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| """ | |
| This module allows to extract facial deatures from videos | |
| """ | |
| import os | |
| import shutil | |
| from retinaface import RetinaFace | |
| from deepface import DeepFace | |
| import json | |
| from video_tools import generate_frames | |
| FRAMES_PATH = "tmp_frames_faces" | |
| def retrieve_faces_data(video_path, rate = 50, show_print = True): | |
| faces_lst = [] | |
| generate_frames(video_path, FRAMES_PATH, rate = rate, show_print = show_print) | |
| for i in sorted([int(s[:-4]) for s in os.listdir(FRAMES_PATH)]): | |
| faces = RetinaFace.extract_faces(FRAMES_PATH + "/" + str(i) + ".png") | |
| data_lst = [] | |
| for face in faces: | |
| try: | |
| face_dict = DeepFace.analyze(face, actions = ["emotion"], detector_backend = "skip") | |
| data_lst.append(face_dict["emotion"]) | |
| except ValueError: | |
| # Face was not detected | |
| continue | |
| faces_lst.append(data_lst) | |
| # Delete temporary directory | |
| #shutil.rmtree(FRAMES_PATH) | |
| return faces_lst | |
| def retrieve_to_file(dest, video_path): | |
| face_data = retrieve_faces_data(video_path, show_print = False) | |
| with open(dest, "w") as output_file: | |
| output_file.writelines([json.dumps(item) + "\n" for item in face_data]) | |
| def retrieve_to_files(dest, video_path): | |
| for file_name in os.listdir(video_path): | |
| retrieve_to_file(dest + "/" + os.path.splitext(file_name)[0] + "_data", video_path + "/" + file_name) | |
| def restore_from_file(file_path): | |
| restored_lst = [] | |
| with open(file_path, "r") as file: | |
| for line in file.readlines(): | |
| if line != "": | |
| restored_lst.append(eval(line)) | |
| return restored_lst | |
| def data_to_vector(data): | |
| vec = [] | |
| for frame in data: | |
| avg = [0, 0, 0, 0, 0, 0, 0] | |
| for face in frame: | |
| avg[0] += face["angry"] | |
| avg[1] += face["disgust"] | |
| avg[2] += face["fear"] | |
| avg[3] += face["happy"] | |
| avg[4] += face["sad"] | |
| avg[5] += face["surprise"] | |
| avg[6] += face["neutral"] | |
| if len(frame) != 0: | |
| for i in range(7): | |
| avg[i] /= len(frame) | |
| vec.append(avg) | |
| return vec | |
| if __name__ == "__main__": | |
| retrieve_to_files("x", "result") |