scripts/render_utils.py

import yaml
import pickle
from subprocess import run
import numpy as np
import torch
import torch.nn.functional as F
# import smplx
import shutil
import os
from pathlib import Path
import trimesh
import pyrender
import math
import matplotlib.pyplot as plt
import cv2
from contextlib import contextmanager
from PIL import Image
from scipy.spatial.transform import Rotation

def project_keypoints_to_2d(keyp_3d, camera_params, img_size=1024):
    """

    Project 3D keypoints to 2D image coordinates using weak perspective camera.

    Applies the same transformation as the mesh renderer (180 degree rotation around X-axis).

    Uses the same projection matrix as WeakPerspectiveCamera.

    

    Args:

        keyp_3d: (N, 3) or (1, N, 3) array of 3D keypoints

        camera_params: [sx, sy, tx, ty] camera parameters

        img_size: image size (assumes square image)

    

    Returns:

        keyp_2d: (N, 2) array of 2D keypoints in image coordinates

    """
    if keyp_3d.ndim == 3:
        keyp_3d = keyp_3d[0]  # (N, 3)
    
    # Apply the same 180-degree rotation around X-axis as in the renderer
    # This matches the transformation in _create_mesh_from_trimesh:
    # transform = trimesh.transformations.rotation_matrix(math.radians(180), [1, 0, 0])
    rot_matrix = np.array([
        [1,  0,  0],
        [0, -1,  0],  # cos(180) = -1, sin(180) = 0
        [0,  0, -1]
    ])
    keyp_3d_transformed = keyp_3d @ rot_matrix.T  # Apply rotation
    
    sx, sy, tx, ty = camera_params
    
    # Apply projection matrix as in WeakPerspectiveCamera.get_projection_matrix()
    # P[0, 0] = sx, P[0, 3] = tx * sx
    # P[1, 1] = sy, P[1, 3] = -ty * sy  <- Note the negative sign!
    x_proj = sx * keyp_3d_transformed[:, 0] + tx * sx
    y_proj = sy * keyp_3d_transformed[:, 1] - ty * sy  # Negative ty!
    
    # Convert from NDC (normalized device coordinates) [-1, 1] to pixel coordinates [0, img_size]
    x_pixel = (x_proj + 1) * img_size / 2
    y_pixel = (1 - y_proj) * img_size / 2  # Flip Y for image coordinates
    
    keyp_2d = np.stack([x_pixel, y_pixel], axis=1)
    return keyp_2d


def draw_keypoints_on_image(image, keyp_2d, radius=5, color=(255, 0, 0), thickness=-1):
    """

    Draw keypoints on image.

    

    Args:

        image: (H, W, 3) or (H, W, 4) numpy array

        keyp_2d: (N, 2) array of 2D keypoint coordinates

        radius: circle radius

        color: RGB color tuple

        thickness: circle thickness (-1 for filled)

    

    Returns:

        image with keypoints drawn

    """
    img = np.array(image).copy()
    
    for i, (x, y) in enumerate(keyp_2d):
        if 0 <= x < img.shape[1] and 0 <= y < img.shape[0]:
            cv2.circle(img, (int(x), int(y)), radius, color, thickness)
            # Optional: add keypoint index
            # cv2.putText(img, str(i), (int(x)+5, int(y)+5), 
            #            cv2.FONT_HERSHEY_SIMPLEX, 0.3, color, 1)
    
    return img


def show_progress_bar(current, total, description="Processing"):
    if total == 0:
        return
    
    progress = (current + 1) / total
    bar_length = 50
    filled_length = int(bar_length * progress)
    bar = '#' * filled_length + '-' * (bar_length - filled_length)
    percentage = progress * 100
    
    print(f"\r{description}: [{bar}] {percentage:.1f}% ({current + 1}/{total})", end="", flush=True)
    
    if current + 1 == total:
        print()

def copy_with_progress_bar(source_path, dest_path, description="Copying"):
    if not os.path.exists(source_path):
        print(f"Warning: {description} source not found at {source_path}")
        return False
    
    # Remove destination if it exists
    if os.path.exists(dest_path):
        shutil.rmtree(dest_path)
    
    # Create destination directory
    os.makedirs(dest_path, exist_ok=True)
    
    if os.path.isdir(source_path):
        # Copy directory contents
        items = list(Path(source_path).rglob("*"))
        files = [item for item in items if item.is_file()]
        
        print(f"{description} {len(files)} files from {source_path} to {dest_path}")
        
        for i, file_path in enumerate(files):
            rel_path = file_path.relative_to(source_path)
            dest_file = Path(dest_path) / rel_path
            dest_file.parent.mkdir(parents=True, exist_ok=True)
            shutil.copy2(file_path, dest_file)
            # Update progress bar
            show_progress_bar(i, len(files), description)
        
        return True
    else:
        # Copy single file
        print(f"{description} single file from {source_path} to {dest_path}")
        shutil.copy2(source_path, dest_path)
        return True

def rsync(src, dst):
    run(["rsync", "-a", src, dst], check=True)

def load_yaml(path):
    with open(path, 'r') as f:
        return yaml.safe_load(f)

def load_pkl(path):
    with open(path, 'rb') as file:
        return pickle.load(file)
    
def pitch():  # Positive: down
    theta = np.deg2rad(np.random.uniform(-10, 100))
        
    rotation_matrix = np.array([
        [1, 0, 0],
        [0, np.cos(theta), -np.sin(theta)],
        [0, np.sin(theta), np.cos(theta)]
    ], dtype=np.float32)
    
    return rotation_matrix, theta > np.deg2rad(50)

def yaw():  # Positive: clockwise
    theta = np.deg2rad(np.random.uniform(-180, 180))
    rotation_matrix = np.array([
        [np.cos(theta), 0, -np.sin(-theta)],
        [0, 1, 0],
        [np.sin(-theta), 0, np.cos(theta)]
    ], dtype=np.float32)
    
    return rotation_matrix

def roll(theta):  # Positive: clockwise
    return np.array([
        [1, 0, 0],
        [0, np.cos(theta), -np.sin(theta)],
        [0, np.sin(theta), np.cos(theta)]
    ], dtype=np.float32)

def rot6d_to_rotmat(rot6d):
    assert rot6d.ndim == 2
    rot6d = rot6d.view(-1, 3, 2)
    a1 = rot6d[:, :, 0]
    a2 = rot6d[:, :, 1]
    b1 = F.normalize(a1)
    b2 = F.normalize(a2 - torch.einsum("bi,bi->b", b1, a2).unsqueeze(-1) * b1)
    b3 = torch.linalg.cross(b1, b2)
    rotmat = torch.stack((b1, b2, b3), dim=-1)
    return rotmat

def create_pose_rotmat(pose, orient):
    orient = rot6d_to_rotmat(torch.tensor(orient).reshape(-1, 6)).reshape(1, 3, 3).numpy()
    pose = rot6d_to_rotmat(torch.tensor(pose).reshape(-1, 6)).reshape(-1, 3, 3).numpy()
    return np.concatenate([orient, pose], axis=0)

def get_bbox(proj):
    min_x, min_y = np.min(proj, axis=0)
    max_x, max_y = np.max(proj, axis=0)
    return (min_x, min_y, max_x, max_y)

# class SMALLayer(smplx.SMPLLayer):
#     NUM_JOINTS = 34
#     NUM_BODY_JOINTS = 34
#     SHAPE_SPACE_DIM = 145

#     def __init__(self, *args, **kwargs):
#         super().__init__(*args, **kwargs)
#         self.vertex_joint_selector.extra_joints_idxs = torch.empty(0, dtype=torch.int32)

def weak_perspective_project(verts, scale, tx, ty):
    proj = verts[:, :2] * scale
    proj[:, 0] += tx
    proj[:, 1] += ty
    return proj

def convert_to_pixel_coords(scale, tx, ty, resolution=1024):
    tx_px = (tx + 0.5) * resolution
    ty_px = (ty + 0.5) * resolution
    s_wp = scale * resolution
    return s_wp, tx_px, ty_px

def save_vertices_obj(vertices, faces, save_path):
    with open(save_path, 'w') as f:
        for v in vertices:
            f.write(f'v {v[0]} {v[1]} {v[2]}\n')
        for face in faces + 1: 
            f.write(f'f {face[0]} {face[1]} {face[2]}\n')

def deduce_weak_perspective_params(verts, img_size=(1024, 1024), random=False):
    """

    Compute weak perspective camera parameters to fit mesh in image.

    

    Args:

        verts: (N, 3) vertices

        img_size: (width, height) image size

        random: if True, add randomness to scale and translation; if False, fit exactly

    

    Returns:

        scale, tx, ty: camera parameters

    """
    min_x, min_y = verts[:, :2].min(axis=0)
    max_x, max_y = verts[:, :2].max(axis=0)

    scale_x = img_size[0] / (max_x - min_x)
    scale_y = img_size[1] / (max_y - min_y)
    scale = min(scale_x, scale_y)

    scale *= 0.9

    if random:
        scale *= np.random.uniform(0.9, 1.0)
    
    scaled_width = scale * (max_x - min_x)
    scaled_height = scale * (max_y - min_y)

    tx_min = 0 - scale * min_x
    tx_max = img_size[0] - scale * max_x
    ty_min = 0 - scale * min_y
    ty_max = img_size[1] - scale * max_y

    if random:
        tx = np.random.uniform(tx_min, tx_max)
        ty_mid = (ty_min + ty_max) / 2
        ty = np.random.uniform(ty_mid, ty_max)
    else:
        # Center the mesh perfectly
        tx = (tx_min + tx_max) / 2
        ty = (ty_min + ty_max) / 2

    return scale, tx, ty

class WeakPerspectiveCamera(pyrender.Camera):
    def __init__(self, scale, translation, znear=10.0, zfar=1000.0):
        super().__init__(znear=znear, zfar=zfar)
        self.scale = np.asarray(scale, dtype=float).ravel()[:2]
        self.translation = np.asarray(translation, dtype=float).ravel()[:2]

    def get_projection_matrix(self, width=None, height=None):
        P = np.eye(4)
        sx, sy = self.scale
        tx, ty = self.translation
        P[0, 0] = sx
        P[1, 1] = sy
        P[0, 3] = tx * sx
        P[1, 3] = -ty * sy
        P[2, 2] = -0.1
        return P


class MeshRenderer:
    def __init__(self, faces=None, resolution=(1024, 1024), randomize_light_orientation=False):
        self.faces = faces
        self.resolution = resolution
        self.randomize_light_orientation = randomize_light_orientation
        self.renderer = pyrender.OffscreenRenderer(*resolution)
        self.scene = pyrender.Scene(
            bg_color=[0, 0, 0, 0],
            ambient_light=(0.5, 0.5, 0.5)
        )
        self.light_nodes = []
        self._setup_lights(randomize_orientation=randomize_light_orientation)
    
    def _setup_lights(self, randomize_orientation=False):
        # Remove existing lights first
        for node in self.light_nodes:
            self.scene.remove_node(node)
        self.light_nodes = []
        
        # Base directions for directional lights (pointing towards origin)
        # DirectionalLight shines along its local -Z axis, so we create rotations
        # that point the light from different directions
        base_directions = np.array([
            [0, -1, 1],   # from above-front
            [0, 1, 1],    # from above-back  
            [1, 1, 2],    # from above-right-back
        ], dtype=np.float64)
        
        # Normalize to get unit direction vectors
        base_directions = base_directions / np.linalg.norm(base_directions, axis=1, keepdims=True)
        
        if randomize_orientation:
            # Apply random 3D rotation to all light directions
            random_rotation = Rotation.random()
            directions = random_rotation.apply(base_directions)
        else:
            directions = base_directions
        
        light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=0.8)
        for direction in directions:
            # Create rotation matrix that aligns -Z axis with the light direction
            # Light shines along -Z in its local frame, so we want -Z to point along 'direction'
            # This means Z should point along '-direction'
            z_axis = -direction  # The light's Z axis (opposite of light direction)
            
            # Create orthonormal basis
            # Pick an arbitrary vector not parallel to z_axis for cross product
            up = np.array([0, 1, 0]) if abs(z_axis[1]) < 0.9 else np.array([1, 0, 0])
            x_axis = np.cross(up, z_axis)
            x_axis = x_axis / np.linalg.norm(x_axis)
            y_axis = np.cross(z_axis, x_axis)
            
            # Build rotation matrix (columns are the local axes in world coordinates)
            pose = np.eye(4)
            pose[:3, 0] = x_axis
            pose[:3, 1] = y_axis
            pose[:3, 2] = z_axis
            
            node = self.scene.add(light, pose=pose)
            self.light_nodes.append(node)

    def _calculate_surface_normals(self, vertices, faces):
        normals = np.cross(
            vertices[faces[:, 1]] - vertices[faces[:, 0]],
            vertices[faces[:, 2]] - vertices[faces[:, 1]]
        )
        normals /= np.linalg.norm(normals, axis=1)[:, None]
        return (normals + 1) / 2 * 255

    def _create_mesh_from_vertices(self, vertices, color=None):
        """Create mesh from vertices and faces (legacy method)"""
        mesh = trimesh.Trimesh(vertices=vertices, faces=self.faces, process=False)
        mesh = mesh.subdivide_loop(iterations=2)
        transform = trimesh.transformations.rotation_matrix(math.radians(180), [1, 0, 0])
        mesh.apply_transform(transform)

        if color is not None:
            material = pyrender.MetallicRoughnessMaterial(
                baseColorFactor=[*color, 1.0],
                metallicFactor=0.1,
                alphaMode="OPAQUE"
            )
            return pyrender.Mesh.from_trimesh(mesh, material=material, smooth=True)
        else:
            normals = self._calculate_surface_normals(mesh.vertices, mesh.faces)
            mesh.visual.face_colors = normals.astype(np.uint8)
            return pyrender.Mesh.from_trimesh(mesh, material=None, smooth=False)
    
    def _create_mesh_from_trimesh(self, trimesh_obj, color=None):
        """Create mesh from a trimesh object (supports textures)"""
        mesh = trimesh_obj.copy()
        
        # Check if mesh has texture - if so, don't subdivide as it breaks UV coordinates
        has_texture = hasattr(mesh.visual, 'uv') and mesh.visual.uv is not None
        
        if not has_texture:
            mesh = mesh.subdivide_loop(iterations=2)
        
        transform = trimesh.transformations.rotation_matrix(math.radians(180), [1, 0, 0])
        mesh.apply_transform(transform)

        if color is not None:
            # Override with solid color
            material = pyrender.MetallicRoughnessMaterial(
                baseColorFactor=[*color, 1.0],
                metallicFactor=0.1,
                alphaMode="OPAQUE"
            )
            return pyrender.Mesh.from_trimesh(mesh, material=material, smooth=True)
        else:
            # Use existing visual (texture or vertex colors)
            # pyrender automatically handles TextureVisuals from trimesh
            return pyrender.Mesh.from_trimesh(mesh, smooth=True)
    
    def render(self, mesh_or_vertices, camera_params, color=None, depth_only=False):
        """

        Render a mesh with camera parameters.

        

        Args:

            mesh_or_vertices: Either a trimesh.Trimesh object or numpy array of vertices

            camera_params: Camera parameters [sx, sy, tx, ty]

            color: Optional color override (R, G, B) in [0, 1]

            depth_only: If True, only return depth map

        """
        # Randomize light orientation on each render call if enabled
        if self.randomize_light_orientation:
            self._setup_lights(randomize_orientation=True)
        
        # Determine if input is a trimesh object or vertices array
        if isinstance(mesh_or_vertices, trimesh.Trimesh):
            pyrender_mesh = self._create_mesh_from_trimesh(mesh_or_vertices, color)
        else:
            # Assume it's a vertices array
            pyrender_mesh = self._create_mesh_from_vertices(mesh_or_vertices, color)
        
        mesh_node = self.scene.add(pyrender_mesh)
    
        cam = WeakPerspectiveCamera(scale=camera_params[:2], translation=camera_params[2:])
        cam_node = self.scene.add(cam, pose=np.eye(4))
    
        if depth_only:
            depth = self.renderer.render(self.scene, flags=pyrender.RenderFlags.DEPTH_ONLY)
            self.scene.remove_node(mesh_node); self.scene.remove_node(cam_node)
            return depth
    
        img_rgba, depth = self.renderer.render(self.scene, flags=pyrender.RenderFlags.RGBA)
    
        # Make a writable copy since pyrender returns read-only arrays
        img_rgba = img_rgba.copy()
        alpha = (depth > 0).astype(np.uint8) * 255
        img_rgba[..., 3] = alpha
    
        self.scene.remove_node(mesh_node); self.scene.remove_node(cam_node)
        return img_rgba

def overlay_rgba_on_rgb(rendered_rgba, background_rgb):
    fg = Image.fromarray(rendered_rgba, mode="RGBA")
    bg = Image.fromarray(background_rgb, mode="RGB")

    if fg.size != bg.size:
        bg = bg.resize(fg.size, resample=Image.BILINEAR)

    out = Image.alpha_composite(bg.convert("RGBA"), fg)
    return np.array(out.convert("RGB"))