Python人脸识别速度慢

Question

Python人脸识别速度慢

pythonpython-3.xopencvreal-timeface-recognition

4

我正在尝试构建一个软件，使用人脸识别库在实时中检测人脸。我已经用摄像头进行了尝试，并得到了令人满意的结果和相当稳定的帧率，但当我切换到.mp4视频时，fps非常低。我正在使用Python 3.6与OpenCV，并且这是我正在使用的代码：

import face_recognition
import cv2


# Load a sample picture and learn how to recognize it.
totti_image = face_recognition.load_image_file("totti.jpg")
totti_face_encoding = face_recognition.face_encodings(totti_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    totti_face_encoding
]
known_face_names = [
    "Francesco Totti"
]
def get_faces(frame):
    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_frame = frame[:, :, ::-1]

    # Find all the faces and face enqcodings in the frame of video
    face_locations = face_recognition.face_locations(rgb_frame)
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    # Loop through each face in this frame of video
    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
        # See if the face is a match for the known face(s)
        matches = face_recognition.compare_faces(known_face_encodings, face_encoding, tolerance=0.50)

        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]

        # 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)

    return frame

在每个帧中都会调用“get_faces”函数，而我得到的性能大约是0.5 fps。如果有人有建议以获得更好的输出fps，请告诉我，谢谢。

编辑： 我使用了以下示例（根据我的需求进行了调整），一切都运行得更好：链接

最终代码：

import face_recognition
import cv2

# Load a sample picture and learn how to recognize it.
image = face_recognition.load_image_file("totti.jpg")
encoding = face_recognition.face_encodings(image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    encoding
]
known_face_names = [
    "Totti",
]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []

def get_faces(frame):

    # Resize frame of video to 1/10 size for faster face recognition processing
    small_frame = cv2.resize(frame, (0, 0), fx=0.1, fy=0.1)

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

    # 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 = "Person"

        # 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)


    # 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 1/10 size
        top *= 10
        right *= 10
        bottom *= 10
        left *= 10

        # 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)

    return frame

- Ajeje_Brazorf

1个回答

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- J. Blackadar · Accepted Answer

为了确定脚本中哪些部分运行时间最长，可以使用分析器。这将输出每个调用的执行时间，因此您可以更好地了解函数中哪些部分不够优化。请参阅Python分析器以获取有关如何分析您的代码的示例。

来自文档：

加速人脸识别

如果您拥有具有多个CPU核心的计算机，则可以并行进行人脸识别。例如，如果您的系统有4个CPU核心，则可以通过并行使用所有CPU核心，在相同的时间内处理大约4倍的图像。如果您使用的是Python 3.4或更高版本，请传递一个--cpus <number_of_cpu_cores_to_use>参数：

face_recognition --cpus 4 ./pictures_of_people_i_know/ ./unknown_pictures/

您还可以传递--cpus -1来使用系统中的所有CPU核心。

使用一个核心和最大数量的核心在计算机上测试操作。如果这显著提高了执行时间，您最好的选择是将多进程实现到自己的脚本中。

2020-08-05更新

进一步研究此问题，因为它仍然受到关注。如果我们查看repository，似乎CLI只是进行一些调用，您可以编写脚本自己获取--cpus参数，以便将其实现到您自己的代码中。具体来说，您可以使用here中的代码程序化地而不是从命令行中使用。以类似的方式使用多进程调用API，或使用def process_images_in_process_pool(images_to_check, number_of_cpus, model):。