Martin Weiss MartinWeiss12
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| image_width, image_height = 600, 450 | |
| data_transforms = { | |
| 'train': transforms.Compose([ | |
| transforms.Resize((image_width, image_height)), | |
| transforms.RandomHorizontalFlip(), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) | |
| ]), | |
| 'test': transforms.Compose([ |
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| import torch | |
| from torch.utils.data import DataLoader | |
| from torchvision import datasets, transforms | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import numpy as np | |
| import cv2 | |
| import matplotlib.pyplot as plt | |
| import matplotlib.cm as cm |
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| # your client id | |
| client_id = '' | |
| # your client secret | |
| client_secret = '' | |
| token_url = 'https://accounts.spotify.com/api/token' | |
| token_data = {'grant_type': 'client_credentials'} | |
| token_response = requests.post(token_url, auth=(client_id, client_secret), data=token_data) | |
| access_token = token_response.json()['access_token'] | |
| headers = {'Authorization': f'Bearer {access_token}'} |
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| album_grid = make_grid(album_images, 'Album') | |
| artist_grid = make_grid(artist_images, 'Artist') |
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| def download_images(folder, type): | |
| top_49 = folder[f'{type} Image URL'][:49] | |
| folder_path = os.path.abspath(f'{type}-Images') | |
| for i, url in enumerate(top_49): | |
| with open(os.path.join(folder_path, f'{type}-{i+1}.png'), 'wb') as file: | |
| file.write(requests.get(url).content) | |
| download_images(top_albums, 'Album') | |
| download_images(top_artists, 'Artist') |
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| import os | |
| import requests | |
| import pandas as pd | |
| from PIL import Image | |
| # path for the excel file for your top albums | |
| top_albums = pd.read_excel('') | |
| # path for the excel file for your top artists | |
| top_artists = pd.read_excel('') | |
| # path for an empty folder named 'Album-Images' where the album images will be saved to |
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| track_artist_album_df['Artist Image URL'] = track_artist_album_df['Artist URI'].map(unique_artist_image_url_dict) | |
| merged_track_artist_album_url_df = pd.merge(cleaned_df, track_artist_album_df, on='Track URI', how='left') |
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| artist_uris = track_artist_album_df['Artist URI'] | |
| unique_artist_uris = list(set(artist_uris)) | |
| n_unique_artist_sublists = len(unique_artist_uris) // 50 + (len(unique_artist_uris) % 50 > 0) | |
| unique_artist_sublists = [unique_artist_uris[i * 50 : (i + 1) * 50] for i in range(n_unique_artist_sublists)] | |
| unique_artist_image_url_dict = {} | |
| start_time = time.time() | |
| for artist_list in unique_artist_sublists: | |
| params = {'ids': ','.join(artist_list)} | |
| response = requests.get('https://api.spotify.com/v1/artists?', headers=headers, params=params) |
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| track_artist_df['Duplicate URI'] = track_artist_df['URI'] | |
| groups = track_artist_df.groupby(['Track', 'Artist']) | |
| for group_name, group_df in groups: | |
| if len(group_df) > 1: | |
| unique_uris = group_df['URI'].unique() | |
| track_artist_df.loc[group_df.index, 'Duplicate URI'] = unique_uris[0] |
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| cleaned_df = pd.DataFrame(list(zip( | |
| filtered_data['master_metadata_track_name'], | |
| filtered_data['master_metadata_album_album_name'], | |
| filtered_data['master_metadata_album_artist_name'], | |
| filtered_data['spotify_track_uri'])), | |
| columns=['Track', 'Album', 'Artist', 'Track URI'] | |
| ) | |
| cleaned_df = ( | |
| cleaned_df[~cleaned_df[['Track', 'Album', 'Artist', 'Track URI']] |
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