diff options
| -rw-r--r-- | face-detection/01_face_capture_dataset.py | 4 | ||||
| -rw-r--r-- | face-detection/02_face_training.py | 31 | ||||
| -rw-r--r-- | face-detection/03_face_recogition.py | 67 |
3 files changed, 100 insertions, 2 deletions
diff --git a/face-detection/01_face_capture_dataset.py b/face-detection/01_face_capture_dataset.py index 327264c..8836ce6 100644 --- a/face-detection/01_face_capture_dataset.py +++ b/face-detection/01_face_capture_dataset.py @@ -6,7 +6,7 @@ cam.set(4, 480) # set video height face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') # For each person, enter one numeric face id face_id = input('\n----Enter User-id and press <return>----') -print("\n Initializing face capture. Look the camera and wait!") +print("\n [INFO] Initializing face capture. Look the camera and wait!") # Initialize individual sampling face count count = 0 while(True): @@ -25,6 +25,6 @@ while(True): elif count >= 30: # Take 30 face sample and stop video capture break # Do a bit of cleanup -print("\n Exiting Program and cleaning up stuff") +print("\n [INFO] Exiting Program and cleaning up stuff") cam.release() cv2.destroyAllWindows() diff --git a/face-detection/02_face_training.py b/face-detection/02_face_training.py new file mode 100644 index 0000000..938761b --- /dev/null +++ b/face-detection/02_face_training.py @@ -0,0 +1,31 @@ +import cv2 +import numpy as np +from PIL import Image +import os + +path = 'dataset' +# Using LBPH(Local Binary Patterns Histograms) recognizer +recognizer = cv2.face.LBPHFaceRecognizer_create() +detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml"); +# function to get the images and label data +def getImagesAndLabels(path): + imagePaths = [os.path.join(path,f) for f in os.listdir(path)] + faceSamples=[] + ids = [] + for imagePath in imagePaths: + PIL_img = Image.open(imagePath).convert('L') # grayscale + img_numpy = np.array(PIL_img,'uint8') + id = int(os.path.split(imagePath)[-1].split(".")[1]) + faces = detector.detectMultiScale(img_numpy) + for (x,y,w,h) in faces: + faceSamples.append(img_numpy[y:y+h,x:x+w]) + ids.append(id) + return faceSamples,ids +print ("\n [INFO] Training faces. It will take a few seconds. Wait ...") +#returns two arrays faces and ids +faces,ids = getImagesAndLabels(path) +recognizer.train(faces, np.array(ids)) +#save the model into trainer/trainer.yml +recognizer.write('trainer/trainer.yml') +# Print the numer of faces trained and end program +print("\n [INFO] {0} faces trained. Exiting Program".format(len(np.unique(ids)))) diff --git a/face-detection/03_face_recogition.py b/face-detection/03_face_recogition.py new file mode 100644 index 0000000..e0da838 --- /dev/null +++ b/face-detection/03_face_recogition.py @@ -0,0 +1,67 @@ +import cv2 +import numpy as np +import os +recognizer = cv2.face.LBPHFaceRecognizer_create() +recognizer.read('trainer/trainer.yml') +cascadePath = "haarcascade_frontalface_default.xml" +faceCascade = cv2.CascadeClassifier(cascadePath); +font = cv2.FONT_HERSHEY_COMPLEX +#font = cv2.FONT_HERSHEY_TRIPLEX +id = 0 +# names related to id +names = ['None', 'Obamna', 'Soda', 'Orange'] +# Initialize and start realtime video capture +cam = cv2.VideoCapture(0) +cam.set(3, 640) # set video widht +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: + ret, img =cam.read() + gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) + + faces = faceCascade.detectMultiScale( + gray, + 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) + id, confidence = recognizer.predict(gray[y:y+h,x:x+w]) + + # If confidence is less them 100 ==> "0" : perfect match + if (confidence < 100): + id = names[id] + confidence = " {0}%".format(round(100 - confidence)) + else: + id = "unknown" + confidence = " {0}%".format(round(100 - confidence)) + + cv2.putText( + img, + str(id), + (x+5,y-5), + font, + 1, + (255,255,255), + 2 + ) + cv2.putText( + img, + str(confidence), + (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 +print("\n [INFO] Exiting Program and cleaning up stuff") +cam.release() +cv2.destroyAllWindows() |