code/ppd/models/pi3/utils/basic.py

import os
import os.path as osp
import math
import cv2
from PIL import Image
import torch
from torchvision import transforms
from plyfile import PlyData, PlyElement
import numpy as np

def load_images_as_tensor(path='data/truck', interval=1, PIXEL_LIMIT=255000):
    """
    Loads images from a directory or video, resizes them to a uniform size,
    then converts and stacks them into a single [N, 3, H, W] PyTorch tensor.
    """
    sources = [] 
    
    # --- 1. Load image paths or video frames ---
    if osp.isdir(path):
        print(f"Loading images from directory: {path}")
        filenames = sorted([x for x in os.listdir(path) if x.lower().endswith(('.png', '.jpg', '.jpeg'))])
        for i in range(0, len(filenames), interval):
            img_path = osp.join(path, filenames[i])
            try:
                sources.append(Image.open(img_path).convert('RGB'))
            except Exception as e:
                print(f"Could not load image {filenames[i]}: {e}")
    elif path.lower().endswith('.mp4'):
        print(f"Loading frames from video: {path}")
        cap = cv2.VideoCapture(path)
        if not cap.isOpened(): raise IOError(f"Cannot open video file: {path}")
        frame_idx = 0
        while True:
            ret, frame = cap.read()
            if not ret: break
            if frame_idx % interval == 0:
                rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                sources.append(Image.fromarray(rgb_frame))
            frame_idx += 1
        cap.release()
    else:
        raise ValueError(f"Unsupported path. Must be a directory or a .mp4 file: {path}")

    if not sources:
        print("No images found or loaded.")
        return torch.empty(0)

    print(f"Found {len(sources)} images/frames. Processing...")

    # --- 2. Determine a uniform target size for all images based on the first image ---
    # This is necessary to ensure all tensors have the same dimensions for stacking.
    first_img = sources[0]
    W_orig, H_orig = first_img.size
    scale = math.sqrt(PIXEL_LIMIT / (W_orig * H_orig)) if W_orig * H_orig > 0 else 1
    W_target, H_target = W_orig * scale, H_orig * scale
    k, m = round(W_target / 14), round(H_target / 14)
    while (k * 14) * (m * 14) > PIXEL_LIMIT:
        if k / m > W_target / H_target: k -= 1
        else: m -= 1
    TARGET_W, TARGET_H = max(1, k) * 14, max(1, m) * 14
    print(f"All images will be resized to a uniform size: ({TARGET_W}, {TARGET_H})")

    # --- 3. Resize images and convert them to tensors in the [0, 1] range ---
    tensor_list = []
    # Define a transform to convert a PIL Image to a CxHxW tensor and normalize to [0,1]
    to_tensor_transform = transforms.ToTensor()
    
    for img_pil in sources:
        try:
            # Resize to the uniform target size
            resized_img = img_pil.resize((TARGET_W, TARGET_H), Image.Resampling.LANCZOS)
            # Convert to tensor
            img_tensor = to_tensor_transform(resized_img)
            tensor_list.append(img_tensor)
        except Exception as e:
            print(f"Error processing an image: {e}")

    if not tensor_list:
        print("No images were successfully processed.")
        return torch.empty(0)

    # --- 4. Stack the list of tensors into a single [N, C, H, W] batch tensor ---
    return torch.stack(tensor_list, dim=0)


def tensor_to_pil(tensor):
    """
    Converts a PyTorch tensor to a PIL image. Automatically moves the channel dimension 
    (if it has size 3) to the last axis before converting.

    Args:
        tensor (torch.Tensor): Input tensor. Expected shape can be [C, H, W], [H, W, C], or [H, W].
    
    Returns:
        PIL.Image: The converted PIL image.
    """
    if torch.is_tensor(tensor):
        array = tensor.detach().cpu().numpy()
    else:
        array = tensor

    return array_to_pil(array)


def array_to_pil(array):
    """
    Converts a NumPy array to a PIL image. Automatically:
        - Squeezes dimensions of size 1.
        - Moves the channel dimension (if it has size 3) to the last axis.
    
    Args:
        array (np.ndarray): Input array. Expected shape can be [C, H, W], [H, W, C], or [H, W].
    
    Returns:
        PIL.Image: The converted PIL image.
    """
    # Remove singleton dimensions
    array = np.squeeze(array)
    
    # Ensure the array has the channel dimension as the last axis
    if array.ndim == 3 and array.shape[0] == 3:  # If the channel is the first axis
        array = np.transpose(array, (1, 2, 0))  # Move channel to the last axis
    
    # Handle single-channel grayscale images
    if array.ndim == 2:  # [H, W]
        return Image.fromarray((array * 255).astype(np.uint8), mode="L")
    elif array.ndim == 3 and array.shape[2] == 3:  # [H, W, C] with 3 channels
        return Image.fromarray((array * 255).astype(np.uint8), mode="RGB")
    else:
        raise ValueError(f"Unsupported array shape for PIL conversion: {array.shape}")


def rotate_target_dim_to_last_axis(x, target_dim=3):
    shape = x.shape
    axis_to_move = -1
    # Iterate backwards to find the first occurrence from the end 
    # (which corresponds to the last dimension of size 3 in the original order).
    for i in range(len(shape) - 1, -1, -1):
        if shape[i] == target_dim:
            axis_to_move = i
            break

    # 2. If the axis is found and it's not already in the last position, move it.
    if axis_to_move != -1 and axis_to_move != len(shape) - 1:
        # Create the new dimension order.
        dims_order = list(range(len(shape)))
        dims_order.pop(axis_to_move)
        dims_order.append(axis_to_move)
        
        # Use permute to reorder the dimensions.
        ret = x.transpose(*dims_order)
    else:
        ret = x

    return ret


def write_ply(
    xyz,
    rgb=None,
    path='output.ply',
) -> None:
    if torch.is_tensor(xyz):
        xyz = xyz.detach().cpu().numpy()

    if torch.is_tensor(rgb):
        rgb = rgb.detach().cpu().numpy()

    if rgb is not None and rgb.max() > 1:
        rgb = rgb / 255.

    xyz = rotate_target_dim_to_last_axis(xyz, 3)
    xyz = xyz.reshape(-1, 3)

    if rgb is not None:
        rgb = rotate_target_dim_to_last_axis(rgb, 3)
        rgb = rgb.reshape(-1, 3)
    
    if rgb is None:
        min_coord = np.min(xyz, axis=0)
        max_coord = np.max(xyz, axis=0)
        normalized_coord = (xyz - min_coord) / (max_coord - min_coord + 1e-8)
        
        hue = 0.7 * normalized_coord[:,0] + 0.2 * normalized_coord[:,1] + 0.1 * normalized_coord[:,2]
        hsv = np.stack([hue, 0.9*np.ones_like(hue), 0.8*np.ones_like(hue)], axis=1)

        c = hsv[:,2:] * hsv[:,1:2]
        x = c * (1 - np.abs( (hsv[:,0:1]*6) % 2 - 1 ))
        m = hsv[:,2:] - c
        
        rgb = np.zeros_like(hsv)
        cond = (0 <= hsv[:,0]*6%6) & (hsv[:,0]*6%6 < 1)
        rgb[cond] = np.hstack([c[cond], x[cond], np.zeros_like(x[cond])])
        cond = (1 <= hsv[:,0]*6%6) & (hsv[:,0]*6%6 < 2)
        rgb[cond] = np.hstack([x[cond], c[cond], np.zeros_like(x[cond])])
        cond = (2 <= hsv[:,0]*6%6) & (hsv[:,0]*6%6 < 3)
        rgb[cond] = np.hstack([np.zeros_like(x[cond]), c[cond], x[cond]])
        cond = (3 <= hsv[:,0]*6%6) & (hsv[:,0]*6%6 < 4)
        rgb[cond] = np.hstack([np.zeros_like(x[cond]), x[cond], c[cond]])
        cond = (4 <= hsv[:,0]*6%6) & (hsv[:,0]*6%6 < 5)
        rgb[cond] = np.hstack([x[cond], np.zeros_like(x[cond]), c[cond]])
        cond = (5 <= hsv[:,0]*6%6) & (hsv[:,0]*6%6 < 6)
        rgb[cond] = np.hstack([c[cond], np.zeros_like(x[cond]), x[cond]])
        rgb = (rgb + m)

    dtype = [
        ("x", "f4"),
        ("y", "f4"),
        ("z", "f4"),
        ("nx", "f4"),
        ("ny", "f4"),
        ("nz", "f4"),
        ("red", "u1"),
        ("green", "u1"),
        ("blue", "u1"),
    ]
    normals = np.zeros_like(xyz)
    elements = np.empty(xyz.shape[0], dtype=dtype)
    attributes = np.concatenate((xyz, normals, rgb * 255), axis=1)
    elements[:] = list(map(tuple, attributes))
    vertex_element = PlyElement.describe(elements, "vertex")
    ply_data = PlyData([vertex_element])
    ply_data.write(path)