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编辑：我在下面找不到我想要的解决方案，所以我将它作为一个简单的写入和读取到 .txt 文件，因为两个应用程序都在同一个物理服务器中。我不会关闭它，因为我相信它仍然是人们可能需要一个真正的解决方案。谢谢。

首先我很抱歉，因为我不确定它是如何被调用的，所以很难用谷歌搜索它。我的问题的概要是这样的：

我正在使用ageitgey 的facial_recognition python 库来识别视频中的人脸。请参阅此代码。因此，您会看到它使用 opencv 来捕获 awhile True:和框架内的每一ret, frame = video_capture.read()帧。

对于每次迭代，如果框架内没有人脸，我将填充一个变量（让我们命名它RETURN_CODE）为0 ，如果人脸未被识别，则为1 ，如果人脸被识别，则为2。

我需要的是，对于每次迭代，我都会在不中断循环的情况下返回此代码，以便另一个应用程序继续检查此状态并根据其值执行其他操作。

我仍在弄清楚如何烧瓶，但这不是这个问题的一部分。

目前我正在打印输出，我读到我可能会使用另一个带有 stdout 的脚本来获取它，但是淹没控制台似乎是错误的。如果 app1 在 app2 打开时尝试写入，写入文件可能会崩溃。

这是我的示例代码，来自上述链接的修改版本： 注意：为了它不会崩溃，它必须在与脚本相同的目录中添加 2 个图像，来自此 repo 的“obama.jpg”和“biden.jpg”：https： //github.com/ageitgey/face_recognition/tree/master/examples

import face_recognition
from imutils.video import VideoStream
import imutils
import cv2
import numpy as np

import time

# our variable
RETURN_CODE = 0



# Load a sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file("obama.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]

# Load a second sample picture and learn how to recognize it.
biden_image = face_recognition.load_image_file("biden.jpg")
biden_face_encoding = face_recognition.face_encodings(biden_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    obama_face_encoding,
    biden_face_encoding
]
known_face_names = [
    "Barack Obama",
    "Joe Biden"
]

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

# start capturing frame by frame
## changed for imutils as is much better and opencv crashes a lot
video_capture = VideoStream(src=0).start()
TEST_START = time.time()
while True:
    # Grab a single frame of video
    frame = video_capture.read()

    # Resize frame of video to 1/4 size for faster face recognition processing
    small_frame = imutils.resize(frame, width=450)

    # THis will resize the frame on screen
    r = frame.shape[1] / float(small_frame.shape[1])

    # 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]

            # Or instead, use the known face with the smallest distance to the new face
            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
            best_match_index = np.argmin(face_distances)
            if matches[best_match_index]:
                name = known_face_names[best_match_index]

            face_names.append(name)

        if name == 'Unkown':
            RETURN_CODE = 1
        else:
            RETURN_CODE = 2

    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 = int(top * r)
        right = int(right * r)
        bottom = int(bottom * r)
        left = int(left * r)

        # 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

    # Currently it's printing the code, later add into a flask
    print(RETURN_CODE)
    #yield RETURN_CODE


    if time.time() - TEST_START >= 10.0:
        break
# Release handle to the webcam
video_capture.stream.release()
video_capture.stop()
cv2.destroyAllWindows()
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1 回答 1

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如果我说得对，您正在寻找进程间通信。返回码就是程序退出时返回的内容。

像您说的那样写入文件是一种方法，但还有（很多）其他方法。

例如看看 Python 管道和队列： https ://docs.python.org/3.4/library/multiprocessing.html?highlight=process#pipes-and-queues

另一种更通用的方法是运行 Redis 之类的队列服务：https ://python-rq.org/

于 2019-04-22T14:50:25.963 回答