Adding Custom Optical Face Recognition to J.A.R.V.I.S

Author: GauravSingh9356

Face-Recognition/Face recognition.py

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+import cv2
+
+recognizer = cv2.face.LBPHFaceRecognizer_create() # Local Binary Patterns Histograms
+recognizer.read('trainer/trainer.yml')   #load trained model
+cascadePath = "haarcascade_frontalface_default.xml"
+faceCascade = cv2.CascadeClassifier(cascadePath) #initializing haar cascade for object detection approach
+
+font = cv2.FONT_HERSHEY_SIMPLEX #denotes the font type
+
+
+id = 2 #number of persons you want to Recognize
+
+
+names = ['','avi']  #names, leave first empty bcz counter starts from 0
+
+
+cam = cv2.VideoCapture(0, cv2.CAP_DSHOW) #cv2.CAP_DSHOW to remove warning
+cam.set(3, 640) # set video FrameWidht
+cam.set(4, 480) # set video FrameHeight
+
+# Define min window size to be recognized as a face
+minW = 0.1*cam.get(3)
+minH = 0.1*cam.get(4)
+
+# flag = True
+
+while True:
+
+    ret, img =cam.read() #read the frames using the above created object
+
+    converted_image = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)  #The function converts an input image from one color space to another
+
+    faces = faceCascade.detectMultiScale( 
+        converted_image,
+        scaleFactor = 1.2,
+        minNeighbors = 5,
+        minSize = (int(minW), int(minH)),
+       )
+
+    for(x,y,w,h) in faces:
+
+        cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2) #used to draw a rectangle on any image
+
+        id, accuracy = recognizer.predict(converted_image[y:y+h,x:x+w]) #to predict on every single image
+
+        # Check if accuracy is less them 100 ==> "0" is perfect match 
+        if (accuracy < 100):
+            id = names[id]
+            accuracy = "  {0}%".format(round(100 - accuracy))
+
+        else:
+            id = "unknown"
+            accuracy = "  {0}%".format(round(100 - accuracy))
+        
+        cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)
+        cv2.putText(img, str(accuracy), (x+5,y+h-5), font, 1, (255,255,0), 1)  
+    
+    cv2.imshow('camera',img) 
+
+    k = cv2.waitKey(10) & 0xff # Press 'ESC' for exiting video
+    if k == 27:
+        break
+
+# Do a bit of cleanup
+print("Thanks for using this program, have a good day.")
+cam.release()
+cv2.destroyAllWindows()

Face-Recognition/LICENSE

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Face-Recognition/Model Trainer.py

@@ -0,0 +1,40 @@
+import cv2
+import numpy as np
+from PIL import Image #pillow package
+import os
+
+path = 'samples' # Path for samples already taken
+
+recognizer = cv2.face.LBPHFaceRecognizer_create() # Local Binary Patterns Histograms
+detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
+#Haar Cascade classifier is an effective object detection approach
+
+
+def Images_And_Labels(path): # function to fetch the images and labels
+
+    imagePaths = [os.path.join(path,f) for f in os.listdir(path)]     
+    faceSamples=[]
+    ids = []
+
+    for imagePath in imagePaths: # to iterate particular image path
+
+        gray_img = Image.open(imagePath).convert('L') # convert it to grayscale
+        img_arr = np.array(gray_img,'uint8') #creating an array
+
+        id = int(os.path.split(imagePath)[-1].split(".")[1])
+        faces = detector.detectMultiScale(img_arr)
+
+        for (x,y,w,h) in faces:
+            faceSamples.append(img_arr[y:y+h,x:x+w])
+            ids.append(id)
+
+    return faceSamples,ids
+
+print ("Training faces. It will take a few seconds. Wait ...")
+
+faces,ids = Images_And_Labels(path)
+recognizer.train(faces, np.array(ids))
+
+recognizer.write('trainer/trainer.yml')  # Save the trained model as trainer.yml
+
+print("Model trained, Now we can recognize your face.")

Face-Recognition/README.md

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+# Face-Recognition in Python
+Face Recognition tutorial refference for https://youtu.be/BG3mpdzk0Rw
+
+
+# Prerequisites
+[ Python 3.6.4 ]
+[ OpenCV 3.4.1 or opencv-contrib-python 4.0 ]
+[ Numpy ]
+[ Pillow ]
+
+
+# steps
+[ run the Sample generator.py and enter a unique numeric id to create face samples with your face ]
+[ run Model Trainer.py ]
+[ run Face recognition.py ]

Face-Recognition/Sample generator.py

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+import cv2
+
+cam = cv2.VideoCapture(0, cv2.CAP_DSHOW) #create a video capture object which is helpful to capture videos through webcam
+cam.set(3, 640) # set video FrameWidth
+cam.set(4, 480) # set video FrameHeight
+
+
+detector = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_frontalface_default.xml')
+#Haar Cascade classifier is an effective object detection approach
+
+face_id = input("Enter a Numeric user ID  here:  ")
+#Use integer ID for every new face (0,1,2,3,4,5,6,7,8,9........)
+
+print("Taking samples, look at camera ....... ")
+count = 0 # Initializing sampling face count
+
+while True:
+
+    ret, img = cam.read() #read the frames using the above created object
+    converted_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #The function converts an input image from one color space to another
+    faces = detector.detectMultiScale(converted_image, 1.3, 5)
+
+    for (x,y,w,h) in faces:
+
+        cv2.rectangle(img, (x,y), (x+w,y+h), (255,0,0), 2) #used to draw a rectangle on any image
+        count += 1
+
+        
+        cv2.imwrite("samples/face." + str(face_id) + '.' + str(count) + ".jpg", converted_image[y:y+h,x:x+w])
+        # To capture & Save images into the datasets folder
+
+        cv2.imshow('image', img) #Used to display an image in a window
+
+    k = cv2.waitKey(100) & 0xff # Waits for a pressed key
+    if k == 27: # Press 'ESC' to stop
+        break
+    elif count >= 10: # Take 50 sample (More sample --> More accuracy)
+         break
+
+print("Samples taken now closing the program....")
+cam.release()
+cv2.destroyAllWindows()