手把手教你玩轉黑白圖片上色【玩轉華為云】

我們都知道，有很多經典的老照片，受限于那個時代的技術，只能以黑白的形式傳世。盡管黑白照片別有一番風味，但是彩色照片有時候能給人更強的代入感。今天在這里為大家提供一種基于華為云ModelArts的AI技術，智能給黑白圖片上色的方法，下面就緊跟小編的步驟，一步一步的來實現吧！

實驗準備

華為云ModelArts平臺，如對該平臺不是很了解，請參考文檔信息成長地圖_AI開發平臺ModelArts_華為云 (huaweicloud.com) ，這些文檔資料可以讓你完成從AI小白到AI大神的蛻變！

實驗流程

一、實驗環境配置

點擊下方連接進入?實例感知圖像上色?的 JupyterLab 頁面，進入代碼操作界面

https://authoring-modelarts-cnnorth4.huaweicloud.com/console/lab?share-url-b64=aHR0cHM6Ly9jbm5vcnRoNC1tb2RlbGh1Yi1tb2RlbHMub2JzLmNuLW5vcnRoLTQubXlod2Nsb3Vkcy5jb20vaXRlbS83OTJjNzlhNS1jYzI5LTRhNjUtYmUzNy01MTRhYjk1OWE2OTIvMS4wLjAvSW5zdENvbG9yaXphdGlvbi5pcHluYg%3D%3D&galleryitemid=792c79a5-cc29-4a65-be37-514ab959a692

不要使用默認的配置信息，這里我們選擇GPU: 1*V100|CPU: 8核 64GB的環境，默認的信息在配置 Detectron2時，會報錯。

切換流程見下圖：

切換完成后，點擊確定

2、 實驗步驟

2.1下載代碼和數據

import os !wget https://obs-aigallery-zc.obs.cn-north-4.myhuaweicloud.com/clf/code/InstColorization.zip os.system('unzip InstColorization.zip')

選擇代碼后，按crtl + enter 鍵運行，或者點擊工具欄的開始按鈕

運行成功后，[*]會變成對應的[1]值，或者工具欄左下方的?Busy?變成?Idle

2.2安裝依賴庫

!gcc --version !pip install torch==1.5 torchvision==0.6 !pip install cython pyyaml==5.1 !pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' !pip install dominate==2.4.0 !pip install detectron2==0.1.3 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/index.html !pip install opencv-python !pip install scikit-image import torch, torchvision print(torch.__version__, torch.cuda.is_available())

2.3準備上色

2.3.1切換目錄

cd InstColorization/

2.3.2配置 Detectron2

from os.path import join, isfile, isdir from os import listdir import os os.environ["CUDA_VISIBLE_DEVICES"] = "0" from argparse import ArgumentParser import detectron2 from detectron2.utils.logger import setup_logger setup_logger() import numpy as np import cv2 # import some common detectron2 utilities from detectron2 import model_zoo from detectron2.engine import DefaultPredictor from detectron2.config import get_cfg import torch cfg = get_cfg() cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml")) cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7 cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml") predictor = DefaultPredictor(cfg)

input_dir = "example" image_list = [f for f in listdir(input_dir) if isfile(join(input_dir, f))] output_npz_dir = "{0}_bbox".format(input_dir) if os.path.isdir(output_npz_dir) is False: print('Create path: {0}'.format(output_npz_dir)) os.makedirs(output_npz_dir)

for image_path in image_list: img = cv2.imread(join(input_dir, image_path)) lab_image = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) l_channel, a_channel, b_channel = cv2.split(lab_image) l_stack = np.stack([l_channel, l_channel, l_channel], axis=2) outputs = predictor(l_stack) save_path = join(output_npz_dir, image_path.split('.')[0]) pred_bbox = outputs["instances"].pred_boxes.to(torch.device('cpu')).tensor.numpy() pred_scores = outputs["instances"].scores.cpu().data.numpy() np.savez(save_path, bbox = pred_bbox, scores = pred_scores)

!ls example_bbox

2.4圖像上色

import sys import time from options.train_options import TestOptions from models import create_model import torch from tqdm import tqdm_notebook from fusion_dataset import Fusion_Testing_Dataset from util import util import multiprocessing multiprocessing.set_start_method('spawn', True) torch.backends.cudnn.benchmark = True sys.argv = [sys.argv[0]] opt = TestOptions().parse()

save_img_path = opt.results_img_dir if os.path.isdir(save_img_path) is False: print('Create path: {0}'.format(save_img_path)) os.makedirs(save_img_path) opt.batch_size = 1 dataset = Fusion_Testing_Dataset(opt, -1) dataset_loader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size) dataset_size = len(dataset) print('#Testing images = %d' % dataset_size)

model = create_model(opt) model.setup_to_test('coco_finetuned_mask_256_ffs')

count_empty = 0 for data_raw in tqdm_notebook(dataset_loader): data_raw['full_img'][0] = data_raw['full_img'][0].cuda() if data_raw['empty_box'][0] == 0: data_raw['cropped_img'][0] = data_raw['cropped_img'][0].cuda() box_info = data_raw['box_info'][0] box_info_2x = data_raw['box_info_2x'][0] box_info_4x = data_raw['box_info_4x'][0] box_info_8x = data_raw['box_info_8x'][0] cropped_data = util.get_colorization_data(data_raw['cropped_img'], opt, ab_thresh=0, p=opt.sample_p) full_img_data = util.get_colorization_data(data_raw['full_img'], opt, ab_thresh=0, p=opt.sample_p) model.set_input(cropped_data) model.set_fusion_input(full_img_data, [box_info, box_info_2x, box_info_4x, box_info_8x]) model.forward() else: count_empty += 1 full_img_data = util.get_colorization_data(data_raw['full_img'], opt, ab_thresh=0, p=opt.sample_p) model.set_forward_without_box(full_img_data) model.save_current_imgs(join(save_img_path, data_raw['file_id'][0] + '.png')) print('{0} images without bounding boxes'.format(count_empty))

2.5展示上色結果

def imshow(img): import IPython import cv2 _, ret = cv2.imencode('.jpg', img) i = IPython.display.Image(data=ret) IPython.display.display(i) img_name_list = ['000000022969', '000000023781', '000000046872', '000000050145'] # 修改對應索引使用不同圖片 0-3 show_index = 1 img = cv2.imread('example/'+img_name_list[show_index]+'.jpg') lab_image = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) l_channel, _, _ = cv2.split(lab_image) img = cv2.imread('results/'+img_name_list[show_index]+'.png') lab_image = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) _, a_pred, b_pred = cv2.split(lab_image) a_pred = cv2.resize(a_pred, (l_channel.shape[1], l_channel.shape[0])) b_pred = cv2.resize(b_pred, (l_channel.shape[1], l_channel.shape[0])) gray_color = np.ones_like(a_pred) * 128 gray_image = cv2.cvtColor(np.stack([l_channel, gray_color, gray_color], 2), cv2.COLOR_LAB2BGR) color_image = cv2.cvtColor(np.stack([l_channel, a_pred, b_pred], 2), cv2.COLOR_LAB2BGR) # save_img_path = 'results_origin/' # if os.path.isdir(save_img_path) is False: # print('Create path: {0}'.format(save_img_path)) # os.makedirs(save_img_path) # cv2.imwrite('results_origin/'+img_name_list[show_index]+'.png', color_image) imshow(np.concatenate([gray_image, color_image], 1))

到此為止，一個官方的示例，就算完成了！

如果想要給其他的黑白圖片上色，又該怎么完成呢？繼續往下看，嘿嘿

在2.5步驟中，有這樣幾句代碼，通過對比可以發現，原始圖集在example文件夾下，通過切換下標，可以給不同的圖片完成上色操作，

img_name_list = ['000000022969', '000000023781', '000000046872', '000000050145'] # 修改對應索引使用不同圖片 0-3 show_index = 1 img = cv2.imread('example/'+img_name_list[show_index]+'.jpg') lab_image = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) l_channel, _, _ = cv2.split(lab_image) img = cv2.imread('results/'+img_name_list[show_index]+'.png') lab_image = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) _, a_pred, b_pred = cv2.split(lab_image)

將本地圖片，通過菜單欄的upload files按鈕，上傳到example文件夾下

重復步驟2.3,2.4,2.5，其中2.5中需要修改照片的信息，將照片123加入數組中，同時指定上色圖片索引為4

def imshow(img): import IPython import cv2 _, ret = cv2.imencode('.jpg', img) i = IPython.display.Image(data=ret) IPython.display.display(i) img_name_list = ['000000022969', '000000023781', '000000046872', '000000050145', '123'] # 修改對應索引使用不同圖片 0-3 show_index = 4 img = cv2.imread('example/'+img_name_list[show_index]+'.jpg') lab_image = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) l_channel, _, _ = cv2.split(lab_image) img = cv2.imread('results/'+img_name_list[show_index]+'.png') lab_image = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) _, a_pred, b_pred = cv2.split(lab_image) a_pred = cv2.resize(a_pred, (l_channel.shape[1], l_channel.shape[0])) b_pred = cv2.resize(b_pred, (l_channel.shape[1], l_channel.shape[0])) gray_color = np.ones_like(a_pred) * 128 gray_image = cv2.cvtColor(np.stack([l_channel, gray_color, gray_color], 2), cv2.COLOR_LAB2BGR) color_image = cv2.cvtColor(np.stack([l_channel, a_pred, b_pred], 2), cv2.COLOR_LAB2BGR) # save_img_path = 'results_origin/' # if os.path.isdir(save_img_path) is False: # print('Create path: {0}'.format(save_img_path)) # os.makedirs(save_img_path) # cv2.imwrite('results_origin/'+img_name_list[show_index]+'.png', color_image) imshow(np.concatenate([gray_image, color_image], 1))

就能完整展示自己的上色圖片啦

AI開發平臺ModelArts

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