 -0
.gitattributes CHANGED
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- *.npz filter=lfs diff=lfs merge=lfs -text
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  *.onnx filter=lfs diff=lfs merge=lfs -text
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  *.onnx filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
.gitignore ADDED
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+ __pycache__
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+ flagged
README.md CHANGED
@@ -1,13 +1,36 @@
1
  ---
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- title: Rtdetr
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- emoji: ⚡
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  colorFrom: pink
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  colorTo: yellow
6
  sdk: gradio
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  sdk_version: 4.14.0
 
8
  app_file: app.py
9
  pinned: false
10
  license: mit
11
  ---
12
 
13
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ title: RT-DETR
3
+ emoji: 🎷
4
  colorFrom: pink
5
  colorTo: yellow
6
  sdk: gradio
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  sdk_version: 4.14.0
8
+ python_version: 3.11.6
9
  app_file: app.py
10
  pinned: false
11
  license: mit
12
  ---
13
 
14
+ ## RT-DETR Demo
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+
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+ ONNX exported from official pretrained models on COCO + Objects365
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+
18
+ | Model | Dataset | Input Size | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | #Params(M) | FPS | checkpoint |
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+ | :-----------: | :-------------: | :--------: | :--------------: | :---------------------------: | :--------: | :-: | :-----------------------------------------------------------------------------------------------------------------------------: |
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+ | rtdetr_18vd | COCO+Objects365 | 640 | 49.0 | 66.5 | 20 | 217 | [url<sup>\*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r18vd_5x_coco_objects365_from_paddle.pth) |
21
+ | rtdetr_r50vd | COCO+Objects365 | 640 | 55.2 | 73.4 | 42 | 108 | [url<sup>\*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r50vd_2x_coco_objects365_from_paddle.pth) |
22
+ | rtdetr_r101vd | COCO+Objects365 | 640 | 56.2 | 74.5 | 76 | 74 | [url<sup>\*</sup>](https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetr_r101vd_2x_coco_objects365_from_paddle.pth) |
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+
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+ ### Run locally
25
+
26
+ Install dependencies
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+
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+ ```sh
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+ poetry install
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+ ```
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+
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+ Run server
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+
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+ ```sh
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+ poetry run gradio app.py
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+ ```
app.py ADDED
@@ -0,0 +1,61 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from glob import glob
2
+ import os
3
+ from timeit import default_timer as timer
4
+
5
+ import gradio as gr
6
+ import numpy as np
7
+ import onnxruntime as ort
8
+ from PIL import ImageOps
9
+
10
+ from utils import draw_overlay
11
+
12
+
13
+ sessions = {
14
+ "rtdetr_18vd": ort.InferenceSession("models/rtdetr_r18vd.onnx"),
15
+ "rtdetr_r50vd": ort.InferenceSession("models/rtdetr_r50vd.onnx"),
16
+ "rtdetr_r101vd": ort.InferenceSession("models/rtdetr_r101vd.onnx"),
17
+ }
18
+
19
+
20
+ def run(image, threshold=0.5, model="rtdetr_18vd"):
21
+ im = ImageOps.pad(image, (640, 640), color="black")
22
+ input = np.asarray(im)
23
+ input = np.transpose(input, (2, 0, 1))
24
+ input = np.expand_dims(input, axis=0)
25
+ input = input.astype(np.float32) / 255
26
+
27
+ inference_start = timer()
28
+ labels, boxes, scores = sessions[model].run(
29
+ None,
30
+ {"images": input, "orig_target_sizes": np.array([[640, 640]])},
31
+ )
32
+ inference_seconds = timer() - inference_start
33
+ draw_overlay(im, threshold, labels, boxes, scores)
34
+
35
+ return im, labels[0], boxes[0], scores[0], inference_seconds * 1000
36
+
37
+
38
+ demo = gr.Interface(
39
+ run,
40
+ inputs=[
41
+ gr.Image(type="pil", label="image"),
42
+ gr.Slider(0, 1, 0.5, label="score threshold"),
43
+ gr.Dropdown(
44
+ ["rtdetr_18vd", "rtdetr_r50vd", "rtdetr_r101vd"],
45
+ value="rtdetr_18vd",
46
+ label="model",
47
+ ),
48
+ ],
49
+ outputs=[
50
+ gr.Image(label="result"),
51
+ gr.Textbox(label="labels"),
52
+ gr.Textbox(label="boxes"),
53
+ gr.Textbox(label="scores"),
54
+ gr.Textbox(label="inference ms"),
55
+ ],
56
+ examples=[glob(os.path.join("samples", "*"))],
57
+ title="RT-DETR",
58
+ description="ONNX exported from official pretrained models on COCO + Objects365",
59
+ )
60
+
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+ demo.launch()
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+ [tool.poetry]
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+ name = "rtdetr-space"
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+ version = "0.1.0"
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+ description = ""
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+ authors = ["Your Name <[email protected]>"]
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+ license = "MIT"
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+ readme = "README.md"
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+
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+ [tool.poetry.dependencies]
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+ python = "^3.11"
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+ [build-system]
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+ requires = ["poetry-core"]
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+ build-backend = "poetry.core.masonry.api"
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+ fsspec==2023.12.2 ; python_version >= "3.11" and python_version < "4.0" \
222
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+ gradio-client==0.8.0 ; python_version >= "3.11" and python_version < "4.0" \
225
+ --hash=sha256:0d817e9ff72fc1ba0866c11612a2b1c995d370ab7ea0fbf2bd0649e6da49ad76 \
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+ gradio==4.14.0 ; python_version >= "3.11" and python_version < "4.0" \
228
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+ h11==0.14.0 ; python_version >= "3.11" and python_version < "4.0" \
231
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784
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785
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787
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788
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789
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791
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792
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793
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794
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795
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796
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797
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798
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799
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800
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801
+ pydantic==2.5.3 ; python_version >= "3.11" and python_version < "4.0" \
802
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803
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804
+ pydub==0.25.1 ; python_version >= "3.11" and python_version < "4.0" \
805
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806
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807
+ pygments==2.17.2 ; python_version >= "3.11" and python_version < "4.0" \
808
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809
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810
+ pyparsing==3.1.1 ; python_version >= "3.11" and python_version < "4.0" \
811
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812
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813
+ pyreadline3==3.4.1 ; sys_platform == "win32" and python_version >= "3.11" and python_version < "4.0" \
814
+ --hash=sha256:6f3d1f7b8a31ba32b73917cefc1f28cc660562f39aea8646d30bd6eff21f7bae \
815
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816
+ python-dateutil==2.8.2 ; python_version >= "3.11" and python_version < "4.0" \
817
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818
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819
+ python-multipart==0.0.6 ; python_version >= "3.11" and python_version < "4.0" \
820
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821
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822
+ pytz==2023.3.post1 ; python_version >= "3.11" and python_version < "4.0" \
823
+ --hash=sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b \
824
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825
+ pyyaml==6.0.1 ; python_version >= "3.11" and python_version < "4.0" \
826
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827
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831
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832
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834
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835
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838
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839
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840
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841
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842
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843
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844
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845
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846
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848
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849
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850
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851
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852
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853
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854
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855
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856
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860
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861
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862
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863
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864
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865
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868
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870
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871
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872
+ --hash=sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c \
873
+ --hash=sha256:fca0e3a251908a499833aa292323f32437106001d436eca0e6e7833256674585 \
874
+ --hash=sha256:fd1592b3fdf65fff2ad0004b5e363300ef59ced41c2e6b3a99d4089fa8c5435d \
875
+ --hash=sha256:fd66fc5d0da6d9815ba2cebeb4205f95818ff4b79c3ebe268e75d961704af52f
876
+ referencing==0.32.1 ; python_version >= "3.11" and python_version < "4.0" \
877
+ --hash=sha256:3c57da0513e9563eb7e203ebe9bb3a1b509b042016433bd1e45a2853466c3dd3 \
878
+ --hash=sha256:7e4dc12271d8e15612bfe35792f5ea1c40970dadf8624602e33db2758f7ee554
879
+ requests==2.31.0 ; python_version >= "3.11" and python_version < "4.0" \
880
+ --hash=sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f \
881
+ --hash=sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1
882
+ rich==13.7.0 ; python_version >= "3.11" and python_version < "4.0" \
883
+ --hash=sha256:5cb5123b5cf9ee70584244246816e9114227e0b98ad9176eede6ad54bf5403fa \
884
+ --hash=sha256:6da14c108c4866ee9520bbffa71f6fe3962e193b7da68720583850cd4548e235
885
+ rpds-py==0.16.2 ; python_version >= "3.11" and python_version < "4.0" \
886
+ --hash=sha256:0474df4ade9a3b4af96c3d36eb81856cb9462e4c6657d4caecfd840d2a13f3c9 \
887
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888
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889
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890
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891
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892
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893
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894
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895
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896
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897
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898
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899
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900
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901
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902
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903
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904
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905
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906
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907
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908
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909
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910
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911
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912
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913
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914
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915
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916
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917
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918
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919
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920
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921
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922
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923
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924
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925
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926
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927
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928
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929
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930
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931
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932
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933
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934
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935
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936
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937
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938
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939
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940
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941
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942
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943
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944
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945
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946
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947
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948
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949
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950
+ --hash=sha256:a9880b4656efe36ccad41edc66789e191e5ee19a1ea8811e0aed6f69851a82f4 \
951
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952
+ --hash=sha256:adc0c3d6fc6ae35fee3e4917628983f6ce630d513cbaad575b4517d47e81b4bb \
953
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954
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955
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956
+ --hash=sha256:b6f5549d6ed1da9bfe3631ca9483ae906f21410be2445b73443fa9f017601c6f \
957
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958
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959
+ --hash=sha256:c647ca87fc0ebe808a41de912e9a1bfef9acb85257e5d63691364ac16b81c1f0 \
960
+ --hash=sha256:ca57468da2d9a660bcf8961637c85f2fbb2aa64d9bc3f9484e30c3f9f67b1dd7 \
961
+ --hash=sha256:cad0f59ee3dc35526039f4bc23642d52d5f6616b5f687d846bfc6d0d6d486db0 \
962
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963
+ --hash=sha256:ccd4e400309e1f34a5095bf9249d371f0fd60f8a3a5c4a791cad7b99ce1fd38d \
964
+ --hash=sha256:cffa76b385dfe1e38527662a302b19ffb0e7f5cf7dd5e89186d2c94a22dd9d0c \
965
+ --hash=sha256:d0dd7ed2f16df2e129496e7fbe59a34bc2d7fc8db443a606644d069eb69cbd45 \
966
+ --hash=sha256:d452817e0d9c749c431a1121d56a777bd7099b720b3d1c820f1725cb40928f58 \
967
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968
+ --hash=sha256:dcbe1f8dd179e4d69b70b1f1d9bb6fd1e7e1bdc9c9aad345cdeb332e29d40748 \
969
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970
+ --hash=sha256:e04e56b4ca7a770593633556e8e9e46579d66ec2ada846b401252a2bdcf70a6d \
971
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972
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973
+ --hash=sha256:e9e557db6a177470316c82f023e5d571811c9a4422b5ea084c85da9aa3c035fc \
974
+ --hash=sha256:eab36eae3f3e8e24b05748ec9acc66286662f5d25c52ad70cadab544e034536b \
975
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976
+ --hash=sha256:ec2e1cf025b2c0f48ec17ff3e642661da7ee332d326f2e6619366ce8e221f018 \
977
+ --hash=sha256:ed99b4f7179d2111702020fd7d156e88acd533f5a7d3971353e568b6051d5c97 \
978
+ --hash=sha256:ee94cb58c0ba2c62ee108c2b7c9131b2c66a29e82746e8fa3aa1a1effbd3dcf1 \
979
+ --hash=sha256:f19afcfc0dd0dca35694df441e9b0f95bc231b512f51bded3c3d8ca32153ec19 \
980
+ --hash=sha256:f1b9d9260e06ea017feb7172976ab261e011c1dc2f8883c7c274f6b2aabfe01a \
981
+ --hash=sha256:f28ac0e8e7242d140f99402a903a2c596ab71550272ae9247ad78f9a932b5698 \
982
+ --hash=sha256:f42e25c016927e2a6b1ce748112c3ab134261fc2ddc867e92d02006103e1b1b7 \
983
+ --hash=sha256:f4bd4578e44f26997e9e56c96dedc5f1af43cc9d16c4daa29c771a00b2a26851 \
984
+ --hash=sha256:f811771019f063bbd0aa7bb72c8a934bc13ebacb4672d712fc1639cfd314cccc
985
+ semantic-version==2.10.0 ; python_version >= "3.11" and python_version < "4.0" \
986
+ --hash=sha256:bdabb6d336998cbb378d4b9db3a4b56a1e3235701dc05ea2690d9a997ed5041c \
987
+ --hash=sha256:de78a3b8e0feda74cabc54aab2da702113e33ac9d9eb9d2389bcf1f58b7d9177
988
+ shellingham==1.5.4 ; python_version >= "3.11" and python_version < "4.0" \
989
+ --hash=sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686 \
990
+ --hash=sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de
991
+ six==1.16.0 ; python_version >= "3.11" and python_version < "4.0" \
992
+ --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \
993
+ --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
994
+ sniffio==1.3.0 ; python_version >= "3.11" and python_version < "4.0" \
995
+ --hash=sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101 \
996
+ --hash=sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384
997
+ starlette==0.35.1 ; python_version >= "3.11" and python_version < "4.0" \
998
+ --hash=sha256:3e2639dac3520e4f58734ed22553f950d3f3cb1001cd2eaac4d57e8cdc5f66bc \
999
+ --hash=sha256:50bbbda9baa098e361f398fda0928062abbaf1f54f4fadcbe17c092a01eb9a25
1000
+ sympy==1.12 ; python_version >= "3.11" and python_version < "4.0" \
1001
+ --hash=sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5 \
1002
+ --hash=sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8
1003
+ tomlkit==0.12.0 ; python_version >= "3.11" and python_version < "4.0" \
1004
+ --hash=sha256:01f0477981119c7d8ee0f67ebe0297a7c95b14cf9f4b102b45486deb77018716 \
1005
+ --hash=sha256:926f1f37a1587c7a4f6c7484dae538f1345d96d793d9adab5d3675957b1d0766
1006
+ toolz==0.12.0 ; python_version >= "3.11" and python_version < "4.0" \
1007
+ --hash=sha256:2059bd4148deb1884bb0eb770a3cde70e7f954cfbbdc2285f1f2de01fd21eb6f \
1008
+ --hash=sha256:88c570861c440ee3f2f6037c4654613228ff40c93a6c25e0eba70d17282c6194
1009
+ tqdm==4.66.1 ; python_version >= "3.11" and python_version < "4.0" \
1010
+ --hash=sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386 \
1011
+ --hash=sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7
1012
+ typer[all]==0.9.0 ; python_version >= "3.11" and python_version < "4.0" \
1013
+ --hash=sha256:50922fd79aea2f4751a8e0408ff10d2662bd0c8bbfa84755a699f3bada2978b2 \
1014
+ --hash=sha256:5d96d986a21493606a358cae4461bd8cdf83cbf33a5aa950ae629ca3b51467ee
1015
+ typing-extensions==4.9.0 ; python_version >= "3.11" and python_version < "4.0" \
1016
+ --hash=sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783 \
1017
+ --hash=sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd
1018
+ tzdata==2023.4 ; python_version >= "3.11" and python_version < "4.0" \
1019
+ --hash=sha256:aa3ace4329eeacda5b7beb7ea08ece826c28d761cda36e747cfbf97996d39bf3 \
1020
+ --hash=sha256:dd54c94f294765522c77399649b4fefd95522479a664a0cec87f41bebc6148c9
1021
+ urllib3==2.1.0 ; python_version >= "3.11" and python_version < "4.0" \
1022
+ --hash=sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3 \
1023
+ --hash=sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54
1024
+ uvicorn==0.25.0 ; python_version >= "3.11" and python_version < "4.0" \
1025
+ --hash=sha256:6dddbad1d7ee0f5140aba5ec138ddc9612c5109399903828b4874c9937f009c2 \
1026
+ --hash=sha256:ce107f5d9bd02b4636001a77a4e74aab5e1e2b146868ebbad565237145af444c
1027
+ websockets==11.0.3 ; python_version >= "3.11" and python_version < "4.0" \
1028
+ --hash=sha256:01f5567d9cf6f502d655151645d4e8b72b453413d3819d2b6f1185abc23e82dd \
1029
+ --hash=sha256:03aae4edc0b1c68498f41a6772d80ac7c1e33c06c6ffa2ac1c27a07653e79d6f \
1030
+ --hash=sha256:0ac56b661e60edd453585f4bd68eb6a29ae25b5184fd5ba51e97652580458998 \
1031
+ --hash=sha256:0ee68fe502f9031f19d495dae2c268830df2760c0524cbac5d759921ba8c8e82 \
1032
+ --hash=sha256:1553cb82942b2a74dd9b15a018dce645d4e68674de2ca31ff13ebc2d9f283788 \
1033
+ --hash=sha256:1a073fc9ab1c8aff37c99f11f1641e16da517770e31a37265d2755282a5d28aa \
1034
+ --hash=sha256:1d2256283fa4b7f4c7d7d3e84dc2ece74d341bce57d5b9bf385df109c2a1a82f \
1035
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1036
+ --hash=sha256:1fdf26fa8a6a592f8f9235285b8affa72748dc12e964a5518c6c5e8f916716f7 \
1037
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1038
+ --hash=sha256:279e5de4671e79a9ac877427f4ac4ce93751b8823f276b681d04b2156713b9dd \
1039
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1040
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1041
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1042
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1043
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1044
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1045
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1046
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1047
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1048
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1049
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1050
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1051
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1052
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1053
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1054
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1055
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1056
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1057
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1058
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1059
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1060
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1061
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1062
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1063
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1064
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1065
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1066
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1067
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1068
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1073
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1074
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1075
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1076
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1077
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1079
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1080
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1081
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1082
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1083
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1084
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1085
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1086
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1087
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1088
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1089
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1096
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1097
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samples/1.jpg ADDED
utils.py ADDED
@@ -0,0 +1,105 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import colorsys
2
+
3
+ from PIL import Image, ImageDraw, ImageFont, ImageOps
4
+
5
+
6
+ COCO_CLASSES = [
7
+ "person",
8
+ "bicycle",
9
+ "car",
10
+ "motorcycle",
11
+ "airplane",
12
+ "bus",
13
+ "train",
14
+ "truck",
15
+ "boat",
16
+ "traffic light",
17
+ "fire hydrant",
18
+ "stop sign",
19
+ "parking meter",
20
+ "bench",
21
+ "bird",
22
+ "cat",
23
+ "dog",
24
+ "horse",
25
+ "sheep",
26
+ "cow",
27
+ "elephant",
28
+ "bear",
29
+ "zebra",
30
+ "giraffe",
31
+ "backpack",
32
+ "umbrella",
33
+ "handbag",
34
+ "tie",
35
+ "suitcase",
36
+ "frisbee",
37
+ "skis",
38
+ "snowboard",
39
+ "sports ball",
40
+ "kite",
41
+ "baseball bat",
42
+ "baseball glove",
43
+ "skateboard",
44
+ "surfboard",
45
+ "tennis racket",
46
+ "bottle",
47
+ "wine glass",
48
+ "cup",
49
+ "fork",
50
+ "knife",
51
+ "spoon",
52
+ "bowl",
53
+ "banana",
54
+ "apple",
55
+ "sandwich",
56
+ "orange",
57
+ "broccoli",
58
+ "carrot",
59
+ "hot dog",
60
+ "pizza",
61
+ "donut",
62
+ "cake",
63
+ "chair",
64
+ "couch",
65
+ "potted plant",
66
+ "bed",
67
+ "dining table",
68
+ "toilet",
69
+ "tv",
70
+ "laptop",
71
+ "mouse",
72
+ "remote",
73
+ "keyboard",
74
+ "cell phone",
75
+ "microwave",
76
+ "oven",
77
+ "toaster",
78
+ "sink",
79
+ "refrigerator",
80
+ "book",
81
+ "clock",
82
+ "vase",
83
+ "scissors",
84
+ "teddy bear",
85
+ "hair drier",
86
+ "toothbrush",
87
+ ]
88
+
89
+
90
+ def draw_overlay(im, threshold, labels, boxes, scores):
91
+ draw = ImageDraw.Draw(im)
92
+ font = ImageFont.load_default()
93
+
94
+ for label, box, score in zip(labels[0], boxes[0], scores[0]):
95
+ if score > threshold:
96
+ color = get_color_by_label(label)
97
+ draw.rectangle(box, outline=color, width=2)
98
+ class_name = COCO_CLASSES[label]
99
+ text = f"{class_name}: {score:.2f}"
100
+ draw.text((box[0] + 4, box[1] + 2), text, fill=color, font=font)
101
+
102
+
103
+ def get_color_by_label(label):
104
+ hue = (((label * 17) % len(COCO_CLASSES))) / len(COCO_CLASSES)
105
+ return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(hue, 1.0, 1.0))