update app.py

app.py

@@ -0,0 +1,81 @@
+
+
+import os
+os.system("pip install xtcocotools>=1.12")
+os.system("pip install 'mmengine>=0.6.0'")
+os.system("pip install 'mmcv>=2.0.0rc4,<2.1.0'")
+os.system("pip install 'mmdet>=3.0.0,<4.0.0'")
+os.system("pip install 'mmpose'")
+
+import PIL
+import cv2
+import mmpose
+import numpy as np
+
+import torch
+from mmpose.apis import MMPoseInferencer
+import gradio as gr
+
+import warnings
+
+warnings.filterwarnings("ignore")
+
+mmpose_model_list = ["human", "hand", "face", "animal", "wholebody",
+                     "vitpose", "vitpose-s", "vitpose-b", "vitpose-l", "vitpose-h"]
+
+
+def save_image(img, img_path):
+    # Convert PIL image to OpenCV image
+    img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+    # Save OpenCV image
+    cv2.imwrite(img_path, img)
+
+
+def download_test_image():
+    # Images
+    torch.hub.download_url_to_file(
+        'https://user-images.githubusercontent.com/59380685/266264420-21575a83-4057-41cf-8a4a-b3ea6f332d79.jpg',
+        'bus.jpg')
+    torch.hub.download_url_to_file(
+        'https://user-images.githubusercontent.com/59380685/266264536-82afdf58-6b9a-4568-b9df-551ee72cb6d9.jpg',
+        'dogs.jpg')
+    torch.hub.download_url_to_file(
+        'https://user-images.githubusercontent.com/59380685/266264600-9d0c26ca-8ba6-45f2-b53b-4dc98460c43e.jpg',
+        'zidane.jpg')
+
+
+def predict_pose(img, model_name, out_dir):
+    img_path = "input_img.jpg"
+    save_image(img, img_path)
+    device = torch.cuda.current_device() if torch.cuda.is_available() else 'cpu'
+    inferencer = MMPoseInferencer(model_name, device=device)
+    result_generator = inferencer(img_path, show=False, out_dir=out_dir)
+    result = next(result_generator)
+    save_dir = './output/visualizations/'
+    out_img_path = save_dir + img_path
+    out_img = PIL.Image.open(out_img_path)
+    return out_img
+
+out_dir = "./output/visualizations/"
+if not os.path.exists(out_dir):
+    os.makedirs(out_dir)
+download_test_image()
+input_image = gr.inputs.Image(type='pil', label="Original Image")
+model_name = gr.inputs.Dropdown(choices=[m for m in mmpose_model_list], label='Model')
+out_dir = gr.inputs.Textbox(label="Output Directory", default="./output")
+output_image = gr.outputs.Image(type="pil", label="Output Image")
+
+examples = [
+    ['zidane.jpg', 'human'],
+    ['dogs.jpg', 'animal'],
+]
+title = "MMPose detection web demo"
+description = "<div align='center'><img src='https://raw.githubusercontent.com/open-mmlab/mmpose/main/resources/mmpose-logo.png' width='450''/><div>" \
+              "<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。" \
+              "OpenMMLab Pose Estimation Toolbox and Benchmark..</p>"
+article = "<p style='text-align: center'><a href='https://github.com/open-mmlab/mmpose'>MMPose</a></p>" \
+          "<p style='text-align: center'><a href='https://github.com/isLinXu'>gradio build by gatilin</a></a></p>"
+
+iface = gr.Interface(fn=predict_pose, inputs=[input_image, model_name, out_dir], outputs=output_image,
+                     examples=examples, title=title, description=description, article=article)
+iface.launch()

requirements.txt

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+wget~=3.2
+opencv-python~=4.6.0.66
+numpy~=1.23.0
+torch~=1.13.1
+torchvision~=0.14.1
+pillow~=9.4.0
+gradio~=3.42.0
+ultralytics~=8.0.169
+pyyaml~=6.0
+wandb~=0.13.11
+tqdm~=4.65.0
+matplotlib~=3.7.1
+pandas~=2.0.0
+seaborn~=0.12.2
+requests~=2.31.0
+psutil~=5.9.4
+thop~=0.1.1-2209072238
+timm~=0.9.2
+super-gradients~=3.2.0
+openmim