Merge pull request #168 from J-B-Mugundh/file-locking

Added File Locking Mechanism with OpenCV

Deep_Learning/File-Locking-Mechanism/Face_Recognition_Script/Face recognition.py

@@ -0,0 +1,182 @@
+import cv2
+import os
+import sys
+import numpy as np
+import FaceDetection
+import warnings
+from os import system
+import os
+warnings.filterwarnings("ignore")
+faces=[]
+labels=[]
+names={}
+dirpath = os.getcwd()
+training_folder = dirpath+"\\Face_Recognition_Script\\training-data"
+
+def newUser():
+    name = input("Enter Your Name: ")
+    dirs = os.listdir(training_folder)
+    os.makedirs(training_folder+'/'+name+'@'+str(len(dirs)+1))
+    cap = cv2.VideoCapture(0)
+    i=0
+    while (True):
+        ret, frame = cap.read()
+        test = frame.copy()
+        frame,frame_crop,rect = FaceDetection.detect_faces(FaceDetection.lbp_face_cascade,frame)
+        cv2.imshow('Smile :) with different moods', frame)
+        cv2.waitKey(50)
+        if frame_crop!="None" and i<100:
+            print(training_folder+"/" + name + '@' + str(len(dirs)+1) + '/' + str(i) + '.jpg')
+            cv2.imwrite(training_folder+"/" + name + '@' + str(len(dirs)+1) + '/' + str(i) + '.jpg', frame_crop)
+            #cv2.imwrite("sample.jpg",test)
+            i+=1
+        elif i>=100:
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+
+def createLables():
+    dirs = os.listdir(training_folder)
+    for users in dirs:
+        lable = int(users[users.find("@")+1:len(users)])
+        names[lable] = users[0:users.find("@")]
+        subfolders = training_folder + "/" + users
+        imagesName = os.listdir(subfolders)
+        for image in imagesName:
+            imagePath = subfolders + "/" + image
+            face = cv2.imread(imagePath)
+            face = cv2.cvtColor(face,cv2.COLOR_BGR2GRAY)
+            #cv2.imshow("Training on this image...",face)
+            #cv2.waitKey(10)
+            #cv2.destroyAllWindows()
+            faces.append(face)
+            labels.append(lable)
+    #print("Labels: "+ str(labels))
+    #print("Total Number of Faces: "+str(len(faces)))
+    #print(names)
+
+face_recognizer = object
+def trainDataLBPH():
+    # create our LBPH face recognizer
+    #face_recognizer = cv2.
+    global face_recognizer
+    if len(labels)>0:
+        face_recognizer = cv2.face.createLBPHFaceRecognizer()
+        face_recognizer.train(faces, np.array(labels))
+    else:
+        print("No train data is present. Add train data using -train flag.")
+        sys.exit()
+def trainDataEigen():
+    # or use EigenFaceRecognizer by replacing above line with
+    if len(labels)>0:
+        face_recognizer = cv2.face.createEigenFaceRecognizer()
+        face_recognizer.train(faces, np.array(labels))
+    else:
+        print("No train data is present. Add train data using -train flag.")
+        sys.exit()
+def trainDataFisher():
+    # or use FisherFaceRecognizer by replacing above line with
+    if len(labels)>0:
+        face_recognizer = cv2.face.createFisherFaceRecognizer()
+        face_recognizer.train(faces, np.array(labels))
+    else:
+        print("No train data is present. Add train data using -train flag.")
+        sys.exit()
+
+
+def draw_rectangle(img, rect):
+    (x, y, w, h) = rect
+    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
+def draw_text(img, text, x, y):
+    cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)
+
+
+def predict(test_img):
+    img = test_img
+    img, face, rect = FaceDetection.detect_faces(FaceDetection.haar_face_cascade,img,1.1)
+    if face=="None":
+        pass
+    else:
+        face = cv2.cvtColor(np.array(face,dtype=np.uint16),cv2.COLOR_BGR2GRAY)
+        label,conf = face_recognizer.predict(np.array(face,dtype=np.uint16))
+        if label==-1:
+        	label_text = "unknown"
+        else:
+        	label_text = names[label]
+        draw_rectangle(img, rect)
+        draw_text(img, label_text, rect[0], rect[1] - 5)
+
+    return img
+
+def newUserTest():
+    cap = cv2.VideoCapture(0)
+    os.system('cls')
+    previous_label = ""
+    while (True):
+        ret, frame = cap.read()
+        #test = frame.copy()
+        frame,frame_crop,rect = FaceDetection.detect_faces(FaceDetection.haar_face_cascade,frame,1.1)
+        if frame_crop == "None":
+            pass
+        else:
+
+            frame_crop = cv2.cvtColor(np.array(frame_crop, dtype=np.uint16), cv2.COLOR_BGR2GRAY)
+            label, conf = face_recognizer.predict(np.array(frame_crop, dtype=np.uint16))
+            if label == -1:
+            	label_text = "unknown"
+            else:
+            	label_text = names[label]
+            #label_text = names[label]
+                # print(face)
+            draw_rectangle(frame, rect)
+            global pass_name
+            if previous_label!=label_text:
+                os.system('cls')
+                previous_label = label_text
+                print(label_text)
+                if label_text==pass_name and pass_name!='':
+                    sys.exit()
+            draw_text(frame, label_text, rect[0], rect[1] - 5)
+        cv2.imshow('Smile :) with different moods', frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            #cv2.imwrite("sample.jpg",test)
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+if __name__ == '__main__':
+    if len(sys.argv)>1:
+        if str(sys.argv[1]) == '-train':
+            newUser()       
+        elif str(sys.argv[1]) == '-run':
+            pass_name=''
+            createLables()
+            os.system('cls')
+            trainDataLBPH()
+            os.system('cls')
+            newUserTest()
+        else:
+            pass_name = sys.argv[1] 
+            createLables()
+            os.system('cls')
+            trainDataLBPH()
+            os.system('cls')
+            newUserTest()
+    else:
+        createLables()
+        os.system('cls')
+        trainDataLBPH()
+        os.system('cls')
+        newUserTest()
+
+    newUser()
+    createLables()
+    os.system('cls')
+    trainDataLBPH()
+    os.system('cls')
+    newUserTest()
+

Deep_Learning/File-Locking-Mechanism/Face_Recognition_Script/FaceDetection.py

@@ -0,0 +1,76 @@
+'''
+detectMultiScale(image, scaleFactor, minNeighbors): 
+    This is a general function to detect objects, in this case, it'll detect faces since we called in the face cascade. 
+    If it finds a face, it returns a list of positions of said face in the form “Rect(x,y,w,h).”, if not, then returns “None”.
+Image: 
+    The first input is the grayscale image. So make sure the image is in grayscale.
+scaleFactor: 
+    This function compensates a false perception in size that occurs when one face appears to be bigger than the other simply because it is closer to the camera.
+minNeighbors: 
+    This is a detection algorithm that uses a moving window to detect objects,
+    it does so by defining how many objects are found near the current one before it can declare the face found.
+'''
+
+import cv2
+
+haar_face_cascade = cv2.CascadeClassifier('E://PYTHON//Windows-Folder-Unlock-Using-Face-Recognition-master//Face_Recognition_Script//haarcascade_frontalface_alt.xml')
+lbp_face_cascade = cv2.CascadeClassifier('E://PYTHON//Windows-Folder-Unlock-Using-Face-Recognition-master//Face_Recognition_Script//lbpcascade_frontalface.xml')
+
+
+def detect_faces(f_cascade, colored_img, scaleFactor=1.1):
+    img_copy = colored_img
+    # convert the test image to gray image as opencv face detector expects gray images
+    gray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
+    # let's detect multiscale (some i
+    # mages may be closer to camera than others) images
+    faces = f_cascade.detectMultiScale(gray, scaleFactor=scaleFactor, minNeighbors=5);
+    # go over list of faces and draw them as rectangles on original colored img
+    x=0
+    y=0
+    z=0
+    w = 0
+    if len(faces)==0:
+        return img_copy,"None","None"
+    for (x, y, w, h) in faces:
+        cv2.rectangle(img_copy, (x, y), (x + w, y + h), (0, 255, 0), 2)
+    return img_copy,img_copy[y:y+w, x:x+h], faces[0]
+
+def staticFaceDetectHaar(img):
+    test1 = cv2.imread(img)
+    test1 = detect_faces(haar_face_cascade,test1)
+    cv2.imshow('finanl',test1)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+def staticFaceDetectLbp(img):
+    test1 = cv2.imread(img)
+    test1 = detect_faces(lbp_face_cascade,test1)
+    cv2.imshow('finanl',test1)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+def liveFaceDetectLbp():
+    cap = cv2.VideoCapture(0)
+    while(True):
+        ret, frame = cap.read()
+        frame = detect_faces(lbp_face_cascade,frame,1.1)
+        cv2.imshow("frame",frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+    cap.release()
+    cv2.destroyAllWindows()
+
+def liveFaceDetectHaar():
+    cap = cv2.VideoCapture(0)
+    while(True):
+        ret, frame = cap.read()
+        frame = detect_faces(haar_face_cascade,frame,1.1)
+        cv2.imshow("frame",frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+#liveFaceDetectHaar()
+#liveFaceDetectLbp()