def load(self, path):
"""
Load classifier from pickle file
:param path: path
"""
clf = EuclideanClassifier()
clf.load(path)
self.clf = clf
def fit(self, folder):
"""
Fit classifier from directory.
Directory must have this structure:
Person 1:
file.jpg
....
file.jpg
Person 2:
file.jpg
...
file.jpg
...
:param folder: root folder path
"""
# Initialize classifier
clf = EuclideanClassifier()
# Load all files
files = []
for ext in config.ALLOWED_IMAGE_TYPES:
files.extend(
glob.glob(os.path.join(folder, "*", ext), recursive=True))
for path in tqdm.tqdm(files):
# Load image
image = cv2.imread(path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Get person name by folder
person = os.path.split(os.path.split(path)[0])[1]
# Get encoding
for encoding, face, box in self.face_encoding(image):
# Add to classifier
clf.fit([encoding], [person])
self.clf = clf
def fit_from_dataframe(self, df, person_col="person", path_col="path"):
"""
Fit classifier from dataframe.
:param df: Pandas dataframe
:param person_col: Dataframe column with person id
:param path_col: Dataframe column with image path
"""
# Initialize classifier
clf = EuclideanClassifier()
for index, row in tqdm.tqdm(df.iterrows(), total=df.shape[0]):
# Load image
image = cv2.imread(row[path_col])
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Get person name by folder
person = row[person_col]
# Get encoding
for encoding, face, box in self.face_encoding(image):
# Add to classifier
clf.fit([encoding], [person])
self.clf = clf