relation.py

# this python file is to analyzing the stream data

# all the packages used for the project
import re
import cv2
import numpy as np
import face_recognition
import os
from datetime import datetime
from kafka import KafkaProducer
import json
#from confluent_kafka import Producer
topic_name1 = 'character_relation'
topic_name2="character_attendance"
def json_serializer(data):
    return json.dumps(data).encode("utf-8")
producer=KafkaProducer(bootstrap_servers=["localhost:9092"],value_serializer=json_serializer)
person_name=[]
images=[]

# loop through the images, and processing
for pic in os.listdir("images"):
    if pic.endswith("png") or pic.endswith("jpg"):
        img=cv2.imread("images/{}".format(pic))
        name=os.path.splitext(pic)[0]
        images.append(img)
        person_name.append(name)

print(person_name)

def encoding(images):
    """encoding the all the images, and find the 128 measurements for the face"""
    images_encoding=[]
    #loop all the images
    for image in images:
        try:
            img=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
            encode = face_recognition.face_encodings(img)[0]
            images_encoding.append(encode)
        except:
            pass
    return images_encoding

encodeListKnown = encoding(images)
print('Encoding Complete')

# record the pic got by webcam
cap = cv2.VideoCapture(0)
name_re=set()
while True:
    """capture the pics from webcam, doing face encoding, face detection and face comparion, return the most matched face name
    draw rectangle around all the faces, and all the information"""
    degree=0.25
    ret, img = cap.read()
    # resize the image tp 0.25 of the orginal one ## make it faster
    imgS = cv2.resize(img,(0,0),None,degree,degree)
    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
    # find the face location in the resized image
    facesCurFrame = face_recognition.face_locations(imgS)
    # encoding the img
    encodesCurFrame = face_recognition.face_encodings(imgS,facesCurFrame)
    format= cv2.FONT_HERSHEY_COMPLEX
    for encode_Face,face_Loc in zip(encodesCurFrame,facesCurFrame):
        matches = face_recognition.compare_faces(encodeListKnown,encode_Face)
        faceDis = face_recognition.face_distance(encodeListKnown,encode_Face)
        print(faceDis)
        matchIndex = np.argmin(faceDis)
        if faceDis[matchIndex]<0.5:
            name = person_name[matchIndex].upper()
            name=res = re.sub(r'[^a-zA-Z]', '', name)
            #print(name)
            y1,x2,y2,x1 = face_Loc
            # scale back the location
            #scale_back=1/degree
            y1, x2, y2, x1 = y1*4,x2*4,y2*4,x1*4
            # draw rectangle on the image
            cv2.rectangle(img,(x1,y1),(x2,y2),(0,255,0),2)
            #cv2.rectangle(img,(x1,y2),(x2,y2),(0,255,0),cv2.FILLED)
            cv2.putText(img,name,(x1+6,y2-6),format,1,(255,255,255),2)
            #Time=datetime.now()
            #current_time=Time.strftime('%Y-%m-%d %H:%M:%S,%f')
            #person_attendance_table.write_row(Time,name)
            json_dic={"name":name}
            print(json_dic)
            #person_attendance_table.write_row(Time,name)
            producer.send(topic_name1, json_dic)
            print("yes")
            if name not in name_re:
                name_re.add(name)
                producer.send(topic_name2, json_dic)
  

    cv2.imshow('Webcam',img)
    if cv2.waitKey(20) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()