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lfw.py
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lfw.py
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import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_lfw_pairs
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
from deepface import DeepFace
from tqdm import tqdm
def get_results(data, dlib_model, distance):
actuals = []
predictions = []
distances = []
for i in tqdm(range(0, len(data.pairs))):
pair = data.pairs[i]
img1 = pair[0]
img2 = pair[1]
# normalization differs depending on the model
if dlib_model in ['Facenet', 'VGGFace', 'ArcFace']:
normalization = dlib_model
else:
normalization = 'base'
# perfom pair comparison
obj = DeepFace.verify(img1, img2,
model_name=dlib_model,
distance_metric=distance,
enforce_detection=False,
prog_bar=False,
normalization=normalization)
# save results
prediction = obj["verified"]
predictions.append(prediction)
label = data.target_names[data.target[i]]
actual = True if data.target[i] == 1 else False
actuals.append(actual)
accuracy = 100 * accuracy_score(actuals, predictions)
return actuals, predictions, accuracy
def main():
# get LFW pair images from sklearn
lfw_pairs = fetch_lfw_pairs(subset='test', color=True, resize=1)
res = {}
for dlib_model in ["ArcFace", "Facenet", "Dlib"]:
for distance in ['cosine', 'euclidean']: # cosine for ArcFace and Facenet, euclidean for Dlib
actuals, predictions, accuracy = get_results(lfw_pairs, dlib_model, distance)
res[dlib_model + '_' + distance] = accuracy
# save results for every model and each distance function
df = pd.DataFrame({'actuals': actuals, 'predictions': predictions})
df.to_csv(dlib_model + '_' + distance+'.csv')
if __name__ == '__main__':
main()