一、前言
本篇文章適合人臉識別初學者。小總結篇。
環境:
- Python 3.3+ or Python 2.7
- macOS or Linux (Windows這個庫說是不支持的,但是應該也有辦法)
下面是這個庫的github地址 face_recognition
基於opencv的人臉實時識別&&face_recognition庫進行本地人臉識別
對視頻中的人臉抓取並匹配照片
安裝 face_recognition
pip install face_recognition
二、需求
我們要做的需求就是,要求能夠實時進行人臉識別,然後並截圖到本地(這裏我本來是想做根據人的識別結果進行截圖的,但是沒整成功,後面再研究一下,或者大家有思路也留言學習下hh)
三、代碼
# -*- coding: utf-8 -*-
import face_recognition
import cv2
import numpy as np
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
cap = cv2.VideoCapture(0)
i=0
# Load a sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file("dataset/me.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("dataset/catch.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 = [
"trump",
"wuyuhui"
]
# Initialize some variables 初始化
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
while True:
# Grab a single frame of video 抓取一幀視頻
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing 將視頻幀的大小調整爲1/4以加快人臉識別處理
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# 將圖像從BGR顏色(OpenCV使用)轉換爲RGB顏色(人臉識別使用)
rgb_small_frame = small_frame[:, :, ::-1]
# 僅每隔一幀處理一次視頻以節省時間
if process_this_frame:
# 查找當前視頻幀中的所有面和麪編碼
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:
# 查看該面是否與已知面匹配
matches = face_recognition.compare_faces(known_face_encodings, face_encoding,tolerance=0.4)
name = "Unknown"
# # 如果在已知的面編碼中找到匹配項,請使用第一個。
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_names[first_match_index]
# 或者,使用與新面的距離最小的已知面
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
print face_distances
# if face_distances[best_match_index]<=0.45:
if matches[best_match_index]:
name = known_face_names[best_match_index]
face_names.append(name)
#抓拍
if False in matches:
ret, frame = cap.read()
cv2.imshow('capture', frame)
cv2.imwrite(r"/Users/sue/desktop/picture/p" + str(i) + ".jpg", frame)
i = i + 1
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()
四、修改部分
1、把需要匹配的圖片放在這裏
2、因爲這個庫對亞洲人和小孩的識別度不高,所以可以通過改變tolerance=0.4
它的參數值來提高準確度。
3、截圖部分
把路徑改一下,然後就可以在文件夾裏看見截圖了,但是這個還需要改善。
五、運行結果
哦這個照片…因爲我放的是obama照片,所以不一樣哈,所以是unknown.大家放自己的照片會有名字的~
截圖:
這個庫還蠻好用的,行了去看論文了,這個還是不可以滿足我的需求T_T
希望有幫助到需要的人8