Master

.gitignore

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+/dataset
+/harcascade
+/trainedModel

cse-2018-2019-maths.csv

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+,Unnamed: 0,Unnamed: 0.1,Unnamed: 0.1.1,Unnamed: 0.1.1.1,Unnamed: 0.1.1.1.1,Sl no.,roll no.,Name,16/2/2020,16/2/2020.1,16/2/2020.1.1,16/2/2020.1.1.1,16/2/2020.1.1.1.1,16/2/2020
+0,0,0,0,0,0,1,32,Nadeem Akhter,A,A,A,A,A,A
+1,1,1,1,1,1,2,35,Atiab Kalam,A,A,A,A,A,A
+2,2,2,2,2,2,3,37,Madhurima Maji,A,A,A,A,A,A
+3,3,3,3,3,3,4,51,Avisek Shaw,A,A,A,A,A,A
+4,4,4,4,4,4,5,60,Agnibesh Mukherjee,A,A,A,A,A,A
+5,5,5,5,5,5,6,64,Abhishek Charan,A,A,A,A,A,A

current.csv

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+,Unnamed: 0,Unnamed: 0.1,Unnamed: 0.1.1,Unnamed: 0.1.1.1,Unnamed: 0.1.1.1.1,Sl no.,roll no.,Name,16/2/2020,16/2/2020.1,16/2/2020.1.1,16/2/2020.1.1.1,16/2/2020.1.1.1.1
+0,0,0,0,0,0,1,32,Nadeem Akhter,A,A,A,A,A
+1,1,1,1,1,1,2,35,Atiab Kalam,A,A,A,A,A
+2,2,2,2,2,2,3,37,Madhurima Maji,A,A,A,A,A
+3,3,3,3,3,3,4,51,Avisek Shaw,A,A,A,A,A
+4,4,4,4,4,4,5,60,Agnibesh Mukherjee,A,A,A,A,A
+5,5,5,5,5,5,6,64,Abhishek Charan,A,A,A,A,A

faceRecognition.py

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+import cv2
+import os
+import numpy as np
+
+def faceDetection(test_img):
+    gray_img = cv2.cvtColor(test_img,cv2.COLOR_BGR2GRAY)
+    face_haar_cascade = cv2.CascadeClassifier("harcascade/haarcascade_frontalface_default.xml")
+    faces = face_haar_cascade.detectMultiScale(gray_img , scaleFactor=1.32 , minNeighbors=5)
+
+    return faces,gray_img
+
+
+def labels_for_training_data(directory):
+    faces = []
+    face_id = []
+
+    for root,sub_directory,filenames in os.walk(directory):
+        for filename in filenames:
+            if filename.startswith("."):
+                print("Skipping this file.....\n")
+                continue
+
+            id = os.path.basename(root)
+            img_path = os.path.join(root,filename)
+            print(f"img_path = {img_path}")
+            print(f"id : {id}")
+            test_img = cv2.imread(img_path)
+
+            if test_img is None:
+                print("Image not loaded properly ")
+                continue
+
+            faces_rect,gray_img = faceDetection(test_img)  
+            if(len(faces_rect)!=1):
+                continue
+
+            (x,y,w,h) = faces_rect[0]
+            roi_gray = gray_img[y:y+w,x:x+h]
+            faces.append(roi_gray)
+            print(id)
+            try:
+                face_id.append(int(id))
+            except:
+                print("Invalid id")
+                continue
+
+    return faces , face_id
+
+
+def train_classifier(faces,face_id):
+    face_recoznizer = cv2.face.LBPHFaceRecognizer_create()
+    face_recoznizer.train(faces,np.array(face_id))
+    return face_recoznizer
+
+def draw_rect(test_img,face):
+    (x,y,w,h) = face
+    cv2.rectangle(test_img,(x,y),(x+w,y+h),(255,0,0),thickness=2)
+
+def put_text(test_img,text,x,y):
+    cv2.putText(test_img,text,(x,y),cv2.FONT_HERSHEY_SIMPLEX , 0.4,(255,0,0),2)
+
+
+

recognize.py

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+import cv2
+import numpy as np
+import os
+import faceRecognition as fr
+import openpyxl as op
+import datetime as dt
+from csv import reader , writer
+import pandas as pd
+
+
+#cap = cv2.VideoCapture(0)
+
+camera = cv2.VideoCapture(0)
+camera_height = 750
+camera_width = 700
+raw_frames = []
+
+
+while(True):
+
+    _, frame = camera.read()
+
+    frame = cv2.flip(frame, 1)
+
+    aspect = frame.shape[1] / float(frame.shape[0])
+    res = int(aspect * camera_height) 
+    frame = cv2.resize(frame, (res, camera_height))
+
+
+    cv2.rectangle(frame, (3, 3), (800, 650), (0, 255, 0), 2)
+
+
+    cv2.imshow("Capturing frames", frame)
+
+    key = cv2.waitKey(1)
+
+
+    if key & 0xFF == ord("q"):
+        # save the frame
+        raw_frames.append(frame)
+        print('1 key pressed - saved TYPE_1 frame')
+        break
+    #elif key & 0xFF == ord("1"):
+
+
+camera.release()
+cv2.destroyAllWindows()
+
+for i, frame in enumerate(raw_frames):
+
+    roi = frame[3+2:800-2, 3+2:650-2]
+
+    roi = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)
+
+    roi = cv2.resize(roi, (224, 224))
+
+    path = 'images/{}.png'.format(i)
+
+    cv2.imwrite(path , cv2.cvtColor(roi,cv2.COLOR_BGR2RGB))
+
+
+label1 = []
+confidence1 = []
+count = 0
+
+
+test_img = cv2.imread("/home/avisek/Desktop/FaceRecognition/images/0.png")
+
+faces_detected,gray_img = fr.faceDetection(test_img)
+print(f"faces_detected : {faces_detected}")
+
+
+
+# faces,face_id = fr.labels_for_training_data("/home/avisek/Desktop/FaceRecognition/dataset/training/Batch-2018-2019")
+# face_recognizer = fr.train_classifier(faces,face_id)
+# face_recognizer.save("trainedModel/Batch-2018-2019.yml")
+
+face_recognizer = cv2.face.LBPHFaceRecognizer_create()
+face_recognizer.read("/home/avisek/Desktop/FaceRecognition/trainedModel/Batch-2018-2019.yml")
+
+# face_recognizer = fr.train_classifier(faces,face_id)
+# face_recognizer.save("trainedModel/Batch-2018-2019.yml")
+# faces,face_id = fr.labels_for_training_data("/home/avisek/Desktop/FaceRecognition/dataset/training/Batch-2018-2019")
+
+name = {35:"Atiab kalam",
+        51:"Avisek shaw",
+        60:"Agnibesh Mukherjee",
+        64:"Abhishek Charan",
+        37:"Madhurima Maji",
+        32:"Arnab kumar Pati"}
+
+for face in faces_detected:
+    (x,y,w,h) = face
+    roi_gray = gray_img[y:y+h,x:x+h]
+    label,confidence = face_recognizer.predict(roi_gray)
+    print(f"confidence : {confidence}")
+    print(f"label : {label}")
+    if confidence>150:
+        fr.draw_rect(test_img,face)
+        fr.put_text(test_img,"Not Registered",x,y)
+    else:
+
+        label1.append(int(label))
+        #confidence1.append(confidence)
+        fr.draw_rect(test_img,face)
+        predicted_name = name[label]
+        fr.put_text(test_img,predicted_name,x,y)
+
+
+resized_imag = cv2.resize(test_img,(1000,700))
+cv2.imshow("face detection",resized_imag)
+cv2.waitKey(0)
+cv2.destroyAllWindows
+
+
+
+
+print(f"label : {label1}")
+
+
+
+
+df = pd.read_excel("attendanceSheet/cse-2018-2019-maths.xlsx",index=False)
+df.to_csv('./current.csv')      
+
+
+
+with open('current.csv', 'r') as read_obj, \
+        open('cse-2018-2019-maths.csv', 'w', newline='') as write_obj:
+
+    csv_reader = reader(read_obj)
+
+    csv_writer = writer(write_obj)
+
+
+    Date = dt.datetime.now()
+    todaysDate = f"{Date.day}/{Date.month}/{Date.year}"
+    for row in csv_reader:
+
+        if (count==0):
+            row.append(todaysDate)
+        else:
+            if (int(row[2]) not in label1):
+                print("absent")
+                status = "A"
+            else:
+                status = "P"
+                print("present")
+            row.append(status)
+        # Add the updated row / list to the output file
+        count = count + 1
+        print(f"row[2] : {row[2]}")
+        csv_writer.writerow(row)
+
+
+read_file = pd.read_csv (r'cse-2018-2019-maths.csv')
+read_file.to_excel (r'attendanceSheet/cse-2018-2019-maths.xlsx', index = None, header=True)

takePicture.py

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+"""
+copyright Avisek shaw
+"""
+import numpy as np
+import cv2
+
+
+camera = cv2.VideoCapture(0)
+camera_height = 600
+raw_frames = []
+
+
+while(True):
+
+    _, frame = camera.read()
+
+    frame = cv2.flip(frame, 1)
+
+    aspect = frame.shape[1] / float(frame.shape[0])
+    res = int(aspect * camera_height) 
+    frame = cv2.resize(frame, (res, camera_height))
+
+
+    cv2.rectangle(frame, (51, 37), (725, 555), (0, 255, 0), 2)
+
+
+    cv2.imshow("Capturing frames", frame)
+
+    key = cv2.waitKey(1)
+
+
+    if key & 0xFF == ord("q"):
+        break
+    elif key & 0xFF == ord("1"):
+        # save the frame
+        raw_frames.append(frame)
+        print('1 key pressed - saved TYPE_1 frame')
+
+camera.release()
+cv2.destroyAllWindows()
+
+for i, frame in enumerate(raw_frames):
+
+    roi = frame[37+2:555-2, 51+2:650-2]
+
+    roi = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)
+
+    roi = cv2.resize(roi, (224, 224))
+
+    cv2.imwrite('images/{}.png'.format(i), cv2.cvtColor(roi,cv2.COLOR_BGR2RGB))