updating requirements and deleting cli examples

example.py

@@ -1,40 +0,0 @@
-"""
-Example script showing how to use MatAnyone programmatically.
-"""
-import os
-import torch
-from inference_matanyone import process_video
-
-# Enable CUDA if available
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-print(f"Using device: {device}")
-
-# Define paths
-input_video = "inputs/video/my_video.mp4"  # Replace with your video path
-mask_image = "inputs/mask/my_mask.png"     # Replace with your mask path
-output_dir = "results"
-
-# Create output directory if it doesn't exist
-os.makedirs(output_dir, exist_ok=True)
-
-# Process the video
-fgr_path, pha_path = process_video(
-    input_path=input_video,
-    mask_path=mask_image,
-    output_path=output_dir,
-    n_warmup=10,           # Number of warmup iterations
-    r_erode=10,            # Erosion kernel size
-    r_dilate=10,           # Dilation kernel size
-    max_size=1280,         # Limit the maximum size (optional)
-    save_image=False       # Whether to save individual frames
-)
-
-print(f"Processing complete!")
-print(f"Foreground video saved to: {fgr_path}")
-print(f"Alpha matte saved to: {pha_path}")
-
-# Example of how to use the outputs
-print("\nNext steps:")
-print("1. Use the foreground video (fgr) with a green background for chroma keying")
-print("2. Use the alpha matte (pha) as a transparency mask in video editing software")
-print("3. Combine them to composite your subject onto a new background") 

example_comfyui.py

@@ -1,77 +0,0 @@
-"""
-Example workflow for using MatAnyone in ComfyUI
-
-This is a Python representation of a ComfyUI workflow.
-You can use this as a reference for how to connect nodes.
-"""
-
-import torch
-import numpy as np
-from PIL import Image
-import os
-
-# This is a representation of how you would use the node in ComfyUI
-# In the actual ComfyUI interface, you would connect nodes visually
-
-def example_workflow():
-    # Step 1: Load video frames
-    # In ComfyUI, you would use a video loader node
-    # Here we're just creating dummy frames for illustration
-    video_frames = torch.rand(10, 3, 512, 512)  # 10 frames, RGB, 512x512
-    
-    # Step 2: Create a mask for the first frame
-    # In ComfyUI, you would use a mask drawing tool or load a mask
-    mask = torch.zeros(512, 512)
-    # Draw a circle in the center as an example foreground
-    center_x, center_y = 256, 256
-    radius = 100
-    for y in range(512):
-        for x in range(512):
-            if (x - center_x)**2 + (y - center_y)**2 < radius**2:
-                mask[y, x] = 1.0
-    
-    # Step 3: Process with MatAnyone
-    # In ComfyUI, this would be the MatAnyone node
-    from __init__ import MatAnyoneNode
-    matanyone_node = MatAnyoneNode()
-    
-    # Make sure the model is downloaded
-    model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "model")
-    os.makedirs(model_dir, exist_ok=True)
-    model_path = os.path.join(model_dir, "matanyone.pth")
-    
-    if not os.path.exists(model_path):
-        print(f"Please download the model from https://github.com/pq-yang/MatAnyone/releases/download/v1.0.0/matanyone.pth")
-        print(f"and place it in {model_dir}")
-        return
-    
-    # Process the video
-    foreground_frames, alpha_frames = matanyone_node.process_video(
-        video_frames=video_frames,
-        mask=mask,
-        warmup_frames=10,
-        erode_kernel=10,
-        dilate_kernel=10,
-        max_size=-1
-    )
-    
-    # Step 4: Use the results
-    # In ComfyUI, you would connect these to other nodes
-    print(f"Foreground frames shape: {foreground_frames.shape}")
-    print(f"Alpha frames shape: {alpha_frames.shape}")
-    
-    # Example: Save the first frame as an image
-    fg_frame = foreground_frames[0].permute(1, 2, 0).cpu().numpy()
-    fg_frame = (fg_frame * 255).astype(np.uint8)
-    fg_image = Image.fromarray(fg_frame)
-    fg_image.save("example_foreground.png")
-    
-    alpha_frame = alpha_frames[0][0].cpu().numpy()
-    alpha_frame = (alpha_frame * 255).astype(np.uint8)
-    alpha_image = Image.fromarray(alpha_frame)
-    alpha_image.save("example_alpha.png")
-    
-    print("Example images saved!")
-
-if __name__ == "__main__":
-    example_workflow() 

requirements.txt

@@ -1,7 +1,6 @@
-torch>=1.13.0
-torchvision>=0.14.0
 numpy>=1.20.0
 opencv-python>=4.5.0
 Pillow>=9.0.0
 omegaconf>=2.1.0
-huggingface_hub>=0.12.0 
+requests
+tqdm