Delete download.py

download.py

@@ -1,153 +0,0 @@
-import json
-from shapely.geometry import shape
-import os
-import collections
-from tqdm import tqdm
-import pandas as pd  
-from multiprocessing import cpu_count 
-from multiprocessing.pool import ThreadPool
-import requests
-import networkx as nx
-import rasterio as rio
-from extract import extract
-from tiling import get_tiles
-
-from huggingface_hub import HfApi
-from datetime import datetime
-from tiling import get_tiles
-
-def download_image(args): 
-    url, fn = args['image_href'], args['image']
-
-    if os.path.exists(fn) is False:
-        try: 
-            r = requests.get(url) 
-            with open(fn, 'wb') as f:
-                f.write(r.content) 
-        except Exception as e: 
-            print('Exception in download_url():', e)
-            return url
-
-    with rio.open(fn, "r") as ds:
-        print(args['date'], args['id'], ds.crs.to_proj4())
-    
-    return url
-
-
-if __name__ == '__main__':
-    api = HfApi()
-
-    image_dir = './dataset/image'
-
-    if os.path.exists('data.json') == False:
-        extract('data.json')
-    
-    with open('data.json') as f:
-        data = json.load(f)
-
-
-    ids = [f['id'] for f in data['features']]
-    duplicated = [item for item, count in collections.Counter(ids).items() if count > 1]
-    for duplicated_instance in duplicated:
-        items = []
-        for f in data['features']:
-            if f['id'] == duplicated_instance:
-                items.append(json.dumps(f))
-        assert len(collections.Counter(items).keys()) == 1, 'Unexpected duplicated item' # Tutti gli elementi che hanno lo stesso id sono completamente identici
-
-    # Prendo tutte le feature che sono univoce a livello di contenuto
-    data['features'] =[json.loads(f) for f in list(set([json.dumps(f) for f in data['features']]))]
-    #data['features'] = data['features'][:2]
-
-    records = []
-    for idx in tqdm(range(len(data['features']))):
-        feature = data['features'][idx]
-
-        gec = feature['assets'].get('GEC')
-        if gec is None:
-            continue
-
-
-        metadata = feature['assets']['metadata']['content']
-        assert len(metadata['collects']) == 1, 'Unexpected situation'
-        assert len(metadata['derivedProducts']['GEC']) == 1, 'Unexpected situation'
-
-        parsed_date = datetime.fromisoformat(metadata['collects'][0]['startAtUTC'])
-        if parsed_date.year < 2024:
-            continue
-            
-
-        records.append({
-            'id': feature['id'],
-            'date': metadata['collects'][0]['startAtUTC'],
-            'bbox': feature['bbox'],
-            'geometry': feature['geometry'],
-            'satellite': metadata['umbraSatelliteName'],
-            'track': metadata['collects'][0]['satelliteTrack'],
-            'direction': metadata['collects'][0]['observationDirection'],
-            'mode': metadata['imagingMode'],
-            'band': metadata['collects'][0]['radarBand'],
-            'polarization': metadata['collects'][0]['polarizations'],
-            'azimuth_res': metadata['derivedProducts']['GEC'][0]['groundResolution']['azimuthMeters'],
-            'range_res': metadata['derivedProducts']['GEC'][0]['groundResolution']['rangeMeters'],
-            'rows': metadata['derivedProducts']['GEC'][0]['numRows'],
-            'cols': metadata['derivedProducts']['GEC'][0]['numColumns'],
-            'size': metadata['derivedProducts']['GEC'][0]['numRows']*metadata['derivedProducts']['GEC'][0]['numColumns'],
-            'image_href': gec['href'],
-            'image': os.path.join(image_dir, '{name}.tiff'.format(name=feature['id']))
-        })
-
-
-
-    cpus = cpu_count() 
-    results = ThreadPool(cpus - 1).imap_unordered(download_image, records) 
-    for result in results: 
-        print('url:', result)
-
-    for record in records:
-        try:
-            with rio.open(record['image']) as src:
-                image_crs = src.crs.to_proj4()
-                record['crs'] = src.crs.to_proj4()
-        except:
-            record['crs'] = 'None'
-            print('Error reading the image')
-    
-    df = pd.DataFrame.from_records(records)
-    df.to_excel('out.xlsx')
-
-
-
-    selected_records = []
-    for record in records:
-        if record['crs'] == '+proj=longlat +datum=WGS84 +no_defs=True':
-            out_dir = 'dataset/tile/{id}'.format(id = record['id'])
-            if os.path.exists(out_dir) is False:
-                os.mkdir(out_dir)
-            selected_records.append({'input_path': record['image'], 'out_dir': out_dir, 'patch_size': 2048})
-    cpus = cpu_count() 
-    results = ThreadPool(cpus - 1).imap_unordered(get_tiles, selected_records) 
-    for result in results: 
-        print('url:', result)
-
-
-
-    api.upload_file(
-        path_or_fileobj='out.xlsx',
-        path_in_repo='out.xlsx',
-        repo_id='fedric95/umbra',
-        repo_type='dataset',
-    )
-
-    api.upload_file(
-        path_or_fileobj='data.json',
-        path_in_repo='data.json',
-        repo_id='fedric95/umbra',
-        repo_type='dataset',
-    )
-
-    api.upload_large_folder(
-        repo_id='fedric95/umbra',
-        repo_type='dataset',
-        folder_path='./dataset/',
-    )