PixHtLab-Src/Demo/PixhtLab/ShadowStyle/inference/utils/make_html.py

import numpy as np
import json
import pdb
import os
from os.path import join
import html
from tqdm import tqdm
import argparse
import pandas as pd
import matplotlib.pyplot as plt

def get_files(folder):
    return [join(folder, f) for f in os.listdir(folder) if os.path.isfile(join(folder, f))]

def get_folders(folder):
    return [join(folder, f) for f in os.listdir(folder) if os.path.isdir(join(folder, f))]

vis_img_folder = 'imgs'
vis_eval_img_folder = 'eval_imgs'
def eval_gen(webpage, output_folder, is_pattern=True):
    def get_file(files, key_world):
        mitsuba_shadow = ''
        for f in files:
            if f.find(key_world) != -1:
                mitsuba_shadow = f
                break
        return mitsuba_shadow

    def flip_shadow(img_file):
        dirname, fname = os.path.dirname(img_file), os.path.splitext(os.path.basename(img_file))[0]
        if img_file == '':
            print('find one zero')
            mts_shadow_np = np.zeros((256,256,3))
        else:
            mts_shadow_np = plt.imread(img_file)
        
        save_path = join(dirname, fname + '_flip.png')
        plt.imsave(save_path, 1.0-mts_shadow_np)
        return save_path

    img_folders = join(output_folder, 'imgs')
    folders = get_folders(img_folders)
    print("There are {} folders".format(len(folders)))
    
    for model in tqdm(folders):
        cur_model_relative = join(vis_img_folder, os.path.basename(model))
        evl_cur_model_relative = join(vis_eval_img_folder, os.path.basename(model))

        if is_pattern:
            ibl_relative = join(cur_model_relative, 'pattern')
        else:
            ibl_relative = join(cur_model_relative, 'real')
        
        # import pdb; pdb.set_trace()
        ibl_folders = get_folders(ibl_relative)
        ibl_folders.sort()
        for ibl in ibl_folders:
            cur_ibl_relative = join(ibl_relative,os.path.basename(ibl))
            gt_files = get_files(cur_ibl_relative)
            mts_shadow = get_file(gt_files, '_shadow.png')

            ibl_name = os.path.basename(ibl)
            ibl = join(cur_ibl_relative, ibl_name + '.png')
           
            mitsuba_shadow = flip_shadow(mts_shadow)

            cur_eval_folder = join(evl_cur_model_relative, join('pattern', ibl_name))
            net_predict = get_file(get_files(cur_eval_folder), 'predict.png')

            # mitsuba_final = join(cur_ibl_relative, 'composite.png')
            # pred_final = join(cur_ibl_relative, 'composite_pred.png')
            
            # print(ibl_name)
            ims, txts, links = [ibl,mitsuba_shadow, net_predict], ['ibl','mitsuba', 'predict'], [ibl,mitsuba_shadow, net_predict]

            webpage.add_images(ims, txts, links)

vis_pattern_folder = '/home/ysheng/Documents/vis_pattern'
vis_real_folder = '/home/ysheng/Documents/vis_real'
def vis_files_in_folder():
    folder = '/home/ysheng/Documents/vis_models'
    webpage = html.HTML(folder, 'models', reflesh=1)    
    img_folders = join(folder, 'imgs')
    files = get_files(img_folders)
    print("There are {} files".format(len(files)))
    
    prefix_set = set()
    for cur_file in tqdm(files):
        cur_name = os.path.splitext(os.path.basename(cur_file))[0]
        prefix_set.add(cur_name[:-3])

    print('there are {} prefixs'.format(len(prefix_set)))
    prefix_set = list(prefix_set)
    prefix_set.sort()

    # import pdb; pdb.set_trace()
    relative_folder = './imgs'
    for i, prefix in enumerate(prefix_set):
        ims = [join(relative_folder, prefix + '{:03d}.png'.format(i)) for i in range(len(files) // len(prefix_set))]
        txts = [prefix + '{:03d}'.format(i) for i in range(len(files) // len(prefix_set))]
        links = ims
        webpage.add_images(ims, txts, links)

    webpage.save()
    print('finished')
    
def vis_files(df_file):
    """ input is a pandas dataframe
        format: path, path,..., name,name, ...
    """
    folder = '.'
    webpage = html.HTML(folder, 'benchmark', reflesh=1)    

    relative_folder = './imgs'
#     for i, prefix in enumerate(prefix_set):
#         ims = [join(relative_folder, prefix + '{:03d}.png'.format(i)) for i in range(len(files) // len(prefix_set))]
#         txts = [prefix + '{:03d}'.format(i) for i in range(len(files) // len(prefix_set))]
#         links = ims
#         webpage.add_images(ims, txts, links)
    
    df = pd.read_csv(df_file)
    for i,v in tqdm(df.iterrows(), total=len(df)):
        img_range = len(v)//2+1
        imgs = [join(relative_folder,v[i]) for i in range(1,img_range)]
        txts = [v[i] for i in range(img_range, len(v))]
        links = imgs
        webpage.add_images(imgs, txts, links)
        
    webpage.save()
    print('finished')
    
if __name__ == "__main__":
    vis_files('/home/ysheng/Documents/paper_project/adobe/soft_shadow/benchmark_results/html.csv')