First mini project

.gitignore

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+.idea
+__pycache__
+venv

.travis.yml

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+language: python
+python:
+  - "3.7"
+# command to install dependencies
+cache: pip
+install:
+  - pip install -r requirements.txt
+# command to run tests
+script: python main.py

Attendance/Attendance_2019-08-24_14-44-53.csv

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+Id,Name,Date,Time
+15,['jawad'],2019-08-24,14:44:39

Attendance/Attendance_2023-05-20_19-59-45.csv

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+,Id,Name,Date,Time
+0,4567,syed,2023-05-20,19:58:38
+1,5076,Riyaz,2023-05-20,19:58:57

Attendance/Attendance_2023-05-23_18-32-27.csv

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+,Id,Name,Date,Time
+0,163,reehu,2023-05-23,18:31:50
+1,1234,riyaz,2023-05-23,18:32:21

Capture_Image.py

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+import csv
+
+import cv2
+import os
+
+import os.path
+# counting the numbers
+
+
+def is_number(s):
+    try:
+        float(s)
+        return True
+    except ValueError:
+        pass
+
+    try:
+        import unicodedata
+        unicodedata.numeric(s)
+        return True
+    except (TypeError, ValueError):
+        pass
+
+    return False
+
+
+
+# Take image function
+
+def takeImages():
+
+
+    Id = input("Enter Your Id: ")
+    name = input("Enter Your Name: ")
+
+    if(is_number(Id) and name.isalpha()):
+        cam = cv2.VideoCapture(0)
+        harcascadePath = "haarcascade_frontalface_default.xml"
+        detector = cv2.CascadeClassifier(harcascadePath)
+        sampleNum = 0
+
+        while(True):
+            ret, img = cam.read()
+            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+            faces = detector.detectMultiScale(gray, 1.3, 5, minSize=(30,30),flags = cv2.CASCADE_SCALE_IMAGE)
+            for(x,y,w,h) in faces:
+                cv2.rectangle(img, (x, y), (x+w, y+h), (10, 159, 255), 2)
+                #incrementing sample number
+                sampleNum = sampleNum+1
+                #saving the captured face in the dataset folder TrainingImage
+                cv2.imwrite("TrainingImage" + os.sep +name + "."+Id + '.' +
+                            str(sampleNum) + ".jpg", gray[y:y+h, x:x+w])
+                #display the frame
+                cv2.imshow('frame', img)
+            #wait for 100 miliseconds
+            if cv2.waitKey(100) & 0xFF == ord('q'):
+                break
+            # break if the sample number is more than 100
+            elif sampleNum > 100:
+                break
+        cam.release()
+        cv2.destroyAllWindows()
+        res = "Images Saved for ID : " + Id + " Name : " + name
+        header=["Id", "Name"]
+        row = [Id, name]
+        if(os.path.isfile("StudentDetails"+os.sep+"StudentDetails.csv")):
+            with open("StudentDetails"+os.sep+"StudentDetails.csv", 'a+') as csvFile:
+                writer = csv.writer(csvFile)
+                writer.writerow(j for j in row)
+            csvFile.close()
+        else:
+            with open("StudentDetails"+os.sep+"StudentDetails.csv", 'a+') as csvFile:
+                writer = csv.writer(csvFile)
+                writer.writerow(i for i in header)
+                writer.writerow(j for j in row)
+            csvFile.close()
+    else:
+        if(is_number(Id)):
+            print("Enter Alphabetical Name")
+        if(name.isalpha()):
+            print("Enter Numeric ID")
+
+

Recognize.py

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+import datetime
+import os
+import time
+
+import cv2
+import pandas as pd
+
+
+#-------------------------
+def recognize_attendence():
+    recognizer = cv2.face.LBPHFaceRecognizer_create()  # cv2.createLBPHFaceRecognizer()
+    recognizer.read("./TrainingImage/Trainner.yml")
+    harcascadePath = "haarcascade_frontalface_default.xml"
+    faceCascade = cv2.CascadeClassifier(harcascadePath)
+    df = pd.read_csv("StudentDetails"+os.sep+"StudentDetails.csv")
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    col_names = ['Id', 'Name', 'Date', 'Time']
+    attendance = pd.DataFrame(columns=col_names)
+
+    # Initialize and start realtime video capture
+    cam = cv2.VideoCapture(0)
+    cam.set(3, 640)  # set video width
+    cam.set(4, 480)  # set video height
+    # Define min window size to be recognized as a face
+    minW = 0.1 * cam.get(3)
+    minH = 0.1 * cam.get(4)
+
+    while True:
+        _,im = cam.read()
+        gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
+        faces = faceCascade.detectMultiScale(gray, 1.2, 5,minSize = (int(minW), int(minH)),flags = cv2.CASCADE_SCALE_IMAGE)
+        for(x, y, w, h) in faces:
+            cv2.rectangle(im, (x, y), (x+w, y+h), (10, 159, 255), 2)
+            Id, conf = recognizer.predict(gray[y:y+h, x:x+w])
+
+            if conf < 100:
+
+                aa = df.loc[df['Id'] == Id]['Name'].values
+                confstr = "  {0}%".format(round(100 - conf))
+                tt = str(Id)+"-"+aa
+
+
+            else:
+                Id = '  Unknown  '
+                tt = str(Id)
+                confstr = "  {0}%".format(round(100 - conf))
+
+            if (100-conf) > 67:
+                ts = time.time()
+                date = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d')
+                timeStamp = datetime.datetime.fromtimestamp(ts).strftime('%H:%M:%S')
+                aa = str(aa)[2:-2]
+                attendance.loc[len(attendance)] = [Id, aa, date, timeStamp]
+
+            tt = str(tt)[2:-2]
+            if(100-conf) > 67:
+                tt = tt + " [Pass]"
+                cv2.putText(im, str(tt), (x+5,y-5), font, 1, (255, 255, 255), 2)
+            else:
+                cv2.putText(im, str(tt), (x + 5, y - 5), font, 1, (255, 255, 255), 2)
+
+            if (100-conf) > 67:
+                cv2.putText(im, str(confstr), (x + 5, y + h - 5), font,1, (0, 255, 0),1 )
+            elif (100-conf) > 50:
+                cv2.putText(im, str(confstr), (x + 5, y + h - 5), font, 1, (0, 255, 255), 1)
+            else:
+                cv2.putText(im, str(confstr), (x + 5, y + h - 5), font, 1, (0, 0, 255), 1)
+
+
+
+        attendance = attendance.drop_duplicates(subset=['Id'], keep='first')
+        cv2.imshow('Attendance', im)
+        if (cv2.waitKey(1) == ord('q')):
+            break
+    ts = time.time()
+    date = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d')
+    timeStamp = datetime.datetime.fromtimestamp(ts).strftime('%H:%M:%S')
+    Hour, Minute, Second = timeStamp.split(":")
+    fileName = "Attendance"+os.sep+"Attendance_"+date+"_"+Hour+"-"+Minute+"-"+Second+".csv"
+    attendance.to_csv(fileName, index=True)
+    print("Attendance Successful")
+    cam.release()
+    cv2.destroyAllWindows()
+
+

StudentDetails/StudentDetails.csv

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+Id,Name

Train_Image.py

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+import os
+import time
+import cv2
+import numpy as np
+from PIL import Image
+from threading import Thread
+
+
+
+# -------------- image labesl ------------------------
+
+def getImagesAndLabels(path):
+    # get the path of all the files in the folder
+    imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
+    # print(imagePaths)
+
+    # create empth face list
+    faces = []
+    # create empty ID list
+    Ids = []
+    # now looping through all the image paths and loading the Ids and the images
+    for imagePath in imagePaths:
+        # loading the image and converting it to gray scale
+        pilImage = Image.open(imagePath).convert('L')
+        # Now we are converting the PIL image into numpy array
+        imageNp = np.array(pilImage, 'uint8')
+        # getting the Id from the image
+        Id = int(os.path.split(imagePath)[-1].split(".")[1])
+        # extract the face from the training image sample
+        faces.append(imageNp)
+        Ids.append(Id)
+    return faces, Ids
+
+
+# ----------- train images function ---------------
+def TrainImages():
+    recognizer = cv2.face.LBPHFaceRecognizer_create()   # recognizer = cv2.face.LBPHFaceRecognizer_create()
+    harcascadePath = "haarcascade_frontalface_default.xml"
+    detector = cv2.CascadeClassifier(harcascadePath)
+    faces, Id = getImagesAndLabels("TrainingImage")
+    Thread(target = recognizer.train(faces, np.array(Id))).start()
+    # Below line is optional for a visual counter effect
+    Thread(target = counter_img("TrainingImage")).start()
+    recognizer.save("TrainingImage"+os.sep+"Trainner.yml")
+    print("All Images")
+
+# Optional, adds a counter for images trained (You can remove it)
+def counter_img(path):
+    imgcounter = 1
+    imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
+    for imagePath in imagePaths:
+        print(str(imgcounter) + " Images Trained", end="\r")
+        time.sleep(0.008)
+        imgcounter += 1
+

automail.py

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+import yagmail
+import os
+import datetime
+date = datetime.date.today().strftime("%B %d, %Y")
+path = 'Attendance'
+os.chdir(path)
+files = sorted(os.listdir(os.getcwd()), key=os.path.getmtime)
+newest = files[-1]
+filename = newest
+sub = "Attendance Report for " + str(date)
+receiver = "[email protected]"
+body = "Attendance report"
+# mail information
+yag = yagmail.SMTP("[email protected]", "jeynagodjcamknoq")
+
+# sent the mail
+yag.send(
+    to=receiver,
+    subject=sub, # email subject
+    contents=body,  # email body
+    attachments= filename  # file attached
+)
+print("Email Sent!")

check_camera.py

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+
+def camer():
+    import cv2
+
+    # Load the cascade
+    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+    # To capture video from webcam.
+    cap = cv2.VideoCapture(0)
+
+    while True:
+        # Read the frame
+        _, img = cap.read()
+
+        # Convert to grayscale
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+        # Detect the faces
+        faces = face_cascade.detectMultiScale(gray, 1.3, 5, minSize=(30, 30),flags = cv2.CASCADE_SCALE_IMAGE)
+
+        # Draw the rectangle around each face
+        for (x, y, w, h) in faces:
+            cv2.rectangle(img, (x, y), (x + w, y + h), (10,159,255), 2)
+
+
+        # Display
+        cv2.imshow('Webcam Check', img)
+
+        # Stop if escape key is pressed
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    # Release the VideoCapture object
+    cap.release()
+    cv2.destroyAllWindows()