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isLinXu
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0e66889
1
Parent(s):
aeac74c
update app.py
Browse files- app.py +81 -0
- requirements.txt +20 -0
app.py
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import os
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os.system("pip install xtcocotools>=1.12")
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os.system("pip install 'mmengine>=0.6.0'")
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os.system("pip install 'mmcv>=2.0.0rc4,<2.1.0'")
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os.system("pip install 'mmdet>=3.0.0,<4.0.0'")
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os.system("pip install 'mmpose'")
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import PIL
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import cv2
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import mmpose
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import numpy as np
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import torch
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from mmpose.apis import MMPoseInferencer
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import gradio as gr
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import warnings
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warnings.filterwarnings("ignore")
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mmpose_model_list = ["human", "hand", "face", "animal", "wholebody",
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"vitpose", "vitpose-s", "vitpose-b", "vitpose-l", "vitpose-h"]
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def save_image(img, img_path):
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# Convert PIL image to OpenCV image
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img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
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# Save OpenCV image
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cv2.imwrite(img_path, img)
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def download_test_image():
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# Images
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torch.hub.download_url_to_file(
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'https://user-images.githubusercontent.com/59380685/266264420-21575a83-4057-41cf-8a4a-b3ea6f332d79.jpg',
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'bus.jpg')
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torch.hub.download_url_to_file(
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'https://user-images.githubusercontent.com/59380685/266264536-82afdf58-6b9a-4568-b9df-551ee72cb6d9.jpg',
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'dogs.jpg')
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torch.hub.download_url_to_file(
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'https://user-images.githubusercontent.com/59380685/266264600-9d0c26ca-8ba6-45f2-b53b-4dc98460c43e.jpg',
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'zidane.jpg')
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def predict_pose(img, model_name, out_dir):
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img_path = "input_img.jpg"
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save_image(img, img_path)
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device = torch.cuda.current_device() if torch.cuda.is_available() else 'cpu'
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inferencer = MMPoseInferencer(model_name, device=device)
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result_generator = inferencer(img_path, show=False, out_dir=out_dir)
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result = next(result_generator)
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save_dir = './output/visualizations/'
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out_img_path = save_dir + img_path
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out_img = PIL.Image.open(out_img_path)
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return out_img
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out_dir = "./output/visualizations/"
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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download_test_image()
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input_image = gr.inputs.Image(type='pil', label="Original Image")
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model_name = gr.inputs.Dropdown(choices=[m for m in mmpose_model_list], label='Model')
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out_dir = gr.inputs.Textbox(label="Output Directory", default="./output")
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output_image = gr.outputs.Image(type="pil", label="Output Image")
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examples = [
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['zidane.jpg', 'human'],
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['dogs.jpg', 'animal'],
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]
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title = "MMPose detection web demo"
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description = "<div align='center'><img src='https://raw.githubusercontent.com/open-mmlab/mmpose/main/resources/mmpose-logo.png' width='450''/><div>" \
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"<p style='text-align: center'><a href='https://github.com/open-mmlab/mmpose'>MMPose</a> MMPose 是一款基于 PyTorch 的姿态分析的开源工具箱,是 OpenMMLab 项目的成员之一。" \
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"OpenMMLab Pose Estimation Toolbox and Benchmark..</p>"
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article = "<p style='text-align: center'><a href='https://github.com/open-mmlab/mmpose'>MMPose</a></p>" \
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"<p style='text-align: center'><a href='https://github.com/isLinXu'>gradio build by gatilin</a></a></p>"
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iface = gr.Interface(fn=predict_pose, inputs=[input_image, model_name, out_dir], outputs=output_image,
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examples=examples, title=title, description=description, article=article)
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iface.launch()
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requirements.txt
ADDED
@@ -0,0 +1,20 @@
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wget~=3.2
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opencv-python~=4.6.0.66
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numpy~=1.23.0
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torch~=1.13.1
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torchvision~=0.14.1
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pillow~=9.4.0
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gradio~=3.42.0
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ultralytics~=8.0.169
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pyyaml~=6.0
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wandb~=0.13.11
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tqdm~=4.65.0
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matplotlib~=3.7.1
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pandas~=2.0.0
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seaborn~=0.12.2
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requests~=2.31.0
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psutil~=5.9.4
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thop~=0.1.1-2209072238
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timm~=0.9.2
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super-gradients~=3.2.0
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openmim
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