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| 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): | |
| img_path = "input_img.jpg" | |
| out_dir = "./output"; | |
| 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) | |
| print(result) | |
| save_dir = './output/visualizations/' | |
| if os.path.exists(save_dir): | |
| out_img_path = save_dir + img_path | |
| print("out_img_path: ", out_img_path) | |
| else: | |
| out_img_path = img_path | |
| out_img = PIL.Image.open(out_img_path) | |
| return (out_img, result) | |
| # 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') | |
| output_image = gr.outputs.Image(type="pil", label="Output Image") | |
| output_text = gr.outputs.Textbox(label="Output Text") | |
| title = "MMPose detection for ShopByShape" | |
| iface = gr.Interface(fn=predict_pose, inputs=[input_image, model_name], outputs=[output_image, output_text], title=title) | |
| iface.launch() | |