國慶出遊神器：魔幻黑科技換天造物，讓vlog秒變科幻大片！

摘要：國慶旅遊景點人太多，拍出來的照片全是人人人、車車車，該怎麼辦？不妨試試這個黑科技，讓你的出遊vlog秒變科幻大片。

本文分享自華為雲社群《國慶出遊神器，魔幻黑科技換天造物，讓vlog秒變科幻大片！》，作者：技術火炬手。

國慶出遊，無論是拍人、拍景或是其他，“天空”都是關鍵元素。比如，一張平平無奇的景物圖加上落日餘暉的天空色調，氛圍感就有了。

當然，自然景觀的天空還不是最酷炫的。今天給大家介紹一款基於原生視訊的AI處理方法，不僅可以一鍵置換天空背景，還可以打造任意“天空之城”。

比如換成《星際迷航》中的浩瀚星空、宇宙飛船，將自己隨手拍的平平無奇vlog秒變為科幻大片，畫面毫無違和感。

該方法源自Github上的開源專案SkyAR，它可以自動識別天空，然後將天空從圖片中切割出來，再將天空替換成目標天空，從而實現魔法換天。

下面，我們將基於SkyAR和ModelArts的JupyterLab從零開始“換天造物”。只要腦洞夠大，利用這項AI技術，就可以創造出無限種玩法。

本案例在CPU和GPU下面均可執行，CPU環境執行預計花費9分鐘，GPU環境執行預計花費2分鐘。

實驗目標

通過本案例的學習：

瞭解影象分割的基本應用；

瞭解運動估計的基本應用；

瞭解影象混合的基本應用。

注意事項

如果您是第一次使用 JupyterLab，請檢視《ModelArts JupyterLab使用指導》瞭解使用方法；
如果您在使用 JupyterLab 過程中碰到報錯，請參考《ModelArts JupyterLab常見問題解決辦法》嘗試解決問題。

實驗步驟

1、安裝和匯入依賴包

import os

import moxing as mox

file_name = 'SkyAR'

if not os.path.exists(file_name):

    mox.file.copy('obs://modelarts-labs-bj4-v2/case_zoo/SkyAR/SkyAR.zip', 'SkyAR.zip')

    os.system('unzip SkyAR.zip')

    os.system('rm SkyAR.zip')

mox.file.copy_parallel('obs://modelarts-labs-bj4-v2/case_zoo/SkyAR/resnet50-19c8e357.pth', '/home/ma-user/.cache/torch/checkpoints/resnet50-19c8e357.pth')

INFO:root:Using MoXing-v1.17.3-43fbf97f

INFO:root:Using OBS-Python-SDK-3.20.7

!pip uninstall opencv-python -y

!pip uninstall opencv-contrib-python -y

Found existing installation: opencv-python 4.1.2.30

Uninstalling opencv-python-4.1.2.30:

  Successfully uninstalled opencv-python-4.1.2.30

WARNING: Skipping opencv-contrib-python as it is not installed.

!pip install opencv-contrib-python==4.5.3.56

Looking in indexes: http://repo.myhuaweicloud.com/repository/pypi/simple

Collecting opencv-contrib-python==4.5.3.56

  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/3f/ce/36772cc6d9061b423b080e86919fd62cdef0837263f29ba6ff92e07f72d7/opencv_contrib_python-4.5.3.56-cp37-cp37m-manylinux2014_x86_64.whl (56.1 MB)

     |████████████████████████████████| 56.1 MB 166 kB/s eta 0:00:01|█████▋                          | 9.8 MB 9.4 MB/s eta 0:00:05 MB 9.4 MB/s eta 0:00:05███▏                | 26.6 MB 9.4 MB/s eta 0:00:04/s eta 0:00:03��██▍           | 35.8 MB 9.4 MB/s eta 0:00:03�███████████▌       | 42.9 MB 9.4 MB/s eta 0:00:02��██████████████▎   | 49.6 MB 166 kB/s eta 0:00:40

Requirement already satisfied: numpy>=1.14.5 in /home/ma-user/anaconda3/envs/PyTorch-1.4/lib/python3.7/site-packages (from opencv-contrib-python==4.5.3.56) (1.20.3)

Installing collected packages: opencv-contrib-python

Successfully installed opencv-contrib-python-4.5.3.56

WARNING: You are using pip version 20.3.3; however, version 21.1.3 is available.

You should consider upgrading via the '/home/ma-user/anaconda3/envs/PyTorch-1.4/bin/python -m pip install --upgrade pip' command.

cd SkyAR/

/home/ma-user/work/Untitled Folder/SkyAR

import time

import json

import base64

import numpy as np

import matplotlib.pyplot as plt

import cv2

import argparse

from networks import *

from skyboxengine import *

import utils

import torch

from IPython.display import clear_output, Image, display, HTML

%matplotlib inline

# 如果存在GPU則在GPU上面執行

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

INFO:matplotlib.font_manager:generated new fontManager

2、預覽一下原視訊

video_name = "test_videos/sky.mp4"

def arrayShow(img):

    img = cv2.resize(img, (0, 0), fx=0.25, fy=0.25, interpolation=cv2.INTER_NEAREST)

    _,ret = cv2.imencode('.jpg', img)

    return Image(data=ret)

# 開啟一個視訊流

cap = cv2.VideoCapture(video_name)

frame_id = 0

while True:

    try:

        clear_output(wait=True) # 清除之前的顯示

        ret, frame = cap.read() # 讀取一幀圖片

        if ret:

            frame_id += 1

            if frame_id > 200:

                break

            cv2.putText(frame, str(frame_id), (5, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)  # 畫frame_id

            tmp = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # 轉換色彩模式

            img = arrayShow(frame)

            display(img) # 顯示圖片

            time.sleep(0.05) # 執行緒睡眠一段時間再處理下一幀圖片

        else:

            break

    except KeyboardInterrupt:

        cap.release()

cap.release()

3、預覽一下要替換的天空圖片

img= cv2.imread('skybox/sky.jpg')

img2 = img[:,:,::-1]

plt.imshow(img2)

<matplotlib.image.AxesImage at 0x7fbea986c590>

4、自定義訓練引數

可以根據自己的需要, 修改下面的引數

skybox_center_crop: 天空體中心偏移

auto_light_matching: 自動亮度匹配

relighting_factor: 補光

recoloring_factor: 重新著色

halo_effect: 光環效應

parameter = {

  "net_G": "coord_resnet50",

  "ckptdir": "./checkpoints_G_coord_resnet50",

  "input_mode": "video",

  "datadir": "./test_videos/sky.mp4",

  "skybox": "sky.jpg",

  "in_size_w": 384,

  "in_size_h": 384,

  "out_size_w": 845,

  "out_size_h": 480,

  "skybox_center_crop": 0.5,

  "auto_light_matching": False,

  "relighting_factor": 0.8,

  "recoloring_factor": 0.5,

  "halo_effect": True,

  "output_dir": "./jpg_output",

  "save_jpgs": False

}

str_json = json.dumps(parameter)

class Struct:

    def __init__(self, **entries):

        self.__dict__.update(entries)

def parse_config():

    data = json.loads(str_json)

    args = Struct(**data)

    return args

args = parse_config()

class SkyFilter():

    def __init__(self, args):

        self.ckptdir = args.ckptdir

        self.datadir = args.datadir

        self.input_mode = args.input_mode

        self.in_size_w, self.in_size_h = args.in_size_w, args.in_size_h

        self.out_size_w, self.out_size_h = args.out_size_w, args.out_size_h

        self.skyboxengine = SkyBox(args)

        self.net_G = define_G(input_nc=3, output_nc=1, ngf=64, netG=args.net_G).to(device)

        self.load_model()

        self.video_writer = cv2.VideoWriter('out.avi',

                                            cv2.VideoWriter_fourcc(*'MJPG'),

                                            20.0,

                                            (args.out_size_w, args.out_size_h))

        self.video_writer_cat = cv2.VideoWriter('compare.avi',

                                                cv2.VideoWriter_fourcc(*'MJPG'),

                                                20.0,

                                                (2*args.out_size_w, args.out_size_h))

        if os.path.exists(args.output_dir) is False:

            os.mkdir(args.output_dir)

        self.output_img_list = []

        self.save_jpgs = args.save_jpgs

    def load_model(self):

        # 載入預訓練的天空摳圖模型

        print('loading the best checkpoint...')

        checkpoint = torch.load(os.path.join(self.ckptdir, 'best_ckpt.pt'),

                                map_location=device)

        self.net_G.load_state_dict(checkpoint['model_G_state_dict'])

        self.net_G.to(device)

        self.net_G.eval()

    def write_video(self, img_HD, syneth):

        frame = np.array(255.0 * syneth[:, :, ::-1], dtype=np.uint8)

        self.video_writer.write(frame)

        frame_cat = np.concatenate([img_HD, syneth], axis=1)

        frame_cat = np.array(255.0 * frame_cat[:, :, ::-1], dtype=np.uint8)

        self.video_writer_cat.write(frame_cat)

        # 定義結果緩衝區

        self.output_img_list.append(frame_cat)

    def synthesize(self, img_HD, img_HD_prev):

        h, w, c = img_HD.shape

        img = cv2.resize(img_HD, (self.in_size_w, self.in_size_h))

        img = np.array(img, dtype=np.float32)

        img = torch.tensor(img).permute([2, 0, 1]).unsqueeze(0)

        with torch.no_grad():

            G_pred = self.net_G(img.to(device))

            G_pred = torch.nn.functional.interpolate(G_pred,

                                                     (h, w),

                                                     mode='bicubic',

                                                     align_corners=False)

            G_pred = G_pred[0, :].permute([1, 2, 0])

            G_pred = torch.cat([G_pred, G_pred, G_pred], dim=-1)

            G_pred = np.array(G_pred.detach().cpu())

            G_pred = np.clip(G_pred, a_max=1.0, a_min=0.0)

        skymask = self.skyboxengine.skymask_refinement(G_pred, img_HD)

        syneth = self.skyboxengine.skyblend(img_HD, img_HD_prev, skymask)

        return syneth, G_pred, skymask

    def cvtcolor_and_resize(self, img_HD):

        img_HD = cv2.cvtColor(img_HD, cv2.COLOR_BGR2RGB)

        img_HD = np.array(img_HD / 255., dtype=np.float32)

        img_HD = cv2.resize(img_HD, (self.out_size_w, self.out_size_h))

        return img_HD

    def process_video(self):

        # 逐幀處理視訊

        cap = cv2.VideoCapture(self.datadir)

        m_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

        img_HD_prev = None

        for idx in range(m_frames):

            ret, frame = cap.read()

            if ret:

                img_HD = self.cvtcolor_and_resize(frame)

                if img_HD_prev is None:

                    img_HD_prev = img_HD

                syneth, G_pred, skymask = self.synthesize(img_HD, img_HD_prev)

                self.write_video(img_HD, syneth)

                img_HD_prev = img_HD

                if (idx + 1) % 50 == 0:

                    print(f'processing video, frame {idx + 1} / {m_frames} ... ')

            else:  # 如果到達最後一幀

                break

5、替換天空

替換後輸出的視訊為out.avi，前後對比的視訊為compare.avi

sf = SkyFilter(args)

sf.process_video()

initialize skybox...

initialize network with normal

loading the best checkpoint...

processing video, frame 50 / 360 ...

processing video, frame 100 / 360 ...

no good point matched

processing video, frame 150 / 360 ...

processing video, frame 200 / 360 ...

processing video, frame 250 / 360 ...

processing video, frame 300 / 360 ...

processing video, frame 350 / 360 ...

6、對比原視訊和替換後的視訊

video_name = "compare.avi"

def arrayShow(img):

    _,ret = cv2.imencode('.jpg', img)

    return Image(data=ret)

# 開啟一個視訊流

cap = cv2.VideoCapture(video_name)

frame_id = 0

while True:

    try:

        clear_output(wait=True) # 清除之前的顯示

        ret, frame = cap.read() # 讀取一幀圖片

        if ret:

            frame_id += 1

            cv2.putText(frame, str(frame_id), (5, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)  # 畫frame_id

            tmp = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # 轉換色彩模式

            img = arrayShow(frame)

            display(img) # 顯示圖片

            time.sleep(0.05) # 執行緒睡眠一段時間再處理下一幀圖片

        else:

            break

    except KeyboardInterrupt:

        cap.release()

cap.release()