face_recognition包人脸检测矩形框怎么进行的实时跟踪
时间: 2023-10-10 17:10:36 浏览: 61
要实现人脸检测框的实时跟踪,可以使用以下步骤:
1. 使用face_recognition包中的`face_locations()`函数检测图像中的人脸位置,并返回每个人脸的矩形框坐标。
2. 对于每个检测到的人脸矩形框,可以使用目标跟踪算法(如Kalman Filter、CamShift等)进行跟踪。这些算法可以根据前一帧的目标位置和当前帧的图像信息,预测目标在当前帧中的位置。
3. 使用跟踪算法输出的目标位置,更新当前帧中的人脸矩形框。如果跟踪算法无法确定当前帧中的目标位置,则需要重新进行人脸检测。
4. 在每个视频帧中重复执行上述步骤,以实现人脸检测矩形框的实时跟踪。
需要注意的是,实时跟踪的精度和效率取决于所使用的目标跟踪算法和计算机硬件性能。
相关问题
基于face_recognition库的人脸识别方案
好的,下面是一个简单的基于face_recognition库的人脸识别方案代码示例:
```python
import face_recognition
import cv2
# 加载已知人脸的图像和名称
known_face_encodings = []
known_face_names = []
image1 = face_recognition.load_image_file("person1.jpg")
image2 = face_recognition.load_image_file("person2.jpg")
image3 = face_recognition.load_image_file("person3.jpg")
known_face_encodings.append(face_recognition.face_encodings(image1)[0])
known_face_encodings.append(face_recognition.face_encodings(image2)[0])
known_face_encodings.append(face_recognition.face_encodings(image3)[0])
known_face_names = ["person1", "person2", "person3"]
# 打开摄像头
cap = cv2.VideoCapture(0)
while True:
# 读取一帧图像
ret, frame = cap.read()
# 将图像转换为RGB格式
rgb_frame = frame[:, :, ::-1]
# 检测人脸
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
# 遍历所有检测到的人脸
for face_encoding, face_location in zip(face_encodings, face_locations):
# 尝试匹配人脸
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
# 如果有匹配到的人脸,则取出其名称
if True in matches:
match_index = matches.index(True)
name = known_face_names[match_index]
else:
name = "unknown"
# 绘制人脸矩形框和名称
top, right, bottom, left = face_location
cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
cv2.putText(frame, name, (left, top-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# 显示图像
cv2.imshow('Face Recognition', frame)
# 按下q键退出
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 释放摄像头和窗口
cap.release()
cv2.destroyAllWindows()
```
在这个代码示例中,我们使用face_recognition库加载了已知人脸的图像和名称,并使用cv2.VideoCapture()方法打开了摄像头。在每一帧图像中,我们使用face_recognition库检测人脸,并使用face_recognition.compare_faces()方法尝试匹配人脸。如果有匹配到的人脸,则取出其名称,并使用cv2.rectangle()和cv2.putText()方法绘制人脸矩形框和名称。最后,我们使用cv2.imshow()方法显示图像,并使用cv2.waitKey()方法等待用户按下q键退出程序。
值得注意的是,face_recognition库是一个基于dlib库的人脸识别库,其检测速度较慢,但是识别精度较高。在实际应用中,我们需要根据具体情况选择不同的人脸识别方法以达到更好的效果。
希望这个代码对您有所帮助!
import face_recognition import cv2 import os unknow_people_list = [i for i in os.listdir('unknow_people') if (i.endswith('.jpg')) or (i.endswith('.png')) or (i.endswith('.jpeg'))] know_people_list = [i for i in os.listdir('know_people') if (i.endswith('.jpg')) or (i.endswith('.png')) or (i.endswith('.jpeg'))] def face_select(): for unknow_people in unknow_people_list: # 读取待识别图片 unknow = face_recognition.load_image_file('unknow_people/' + unknow_people) # 将待识别图片转化为特征向量 unknow_encode = face_recognition.face_encodings(unknow)[0] flag = False for know_people in know_people_list: # 读取计算机已经认识的图片 know = face_recognition.load_image_file('know_people/' + know_people) # 获得面部位置 face_location1 = face_recognition.face_locations(know) face_location2 = face_recognition.face_locations(unknow) # 提取面部关键点 face_landmarks_list1 = face_recognition.face_landmarks(know) face_landmarks_list2 = face_recognition.face_landmarks(unknow) # 图片转化为特征向量 know_encode = face_recognition.face_encodings(know)[0] # 两张图片进行比较的结果 res = face_recognition.compare_faces([know_encode], unknow_encode, tolerance=0.5) if res[0]: flag = True name = know_people.split(".")[0] break if flag: print(f'{name}匹配成功!') else: print(f'匹配失败') name = "UNKNOWN" # 绘制人脸特征点和矩形框 for (x1, y1, w1, h1) in face_location1: cv2.rectangle(know, (y1, x1), (h1, w1), (255, 0, 0), 2) cv2.putText(know, name, (y1 - 10, x1 - 10), cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 0), 2) for face_landmarks in face_landmarks_list1: for facial_feature in face_landmarks.keys(): for pt_pos in face_landmarks[facial_feature]: cv2.circle(know, pt_pos, 1, (192, 192, 192), 2) for (x1, y1, w1, h1) in face_location2: cv2.rectangle(unknow, (y1, x1), (h1, w1), (255, 0, 0), 2) cv2.putText(unknow, name, (y1 - 10, x1 - 10), cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 0), 2) for face_landmarks in face_landmarks_list2: for facial_feature in face_landmarks.keys(): for pt_pos in face_landmarks[facial_feature]: cv2.circle(unknow, pt_pos, 1, (192, 192, 192), 2) # 显示图片 cv2.imshow("known", know) cv2.imshow("unknown", unknow) cv2.waitKey(0) if __name__ == '__main__': face_select()
这段代码使用 face_recognition 和 OpenCV 库来进行人脸识别。它将 "know_people" 和 "unknow_people" 两个文件夹中的图像进行比较,以查找已知人脸。以下是代码的主要步骤:
1. 导入所需库。
```python
import face_recognition
import cv2
import os
```
2. 定义要识别的人脸图像列表。
```python
unknow_people_list = [i for i in os.listdir('unknow_people') if (i.endswith('.jpg')) or (i.endswith('.png')) or (i.endswith('.jpeg'))]
know_people_list = [i for i in os.listdir('know_people') if (i.endswith('.jpg')) or (i.endswith('.png')) or (i.endswith('.jpeg'))]
```
3. 定义人脸识别函数。
```python
def face_select():
for unknow_people in unknow_people_list:
# 读取待识别图片
unknow = face_recognition.load_image_file('unknow_people/' + unknow_people)
# 将待识别图片转化为特征向量
unknow_encode = face_recognition.face_encodings(unknow)[0]
flag = False
for know_people in know_people_list:
# 读取计算机已经认识的图片
know = face_recognition.load_image_file('know_people/' + know_people)
# 获得面部位置
face_location1 = face_recognition.face_locations(know)
face_location2 = face_recognition.face_locations(unknow)
# 提取面部关键点
face_landmarks_list1 = face_recognition.face_landmarks(know)
face_landmarks_list2 = face_recognition.face_landmarks(unknow)
# 图片转化为特征向量
know_encode = face_recognition.face_encodings(know)[0]
# 两张图片进行比较的结果
res = face_recognition.compare_faces([know_encode], unknow_encode, tolerance=0.5)
if res[0]:
flag = True
name = know_people.split(".")[0]
break
if flag:
print(f'{name}匹配成功!')
else:
print(f'匹配失败')
name = "UNKNOWN"
# 绘制人脸特征点和矩形框
for (x1, y1, w1, h1) in face_location1:
cv2.rectangle(know, (y1, x1), (h1, w1), (255, 0, 0), 2)
cv2.putText(know, name, (y1 - 10, x1 - 10), cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 0), 2)
for face_landmarks in face_landmarks_list1:
for facial_feature in face_landmarks.keys():
for pt_pos in face_landmarks[facial_feature]:
cv2.circle(know, pt_pos, 1, (192, 192, 192), 2)
for (x1, y1, w1, h1) in face_location2:
cv2.rectangle(unknow, (y1, x1), (h1, w1), (255, 0, 0), 2)
cv2.putText(unknow, name, (y1 - 10, x1 - 10), cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 0), 2)
for face_landmarks in face_landmarks_list2:
for facial_feature in face_landmarks.keys():
for pt_pos in face_landmarks[facial_feature]:
cv2.circle(unknow, pt_pos, 1, (192, 192, 192), 2)
# 显示图片
cv2.imshow("known", know)
cv2.imshow("unknown", unknow)
cv2.waitKey(0)
```
该函数首先将待识别图片转化为特征向量,然后将其与已知人脸图像进行比较,以查找匹配。如果找到匹配,则在图像上绘制人脸特征点和矩形框,并显示人名。如果未找到匹配,则在图像上显示 "UNKNOWN"。
4. 在主函数中调用人脸识别函数。
```python
if __name__ == '__main__':
face_select()
```
在主函数中,我们只需要调用 `face_select()` 函数即可开始人脸识别。
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