increase the performance and logical
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Tang1705
@@ -6,8 +6,11 @@ import cv2
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import pandas as pd
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import os
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import time
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import facenet
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from PIL import Image, ImageDraw, ImageFont
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from model import create_model
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start_time = 0
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# 1. Dlib 正向人脸检测器
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# detector = dlib.get_frontal_face_detector()
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@@ -22,6 +25,9 @@ predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmar
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# 3. Dlib Resnet 人脸识别模型，提取 128D 的特征矢量
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face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
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nn4_small2_pretrained = create_model()
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nn4_small2_pretrained.load_weights('weights/nn4.small2.v1.h5')
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class Face_Recognizer:
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def __init__(self):
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@@ -32,6 +38,9 @@ class Face_Recognizer:
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self.name_known_cnt = 0
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self.name_known_list = []
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self.metadata = []
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self.embedded = []
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# 存储当前摄像头中捕获到的所有人脸的坐标名字
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self.pos_camera_list = []
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self.name_camera_list = []
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@@ -46,38 +55,39 @@ class Face_Recognizer:
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# 从 "features_all.csv" 读取录入人脸特征
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def get_face_database(self):
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if os.path.exists("data/features_all.csv"):
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path_features_known_csv = "data/features_all.csv"
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csv_rd = pd.read_csv(path_features_known_csv, header=None)
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# 2. 读取已知人脸数据
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for i in range(csv_rd.shape[0]):
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features_someone_arr = []
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for j in range(0, 128):
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if csv_rd.iloc[i][j] == '':
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features_someone_arr.append('0')
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else:
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features_someone_arr.append(csv_rd.iloc[i][j])
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self.features_known_list.append(features_someone_arr)
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self.name_known_list.append("Person_" + str(i + 1))
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self.name_known_cnt = len(self.name_known_list)
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print("Faces in Database：", len(self.features_known_list))
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if os.path.exists("data/data_faces_from_camera/"):
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self.metadata = facenet.load_metadata("data/data_faces_from_camera/")
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self.name_known_cnt = self.metadata.shape[0]
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self.embedded = np.zeros((self.metadata.shape[0], 128))
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for i, m in enumerate(self.metadata):
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for j, n in enumerate(m):
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img = facenet.load_image(n.image_path())
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# img = align_image(img)
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img = cv2.resize(img, (96, 96))
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# scale RGB values to interval [0,1]
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img = (img / 255.).astype(np.float32)
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# obtain embedding vector for image
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self.embedded[i] = nn4_small2_pretrained.predict(np.expand_dims(img, axis=0))[0]
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# self.embedded[i] = self.embedded[i] / len(m)
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self.name_known_list.append('')
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return 1
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else:
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print('##### Warning #####', '\n')
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print("'features_all.csv' not found!")
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print(
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"Please run 'get_faces_from_camera.py' and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'",
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"Please run 'get_faces_from_camera.py' before 'face_reco_from_camera.py'",
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'\n')
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print('##### End Warning #####')
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return 0
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# 计算两个128D向量间的欧式距离
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@staticmethod
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def return_euclidean_distance(feature_1, feature_2):
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feature_1 = np.array(feature_1)
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feature_2 = np.array(feature_2)
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dist = np.sqrt(np.sum((feature_1 - feature_2) ** 2))
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return dist
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# @staticmethod
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# def return_euclidean_distance(feature_1, feature_2):
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# feature_1 = np.array(feature_1)
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# feature_2 = np.array(feature_2)
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# dist = np.sqrt(np.sum((feature_1 - feature_2) ** 2))
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# return dist
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# 更新 FPS
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def update_fps(self):
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@@ -111,8 +121,8 @@ class Face_Recognizer:
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# Default known name: person_1, person_2, person_3
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self.name_known_list[0] = '唐麒'.encode('utf-8').decode()
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self.name_known_list[1] = '段海燕'.encode('utf-8').decode()
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# self.name_known_list[2] ='xx'.encode('utf-8').decode()
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# self.name_known_list[3] ='xx'.encode('utf-8').decode()
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# self.name_known_list[2] = '唐保生'.encode('utf-8').decode()
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# self.name_known_list[3] = '唐麒'.encode('utf-8').decode()
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# self.name_known_list[4] ='xx'.encode('utf-8').decode()
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# 处理获取的视频流，进行人脸识别
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@@ -142,18 +152,18 @@ class Face_Recognizer:
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# 2. 检测到人脸
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if faces.shape[2] != 0:
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# 3. 获取当前捕获到的图像的所有人脸的特征，存储到 self.features_camera_list
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for i in range(0, faces.shape[2]):
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confidence = faces[0, 0, i, 2]
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# filter out weak detections by ensuring the `confidence` is
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# greater than the minimum confidence
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if confidence < 0.5:
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continue
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box = faces[0, 0, i, 3:7] * np.array([w, h, w, h])
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(startX, startY, endX, endY) = box.astype("int")
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rect = dlib.rectangle(startX, startY, endX, endY)
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shape = predictor(img_rd, rect)
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self.features_camera_list.append(face_reco_model.compute_face_descriptor(img_rd, shape))
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# for i in range(0, faces.shape[2]):
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# confidence = faces[0, 0, i, 2]
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#
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# # filter out weak detections by ensuring the `confidence` is
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# # greater than the minimum confidence
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# if confidence < 0.5:
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# continue
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# box = faces[0, 0, i, 3:7] * np.array([w, h, w, h])
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# (startX, startY, endX, endY) = box.astype("int")
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# rect = dlib.rectangle(startX, startY, endX, endY)
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# shape = predictor(img_rd, rect)
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# self.features_camera_list.append(face_reco_model.compute_face_descriptor(img_rd, shape))
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# 4. 遍历捕获到的图像中所有的人脸
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for k in range(0, faces.shape[2]):
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@@ -164,13 +174,13 @@ class Face_Recognizer:
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# greater than the minimum confidence
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if confidence < 0.5:
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continue
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self.faces_cnt+=1
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self.faces_cnt += 1
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# print("##### camera person", k + 1, "#####")
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# 让人名跟随在矩形框的上方
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# 确定人名的位置坐标
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# 先默认所有人不认识，是 unknown
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# Set the default names of faces with "unknown"
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self.name_camera_list.append("unknown")
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self.name_camera_list.append("陌生人")
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# 每个捕获人脸的名字坐标
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box = faces[0, 0, k, 3:7] * np.array([w, h, w, h])
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@@ -178,25 +188,39 @@ class Face_Recognizer:
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self.pos_camera_list.append(tuple(
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[int(startX + 5), int(startY - 30)]))
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height = (endY - startY)
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width = (endX - startX)
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img_blank = np.zeros((height, width, 3), np.uint8)
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for ii in range(height):
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for jj in range(width):
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img_blank[ii][jj] = img_rd[startY + ii][startX + jj]
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img = cv2.resize(img_blank, (96, 96))
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img = (img / 255.).astype(np.float32)
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img = nn4_small2_pretrained.predict(np.expand_dims(img, axis=0))[0]
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# 5. 对于某张人脸，遍历所有存储的人脸特征
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e_distance_list = []
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for i in range(len(self.features_known_list)):
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# 如果 person_X 数据不为空
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if str(self.features_known_list[i][0]) != '0.0':
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# print("with person", str(i + 1), "the e distance: ", end='')
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e_distance_tmp = self.return_euclidean_distance(self.features_camera_list[k],
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self.features_known_list[i])
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# print(e_distance_tmp)
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e_distance_list.append(e_distance_tmp)
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else:
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# 空数据 person_X
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e_distance_list.append(999999999)
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# 6. 寻找出最小的欧式距离匹配
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for i in range(0, len(self.embedded)):
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e_distance_list.append(facenet.distance(self.embedded[i], img))
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# for i in range(len(self.features_known_list)):
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# # 如果 person_X 数据不为空
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# if str(self.features_known_list[i][0]) != '0.0':
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# # print("with person", str(i + 1), "the e distance: ", end='')
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# e_distance_tmp = self.return_euclidean_distance(self.features_camera_list[k],
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# self.features_known_list[i])
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# # print(e_distance_tmp)
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# e_distance_list.append(e_distance_tmp)
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# else:
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# # 空数据 person_X
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# e_distance_list.append(999999999)
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# # 6. 寻找出最小的欧式距离匹配
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similar_person_num = e_distance_list.index(min(e_distance_list))
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# print("Minimum e distance with person", self.name_known_list[similar_person_num])
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if min(e_distance_list) < 1:
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self.name_camera_list[k] = self.name_known_list[similar_person_num]
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# print(min(e_distance_list))
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if min(e_distance_list) < 0.58:
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self.name_camera_list[k] = self.name_known_list[similar_person_num % 8]
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# print("May be person " + str(self.name_known_list[similar_person_num]))
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else:
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pass
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@@ -205,10 +229,16 @@ class Face_Recognizer:
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# 矩形框
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for kk, d in enumerate(faces):
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# 绘制矩形框
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cv2.rectangle(img_rd, tuple([startX, startY]), tuple([endX, endY]),
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(0, 255, 0), 2)
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cv2.rectangle(img_rd, tuple([startX, startY - 35]), tuple([endX, startY]),
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(0, 255, 0), cv2.FILLED)
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if self.name_camera_list[k] != '陌生人':
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cv2.rectangle(img_rd, tuple([startX, startY]), tuple([endX, endY]),
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(0, 255, 0), 2)
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cv2.rectangle(img_rd, tuple([startX, startY - 35]), tuple([endX, startY]),
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(0, 255, 0), cv2.FILLED)
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else:
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cv2.rectangle(img_rd, tuple([startX, startY]), tuple([endX, endY]),
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(0, 0, 255), 2)
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cv2.rectangle(img_rd, tuple([startX, startY - 35]), tuple([endX, startY]),
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(0, 0, 255), cv2.FILLED)
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# print('\n')
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# self.faces_cnt = faces.shape[2]
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# if len(self.name_camera_list) > 0:
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@@ -226,7 +256,7 @@ class Face_Recognizer:
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cv2.imshow("camera", img_with_name)
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# 9. 更新 FPS / Update stream FPS
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# self.update_fps()
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self.update_fps()
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# OpenCV 调用摄像头并进行 process
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def run(self):
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@@ -195,7 +195,8 @@ class Face_Register:
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color_rectangle, 2)
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# 7. 根据人脸大小生成空的图像
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img_blank = np.zeros((int(height * 2), width * 2, 3), np.uint8)
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# img_blank = np.zeros((int(height * 2), width * 2, 3), np.uint8)
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img_blank = np.zeros((height, width, 3), np.uint8)
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if save_flag:
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# 8. 按下 's' 保存摄像头中的人脸到本地
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@@ -205,9 +206,9 @@ class Face_Register:
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self.ss_cnt += 1
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if self.index <= 7:
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for ii in range(height * 2):
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for jj in range(width * 2):
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img_blank[ii][jj] = img_rd[startY - hh + ii][startX - ww + jj]
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for ii in range(height):
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for jj in range(width):
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img_blank[ii][jj] = img_rd[startY + ii][startX + jj]
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cv2.imwrite(current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", img_blank)
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print("写入本地 / Save into：",
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str(current_face_dir) + "/img_face_" + str(self.ss_cnt) + ".jpg")
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