[Ubuntu] [Python] Gesichtsorganerkennung mit dlib

Gesichtsorganerkennung, die derzeit in dlib, aber nicht in OpenCV ist.

Vorerst habe ich es gewaltsam gefärbt, aber es muss einen klügeren Weg geben. Oder besser gesagt, ich denke nur, dass die Referenz nicht richtig geladen ist. .. ..

Das Video ist unten. Es ist peinlich, mein Gesicht zu zeigen, deshalb benutze ich die Gesichtserkennung, um es zu verbergen. https://www.youtube.com/watch?v=s2YtXqcBuPY [](https://www.youtube.com/watch? v = s2YtXqcBuPY)

Der Quellcode ist unten. Damit es funktioniert, legen Sie die trainierten Daten der Gesichtsorgane in dasselbe Verzeichnis wie die py-Datei. Muss platziert werden. →shape_predictor_68_face_landmarks.dat（http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2）

#!/usr/bin/env python
# -*- coding: utf-8 -*-

'''
face_landmark_detector.py.

Usage:
  face_landmark_detector.py [<video source>] [<resize rate>] [<privacy mask>]
'''

import sys
import dlib
import cv2
import time
import copy

try:
    fn = sys.argv[1]
    if fn.isdigit() == True:
        fn = int(fn)
except:
    fn = 0

try:
    resize_rate = sys.argv[2]
    resize_rate = int(resize_rate)
except:
    resize_rate = 1

try:
    privacy_mask = sys.argv[3]
    privacy_mask = int(privacy_mask)
except:
    privacy_mask = 0

predictor_path = "./shape_predictor_68_face_landmarks.dat"

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)

video_input = cv2.VideoCapture(fn)

while(video_input.isOpened() == True):
    ret, frame = video_input.read()
    temp_frame = copy.deepcopy(frame)

    #Reduzierung des Zielrahmens zur Reduzierung der Verarbeitungslast (wenn ein Argument angegeben wird)
    height, width = frame.shape[:2]
    temp_frame = cv2.resize(frame, (int(width/resize_rate), int(height/resize_rate)))

    #Gesichtserkennung
    start = time.time()
    dets = detector(temp_frame, 1)
    elapsed_time = time.time() - start
    print ("detector processing time:{0}".format(elapsed_time)) + "[sec]"

    for k, d in enumerate(dets):
        print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
            k, d.left(), d.top(), d.right(), d.bottom()))

        #Gesichtsorganerkennung
        start = time.time()
        shape = predictor(temp_frame, d)
        elapsed_time = time.time() - start
        print ("predictor processing time:{0}".format(elapsed_time)) + "[sec]"

        #Zeichnung
        rect_offset = 20
        if privacy_mask == 1:
            cv2.rectangle(frame, (int(d.left() * resize_rate) - rect_offset, int(d.top() * resize_rate) - rect_offset), \
                (int(d.right() * resize_rate) + rect_offset, int(d.bottom() * resize_rate) + rect_offset), (255, 255, 255), -1)

        for shape_point_count in range(shape.num_parts):
            shape_point = shape.part(shape_point_count)
            if shape_point_count < 17: # [0-16]:Kontur
                cv2.circle(frame, (int(shape_point.x * resize_rate), int(shape_point.y * resize_rate)), 2, (0, 0, 255), -1)
            elif shape_point_count < 22: # [17-21]Augenbrauen (rechts)
                cv2.circle(frame, (int(shape_point.x * resize_rate), int(shape_point.y * resize_rate)), 2, (0, 255, 0), -1)
            elif shape_point_count < 27: # [22-26]Augenbrauen (links)
                cv2.circle(frame, (int(shape_point.x * resize_rate), int(shape_point.y * resize_rate)), 2, (255, 0, 0), -1)
            elif shape_point_count < 31: # [27-30]Nase zurück
                cv2.circle(frame, (int(shape_point.x * resize_rate), int(shape_point.y * resize_rate)), 2, (0, 255, 255), -1)
            elif shape_point_count < 36: # [31-35]Nasenflügel, Nasenspitzen
                cv2.circle(frame, (int(shape_point.x * resize_rate), int(shape_point.y * resize_rate)), 2, (255, 255, 0), -1)
            elif shape_point_count < 42: # [36-4142 Augen 47)
                cv2.circle(frame, (int(shape_point.x * resize_rate), int(shape_point.y * resize_rate)), 2, (255, 0, 255), -1)
            elif shape_point_count < 48: # [42-47]Augen (links)
                cv2.circle(frame, (int(shape_point.x * resize_rate), int(shape_point.y * resize_rate)), 2, (0, 0, 128), -1)
            elif shape_point_count < 55: # [48-54]Oberlippe (Oberkontur)
                cv2.circle(frame, (int(shape_point.x * resize_rate), int(shape_point.y * resize_rate)), 2, (0, 128, 0), -1)
            elif shape_point_count < 60: # [54-59]Unterlippe (Unterkontur)
                cv2.circle(frame, (int(shape_point.x * resize_rate), int(shape_point.y * resize_rate)), 2, (128, 0, 0), -1)
            elif shape_point_count < 65: # [60-64]Oberlippe (Unterkontur)
                cv2.circle(frame, (int(shape_point.x * resize_rate), int(shape_point.y * resize_rate)), 2, (0, 128, 255), -1)
            elif shape_point_count < 68: # [65-67]Unterlippe (Oberkontur)
                cv2.circle(frame, (int(shape_point.x * resize_rate), int(shape_point.y * resize_rate)), 2, (128, 255, 0), -1)

    cv2.imshow('face landmark detector', frame)

    c = cv2.waitKey(50) & 0xFF

    if c==27: # ESC
        break

video_input.release()
cv2.destroyAllWindows()

das ist alles.