auto_logger.py

# -*- coding: utf-8 -*-
"""
Created on Sun Nov 24 19:59:49 2019

@author: batho
"""

import face_recognition
import cv2
import numpy as np
import os
import glob
from datetime import datetime
import math
import time

faces=glob.glob("faces/*.jpg")
print(faces)
#print(faces[0].find('.'))
#print(faces[0][5+1:10])
# 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)
known_face_encodings=[]
known_face_names=[]
for face in faces:
    image=face_recognition.load_image_file(face)
    encoding=face_recognition.face_encodings(image)[0]
    known_face_encodings.append(encoding)
    slash_ind=face.find('\\')
    dot_index=face.find('.')
    known_face_names.append(face[slash_ind+1:dot_index])
    

    
# 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.
abir_image = face_recognition.load_image_file("abir.jpg")
abir_face_encoding = face_recognition.face_encodings(abir_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    obama_face_encoding,
    abir_face_encoding
]
known_face_names = [
    "Barack Obama",
    "Abir"
]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
'''
process_this_frame = True
count=0
workers=['Abir','Quang', 'Thomas', 'Prof Hartanto']
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)

    if name in workers:
        count+=1
        if count==20:
            break
    # 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()


#Putting coming in and going out logic
time.sleep(2)
cap = cv2.VideoCapture(0)
entry=''  
count_in=0
count_out=0 
while(True):
    try:    
        ret, frame = cap.read()
        frame=cv2.flip(frame,1)
        kernel = np.ones((3,3),np.uint8)
        
        #define region of interest
        roi=frame[100:300, 100:300]
        
        
        cv2.rectangle(frame,(100,100),(300,300),(0,255,0),0)    
        hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
        
        
         
    # define range of skin color in HSV
        lower_skin = np.array([0,20,70], dtype=np.uint8)
        upper_skin = np.array([20,255,255], dtype=np.uint8)
        
     #extract skin colur imagw  
        mask = cv2.inRange(hsv, lower_skin, upper_skin)
        
       
        
    #extrapolate the hand to fill dark spots within
        mask = cv2.dilate(mask,kernel,iterations = 4)
        
    #blur the image
        mask = cv2.GaussianBlur(mask,(5,5),100) 
        
        
        
    #find contours
        contours,hierarchy= cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    
       #find contour of max area(hand)
        cnt = max(contours, key = lambda x: cv2.contourArea(x))
        
    #approx the contour a little
        epsilon = 0.0005*cv2.arcLength(cnt,True)
        approx= cv2.approxPolyDP(cnt,epsilon,True)
       
        
    #make convex hull around hand
        hull = cv2.convexHull(cnt)
        
     #define area of hull and area of hand
        areahull = cv2.contourArea(hull)
        areacnt = cv2.contourArea(cnt)
      
    #find the percentage of area not covered by hand in convex hull
        arearatio=((areahull-areacnt)/areacnt)*100
    
     #find the defects in convex hull with respect to hand
        hull = cv2.convexHull(approx, returnPoints=False)
        defects = cv2.convexityDefects(approx, hull)
        
    # l = no. of defects
        l=0
        
    #code for finding no. of defects due to fingers
        for i in range(defects.shape[0]):
            s,e,f,d = defects[i,0]
            start = tuple(approx[s][0])
            end = tuple(approx[e][0])
            far = tuple(approx[f][0])
            pt= (100,180)
            
            
            # find length of all sides of triangle
            a = math.sqrt((end[0] - start[0])**2 + (end[1] - start[1])**2)
            b = math.sqrt((far[0] - start[0])**2 + (far[1] - start[1])**2)
            c = math.sqrt((end[0] - far[0])**2 + (end[1] - far[1])**2)
            s = (a+b+c)/2
            ar = math.sqrt(s*(s-a)*(s-b)*(s-c))
            
            #distance between point and convex hull
            d=(2*ar)/a
            
            # apply cosine rule here
            angle = math.acos((b**2 + c**2 - a**2)/(2*b*c)) * 57
            
        
            # ignore angles > 90 and ignore points very close to convex hull(they generally come due to noise)
            if angle <= 90 and d>30:
                l += 1
                cv2.circle(roi, far, 3, [255,0,0], -1)
            
            #draw lines around hand
            cv2.line(roi,start, end, [0,255,0], 2)
            
            
        l+=1
        
        #print corresponding gestures which are in their ranges
        font = cv2.FONT_HERSHEY_SIMPLEX
        if l==1:
            if areacnt<2000:
                cv2.putText(frame,'Put rock for coming in and any other for going out inside the box',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA)
            else:
                if arearatio<12:
                    cv2.putText(frame,'Could not recognize try again',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA)
                elif arearatio<17.5:
                    cv2.putText(frame,'Best of luck',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA)
            
                    count_in+=1
                   
                else:
                    cv2.putText(frame,'Could not recognize try again',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA)
                    
        elif l==2:
            cv2.putText(frame,'Could not recognize try again',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA)
            
        elif l==3:
         
              if arearatio<27:
                    cv2.putText(frame,'Could not recognize try again',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA)
              else:
                    cv2.putText(frame,'Could not recognize try again',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA)
                    
        elif l==4:
            cv2.putText(frame,'Going out registered',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA)
            count_out+=1
            
        elif l==5:
            cv2.putText(frame,'Going out registered',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA)
            count_out+=1
            
        elif l==6:
            cv2.putText(frame,'reposition',(0,50), font, 2, (0,0,255), 3, cv2.LINE_AA)
            
        else :
            cv2.putText(frame,'reposition',(10,50), font, 2, (0,0,255), 3, cv2.LINE_AA)
            
        if count_in==20:
            entry+='in'
            break
        elif count_out==20:
            entry+='out'
            break
            
        #show the windows
        cv2.imshow('mask',mask)
        cv2.imshow('frame',frame)
    except:pass
    
    if cv2.waitKey(1) & 0xFF == ord('q'):break
    
cv2.destroyAllWindows()
cap.release()   












now=datetime.now()
log=os.listdir('logbook/')
month=now.strftime("%B")
year=str(now.year)
day=str(now.day)
hour=str(now.hour)
minute=str(now.minute)
print(log)


if month+' '+year not in log:
    os.mkdir('logbook/'+(now.strftime("%B")+' '+year))
day_glob=os.listdir("logbook/"+month+' '+year)
print(day_glob)
file_path="logbook/"+(now.strftime("%B")+' '+year)
file_name=day+'-'+month+'-'+year+'.txt'
full_path=os.path.join(file_path, file_name)
if day+'-'+month+'-'+year+'.txt' not in day_glob:
    f=open(full_path,"a+")
    if 'in' in entry:
        f.write('[Check In++] Username: '+name+' '+day+'-'+ str(now.month) +'-'+year+' '+ str(now.strftime("%H:%M"))+'\n')
    else:
        f.write('[--Check Out] Username: '+name+' '+day+'-'+ str(now.month) +'-'+year+' '+str(now.strftime("%H:%M"))+'\n') 
else:
    f=open(full_path,"r")
    a=f.read()
    #f=open(full_path,"a+")
    if 'in' in entry:
        if '[Check In] Username: '+name in a:
            print('You are already in. Try again with hi-five to go out')
        else:
            g=open(full_path,"a+")
            g.write('[Check In++] Username: '+name+' '+day+'-'+ str(now.month) +'-'+year+' '+ str(now.strftime("%H:%M"))+'\n')
            g.close()
    else:
        if '[Check Out] Username: '+name in a:
            print('You are already out. Try again with fist bump to come in')
        else:
             g=open(full_path,"a+")
             g.write('[--Check Out] Username: '+name+' '+day+'-'+ str(now.month) +'-'+year+' '+str(now.strftime("%H:%M"))+'\n')
             g.close()
    
f.close()