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How to detect face from 5 meters ?

Hello can somebody help me please
I try to run face detection
And face detection working only in red area on the picture.
What can i change in code for detecting face in doors ?? there is 5 meter
image description

This is code :

import face_recognition
import cv2
import numpy as np
import os

# This is a demo of running face recognition on live video from your webcam. It's a little more complicated 
# than the other example, but it includes some basic performance tweaks to make things run a lot faster:
#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
#   2. Only detect faces in every other frame of video.

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.

# rtsp streaming URL
video_addr = 'rtsp://xxx:xxx@xxx.ducksdns.org:1222/cam/realmonitor?channel=1&subtype=02'

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture("rtsp://xxx:xxx@xxx.duckdns.org:1222/cam/realmonitor?channel=1&subtype=02")
#video_capture = cv2.VideoCapture(0)

# Known face pictures directory
#known_face_dir = './known_face/'
known_face_dir = '/home/rafal/face/known_face/'
files = os.listdir(known_face_dir)
print(files)

#for root, dirs, files in os.walk(known_face_dir, topdown=False):
#    print(dirs)
#    print(files)

# Loading all the known face pictures
known_face_encodings = []
known_face_names = []
face_encodings_handle = locals()
for file_name in files:
    face_encodings_handle[file_name] = face_recognition.face_encodings(face_recognition.load_image_file(known_face_dir + file_name))[0]
    known_face_encodings.append(face_encodings_handle[file_name])
    known_face_names.append(file_name.rsplit('.', 1)[0])

# Load a sample picture and learn how to recognize it.
#obama_image = face_recognition.load_image_file(known_face_dir + "/zhzl.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(known_face_dir + "/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 video frame frequency
process_frame_freq = 4
process_this_frame = process_frame_freq

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=0.50, fy=0.50)

    # 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 == process_frame_freq:
        # 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)

    process_this_frame += 1
    process_this_frame = process_this_frame % (process_frame_freq + 1)


    # 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 *= 2
        right *= 2
        bottom *= 2
        left *= 2

        # 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()