Hugging Face's logo

createcolor
/
NPRC24

Image-to-Image
imaging
isp
raw2rgb
Model card Files Community
NPRC24
File size: 6,940 Bytes 
e91104d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
 
###################################################
# Night challenge 2024
###################################################


import argparse
import threading
from pathlib import Path
import os
import queue
from time import time
from tqdm import tqdm

import blocks as B
import cv2
import numpy as np
import skimage.color as cl
from skimage.transform import resize
from raw_prc_pipeline import (expected_img_ext, expected_landscape_img_height,
                              expected_landscape_img_width)
from raw_prc_pipeline.pipeline import RawProcessingPipelineDemo
from tqdm import tqdm
from utils import fraction_from_json, json_read


def parse_args():
    parser = argparse.ArgumentParser(
        description='Script for processing PNG images with given metadata files.')
    # folder params
    parser.add_argument('-p', '--png_dir', type=Path, required=True,
                        help='Path of the directory containing PNG images with metadata files.')
    parser.add_argument('-o', '--out_dir', type=Path, default='./results',
                        help='Path to the directory where processed images will be saved. Images will be saved in JPG format.')
    # raw processing params
    parser.add_argument('-ie', '--illumination_estimation', type=str, default='',
                        help='Options for illumination estimation algorithms: "gw", "wp", "sog", "iwp".')
    parser.add_argument('-tm', '--tone_mapping', type=str, default='Flash',
                        help='Options for tone mapping algorithms: "Base", "Flash", "Storm", "Linear", "Drago", "Mantiuk", "Reinhard".')
    # srgb processing params
    parser.add_argument('-gc', '--gamma_correction', type=float, default=1 / 1.4,
                        help='Global gamma correction.')
    parser.add_argument('-dm', '--denoise_mask', type=float, default=0.6,
                        help='Value to control denoising effect in bright regions. Should be between 0 and 1')
    args = parser.parse_args()

    if args.out_dir is None:
        args.out_dir = args.png_dir

    return args


class PNGProcessing():
    def __init__(self, ie_method, tone_mapping, gamma_correction, denoise_mask):
        self.pipeline_params = {
            'illumination_estimation': ie_method,
            # 'tone_mapping': tone_mapping,
            'out_landscape_width': expected_landscape_img_width,
            'out_landscape_height': expected_landscape_img_height
        }

        self.pipeline = RawProcessingPipelineDemo(**self.pipeline_params)
        self.gamma_correction = gamma_correction
        self.denoise_mask = denoise_mask

    def pipeline_exec(self, raw_image, metadata):

        normalized_image = self.pipeline.normalize(raw_image, metadata)

        demosaiced_image = self.pipeline.demosaic(normalized_image, metadata)
        # check the original demosaicing to see if results are the same
        
        demosaiced_image = resize(demosaiced_image, (768, 1024), preserve_range=True, anti_aliasing=True)

        wb_image = self.pipeline.white_balance(demosaiced_image, metadata)

        xyz_image = self.pipeline.xyz_transform(wb_image, metadata)
        srgb_image = self.pipeline.srgb_transform(xyz_image, metadata)

        denoised_image = B.denoise_raw(
            srgb_image, l_w=1, ch_w=7)
            # srgb_image, l_w=4.5, ch_w=20)
            # srgb_image, l_w=1.659923974475318, ch_w=5.459274910995606)

        light_enhancer = B.LCC(2)
        # light_enhancer = B.LCC(sigma=6.463076463115174)
        light_image = light_enhancer(denoised_image).clip(0)

        contrast_image = B.global_mean_contrast(light_image, beta=1.5).clip(0)
        # contrast_image = B.global_mean_contrast(light_image, beta=0.8653634653721171).clip(0)

        gamma_image = B.scurve(contrast_image, alpha=0, lmbd=(1 / 1.8)).clip(0)
        # gamma_image = B.scurve(contrast_image, alpha=0.7050463096367395, lmbd=0.9740931227248038).clip(0)

        black_adj_image = B.imadjust(gamma_image, 0.99).clip(0)
        # black_adj_image = B.imadjust(gamma_image, 0.9957007298972433, 0.01697803128505186).clip(0)

        im_h = cl.rgb2hsv(black_adj_image)[:, :, 2]
        if im_h.mean() < 0.2:
            black_adj_image = B.scurve_central(black_adj_image, lmbd=(1 / 1.8)).clip(0)
            # black_adj_image = B.scurve_central(black_adj_image, lmbd=0.6913136563678325).clip(0)
        elif im_h.mean() < 0.25:
            black_adj_image = B.scurve_central(black_adj_image, lmbd=(1 / 1.4)).clip(0)
            # black_adj_image = B.scurve_central(black_adj_image, lmbd=0.3612134419536918).clip(0)
        elif im_h.mean() > 0.4:
            black_adj_image = B.gamma_correction(black_adj_image, 1.6).clip(0)
            # black_adj_image = B.gamma_correction(black_adj_image, 3.5208650132731356).clip(0)

        sharp_image = B.sharpening(black_adj_image, sigma=1).clip(0)
        # sharp_image = B.sharpening(black_adj_image, 1.5389081796026578, 0.05456721376794549).clip(0)

        wb_image = B.white_balance(sharp_image, denoise_first=True).clip(0)
        # wb_image = B.white_balance(sharp_image, 0.740831363817609, 0.004044358054560114).clip(0)
        
        uint8_image = self.pipeline.to_uint8(wb_image, metadata)
        # resized_image = self.pipeline.resize(uint8_image, metadata)
        resulted_image = self.pipeline.fix_orientation(uint8_image, metadata)


        return resulted_image

    def __call__(self, png_path: Path, out_path: Path):

        # parse raw img
        raw_image = cv2.imread(str(png_path), cv2.IMREAD_UNCHANGED)
        # parse metadata
        metadata = json_read(png_path.with_suffix(
            '.json'), object_hook=fraction_from_json)

        start = time()
        output_image = self.pipeline_exec(raw_image, metadata)
        end = time()
        
        # save results
        output_image = cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR)
        cv2.imwrite(str(out_path), output_image, [
                    cv2.IMWRITE_JPEG_QUALITY, 100])
        return end - start


def process(png_processor, out_dir, png_paths):
    out_paths = [
        out_dir / png_path.with_suffix(expected_img_ext).name for png_path in png_paths]
    times = []
    pbar = tqdm(total=len(png_paths), ncols=100)
    for png_path, out_path in zip(png_paths, out_paths):
        runtime = png_processor(png_path, out_path)
        times.append(runtime)
        pbar.update()
    return times
    

def main(png_dir, out_dir, illumination_estimation, tone_mapping, gamma_correction, denoise_mask):
    # out_dir.mkdir(exist_ok=True)
    os.makedirs(out_dir, exist_ok=True)

    png_paths = list(png_dir.glob('*.png'))

    png_processor = PNGProcessing(
        illumination_estimation, tone_mapping, gamma_correction, denoise_mask)

    times = process(png_processor, out_dir, png_paths)
    print(f'Average time: {np.mean(times)} seconds.')


if __name__ == '__main__':
    args = parse_args()
    main(**vars((args)))