Question

我正在尝试构建一个使用面部识别库实时检测面部的软件。我使用网络摄像头进行了尝试，并获得了可喜的结果和相当稳定的帧频，但是当我切换到.mp4视频时，以fps而言效果非常差。我正在将Python 3.6与OpenCV结合使用，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

函数“ get_faces”在每帧的while循环内被调用，而我得到的性能约为0.5 fps。如果有人提出建议以提高输出的fps，请告诉我，谢谢。

编辑： 我使用下面的示例（使其适应我的需求），并且一切工作得更好： link

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

Answer 1

要确定脚本的哪些部分运行时间最长，请使用 profiler 。这将输出执行每个调用的时间，因此您可以更好地了解函数的哪些部分不是最佳的。有关如何配置代码的示例，请参见The Python Profilers。

来自documentation：

快速识别面部

如果您的计算机具有以下功能，则可以并行进行人脸识别   多个CPU内核。例如，如果您的系统具有4个CPU内核，则您   可以在相同的时间内处理大约4倍的图像   并行使用所有CPU内核。如果您使用的是Python 3.4或   较新的，传入--cpus参数：

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

您还可以传入--cpus -1以使用系统中的所有CPU内核。

使用一个，然后使用最大核数来测试计算机上的操作。如果这可以显着缩短执行时间，那么最好的做法是在自己的脚本中实施多处理。

Python人脸识别速度慢

1 个答案: