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| import numpy as np | |
| import gradio as gr | |
| import os | |
| import tempfile | |
| import shutil | |
| # from gradio_inter.predict_from_file import predict_from_file | |
| from gradio_inter.create_bash_file import create_bash_file | |
| from sample.reconstruct_data_taco import reconstruct_from_file | |
| def create_temp_file(path: str) -> str: | |
| temp_dir = tempfile.gettempdir() | |
| temp_folder = os.path.join(temp_dir, "denoising") | |
| os.makedirs(temp_folder, exist_ok=True) | |
| # Clean up directory | |
| # for i in os.listdir(temp_folder): | |
| # print("Removing", i) | |
| # os.remove(os.path.join(temp_folder, i)) | |
| temp_path = os.path.join(temp_folder, path.split("/")[-1]) | |
| shutil.copy2(path, temp_path) | |
| return temp_path | |
| # from gradio_inter.predict import predict_from_data | |
| # from gradio_inter.predi | |
| def transpose(matrix): | |
| return matrix.T | |
| def predict(file_path: str): | |
| temp_file_path = create_temp_file(file_path) | |
| # predict_from_file | |
| temp_bash_file = create_bash_file(temp_file_path) | |
| os.system(f"bash {temp_bash_file}") | |
| res_file_path = "/tmp/denoising/save/predicted_infos_seed_0_tag_20231104_017_jts_spatial_t_100__st_0.npy" | |
| saved_path = reconstruct_from_file(temp_file_path) | |
| return saved_path | |
| def create_demo(): | |
| USAGE = """# GeneOH Diffusion: Towards Generalizable Hand-Object Interaction Denoising via Denoising Diffusion | |
| **[Project](https://meowuu7.github.io/GeneOH-Diffusion/) | [Paper](https://openreview.net/pdf?id=FvK2noilxT) | [Github](https://github.com/Meowuu7/GeneOH-Diffusion)** | |
| ## Input data format | |
| Currently, the demo accepts a `.pkl` file containing an hand-object sequence organized as the following format: | |
| ```python | |
| { | |
| "hand_pose": numpy.ndarray(seq_length, 48), # MANO pose at each frame | |
| "hand_trans": numpy.ndarray(seq_length, 3), # hand global translation at each frmae | |
| "hand_shape": numpy.ndarray(10), # MANO shape coefficients | |
| "hand_verts": numpy.ndarray(seq_length, 778, 3), # MANO hand vertices | |
| "hand_faces": numpy.ndarray(1538, 3), # MANO hand faces | |
| "obj_verts": numpy.ndarray(seq_length, num_obj_verts, 3), # object vertices at each frame | |
| "obj_faces": numpy.ndarray(num_obj_faces, 3), # object faces | |
| "obj_pose": numpy.ndarray(seq_length, 4, 4), # object pose at each frame | |
| } | |
| ``` | |
| We provide an example [here](https://drive.google.com/file/d/17oqKMhQNpRqSdApyuuCmTrPkrFl0Cqp6/view?usp=sharing). **The demo is under developing and will support more data formats in the future.** | |
| ## To run the demo, | |
| 1. Upload a `pickle` file to the left box by draging your file or clicking the box to open the file explorer. | |
| 2. Clik the `Submit` button to run the demo. | |
| 3. The denoised sequence will be output as a `.npy` file and can be downloaded from the right box. | |
| Since the model runs on CPU currently, the speed is not very fast. For instance, it takes abount 1200s to process the [example](https://drive.google.com/file/d/17oqKMhQNpRqSdApyuuCmTrPkrFl0Cqp6/view?usp=sharing) mentioned above which contains 288 frames. Please be patient and wait for the result. | |
| To run the model faster, please visit our [github repo](https://github.com/Meowuu7/GeneOH-Diffusion), follow the instructions and run the model on your own server or local machine. | |
| ## Output data format | |
| The output is a `.npy` file containing the denoised sequence organized as the following format: | |
| ```python | |
| { | |
| "predicted_info": { | |
| "targets": numpy.ndarray(seq_length, num_mano_joints, 3), # input MANO joints | |
| "outputs": numpy.ndarray(seq_length, num_mano_joints, 3), # denoised MANO joints | |
| "obj_verts": numpy.ndarray(seq_length, num_obj_verts, 3), # object vertices at each frame | |
| "obj_faces": numpy.ndarray(num_obj_faces, 3), # object faces | |
| ... # others | |
| } | |
| "bf_ct_verts": numpy.ndarray(seq_length, 778, 3), # denoised MANO vertices | |
| "bf_ct_rot_var": numpy.ndarray(seq_length, 3), # denoised MANO global rotation coefficients | |
| "bf_ct_theta_var": numpy.ndarray(seq_length, 45), # denoised MANO global pose coefficients | |
| "bf_ct_beta_var": numpy.ndarray(1, 10), # denoised MANO shape coefficients | |
| "bf_ct_transl_var": numpy.ndarray(seq_length, 3), # denoised hand global translation | |
| } | |
| ``` | |
| The corresponding output file of the [example](https://drive.google.com/file/d/17oqKMhQNpRqSdApyuuCmTrPkrFl0Cqp6/view?usp=sharing) mentioned above can be downloaded [here](https://drive.google.com/file/d/1Ah-qwV6LXlOyaBBe0qQRu1lN-BpKt2Y3/view?usp=sharing). | |
| """ | |
| with gr.Blocks() as demo: | |
| gr.Markdown(USAGE) | |
| # # demo = | |
| # gr.Interface( | |
| # predict, | |
| # # gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3), | |
| # gr.File(type="filepath"), | |
| # gr.File(type="filepath"), | |
| # cache_examples=False | |
| # ) | |
| input_file = gr.File(type="filepath") | |
| output_file = gr.File(type="filepath") | |
| gr.Interface( | |
| predict, | |
| # gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3), | |
| input_file, | |
| output_file, | |
| cache_examples=False | |
| ) | |
| inputs = input_file | |
| outputs = output_file | |
| gr.Examples( | |
| examples=[os.path.join(os.path.dirname(__file__), "./gradio_inter/20231104_017.pkl"), os.path.join(os.path.dirname(__file__), "./gradio_inter/20231104_010.pkl")], | |
| inputs=inputs, | |
| fn=predict, | |
| outputs=outputs, | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| demo = create_demo() | |
| demo.launch() |