def face_rec(num):
dataset_path = 'data\\dataset\\test\\test' + str(num) + '.csv'
label_ID = []
with open(dataset_path, encoding='utf-8') as f:
reader = f.readlines()
embedded = np.zeros((len(reader) + 1, 128))
i = 1
for row in reader:
label_ID.append(row.split(',')[0])
feature = np.array(list(map(float, row.split(',')[1:])))
embedded[i, :] = feature
i += 1
f.close()
path = 'data\\dataset\\Yuchao'
imganchorpath = [path + '\\' + i for i in os.listdir(path)]
for imganchor in imganchorpath:
image1 = cv2.imread(imganchor)
faces = facebounding(image1)
size = []
for i in faces:
h, w, c = i.image.shape
size.append(h)
index = size.index(max(size))
image1 = preprocess_image(faces[index].image)
embedded[0] = nn4_small2_pretrained.predict(
np.expand_dims(image1, axis=0))[0]
imgDistanceList = []
for i in range(len(embedded) - 1):
imgDistanceList.append(distance(embedded[0], embedded[i + 1]))
minIndex = imgDistanceList.index(min(imgDistanceList))
cv2.rectangle(
faces[index].container_image,
(faces[index].bounding_box[0], faces[index].bounding_box[1]),
(faces[index].bounding_box[2], faces[index].bounding_box[3]),
(0, 155, 255), 5)
text = label_ID[minIndex]
cv2.putText(
faces[index].container_image, text,
(faces[index].bounding_box[0], faces[index].bounding_box[1] - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, (225, 155, 0), 1)
print(label_ID[minIndex])
print(min(imgDistanceList))
# cv2.namedWindow('image', 2)
# cv2.namedWindow('result', 2)
cv2.imshow(imganchor.split('\\')[-1], faces[0].container_image)
cv2.imshow(
'result_' + imganchor.split('\\')[-1],
cv2.imread('data\\dataset\\test\\test' + str(num) + '\\' +
label_ID[minIndex]))
cv2.waitKey()
def get_feature():
global path
global faceitem
global Personal_ID
global pass_state
if pass_state == 'pass':
csvPath = path + '\\Facedataset.csv'
cavepath = path + '\\Facedataset\\' + Personal_ID + '.jpg'
cv2.imwrite(cavepath, faceitem.image)
with open(csvPath, 'a', encoding='utf-8') as f:
faceimg = faceitem.image
testface = preprocess_image(faceimg)
face_feature = nn4_small2_pretrained.predict(np.expand_dims(testface, axis=0))[0]
face_feature = list(map(str, face_feature))
face_feature = ','.join(face_feature)
logs = Personal_ID + ',' + phone_number + ',' + face_feature + '\n'
f.writelines(logs)
print('Pass')
f.close()
pass_state = None
def Face_Recognition_Interface(image):
dataset_path = 'data\\dataset\\FaceDataset50_1.csv'
label_ID = []
phone = []
with open(dataset_path, encoding='utf-8') as f:
reader = f.readlines()
embedded = np.zeros((len(reader) + 1, 128))
i = 1
for row in reader:
label_ID.append(row.split(',')[0])
phone.append(row.split(',')[1])
feature = np.array(list(map(float, row.split(',')[2:])))
embedded[i, :] = feature
i += 1
f.close()
faces = facebounding(image)
faceitem = faces[0]
faceimage = faceitem.image
faceimage = preprocess_image(faceimage)
embedded[0] = nn4_small2_pretrained.predict(np.expand_dims(faceimage, axis=0))[0]
imgDistanceList = []
for i in range(len(embedded) - 1):
imgDistanceList.append(distance(embedded[0], embedded[i + 1]))
minIndex = imgDistanceList.index(min(imgDistanceList))
print(label_ID[minIndex])
print(phone[minIndex])
print(min(imgDistanceList))
imageSet_path = dataset_path.split('.')[0]
image_path = [imageSet_path + '\\' + i for i in os.listdir(imageSet_path)]
for i in image_path:
if i.split('\\')[-1].split('_')[0] == label_ID[minIndex]:
targret = i
print(targret)
image_target = cv2.imread(targret)
cv2.imshow('1', image_target)
cv2.imshow('2', image)
cv2.waitKey(10)
def video_test():
capture = cv2.VideoCapture('data\\dataset\\yuchao.mp4')
cv2.namedWindow('frame', 2)
cv2.namedWindow('face', 2)
dataset_path = 'data\\dataset\\test\\test100.csv'
label_ID = []
with open(dataset_path, encoding='utf-8') as f:
reader = f.readlines()
embedded = np.zeros((len(reader) + 1, 128))
i = 1
for row in reader:
label_ID.append(row.split(',')[0])
feature = np.array(list(map(float, row.split(',')[1:])))
embedded[i, :] = feature
i += 1
f.close()
while True:
ret, frame = capture.read()
if ret == False:
break
image = np.array(frame)
faces = facebounding(image)
text = ''
try:
faceitem = faces[0]
image = faceitem.image
cv2.imshow('face', image)
image1 = preprocess_image(image)
embedded[0] = nn4_small2_pretrained.predict(
np.expand_dims(image1, axis=0))[0]
imgDistanceList = []
for i in range(len(embedded) - 1):
imgDistanceList.append(distance(embedded[0], embedded[i + 1]))
minIndex = imgDistanceList.index(min(imgDistanceList))
if float(min(imgDistanceList)) < 0.23:
print(label_ID[minIndex])
print(min(imgDistanceList))
text = label_ID[minIndex]
cv2.rectangle(
frame,
(faces[0].bounding_box[0], faces[0].bounding_box[1]),
(faces[0].bounding_box[2], faces[0].bounding_box[3]),
(0, 155, 255), 2)
cv2.putText(
frame, text,
(faces[0].bounding_box[0], faces[0].bounding_box[1] - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 155, 255), 2)
cv2.imshow('frame', frame)
cv2.imshow(
'person',
cv2.imread('data\\dataset\\test\\test100\\' +
label_ID[minIndex]))
cv2.waitKey(5)
except:
cv2.imshow('face', image)
cv2.imshow('frame', frame)
key = cv2.waitKey(1)
if key == ord('q'): # 判断是哪一个键按下
cv2.destroyAllWindows()
break