def train_dataframe(company_name):
print("Loading Started...")
known_faces = get_images_with_tag(company_name)
all_ids = []
all_locations = []
fr = FaceRecognition()
try:
for name, known_file in known_faces:
all_ids.append(name)
all_locations.append(known_file)
print("Loaded: " + name)
except:
print("Error for " + known_file + " tag: " + name)
df = pd.DataFrame(list(zip(all_ids, all_locations)), columns =['person', 'path'])
print("Fitting started")
fr.fit_from_dataframe(df)
fr.save('/var/www/attendancekeeper_' + company_name + '/detector/knn.pkl')
print("Fitting done")
import os
from face_recognition import FaceRecognition
from face_detection import get_detected_face
if __name__ == "__main__":
model_name = "face_recognition.h5"
image_path = 'ronaldo.jpg'
face_recognition = FaceRecognition()
face_recognition.train()
face_recognition.save(model_name)
# model = FaceRecognition.load_saved_model(os.path.join("model", model_name))
# f = FaceRecognition.model_predict(image_path, os.path.join("model", model_name),
#os.path.join("model", "face_recognition_class_names.npy"))
#print(f"This is {f}")
