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from pathlib import Path |
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import datasets |
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import pandas as pd |
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_VERSION = "1.2.1" |
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_CITATION = f""" |
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@dataset{{unsplash-lite-dataset, |
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title = {{Unsplash Lite Dataset {_VERSION}}}, |
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url = {{\\url{{https://github.com/unsplash/datasets}}}}, |
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author = {{Unsplash}}, |
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year = {{2023}}, |
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month = {{May}}, |
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day = {{02}}, |
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}} |
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""" |
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_DESCRIPTION = """ |
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This dataset, available for commercial and noncommercial usage, |
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contains 25k nature-themed Unsplash photos, 25k keywords, and 1M searches. |
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""" |
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_HOMEPAGE = f"https://github.com/unsplash/datasets/tree/{_VERSION}" |
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_URL = f"https://unsplash.com/data/lite/{_VERSION}" |
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_LICENSE = "Unsplash Dataset License" |
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_TSV = ( |
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"collections", |
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"colors", |
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"conversions", |
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"keywords", |
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"photos", |
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) |
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_FEATURES = datasets.Features( |
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{ |
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"photo": { |
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"id": datasets.Value("string"), |
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"url": datasets.Value("string"), |
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"image_url": datasets.Value("string"), |
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"submitted_at": datasets.Value("string"), |
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"featured": datasets.Value("bool"), |
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"width": datasets.Value("uint16"), |
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"height": datasets.Value("uint16"), |
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"aspect_ratio": datasets.Value("float32"), |
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"description": datasets.Value("string"), |
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"blur_hash": datasets.Value("string"), |
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}, |
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"photographer": { |
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"username": datasets.Value("string"), |
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"first_name": datasets.Value("string"), |
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"last_name": datasets.Value("string"), |
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}, |
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"exif": { |
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"camera_make": datasets.Value("string"), |
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"camera_model": datasets.Value("string"), |
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"iso": datasets.Value("string"), |
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"aperture_value": datasets.Value("string"), |
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"focal_length": datasets.Value("string"), |
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"exposure_time": datasets.Value("string"), |
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}, |
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"location": { |
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"name": datasets.Value("string"), |
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"latitude": datasets.Value("float32"), |
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"longitude": datasets.Value("float32"), |
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"country": datasets.Value("string"), |
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"city": datasets.Value("string"), |
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}, |
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"stats": { |
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"views": datasets.Value("uint32"), |
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"downloads": datasets.Value("uint32"), |
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}, |
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"ai": { |
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"description": datasets.Value("string"), |
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"primary_landmark_name": datasets.Value("string"), |
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"primary_landmark_latitude": datasets.Value("string"), |
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"primary_landmark_longitude": datasets.Value("string"), |
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"primary_landmark_confidence": datasets.Value("string"), |
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}, |
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"keywords": [ |
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{ |
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"keyword": datasets.Value("string"), |
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"ai_service_1_confidence": datasets.Value("string"), |
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"ai_service_2_confidence": datasets.Value("string"), |
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"suggested_by_user": datasets.Value("bool"), |
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}, |
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], |
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"collections": [ |
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{ |
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"collection_id": datasets.Value("string"), |
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"collection_title": datasets.Value("string"), |
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"photo_collected_at": datasets.Value("string"), |
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}, |
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], |
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"conversions": [ |
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{ |
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"converted_at": datasets.Value("string"), |
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"conversion_type": datasets.Value("string"), |
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"keyword": datasets.Value("string"), |
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"anonymous_user_id": datasets.Value("string"), |
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"conversion_country": datasets.Value("string"), |
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}, |
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], |
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"colors": [ |
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{ |
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"hex": datasets.Value("string"), |
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"red": datasets.Value("uint8"), |
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"green": datasets.Value("uint8"), |
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"blue": datasets.Value("uint8"), |
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"keyword": datasets.Value("string"), |
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"ai_coverage": datasets.Value("float32"), |
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"ai_score": datasets.Value("float32"), |
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}, |
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], |
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}, |
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) |
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def df_withprefix(df, prefix, exclude=None): |
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columns = [col for col in df.columns if col.startswith(prefix)] |
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if exclude is not None: |
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columns = [col for col in columns if exclude not in col] |
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if "photo_id" not in columns: |
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columns.append("photo_id") |
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return df[columns].rename(columns=lambda col: col.removeprefix(prefix)) |
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class Unsplash(datasets.GeneratorBasedBuilder): |
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"""The Unsplash Lite dataset.""" |
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DEFAULT_WRITER_BATCH_SIZE = 100 |
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def _info(self): |
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return datasets.DatasetInfo( |
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features=_FEATURES, |
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supervised_keys=None, |
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description=_DESCRIPTION, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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version=_VERSION, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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archive_path = Path(dl_manager.download_and_extract(_URL)) |
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dataframes = {} |
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for doc in _TSV: |
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frames = [] |
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for filename in archive_path.glob(f"{doc}.tsv*"): |
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frame = pd.read_csv(filename, sep="\t", header=0) |
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frames.append(frame) |
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concat_frames = pd.concat(frames, axis=0, ignore_index=True) |
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if doc != "photos": |
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dataframes[doc] = concat_frames |
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else: |
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dataframes["photo"] = df_withprefix(concat_frames, "photo_", "location") |
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dataframes["photo"]["blur_hash"] = concat_frames["blur_hash"] |
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dataframes["photographer"] = df_withprefix(concat_frames, "photographer_") |
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dataframes["exif"] = df_withprefix(concat_frames, "exif_") |
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dataframes["location"] = df_withprefix(concat_frames, "photo_location_") |
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dataframes["stats"] = df_withprefix(concat_frames, "stats_") |
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dataframes["ai"] = df_withprefix(concat_frames, "ai_") |
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dataframes["photo"]["featured"] = dataframes["photo"]["featured"].map({"t": True, "f": False}) |
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dataframes["keywords"]["suggested_by_user"] = dataframes["keywords"]["suggested_by_user"].map({"t": True, "f": False}) |
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for doc in dataframes.keys(): |
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if doc in _TSV: |
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features = _FEATURES[doc][0] |
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else: |
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features = _FEATURES[doc] |
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dataframes[doc].astype({ |
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key: features[key].dtype |
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for key in features.keys() |
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}) |
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for key in _TSV[:-1]: |
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dataframes[key] = dataframes[key].groupby("photo_id") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"dataframes": dataframes}, |
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), |
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] |
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def _generate_examples(self, dataframes): |
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photo_id_frames = {} |
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for index, row in dataframes["photo"].iterrows(): |
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photo_id = row["id"] |
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photographer = dataframes["photographer"].iloc[index] |
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exif = dataframes["exif"].iloc[index] |
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location = dataframes["location"].iloc[index] |
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stats = dataframes["stats"].iloc[index] |
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ai = dataframes["ai"].iloc[index] |
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for key in _TSV[:-1]: |
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try: |
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photo_id_frames[key] = dataframes[key].get_group(photo_id) |
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except: |
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photo_id_frames[key] = pd.DataFrame() |
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data = { |
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"photo": { |
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"id": photo_id, |
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"url": row["url"], |
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"image_url": row["image_url"], |
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"submitted_at": row["submitted_at"], |
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"featured": row["featured"], |
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"width": row["width"], |
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"height": row["height"], |
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"aspect_ratio": row["aspect_ratio"], |
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"description": row["description"], |
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"blur_hash": row["blur_hash"], |
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}, |
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"photographer": { |
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"username": photographer["username"], |
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"first_name": photographer["first_name"], |
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"last_name": photographer["last_name"], |
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}, |
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"exif": { |
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"camera_make": exif["camera_make"], |
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"camera_model": exif["camera_model"], |
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"iso": exif["iso"], |
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"aperture_value": exif["aperture_value"], |
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"focal_length": exif["focal_length"], |
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"exposure_time": exif["exposure_time"], |
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}, |
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"location": { |
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"name": location["name"], |
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"latitude": location["latitude"], |
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"longitude": location["longitude"], |
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"country": location["country"], |
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"city": location["city"], |
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}, |
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"stats": { |
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"views": stats["views"], |
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"downloads": stats["downloads"], |
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}, |
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"ai": { |
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"description": ai["description"], |
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"primary_landmark_name": ai["primary_landmark_name"], |
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"primary_landmark_latitude": ai["primary_landmark_latitude"], |
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"primary_landmark_longitude": ai["primary_landmark_longitude"], |
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"primary_landmark_confidence": ai["primary_landmark_confidence"], |
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}, |
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"keywords": [ |
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{ |
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"keyword": keyword["keyword"], |
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"ai_service_1_confidence": keyword["ai_service_1_confidence"], |
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"ai_service_2_confidence": keyword["ai_service_2_confidence"], |
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"suggested_by_user": keyword["suggested_by_user"], |
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} |
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for _, keyword in photo_id_frames["keywords"].iterrows() |
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], |
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"collections": [ |
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{ |
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"collection_id": collection["collection_id"], |
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"collection_title": str(collection["collection_title"]), |
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"photo_collected_at": collection["photo_collected_at"], |
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} |
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for _, collection in photo_id_frames["collections"].iterrows() |
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], |
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"conversions": [ |
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{ |
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"converted_at": conversion["converted_at"], |
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"conversion_type": conversion["conversion_type"], |
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"keyword": conversion["keyword"], |
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"anonymous_user_id": conversion["anonymous_user_id"], |
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"conversion_country": str(conversion["conversion_country"]), |
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} |
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for _, conversion in photo_id_frames["conversions"].iterrows() |
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], |
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"colors": [ |
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{ |
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"hex": color["hex"], |
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"red": color["red"], |
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"green": color["green"], |
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"blue": color["blue"], |
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"keyword": color["keyword"], |
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"ai_coverage": color["ai_coverage"], |
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"ai_score": color["ai_score"], |
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} |
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for _, color in photo_id_frames["colors"].iterrows() |
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], |
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} |
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yield index, data |
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