Duplicate from Daniellesry/TransPhy3D

Co-authored-by: Shaocong.Xu <Daniellesry@users.noreply.huggingface.co>

.gitattributes

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+*.pcm filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: apache-2.0
+task_categories:
+- depth-estimation
+tags:
+- transparency
+- video-depth-estimation
+- computer-vision
+---
+
+# TransPhy3D
+
+[**Project Page**](https://daniellli.github.io/projects/DKT/) | [**Paper**](https://huggingface.co/papers/2512.23705) | [**Code**](https://github.com/Daniellli/DKT)
+
+TransPhy3D is a synthetic video corpus of transparent and reflective scenes, consisting of 11k sequences rendered with Blender/Cycles. It provides high-quality RGB frames along with physically based depth and normal labels. The dataset was introduced in the paper "Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimation".
+
+## Introduction 
+
+This dataset aims to provide the first transparent-object oriented video dataset with perfect depth and normal labels, and diverse categories and shapes. Scenes are assembled from a curated bank of category-rich static assets and shape-rich procedural assets paired with glass/plastic/metal materials.
+
+## Quick Start
+
+The dataset repository includes a demo script to load and visualize the data:
+
+```bash
+python load_demo.py --data_path test/0826_0006_materials.000000.tar --output outputs
+```
+
+The results will be saved in the `outputs/` directory as follows:
+```text
+outputs/
+|-- output_depth.mp4
+|-- output_normal.mp4
+`-- output_rgb.mp4
+```
+
+## Data Structure
+
+The dataset is organized as follows:
+
+```text
+|-- parametric_train #* the shape-rich dataset
+    |-- test
+        |-- 1_materials.000000.tar
+        |-- ...
+    |-- training
+    `-- validation
+|-- test #* TransPhy3D-Test
+`-- train #* the category-rich dataset
+```
+
+## Citation 
+
+If you use this dataset in your research, please cite the following paper:
+
+```bibtex
+@article{dkt2025,
+  title   = {Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimation},
+  author  = {Shaocong Xu and Songlin Wei and Qizhe Wei and Zheng Geng and Hong Li and Licheng Shen and Qianpu Sun and Shu Han and Bin Ma and Bohan Li and Chongjie Ye and Yuhang Zheng and Nan Wang and Saining Zhang and Hao Zhao},
+  journal = {https://arxiv.org/abs/2512.23705},
+  year    = {2025}
+}
+```

load_demo.py

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+
+
+from os.path import dirname, join
+import webdataset as wds
+from PIL import Image
+import io
+import json
+import numpy as np
+import argparse
+import matplotlib
+import matplotlib.pyplot as plt 
+import os
+try:
+    import imageio
+    HAS_IMAGEIO = True
+except ImportError:
+    HAS_IMAGEIO = False
+    try:
+        import cv2
+        HAS_CV2 = True
+    except ImportError:
+        HAS_CV2 = False
+
+
+
+
+def dump_video(image_seq, output_path, fps=30, codec='libx264', quality=8):
+    """
+    Dump a sequence of PIL Images to a video file.
+    
+    Args:
+        image_seq: List of PIL Images
+        output_path: Output video file path (e.g., 'output.mp4')
+        fps: Frames per second (default: 30)
+        codec: Video codec (default: 'libx264')
+        quality: Video quality, 0-10, higher is better (default: 8)
+    """
+    if not image_seq:
+        print("Warning: Empty image sequence, skipping video dump")
+        return
+    
+    if HAS_IMAGEIO:
+        # Use imageio (simpler API)
+        frames = []
+        for img in image_seq:
+            # Convert PIL Image to numpy array
+            frames.append(np.array(img))
+        
+        # Write video
+        # imageio v2 uses mimwrite, v3 uses get_writer
+        try:
+            # Try imageio v2 API
+            imageio.mimwrite(output_path, frames, fps=fps, codec=codec, quality=quality)
+        except (AttributeError, TypeError):
+            # Fallback for imageio v3
+            try:
+                writer = imageio.get_writer(output_path, fps=fps, codec=codec)
+                for frame in frames:
+                    writer.append_data(frame)
+                writer.close()
+            except Exception:
+                # Final fallback without codec
+                writer = imageio.get_writer(output_path, fps=fps)
+                for frame in frames:
+                    writer.append_data(frame)
+                writer.close()
+        print(f"Video saved to {output_path} using imageio")
+    
+    elif HAS_CV2:
+        # Use OpenCV as fallback
+        if not image_seq:
+            return
+        
+        # Get image dimensions
+        first_img = image_seq[0]
+        height, width = first_img.size[1], first_img.size[0]
+        
+        # Define codec and create VideoWriter
+        fourcc = cv2.VideoWriter_fourcc(*codec if len(codec) == 4 else 'mp4v')
+        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+        
+        for img in image_seq:
+            # Convert PIL Image to numpy array (RGB -> BGR for OpenCV)
+            img_array = np.array(img)
+            if len(img_array.shape) == 3:
+                if img_array.shape[2] == 3:
+                    img_array = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
+                elif img_array.shape[2] == 4:
+                    img_array = cv2.cvtColor(img_array, cv2.COLOR_RGBA2BGR)
+            out.write(img_array)
+        
+        out.release()
+        print(f"Video saved to {output_path} using OpenCV")
+    
+    else:
+        raise ImportError("Neither imageio nor cv2 is available. Please install one: pip install imageio[ffmpeg] or pip install opencv-python")
+
+
+# Parse 16bit depth to actual depth values
+def parse_depth_16bit(depth_image: Image.Image, max_depth: float) -> np.ndarray:
+    """Parse 16-bit depth image back to actual depth values."""
+    # Convert PIL image to numpy array
+    depth_array = np.array(depth_image, dtype=np.uint16)
+    
+    # Normalize to [0, 1] and multiply by max_depth to get actual depth
+    depth_normalized = depth_array.astype(np.float32) / 65535.0
+    actual_depth = depth_normalized * max_depth
+    
+    return actual_depth
+
+
+
+
+def colorize_depth_map(depth, mask=None, reverse_color=False):
+    from decord import VideoReader,cpu
+    
+    cm = matplotlib.colormaps["Spectral"]
+
+    # colorize
+    if reverse_color:
+        img_colored_np = cm(1 - depth, bytes=False)[:, :, 0:3]  # Invert the depth values before applying colormap
+    else:
+        img_colored_np = cm(depth, bytes=False)[:, :, 0:3] # (h,w,3)
+
+    depth_colored = (img_colored_np * 255).astype(np.uint8) 
+    # if mask is not None:
+    #     masked_image = np.zeros_like(depth_colored)
+    #     masked_image[mask.numpy()] = depth_colored[mask.numpy()]
+    #     depth_colored_img = Image.fromarray(masked_image)
+    # else:
+    depth_colored_img = Image.fromarray(depth_colored)
+    return depth_colored_img
+
+
+if __name__ == '__main__':
+
+    args = argparse.ArgumentParser()
+    args.add_argument('--data_path', type=str, default='data/TransPhy3D/parametric_train/training/0_materials.000000.tar')
+    args.add_argument('--output_path', type=str, default='output')
+    args = args.parse_args()
+
+    os.makedirs(args.output_path, exist_ok=True)
+
+    # Use WebDataset's built-in verification
+    dataset = wds.WebDataset(args.data_path)
+    data = {}
+
+    depth_seq_raw = []  # Store raw 16bit depth images
+    depth_max_values = []  # Store max_depth for each frame
+    normal_seq = []
+    rgbs_seq =  []
+    meta_info =[]
+
+    # First pass: load all data including 16bit depth
+    for sample in dataset:
+        depth_img = None
+        max_depth = None
+        for key, value in sample.items():            
+            # Ensure value is bytes-like
+            if not isinstance(value, (bytes, bytearray)):
+                continue
+            
+            # Match exact key names or check file extension
+            if key == 'depth.png':
+                # Load 16bit depth image
+                depth_img = Image.open(io.BytesIO(value))
+            elif key == 'depth.json':
+                # Load max_depth value
+                depth_info = json.loads(value)
+                max_depth = depth_info.get('max_depth', None)
+            elif key == 'normal.png':
+                img = Image.open(io.BytesIO(value))
+                normal_seq.append(img)
+            elif key == 'image.png':
+                img = Image.open(io.BytesIO(value))
+                rgbs_seq.append(img)
+            elif key.endswith('.json'):
+                meta_info.append(json.loads(value))
+        
+        # Store depth data if both image and max_depth are available
+        if depth_img is not None:
+            depth_seq_raw.append(depth_img)
+            depth_max_values.append(max_depth)
+    
+
+    
+    #* depth processing 
+    depth_seq_vis = []
+    for depth_img, max_depth in zip(depth_seq_raw, depth_max_values):
+        if max_depth is not None:
+            # Parse 16bit depth to actual depth values
+            #* show how to convert to original depth unit
+            actual_depth = parse_depth_16bit(depth_img, max_depth)
+            
+            # Normalize for visualization (0-255)
+            depth_normalized = actual_depth / max_depth  # [0, 1]
+            depth_colored_img = colorize_depth_map(depth_normalized)
+            
+            depth_seq_vis.append(depth_colored_img)
+        else:
+            # Fallback: use raw depth image if max_depth not available
+            depth_seq_vis.append(depth_img.convert('L'))
+
+
+
+    dump_video(rgbs_seq, join(args.output_path, 'output_rgb.mp4'), fps=30)
+    dump_video(normal_seq, join(args.output_path, 'output_normal.mp4'), fps=30)
+    dump_video(depth_seq_vis, join(args.output_path, 'output_depth.mp4'), fps=30)
+    

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