import os
import numpy as np
import imageio
from utils.camera_utils import generate_interpolated_path

def save_interpolate_pose(model_path, iter, n_views):
    org_pose = np.load(model_path + f"pose/pose_{iter}.npy")    
    # visualizer(org_pose, ["green" for _ in org_pose], model_path + "pose/poses_optimized.png")
    n_interp = int(10 * 30 / n_views)  # 10second, fps=30
    all_inter_pose = []
    for i in range(n_views-1):
        tmp_inter_pose = generate_interpolated_path(poses=org_pose[i:i+2], n_interp=n_interp)
        all_inter_pose.append(tmp_inter_pose)
    all_inter_pose = np.array(all_inter_pose).reshape(-1, 3, 4)

    inter_pose_list = []
    for p in all_inter_pose:
        tmp_view = np.eye(4)
        tmp_view[:3, :3] = p[:3, :3]
        tmp_view[:3, 3] = p[:3, 3]
        inter_pose_list.append(tmp_view)
    inter_pose = np.stack(inter_pose_list, 0)
    # visualizer(inter_pose, ["blue" for _ in inter_pose], model_path + "pose/poses_interpolated.png")
    np.save(model_path + "pose/pose_interpolated.npy", inter_pose)


def images_to_video(image_folder, output_video_path, fps=30):
    """
    Convert images in a folder to a video.

    Args:
    - image_folder (str): The path to the folder containing the images.
    - output_video_path (str): The path where the output video will be saved.
    - fps (int): Frames per second for the output video.
    """
    images = []

    for filename in sorted(os.listdir(image_folder)):
        if filename.endswith(('.png', '.jpg', '.jpeg')):
            image_path = os.path.join(image_folder, filename)
            image = imageio.imread(image_path)
            images.append(image)

    imageio.mimwrite(output_video_path, images, fps=fps)