Asynchronous execution in one Python script?

Question

Python

Hi friends! I studied machine learning on Sozykin's channel, and in one of the lessons it was necessary to find a person from photographs, such as uploading an image and he suggested how similar he was. In principle, everything is clear, and I decided to set a task for myself, but what if everything is the same, but only from Life-broadcasts. I found a suitable example and launched it, and it is executed after the completion of the program itself. And I came across such a difficulty that Python executes the code line by line, and that I need some kind of asynchrony for it to work, process it and turn it off on click. But first I need advice on how to optimize my code. Thank you all in advance. I will provide the script below.

import pickle

import cv2
import face_recognition
import postgresql


class VidCam():
    video_capture = cv2.VideoCapture(0)

    db = postgresql.open()
    fases_db = db.query("SELECT name, face FROM faces")
    known_face_names = []
    known_face_encodings = []
    for name in fases_db:
        known_face_names.append(name[0])
        known_face_encodings.append(pickle.loads(name[1]))

    face_locations = []
    face_encodings = []
    face_names = []
    process_this_frame = True

    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"
                ###############################################################################              
                #                Здесь Если скрипт определяет лицо, оно его фотографирует


                count = 0
                cv2.imwrite('frame%d.jpg' % count, frame)

                ##############################################################################
                # 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()

Answer 1

1. Learn how to measure code execution time
   
2. Learn to use a profiler
   
3. Measure how long code spends inside OpenCV
   
4. If less than 40ms - you have a chance
   
5. Then, in our code, we look for the most time-consuming places and figure out what is wrong with them.