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import os, glob, sys, logging
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import argparse, datetime, time
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import numpy as np
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import cv2
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from PIL import Image
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from models import model, basic
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from utils import util
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def setup_model(checkpt_path, device="cuda"):
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"""Load the model into memory to make running multiple predictions efficient"""
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colorLabeler = basic.ColorLabel(device=device)
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colorizer = model.AnchorColorProb(inChannel=1, outChannel=313, enhanced=True, colorLabeler=colorLabeler)
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colorizer = colorizer.to(device)
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assert os.path.exists(checkpt_path), "No checkpoint found!"
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data_dict = torch.load(checkpt_path, map_location=torch.device('cpu'))
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colorizer.load_state_dict(data_dict['state_dict'])
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colorizer.eval()
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return colorizer, colorLabeler
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def resize_ab2l(gray_img, lab_imgs, vis=False):
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H, W = gray_img.shape[:2]
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reszied_ab = cv2.resize(lab_imgs[:,:,1:], (W,H), interpolation=cv2.INTER_LINEAR)
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if vis:
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gray_img = cv2.resize(lab_imgs[:,:,:1], (W,H), interpolation=cv2.INTER_LINEAR)
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return np.concatenate((gray_img[:,:,np.newaxis], reszied_ab), axis=2)
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else:
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return np.concatenate((gray_img, reszied_ab), axis=2)
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def prepare_data(rgb_img, target_res):
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rgb_img = np.array(rgb_img / 255., np.float32)
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lab_img = cv2.cvtColor(rgb_img, cv2.COLOR_RGB2LAB)
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org_grays = (lab_img[:,:,[0]]-50.) / 50.
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lab_img = cv2.resize(lab_img, target_res, interpolation=cv2.INTER_LINEAR)
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lab_img = torch.from_numpy(lab_img.transpose((2, 0, 1)))
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gray_img = (lab_img[0:1,:,:]-50.) / 50.
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ab_chans = lab_img[1:3,:,:] / 110.
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input_grays = gray_img.unsqueeze(0)
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input_colors = ab_chans.unsqueeze(0)
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return input_grays, input_colors, org_grays
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def colorize_grayscale(colorizer, color_class, rgb_img, hint_img, n_anchors, is_high_res, is_editable, device="cuda"):
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n_anchors = int(n_anchors)
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n_anchors = max(n_anchors, 3)
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n_anchors = min(n_anchors, 14)
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target_res = (512,512) if is_high_res else (256,256)
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input_grays, input_colors, org_grays = prepare_data(rgb_img, target_res)
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input_grays = input_grays.to(device)
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input_colors = input_colors.to(device)
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if is_editable:
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print('>>>:editable mode')
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sampled_T = -1
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_, input_colors, _ = prepare_data(hint_img, target_res)
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input_colors = input_colors.to(device)
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pal_logit, ref_logit, enhanced_ab, affinity_map, spix_colors, hint_mask = colorizer(input_grays, \
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input_colors, n_anchors, sampled_T)
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else:
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print('>>>:automatic mode')
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sampled_T = 0
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pal_logit, ref_logit, enhanced_ab, affinity_map, spix_colors, hint_mask = colorizer(input_grays, \
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input_colors, n_anchors, sampled_T)
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pred_labs = torch.cat((input_grays,enhanced_ab), dim=1)
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lab_imgs = basic.tensor2array(pred_labs).squeeze(axis=0)
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lab_imgs = resize_ab2l(org_grays, lab_imgs)
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lab_imgs[:,:,0] = lab_imgs[:,:,0] * 50.0 + 50.0
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lab_imgs[:,:,1:3] = lab_imgs[:,:,1:3] * 110.0
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rgb_output = cv2.cvtColor(lab_imgs[:,:,:], cv2.COLOR_LAB2RGB)
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return (rgb_output*255.0).astype(np.uint8)
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def predict_anchors(colorizer, color_class, rgb_img, n_anchors, is_high_res, is_editable, device="cuda"):
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n_anchors = int(n_anchors)
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n_anchors = max(n_anchors, 3)
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n_anchors = min(n_anchors, 14)
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target_res = (512,512) if is_high_res else (256,256)
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input_grays, input_colors, org_grays = prepare_data(rgb_img, target_res)
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input_grays = input_grays.to(device)
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input_colors = input_colors.to(device)
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sampled_T, sp_size = 0, 16
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pal_logit, ref_logit, enhanced_ab, affinity_map, spix_colors, hint_mask = colorizer(input_grays, \
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input_colors, n_anchors, sampled_T)
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pred_probs = pal_logit
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guided_colors = color_class.decode_ind2ab(ref_logit, T=0)
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guided_colors = basic.upfeat(guided_colors, affinity_map, sp_size, sp_size)
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anchor_masks = basic.upfeat(hint_mask, affinity_map, sp_size, sp_size)
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marked_labs = basic.mark_color_hints(input_grays, guided_colors, anchor_masks, base_ABs=None)
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lab_imgs = basic.tensor2array(marked_labs).squeeze(axis=0)
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lab_imgs = resize_ab2l(org_grays, lab_imgs, vis=True)
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lab_imgs[:,:,0] = lab_imgs[:,:,0] * 50.0 + 50.0
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lab_imgs[:,:,1:3] = lab_imgs[:,:,1:3] * 110.0
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rgb_output = cv2.cvtColor(lab_imgs[:,:,:], cv2.COLOR_LAB2RGB)
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return (rgb_output*255.0).astype(np.uint8) |
