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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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