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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) |