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| import argparse | |
| import cv2 | |
| import numpy as np | |
| import os | |
| from tqdm import tqdm | |
| import torch | |
| from basicsr.archs.ddcolor_arch import DDColor | |
| import torch.nn.functional as F | |
| class ImageColorizationPipeline(object): | |
| def __init__(self, model_path, input_size=256, model_size='large'): | |
| self.input_size = input_size | |
| if torch.cuda.is_available(): | |
| self.device = torch.device('cuda') | |
| else: | |
| self.device = torch.device('cpu') | |
| if model_size == 'tiny': | |
| self.encoder_name = 'convnext-t' | |
| else: | |
| self.encoder_name = 'convnext-l' | |
| self.decoder_type = "MultiScaleColorDecoder" | |
| if self.decoder_type == 'MultiScaleColorDecoder': | |
| self.model = DDColor( | |
| encoder_name=self.encoder_name, | |
| decoder_name='MultiScaleColorDecoder', | |
| input_size=[self.input_size, self.input_size], | |
| num_output_channels=2, | |
| last_norm='Spectral', | |
| do_normalize=False, | |
| num_queries=100, | |
| num_scales=3, | |
| dec_layers=9, | |
| ).to(self.device) | |
| else: | |
| self.model = DDColor( | |
| encoder_name=self.encoder_name, | |
| decoder_name='SingleColorDecoder', | |
| input_size=[self.input_size, self.input_size], | |
| num_output_channels=2, | |
| last_norm='Spectral', | |
| do_normalize=False, | |
| num_queries=256, | |
| ).to(self.device) | |
| self.model.load_state_dict( | |
| torch.load(model_path, map_location=torch.device('cpu'))['params'], | |
| strict=False) | |
| self.model.eval() | |
| def process(self, img): | |
| self.height, self.width = img.shape[:2] | |
| # print(self.width, self.height) | |
| # if self.width * self.height < 100000: | |
| # self.input_size = 256 | |
| img = (img / 255.0).astype(np.float32) | |
| orig_l = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)[:, :, :1] # (h, w, 1) | |
| # resize rgb image -> lab -> get grey -> rgb | |
| img = cv2.resize(img, (self.input_size, self.input_size)) | |
| img_l = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)[:, :, :1] | |
| img_gray_lab = np.concatenate((img_l, np.zeros_like(img_l), np.zeros_like(img_l)), axis=-1) | |
| img_gray_rgb = cv2.cvtColor(img_gray_lab, cv2.COLOR_LAB2RGB) | |
| tensor_gray_rgb = torch.from_numpy(img_gray_rgb.transpose((2, 0, 1))).float().unsqueeze(0).to(self.device) | |
| output_ab = self.model(tensor_gray_rgb).cpu() # (1, 2, self.height, self.width) | |
| # resize ab -> concat original l -> rgb | |
| output_ab_resize = F.interpolate(output_ab, size=(self.height, self.width))[0].float().numpy().transpose(1, 2, 0) | |
| output_lab = np.concatenate((orig_l, output_ab_resize), axis=-1) | |
| output_bgr = cv2.cvtColor(output_lab, cv2.COLOR_LAB2BGR) | |
| output_img = (output_bgr * 255.0).round().astype(np.uint8) | |
| return output_img | |
| colorizer = ImageColorizationPipeline(model_path='/content/DDColor/models/pytorch_model.pt', input_size=512) | |
| from PIL import Image | |
| import gradio as gr | |
| import subprocess | |
| import shutil, os | |
| from gradio_imageslider import ImageSlider | |
| def generate(image): | |
| image_in = cv2.imread(image) | |
| image_out = colorizer.process(image_in) | |
| cv2.imwrite('/content/DDColor/out.jpg', image_out) | |
| image_in_pil = Image.fromarray(cv2.cvtColor(image_in, cv2.COLOR_BGR2RGB)) | |
| image_out_pil = Image.fromarray(cv2.cvtColor(image_out, cv2.COLOR_BGR2RGB)) | |
| return (image_in_pil, image_out_pil) | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| image = gr.Image(type='filepath') | |
| button = gr.Button() | |
| output_image = ImageSlider(show_label=False, type="filepath", interactive=False) | |
| button.click(fn=generate, inputs=[image], outputs=[output_image]) | |
| demo.queue().launch(inline=False, debug=True) |