a

app.py

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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
+
+
+
+
+def create_bash_file(single_path_seq):
+    
+    ori_bash_file = "./scripts_demo/train_grab_pointset_points_dyn_s1.sh"
+    with open(ori_bash_file) as rf:
+        bash_string = rf.read()
+    bash_string = bash_string.replace("./data/102_grab_all_data.npy", single_path_seq)
+    dst_bash_file = "./scripts_demo/train_grab_pointset_points_dyn_s1.sh"
+    with open(dst_bash_file, "w") as wf:    
+        wf.write(bash_string)
+    return dst_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}")
+    
+    temp_dir = tempfile.gettempdir()
+    base_exp_dir = os.path.join(temp_dir, "quasi_sim")
+    os.makedirs(base_exp_dir, exist_ok=True)
+    base_exp_dir = os.path.join(base_exp_dir, "exp")
+    os.makedirs(base_exp_dir, exist_ok=True)
+    base_exp_dir = f"{base_exp_dir}/wmask"
+    
+    print(f"self.base_exp_dir:", base_exp_dir)
+    base_exp_dir = base_exp_dir + f"_reverse_value_totviews_"
+    os.makedirs(base_exp_dir, exist_ok=True)
+    # self.dataset = Dataset(self.conf['dataset'])
+    
+        
+    mano_pts_retar_sv_info_fn = os.path.join(base_exp_dir, f"ts_to_hand_obj_verts.npy")
+    return mano_pts_retar_sv_info_fn
+    
+    
+    # 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 = """## 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": xxx
+            "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")],
+            inputs=inputs,
+            fn=predict,
+            outputs=outputs,
+        )
+        
+
+    return demo
+    
+if __name__ == "__main__":
+    demo = create_demo()
+    demo.launch()

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