计划永远赶不上变化。本想着入门一下TensorFlow训练一个自己学校的人脸库的,结果失败告终。但是某天无意中看到这样的链接-face_recognition决定尝试一下。

  • 首先目录如下(photos存放爬取得到的证件照并以学号命名),全校证件照获取链接
  • 加载photos文件夹中的证件照(相当于人脸库),并返回人脸数据以及对应的学号。
def load_photo(path):
imageList = []
imgs = os.listdir(path)
for img in imgs:
if img.endswith('jpg'):
imageList.append(img)
return imageList

def load_face(face_path):
known_face_encodings = []
known_face_names = []
imageList = load_photo(face_path)
i = 0
for image in imageList:
print(i," 照片")
i=i+1
try:
Image.open(face_path+"/"+image)
except IOError:
print(image," 图片损坏")
continue
face_image = face_recognition.load_image_file(face_path+"/"+image)
face_locations = face_recognition.face_locations(face_image)
if not face_locations :
print(image,"未检测到人脸")
continue
face_encoding = face_recognition.face_encodings(face_image)[0]
known_face_names.append(image)
known_face_encodings.append(face_encoding)
return known_face_encodings,known_face_names

吐槽一下,不知道为啥里面有一些证件照失效了,还有一些没人脸的。可能拍照的时候请假什么的吧,到现在也没有补拍。

那么问题来了,2w7多张的照片加载需要多少时间呢?

  • 加载完图片后,opencv调用摄像头,实时检测人脸。
video_capture = cv2.VideoCapture(0)

while True:
# Grab a single frame of video
ret, frame = video_capture.read()

# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]

# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"

# If a match was found in known_face_encodings, just use the first one.
if True in matches:
first_match_index = matches.index(True)
name = known_face_names[first_match_index]

face_names.append(name)

process_this_frame = not process_this_frame


# Display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4

# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

# Display the resulting image
cv2.imshow('Video', frame)

# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()

我学号122结尾?看来检测精度有待提高呀。而且识别出了多个学号。

  • 回到原项目中查看一下。

这就有点尴尬了(摊手.jpg),不过总体来说还是不错的,可以调整容错率来提高识别的精度。

  • 最后,这个项目非常适合入门python图像处理的新手。

项目:链接


2 条评论

aruki · 2019年3月27日 13:56

请问这个博客是用什么建的呢

    @_wts_@ · 2019年3月30日 21:56

    wordpress

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