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| import os, glob, sys, logging | |
| import argparse, datetime, time | |
| import numpy as np | |
| import cv2 | |
| from PIL import Image | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from models import model, basic | |
| from utils import util | |
| def setup_model(checkpt_path, device="cuda"): | |
| seed = 130 | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed(seed) | |
| #print('--------------', torch.cuda.is_available()) | |
| """Load the model into memory to make running multiple predictions efficient""" | |
| colorLabeler = basic.ColorLabel(device=device) | |
| colorizer = model.AnchorColorProb(inChannel=1, outChannel=313, enhanced=True, colorLabeler=colorLabeler) | |
| colorizer = colorizer.to(device) | |
| #checkpt_path = "./checkpoints/disco-beta.pth.rar" | |
| assert os.path.exists(checkpt_path) | |
| data_dict = torch.load(checkpt_path, map_location=torch.device('cpu')) | |
| colorizer.load_state_dict(data_dict['state_dict']) | |
| colorizer.eval() | |
| return colorizer, colorLabeler | |
| def resize_ab2l(gray_img, lab_imgs, vis=False): | |
| H, W = gray_img.shape[:2] | |
| reszied_ab = cv2.resize(lab_imgs[:,:,1:], (W,H), interpolation=cv2.INTER_LINEAR) | |
| if vis: | |
| gray_img = cv2.resize(lab_imgs[:,:,:1], (W,H), interpolation=cv2.INTER_LINEAR) | |
| return np.concatenate((gray_img[:,:,np.newaxis], reszied_ab), axis=2) | |
| else: | |
| return np.concatenate((gray_img, reszied_ab), axis=2) | |
| def prepare_data(rgb_img, target_res): | |
| rgb_img = np.array(rgb_img / 255., np.float32) | |
| lab_img = cv2.cvtColor(rgb_img, cv2.COLOR_RGB2LAB) | |
| org_grays = (lab_img[:,:,[0]]-50.) / 50. | |
| lab_img = cv2.resize(lab_img, target_res, interpolation=cv2.INTER_LINEAR) | |
| lab_img = torch.from_numpy(lab_img.transpose((2, 0, 1))) | |
| gray_img = (lab_img[0:1,:,:]-50.) / 50. | |
| ab_chans = lab_img[1:3,:,:] / 110. | |
| input_grays = gray_img.unsqueeze(0) | |
| input_colors = ab_chans.unsqueeze(0) | |
| return input_grays, input_colors, org_grays | |
| def colorize_grayscale(colorizer, color_class, rgb_img, hint_img, n_anchors, is_high_res, is_editable, device="cuda"): | |
| n_anchors = int(n_anchors) | |
| n_anchors = max(n_anchors, 3) | |
| n_anchors = min(n_anchors, 14) | |
| target_res = (512,512) if is_high_res else (256,256) | |
| input_grays, input_colors, org_grays = prepare_data(rgb_img, target_res) | |
| input_grays = input_grays.to(device) | |
| input_colors = input_colors.to(device) | |
| if is_editable: | |
| print('>>>:editable mode') | |
| sampled_T = -1 | |
| _, input_colors, _ = prepare_data(hint_img, target_res) | |
| input_colors = input_colors.to(device) | |
| pal_logit, ref_logit, enhanced_ab, affinity_map, spix_colors, hint_mask = colorizer(input_grays, \ | |
| input_colors, n_anchors, sampled_T) | |
| else: | |
| print('>>>:automatic mode') | |
| sampled_T = 0 | |
| pal_logit, ref_logit, enhanced_ab, affinity_map, spix_colors, hint_mask = colorizer(input_grays, \ | |
| input_colors, n_anchors, sampled_T) | |
| pred_labs = torch.cat((input_grays,enhanced_ab), dim=1) | |
| lab_imgs = basic.tensor2array(pred_labs).squeeze(axis=0) | |
| lab_imgs = resize_ab2l(org_grays, lab_imgs) | |
| lab_imgs[:,:,0] = lab_imgs[:,:,0] * 50.0 + 50.0 | |
| lab_imgs[:,:,1:3] = lab_imgs[:,:,1:3] * 110.0 | |
| rgb_output = cv2.cvtColor(lab_imgs[:,:,:], cv2.COLOR_LAB2RGB) | |
| return (rgb_output*255.0).astype(np.uint8) | |
| def predict_anchors(colorizer, color_class, rgb_img, n_anchors, is_high_res, is_editable, device="cuda"): | |
| n_anchors = int(n_anchors) | |
| n_anchors = max(n_anchors, 3) | |
| n_anchors = min(n_anchors, 14) | |
| target_res = (512,512) if is_high_res else (256,256) | |
| input_grays, input_colors, org_grays = prepare_data(rgb_img, target_res) | |
| input_grays = input_grays.cuda(non_blocking=True) | |
| input_colors = input_colors.cuda(non_blocking=True) | |
| sampled_T, sp_size = 0, 16 | |
| pal_logit, ref_logit, enhanced_ab, affinity_map, spix_colors, hint_mask = colorizer(input_grays, \ | |
| input_colors, n_anchors, sampled_T) | |
| pred_probs = pal_logit | |
| guided_colors = color_class.decode_ind2ab(ref_logit, T=0) | |
| guided_colors = basic.upfeat(guided_colors, affinity_map, sp_size, sp_size) | |
| anchor_masks = basic.upfeat(hint_mask, affinity_map, sp_size, sp_size) | |
| marked_labs = basic.mark_color_hints(input_grays, guided_colors, anchor_masks, base_ABs=None) | |
| lab_imgs = basic.tensor2array(marked_labs).squeeze(axis=0) | |
| lab_imgs = resize_ab2l(org_grays, lab_imgs, vis=True) | |
| lab_imgs[:,:,0] = lab_imgs[:,:,0] * 50.0 + 50.0 | |
| lab_imgs[:,:,1:3] = lab_imgs[:,:,1:3] * 110.0 | |
| rgb_output = cv2.cvtColor(lab_imgs[:,:,:], cv2.COLOR_LAB2RGB) | |
| return (rgb_output*255.0).astype(np.uint8) |