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}")