import numpy as np
import matplotlib

def visualize_depth(depth: np.ndarray, 
                    depth_min=None, 
                    depth_max=None, 
                    percentile=5, 
                    ret_minmax=False,
                    cmap='Spectral'):
    if depth_min is None: depth_min = np.percentile(depth, percentile)
    if depth_max is None: depth_max = np.percentile(depth, 100 - percentile)
    if depth_min == depth_max:
        depth_min = depth_min - 1e-6
        depth_max = depth_max + 1e-6
    cm = matplotlib.colormaps[cmap]
    depth = ((depth - depth_min) / (depth_max - depth_min)).clip(0, 1)
    img_colored_np = cm(depth[None], bytes=False)[:, :, :, 0:3]  # value from 0 to 1
    img_colored_np = (img_colored_np[0] * 255.0).astype(np.uint8)
    if ret_minmax:
        return img_colored_np, depth_min, depth_max
    else:
        return img_colored_np
    

def unproject_depth(depth, 
                    ixt, 
                    depth_min=0.01, 
                    depth_max=None, 
                    color=None, 
                    ext=None, 
                    conf=None, 
                    ret_pcd=False, 
                    clip_box=None):
    height, width = depth.shape
    x = np.arange(0, width)
    y = np.arange(0, height)
    xx, yy = np.meshgrid(x, y)
    xx = xx.reshape(-1)
    yy = yy.reshape(-1)
    zz = depth.reshape(-1)
    mask = np.ones_like(xx, dtype=np.bool_)
    if depth_min is not None:
        mask &= zz >= depth_min
    if depth_max is not None:
        mask &= zz <= depth_max
    if conf is not None:
        mask &= conf.reshape(-1) == 2
    xx = xx[mask]
    yy = yy[mask]
    zz = zz[mask]
    pcd = np.stack([xx, yy, np.ones_like(xx)], axis=1)
    pcd = pcd * zz[:, None]
    pcd = np.dot(pcd, np.linalg.inv(ixt).T)
    if ext is not None:
        pcd = np.concatenate([pcd, np.ones((pcd.shape[0], 1))], axis=1)
        pcd = np.dot(pcd, np.linalg.inv(ext).T)
    new_mask = np.ones_like(pcd[:, 0]).astype(np.bool_)
    if clip_box is not None:
        assert len(clip_box) == 6
        for i, val in enumerate(clip_box):
            if val is None:
                continue
            if i == 0: new_mask &= (pcd[:, 0] <= val)
            elif i == 1: new_mask &= (pcd[:, 1] <= val)
            elif i == 2: new_mask &= (pcd[:, 2] <= val)
            elif i == 3: new_mask &= (pcd[:, 0] >= val)
            elif i == 4: new_mask &= (pcd[:, 1] >= val)
            elif i == 5: new_mask &= (pcd[:, 2] >= val)
    if color is not None:
        if color.dtype == np.uint8:
            color = color.astype(np.float32) / 255.
        if ret_pcd:
            points = pcd
            import open3d as o3d
            pcd = o3d.geometry.PointCloud()
            pcd.points = o3d.utility.Vector3dVector(points[:, :3][new_mask])
            pcd.colors = o3d.utility.Vector3dVector(color.reshape(-1, 3)[mask][new_mask])
        else:
            return pcd[:, :3][new_mask], color.reshape(-1, 3)[mask][new_mask]
    else:
        if ret_pcd:
            import open3d as o3d
            points = pcd
            pcd = o3d.geometry.PointCloud()
            pcd.points = o3d.utility.Vector3dVector(pcd[:, :3][new_mask])
        else:
            return pcd[:, :3][new_mask]
    return pcd

if __name__ == '__main__':
    depth = np.random.rand(100, 100)
    visualize_depth(depth)