import cv2
import os

def crop_center_square(img):

    
    # 获取图像的宽度和高度
    height, width = img.shape[:2]
    
    # 计算裁剪区域的左上角和右下角坐标
    if width < height:
        top = (height - width) // 2
        bottom = top + width
        left = 0
        right = width
    else:
        left = (width - height) // 2
        right = left + height
        top = 0
        bottom = height

    # 裁剪图像为正方形
    img_cropped = img[top:bottom, left:right]

    return img_cropped
# 指定目录路径
directory = '/data_160TB/2021/yangkun/gha-gaussian/datasets/real_data/Nersemble_all/data/sequence_EMO-1-shout+laugh_part-2/126/EMO-1-shout+laugh'

# 列出目录中的所有文件
files = os.listdir(directory)

# 筛选以"cam"开头且以".mp4"结尾的文件路径
mp4_files = [os.path.join(directory, file) for file in files if file.startswith('cam') and file.endswith('.mp4')]
# 对文件路径进行排序
mp4_files = sorted(mp4_files)
# 打印筛选出的MP4文件路径
view_count = 0
for file_path in mp4_files:
    print(file_path)
    # 读取视频文件
    video_path = file_path
    cap = cv2.VideoCapture(video_path)

    # 逐帧读取视频并保存为图像
    frame_count = 0
    while cap.isOpened():
        os.makedirs(os.path.join(directory, 'people', f'frame_{str(frame_count).zfill(4)}', 'images'), exist_ok=True)
        write_path = os.path.join(directory, 'people', f'frame_{str(frame_count).zfill(4)}', 'images')
        ret, frame = cap.read()
        if not ret:
            break
        cv2.imwrite(write_path + f'/{str(view_count).zfill(4)}.png', crop_center_square(frame))
        frame_count += 1
    view_count +=1
    # 释放视频对象
    cap.release()

import json
import numpy as np
import shutil

# 读取JSON文件
# with open('/data_160TB/2021/yangkun/gha-gaussian/datasets/real_data/Nersemble/nersemble/036/camera_params.json', 'r') as file:
#     data = json.load(file)

# # 对外参按照键值进行排序
# sorted_world_2_cam = {k: v for k, v in sorted(data['world_2_cam'].items())}
# inverted_world_2_cam = {k: np.linalg.inv(v) for k, v in sorted_world_2_cam.items()}
# # 从字典中获取 NumPy 数组的列表
# array_list = [array for array in inverted_world_2_cam.values()]

# # 连接所有 NumPy 数组成一个新的 NumPy 数组
# para_ext =  np.concatenate([arr[np.newaxis] for arr in array_list], axis=0)
# # 保存排序好的外参为npy文件
# np.save('/data_160TB/2021/yangkun/gha-gaussian/datasets/real_data/Nersemble/nersemble/036/EMO-1-shout+laugh/cameras.npy', para_ext)

# # 提取内参并按照指定顺序保存为npy文件
# intrinsics = []
# params = data['intrinsics']
# params =  np.array(params)
# # H, W, fx, fy, cx, cy = params[0][0], params[1][1], params[0][2], params[1][2], params[0][3], params[1][3]
# H, W, fx, fy, cx, cy = 1099.2677002*2, 1099.2677002*2, 8198.8134765625, 8198.8134765625, 1099.2677002, 1099.2677002
# intrinsics.append([H, W, fx, fy, cx, cy])
# intrinsics = np.array(intrinsics)
# repeated_array = np.tile(intrinsics, (16, 1))

# # 保存内参为npy文件
# np.save('/data_160TB/2021/yangkun/gha-gaussian/datasets/real_data/Nersemble/nersemble/036/EMO-1-shout+laugh/images.npy', repeated_array)

def copy_folders(path):
    f2 = '/data_160TB/2021/yangkun/gha-gaussian/datasets/real_data/Nersemble/nersemble/036/EMO-1-shout+laugh/cameras.npy'
    f3 = '/data_160TB/2021/yangkun/gha-gaussian/datasets/real_data/Nersemble/nersemble/036/EMO-1-shout+laugh/images.npy'
    for root, dirs, files in os.walk(path):
        for dir_name in dirs:
            # if dir_name == "images" or dir_name == "mesh3D" or dir_name == "point_cloud":
            #     full_path = os.path.join(root, dir_name)
            #     shutil.rmtree(full_path)
            all_path = os.path.join(root, dir_name)
            os.makedirs(os.path.join(all_path, 'mesh3D'), exist_ok=True)
            final_path = os.path.join(all_path, 'mesh3D')
            # shutil.copytree(f1, all_path+'/')
            # shutil.copy(f1, all_path)
            shutil.copy(f2, final_path)
            shutil.copy(f3, final_path)

path = "/data_160TB/2021/yangkun/gha-gaussian/datasets/real_data/Nersemble_all/data/sequence_EMO-1-shout+laugh_part-2/126/EMO-1-shout+laugh/"
copy_folders(path)