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7_recognise_face_multiple.py
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7_recognise_face_multiple.py
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#Face recognition for multiple face
import dlib
import numpy as np
import cv2
import os
import pandas as pd
import time
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('models/model_dlib/shape_predictor_68_face_landmarks.dat')
face_reco_model = dlib.face_recognition_model_v1("models/model_dlib/dlib_face_recognition_resnet_model_v1.dat")
class Face_Recognizer:
def __init__(self):
self.font = cv2.FONT_ITALIC
# For FPS
self.frame_time = 0
self.frame_start_time = 0
self.fps = 0
# cnt for frame
self.frame_cnt = 0
self.features_known_list = []
self.name_known_list = []
self.last_frame_centroid_list = []
self.current_frame_centroid_list = []
self.last_frame_names_list = []
self.current_frame_face_name_list = []
self.last_frame_face_cnt = 0
self.current_frame_face_cnt = 0
self.current_frame_face_X_e_distance_list = []
self.current_frame_face_position_list = []
self.current_frame_face_features_list = []
# e distance between centroid of ROI in last and current frame
self.last_current_frame_centroid_e_distance = 0
def get_face_database(self):
if os.path.exists("models/features_all.csv"):
path_features_known_csv = "models/features_all.csv"
csv_rd = pd.read_csv(path_features_known_csv, header=None)
for i in range(csv_rd.shape[0]):
features_someone_arr = []
for j in range(0, 128):
if csv_rd.iloc[i][j] == '':
features_someone_arr.append('0')
else:
features_someone_arr.append(csv_rd.iloc[i][j])
self.features_known_list.append(features_someone_arr)
self.name_known_list.append("Person_" + str(i + 1))
print("Faces in Database：", len(self.features_known_list))
return 1
else:
print('##### Warning #####', '\n')
print("'features_all.csv' not found!")
print(
"Please run '3_capture_faces_fom_cam.py' and '4_feature_extraction_as_csv.py' before '5_recognise_face_from_cam.py'",
'\n')
print('##### End Warning #####')
return 0
def update_fps(self):
now = time.time()
self.frame_time = now - self.frame_start_time
self.fps = 1.0 / self.frame_time
self.frame_start_time = now
@staticmethod
def return_euclidean_distance(feature_1, feature_2):
feature_1 = np.array(feature_1)
feature_2 = np.array(feature_2)
dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))
return dist
def centroid_tracker(self):
for i in range(len(self.current_frame_centroid_list)):
e_distance_current_frame_person_x_list = []
# For object 1 in current_frame, compute e-distance with object 1/2/3/4/... in last frame
for j in range(len(self.last_frame_centroid_list)):
self.last_current_frame_centroid_e_distance = self.return_euclidean_distance(
self.current_frame_centroid_list[i], self.last_frame_centroid_list[j])
e_distance_current_frame_person_x_list.append(
self.last_current_frame_centroid_e_distance)
last_frame_num = e_distance_current_frame_person_x_list.index(
min(e_distance_current_frame_person_x_list))
self.current_frame_face_name_list[i] = self.last_frame_face_name_list[last_frame_num]
def draw_note(self, img_rd):
cv2.putText(img_rd, "Face recognizer with OT", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA)
cv2.putText(img_rd, "FPS: " + str(self.fps.__round__(2)), (20, 100), self.font, 0.8, (0, 255, 0), 1,
cv2.LINE_AA)
cv2.putText(img_rd, "Faces: " + str(self.current_frame_face_cnt), (20, 130), self.font, 0.8, (0, 255, 0), 1,
cv2.LINE_AA)
cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
for i in range(len(self.current_frame_face_name_list)):
cv2.putText(img_rd, "Face " + str(i + 1), tuple(
[int(self.current_frame_centroid_list[i][0]), int(self.current_frame_centroid_list[i][1])]), self.font,
0.8, (255, 190, 0),
1,
cv2.LINE_AA)
def process(self, stream):
if self.get_face_database():
while stream.isOpened():
self.frame_cnt += 1
print(">>> Frame " + str(self.frame_cnt) + " starts")
flag, img_rd = stream.read()
kk = cv2.waitKey(1)
faces = detector(img_rd, 0)
if self.current_frame_face_name_list == ['Person_2', 'Person_2']:
break
# Update cnt for faces in frames
self.last_frame_face_cnt = self.current_frame_face_cnt
self.current_frame_face_cnt = len(faces)
# Update the face name list in last frame
self.last_frame_face_name_list = self.current_frame_face_name_list[:]
# update frame centroid list
self.last_frame_centroid_list = self.current_frame_centroid_list
self.current_frame_centroid_list = []
print(" >>> current_frame_face_cnt: ", self.current_frame_face_cnt)
# 2.1. if cnt not changes
if self.current_frame_face_cnt == self.last_frame_face_cnt:
print(" >>> scene 1: no faces cnt changes in this frame!!!")
self.current_frame_face_position_list = []
if self.current_frame_face_cnt != 0:
# 2.1.1 Get ROI positions
for k, d in enumerate(faces):
self.current_frame_face_position_list.append(tuple(
[faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
self.current_frame_centroid_list.append(
[int(faces[k].left() + faces[k].right()) / 2,
int(faces[k].top() + faces[k].bottom()) / 2])
height = (d.bottom() - d.top())
width = (d.right() - d.left())
hh = int(height / 2)
ww = int(width / 2)
cv2.rectangle(img_rd,
tuple([d.left() - ww, d.top() - hh]),
tuple([d.right() + ww, d.bottom() + hh]),
(255, 255, 255), 2)
# multi-faces in current frames, use centroid tracker to track
if self.current_frame_face_cnt != 1:
self.centroid_tracker()
for i in range(self.current_frame_face_cnt):
# 6.2 write names under ROI
cv2.putText(img_rd, self.current_frame_face_name_list[i],
self.current_frame_face_position_list[i], self.font, 0.8, (0, 255, 255), 1,
cv2.LINE_AA)
# 2.2 if cnt of faces changes, 0->1 or 1->0 or ...
else:
print(" >>> scene 2: Faces cnt changes in this frame")
self.current_frame_face_position_list = []
self.current_frame_face_X_e_distance_list = []
# 2.2.1 face cnt decrease: 1->0, 2->1, ...
if self.current_frame_face_cnt == 0:
print(" >>> scene 2.1 No guy in this frame!!!")
# clear list of names and features
self.current_frame_face_name_list = []
self.current_frame_face_features_list = []
# 2.2.2 face cnt increase: 0->1, 0->2, ..., 1->2, ...
else:
print(" >>> scene 2.2 Do face recognition for people detected in this frame")
self.current_frame_face_name_list = []
for i in range(len(faces)):
shape = predictor(img_rd, faces[i])
self.current_frame_face_features_list.append(
face_reco_model.compute_face_descriptor(img_rd, shape))
self.current_frame_face_name_list.append("unknown")
for k in range(len(faces)):
print(" >>> For face " + str(k+1) + " in current frame:")
self.current_frame_centroid_list.append(
[int(faces[k].left() + faces[k].right()) / 2,
int(faces[k].top() + faces[k].bottom()) / 2])
self.current_frame_face_X_e_distance_list = []
self.current_frame_face_position_list.append(tuple(
[faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
# For every faces detected, compare the faces in the database
for i in range(len(self.features_known_list)):
if str(self.features_known_list[i][0]) != '0.0':
print(" >>> with person", str(i + 1), "the e distance: ", end='')
e_distance_tmp = self.return_euclidean_distance(
self.current_frame_face_features_list[k],
self.features_known_list[i])
print(e_distance_tmp)
self.current_frame_face_X_e_distance_list.append(e_distance_tmp)
else:
self.current_frame_face_X_e_distance_list.append(999999999)
similar_person_num = self.current_frame_face_X_e_distance_list.index(
min(self.current_frame_face_X_e_distance_list))
if min(self.current_frame_face_X_e_distance_list) < 0.4:
self.current_frame_face_name_list[k] = self.name_known_list[similar_person_num]
print(" >>> recognition result for face " + str(k+1) +": "+ self.name_known_list[similar_person_num])
else:
print(" >>> recognition result for face " + str(k + 1) + ": " + "unknown")
self.draw_note(img_rd)
if kk == ord('q'):
break
self.update_fps()
cv2.namedWindow("camera", 1)
cv2.imshow("camera", img_rd)
print(">>> Frame ends\n\n")
def run(self):
cap = cv2.VideoCapture(0)
self.process(cap)
cap.release()
cv2.destroyAllWindows()
def main():
Face_Recognizer_con = Face_Recognizer()
Face_Recognizer_con.run()
if __name__ == '__main__':
main()