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Runtime error
| import face_recognition | |
| import requests | |
| import pandas as pd | |
| from io import BytesIO | |
| from tqdm import tqdm | |
| from time import time | |
| def get_image(url: str): | |
| headers = { | |
| "User-Agent": "Actors matching app 1.0" | |
| } | |
| response = requests.get(url, headers=headers) | |
| response.raise_for_status() | |
| img_file_object = BytesIO(response.content) | |
| return face_recognition.load_image_file(img_file_object) | |
| def get_embeddings(url: str): | |
| try: | |
| image = get_image(url) | |
| embeddings = face_recognition.face_encodings(image, num_jitters=2, model="large") | |
| return list(embeddings[0]) | |
| except Exception as e: | |
| print(e) | |
| def process_all_images(input_file, output_file): | |
| df = pd.read_csv(input_file)[["nconst","contentUrl","resultPosition"]] | |
| try: | |
| df_emb = pd.read_csv(output_file) | |
| df = df[~df["contentUrl"].isin(df_emb["contentUrl"])] | |
| except: | |
| # file does not exists yet | |
| df_emb = pd.DataFrame(columns=list(df.columns) + ["embeddings"]) | |
| print(f"Start processing of {df.shape[0]} images") | |
| df = df.sort_values("resultPosition", ascending=True) | |
| #df = df.sample(frac=1) # shuffle so you get some images for everybody while it's running | |
| for i, row in tqdm(df.iterrows(), total=df.shape[0]): | |
| embeddings = get_embeddings(row["contentUrl"]) | |
| new_row = row.copy() | |
| new_row["embeddings"] = embeddings | |
| new_row = new_row[["nconst", "contentUrl", "embeddings"]] | |
| df_emb = df_emb.append(new_row, ignore_index=True) | |
| if i % 5 == 0: | |
| df_emb.to_csv(output_file, index=False) | |
| df_emb.to_csv(output_file, index=False) | |
| return df_emb | |
| def build_annoy_index(): | |
| pass | |
| if __name__ == "__main__": | |
| output_file = "../data/actors_embeddings.csv" | |
| df_embeddings = process_all_images(input_file="../data/actors_images.csv", output_file=output_file) |