Python Face Detect Offline

python版本 3.7.0 

一、 安装 cmake

pip install cmake 

二、安装 boost

pip install boost 

三、安装 dlib

pip install dlib 

四、安装 face_recognition

pip install face_recognition 

五、验证

face_recognition 本地模型路径 要识别图片路径 
输出：文件名 识别的人名 

注意：文件名以人名命名 

六、寻找人脸位置

face_detection “路径” 
输出：人脸像素坐标 

七、调整灵敏度

face_recognition –tolerance 灵敏度 本地模型路径 要识别图片路径 
注：默认0.6,识别度越低识别难度越高 

八、计算每次面部距离

face_recognition –show-distance true 本地模型路径 要识别图片路径 

九、只是想知道每张照片中人物的姓名，却不关心文件名，能够这样作：

face_recognition 本地模型路径 要识别图片路径 | cut -d ‘,’ -f2

十、加速识别

face_recognition –cpus 使用内核数 本地模型路径 要识别图片路径 
使用四核识别： 
face_recognition –cpus 4 本地模型路径 要识别图片路径 
 
使用所有内核识别： 
face_recognition –cpus -1 本地模型路径 要识别图片路径

十一、自动查找图像中的全部面孔

import face_recognition

image = face_recognition.load_image_file(“吴京.jpg”) 
face_locations = face_recognition.face_locations(image)

 

import face_recognition
import cv2
import numpy as np

# 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.

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(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

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"

            # # 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 = 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()　　

彩蛋

import cv2
import threading
import face_recognition
import numpy as np
import os

class camThread(threading.Thread):
    def __init__(self, previewName, camID):
        threading.Thread.__init__(self)
        self.previewName = previewName
        self.camID = camID
    def run(self):
        print("Starting " + self.previewName)
        camPreview(self.previewName, self.camID)

def camPreview(previewName, camID):
    cv2.namedWindow(previewName)
    video_capture = cv2.VideoCapture(camID)
    if video_capture.isOpened():
        rval, frame = video_capture.read()
    else:
        rval = False

    known_face_encodings = []
    known_face_names = []

    imagelist = os.listdir('./face/')
    for imagename in imagelist:
        image = face_recognition.load_image_file("./face/"+imagename)
        face_encoding = face_recognition.face_encodings(image)[0]
        known_face_encodings.append(face_encoding)
        subname=imagename.split('.')[0]
        known_face_names.append(subname)
        face_locations = []
        face_encodings = []
        face_names = []
        process_this_frame = True

    while rval:
        #cv2.imshow(previewName, frame)
        rval, frame = video_capture.read()
        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
        rgb_small_frame = small_frame[:, :, ::-1]
        if process_this_frame:
            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:
                matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
                name = "Unknown"

                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 = not process_this_frame

        for (top, right, bottom, left), name in zip(face_locations, face_names):
            top *= 4
            right *= 4
            bottom *= 4
            left *= 4

            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

            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)

        cv2.imshow(previewName, frame)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    cv2.destroyWindow(previewName)

thread1 = camThread("Camera 1", 0)
thread2 = camThread("Camera 2", 1)

thread1.start()
thread2.start()