Integrated hardware commands to Facial Recognition Software.
This commit is contained in:
parent
c5bf048621
commit
cf228272a8
7 changed files with 170 additions and 177 deletions
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@ -7,11 +7,13 @@ import numpy as np
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import Facial_Recognition_Render as fr
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import _pickle as cPickle
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import glob
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'import Hardware.Motor' #Line 225-228
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faceWidth = 320
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faceHeight = 320
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SKIP_FRAMES = 1
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def alignFace(imFace, landmarks):
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l_x = landmarks[39][0]
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l_y = landmarks[39][1]
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@ -22,19 +24,19 @@ def alignFace(imFace, landmarks):
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# Convert from radians to degrees
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angle = math.atan2(dy, dx) * 180.0 / math.pi
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eyesCenter = ((l_x + r_x)*0.5, (l_y + r_y)*0.5)
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eyesCenter = ((l_x + r_x) * 0.5, (l_y + r_y) * 0.5)
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rotMatrix = cv2.getRotationMatrix2D(eyesCenter, angle, 1)
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alignedImFace = np.zeros(imFace.shape, dtype=np.uint8)
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alignedImFace = cv2.warpAffine(imFace, rotMatrix, (imFace.shape[1],imFace.shape[0]))
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alignedImFace = cv2.warpAffine(imFace, rotMatrix, (imFace.shape[1], imFace.shape[0]))
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return alignedImFace
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def face_detector_haarcascade(image):
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def face_detector_haarcascade(image):
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grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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resize_fx = 1
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resize_fy = 1
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grey = cv2.resize(grey, dsize=None, fx=resize_fx, fy=resize_fy, interpolation = cv2.INTER_AREA)
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grey = cv2.resize(grey, dsize=None, fx=resize_fx, fy=resize_fy, interpolation=cv2.INTER_AREA)
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pwd = sys.path[0]
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classfier = cv2.CascadeClassifier(pwd + "/Facial_models/haarcascade_frontalface_alt2.xml")
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@ -44,30 +46,31 @@ def face_detector_haarcascade(image):
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if len(faceRects) > 0:
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for faceRect in faceRects:
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x, y, w, h = faceRect
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x = int(x/resize_fx)
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y = int(y/resize_fy)
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w = int(w/resize_fx)
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h = int(h/resize_fy)
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x = int(x / resize_fx)
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y = int(y / resize_fy)
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w = int(w / resize_fx)
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h = int(h / resize_fy)
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cv2.rectangle(image, (x - 10, y - 10), (x + w + 10, y + h + 10), (0, 255, 0), 5)
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return image
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def face_detector_ssd(image):
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def face_detector_ssd(image):
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pwd = sys.path[0]
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net = cv2.dnn.readNetFromCaffe(pwd+"/Facial_models/deploy.prototxt", pwd+"/Facial_models/res10_300x300_ssd_iter_140000_fp16.caffemodel")
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net = cv2.dnn.readNetFromCaffe(pwd + "/Facial_models/deploy.prototxt",
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pwd + "/Facial_models/res10_300x300_ssd_iter_140000_fp16.caffemodel")
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resize = (300, 300)
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confidence_thres = 0.65
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blob = cv2.dnn.blobFromImage(cv2.resize(image, dsize=resize), 1.0, resize, (104.0, 177.0, 123.0))
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# blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
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# blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
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net.setInput(blob)
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detections = net.forward()
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h,w,c=image.shape
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h, w, c = image.shape
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for i in range(0, detections.shape[2]):
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confidence = detections[0, 0, i, 2]
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@ -76,13 +79,14 @@ def face_detector_ssd(image):
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(startX, startY, endX, endY) = box.astype("int")
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text = "{:.2f}%".format(confidence * 100)
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y = startY - 10 if startY - 10 > 10 else startY + 10
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cv2.rectangle(image, (startX, startY), (endX, endY),(0, 255,0), 5)
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cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 5)
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cv2.putText(image, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 1.00, (0, 255, 0), 3)
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return image
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def training_data_loader():
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imagesFolder = sys.path[0]+"/Facial_images/face_rec/train/"
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imagesFolder = sys.path[0] + "/Facial_images/face_rec/train/"
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subfolders = []
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for x in os.listdir(imagesFolder):
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@ -107,7 +111,7 @@ def training_data_loader():
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labelsFaceTrain = []
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faceDetector = dlib.get_frontal_face_detector()
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landmarkDetector = dlib.shape_predictor(sys.path[0]+"/Facial_models/shape_predictor_68_face_landmarks.dat")
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landmarkDetector = dlib.shape_predictor(sys.path[0] + "/Facial_models/shape_predictor_68_face_landmarks.dat")
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for j, imagePath in enumerate(imagePaths):
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im = cv2.imread(imagePath, 0)
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@ -120,10 +124,10 @@ def training_data_loader():
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if len(landmarks) == 68:
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x1Limit = landmarks[0][0] - (landmarks[36][0] - landmarks[0][0])
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x2Limit = landmarks[16][0] + (landmarks[16][0] - landmarks[45][0])
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y1Limit = landmarks[27][1] - 3*(landmarks[30][1] - landmarks[27][1])
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y1Limit = landmarks[27][1] - 3 * (landmarks[30][1] - landmarks[27][1])
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y2Limit = landmarks[8][1] + (landmarks[30][1] - landmarks[29][1])
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x1 = max(x1Limit,0)
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x1 = max(x1Limit, 0)
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x2 = min(x2Limit, imWidth)
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y1 = max(y1Limit, 0)
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y2 = min(y2Limit, imHeight)
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@ -132,63 +136,64 @@ def training_data_loader():
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alignedFace = alignFace(imFace, landmarks)
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alignedFace = cv2.resize(alignedFace, (faceHeight, faceWidth))
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imagesFaceTrain.append(np.float32(alignedFace)/255.0)
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imagesFaceTrain.append(np.float32(alignedFace) / 255.0)
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labelsFaceTrain.append(labels[j])
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return imagesFaceTrain, labelsFaceTrain, labelsMap
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def training_recognizer(rec_type):
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def training_recognizer(rec_type):
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imagesFaceTrain, labelsFaceTrain, labelsMap = training_data_loader()
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if (rec_type=='LBPH'):
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if (rec_type == 'LBPH'):
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faceRecognizer = cv2.face.LBPHFaceRecognizer_create()
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print("Training using LBPH Faces")
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elif (rec_type=='Eigen'):
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elif (rec_type == 'Eigen'):
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faceRecognizer = cv2.face.EigenFaceRecognizer_create()
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print("Training using Eigen Faces")
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elif (rec_type=='Fisher'):
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elif (rec_type == 'Fisher'):
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faceRecognizer = cv2.face.FisherFaceRecognizer_create()
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print("Training using Fisher Faces")
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faceRecognizer.train(imagesFaceTrain, np.array(labelsFaceTrain))
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faceRecognizer.write(sys.path[0]+'/Facial_models/face_rec_model.yml')
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faceRecognizer.write(sys.path[0] + '/Facial_models/face_rec_model.yml')
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with open(sys.path[0]+'/Facial_models/labels_map.pkl', 'wb') as f:
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with open(sys.path[0] + '/Facial_models/labels_map.pkl', 'wb') as f:
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cPickle.dump(labelsMap, f)
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def face_recognition_inference(rec_type):
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#testFiles = glob.glob(sys.path[0]+'/Facial_test_images/face_rec/test/*.jpg')
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#testFiles.sort()
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# testFiles = glob.glob(sys.path[0]+'/Facial_test_images/face_rec/test/*.jpg')
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# testFiles.sort()
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i = 0
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correct = 0
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error = 0
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faceDetector = dlib.get_frontal_face_detector()
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print(sys.path[0])
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landmarkDetector = dlib.shape_predictor(sys.path[0]+'/Facial_models/shape_predictor_68_face_landmarks.dat')
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landmarkDetector = dlib.shape_predictor(sys.path[0] + '/Facial_models/shape_predictor_68_face_landmarks.dat')
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if (rec_type=='LBPH'):
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if (rec_type == 'LBPH'):
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faceRecognizer = cv2.face.LBPHFaceRecognizer_create()
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print("Test using LBPH Faces")
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elif (rec_type=='Eigen'):
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elif (rec_type == 'Eigen'):
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faceRecognizer = cv2.face.EigenFaceRecognizer_create()
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print("Test using Eigen Faces")
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elif (rec_type=='Fisher'):
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elif (rec_type == 'Fisher'):
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faceRecognizer = cv2.face.FisherFaceRecognizer_create()
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print("Test using Fisher Faces")
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faceRecognizer.read(sys.path[0]+'/Facial_models/face_rec_model.yml')
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labelsMap = np.load(sys.path[0]+'/Facial_models/labels_map.pkl', allow_pickle=True)
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faceRecognizer.read(sys.path[0] + '/Facial_models/face_rec_model.yml')
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labelsMap = np.load(sys.path[0] + '/Facial_models/labels_map.pkl', allow_pickle=True)
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cam = cv2.VideoCapture(0)
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while(True):
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#imagePath = testFiles[i]
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while (True):
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# imagePath = testFiles[i]
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success, original = cam.read()
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im = cv2.resize(original, (640, 480))
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i += 1
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im = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
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im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
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imHeight, imWidth = im.shape[:2]
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landmarks = fr.getLandmarks(faceDetector, landmarkDetector, im)
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@ -197,10 +202,10 @@ def face_recognition_inference(rec_type):
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if len(landmarks) == 68:
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x1Limit = landmarks[0][0] - (landmarks[36][0] - landmarks[0][0])
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x2Limit = landmarks[16][0] + (landmarks[16][0] - landmarks[45][0])
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y1Limit = landmarks[27][1] - 3*(landmarks[30][1] - landmarks[27][1])
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y1Limit = landmarks[27][1] - 3 * (landmarks[30][1] - landmarks[27][1])
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y2Limit = landmarks[8][1] + (landmarks[30][1] - landmarks[29][1])
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x1 = max(x1Limit,0)
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x1 = max(x1Limit, 0)
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x2 = min(x2Limit, imWidth)
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y1 = max(y1Limit, 0)
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y2 = min(y2Limit, imHeight)
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@ -208,26 +213,29 @@ def face_recognition_inference(rec_type):
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alignedFace = alignFace(imFace, landmarks)
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alignedFace = cv2.resize(alignedFace, (faceHeight, faceWidth))
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imFaceFloat = np.float32(alignedFace)/255.0
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imFaceFloat = np.float32(alignedFace) / 255.0
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predictedLabel = -1
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predictedLabel, score = faceRecognizer.predict(imFaceFloat)
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center = ( int((x1 + x2) /2), int((y1 + y2)/2) )
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radius = int((y2-y1)/2.0)
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text = '{} {}%'.format(labelsMap[predictedLabel],round(score, 5))
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center = (int((x1 + x2) / 2), int((y1 + y2) / 2))
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radius = int((y2 - y1) / 2.0)
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text = '{} {}%'.format(labelsMap[predictedLabel], round(score, 5))
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cv2.rectangle(original, (x1, y1), (x2, y2), (0, 255, 0), 5)
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cv2.putText(original, text, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 3)
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cv2.putText(original, text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 3)
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'Hardware.Motor.Motor.stop_motor()'
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'Hardware.Motor.Motor.start_motor()'
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'Hardware.Motor.Motor.stop_motor()'
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'Hardware.Motor.Motor.start_alarm()'
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cv2.imshow('Face Recognition Demo', original)
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k = cv2.waitKey(10)
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cam.release()
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cv2.destroyAllWindows()
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if __name__=="__main__":
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if __name__ == "__main__":
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mode = 'test'
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rec_type = 'Fisher' # 'LBPH' 'Fisher' 'Eigen'
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@ -236,10 +244,6 @@ if __name__=="__main__":
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elif (mode == 'test'):
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face_recognition_inference(rec_type)
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# video process (keep it in case if needed)
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'''
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cameraCapture = cv2.VideoCapture(1)
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@ -274,6 +278,3 @@ if __name__=="__main__":
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cv2.waitKey()
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cv2.destroyAllWindows()
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'''
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@ -1,105 +0,0 @@
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import RPi.GPIO as GPIO
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from time import sleep
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class Motor:
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print("Starting of the program")
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def __init__(self):
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GPIO.setmode(GPIO.BCM)
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GPIO.setwarnings(False)
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#preset GPIO ports for 2 motors
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self.Motor1 = {'EN': 25, 'input1': 24, 'input2': 23}
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self.Motor2 = {'EN': 17, 'input1': 27, 'input2': 22}
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# preset the port for buttons and alarm
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GPIO.setup(5,GPIO.IN) # start motor button, initially True
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GPIO.setup(13,GPIO.IN) # stop motor button, initially True
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GPIO.setup(16,GPIO.IN) # start alarm button, initially True
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GPIO.setup(26,GPIO.OUT) # alarm output
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for x in self.Motor1:
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GPIO.setup(self.Motor1[x], GPIO.OUT)
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GPIO.setup(self.Motor2[x], GPIO.OUT)
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#utilize PWM function, enable motors and frequency is 100Hz
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self.EN1 = GPIO.PWM(self.Motor1['EN'], 100)
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self.EN2 = GPIO.PWM(self.Motor2['EN'], 100)
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self.EN1.start(0)
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self.EN2.start(0)
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#stop signals for motors and alarm
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self.motorStop=False
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self.alarmStop=False
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def start_motor(self):
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while (not self.motorStop) or (not GPIO.input(5)): #break the loop when motor stop signal is detected
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print ("FORWARD MOTION")
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self.motorStop=self.stop_motor()
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self.EN1.ChangeDutyCycle(50)
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self.EN2.ChangeDutyCycle(50)
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GPIO.output(self.Motor1['input1'], GPIO.HIGH)
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GPIO.output(self.Motor1['input2'], GPIO.LOW)
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GPIO.output(self.Motor2['input1'], GPIO.HIGH)
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GPIO.output(self.Motor2['input2'], GPIO.LOW)
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GPIO.cleanup()
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def stop_motor(self):
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userStop=input("Stop the motor? choose between Y/N")
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if (userStop=="Y") or (not GPIO.input(13)):
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print("stopping motor...")
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self.EN1.ChangeDutyCycle(0)
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self.EN2.ChangeDutyCycle(0)
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print("motor stops")
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return True
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elif userStop=="N":
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return False
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else:
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self.stop_motor(self)
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def start_alarm(self):
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while (not self.alarmStop) or (not GPIO.input(16)):
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self.alarmStop=self.stop_alarm()
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GPIO.output(26,True)
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GPIO.cleanup()
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def stop_alarm(self):
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stopRequest=input("Turn off the alarm? choose between Y/N")
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if stopRequest=="Y":
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print("Alarm turning off...")
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GPIO.output(26,False)
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print("Alarm is off")
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return True
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elif stopRequest=="N":
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return False
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else:
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self.stop_alarm()
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if __name__=="__main__":
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#print("Execute function...")
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motor1=Motor()
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#motor1.start_motor()
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motor1.start_alarm()
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98
Hardware/Motor.py
Normal file
98
Hardware/Motor.py
Normal file
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@ -0,0 +1,98 @@
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import RPi.GPIO as GPIO
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from time import sleep
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class Motor:
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def __init__(self):
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print("Starting of the program")
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GPIO.setmode(GPIO.BCM)
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GPIO.setwarnings(False)
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# preset GPIO ports for 2 motors
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self.Motor1 = {'EN': 25, 'input1': 24, 'input2': 23}
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self.Motor2 = {'EN': 17, 'input1': 27, 'input2': 22}
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# preset the port for buttons and alarm
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GPIO.setup(5, GPIO.IN) # start motor button, initially True
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GPIO.setup(13, GPIO.IN) # stop motor button, initially True
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GPIO.setup(16, GPIO.IN) # start alarm button, initially True
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GPIO.setup(26, GPIO.OUT) # alarm output
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for x in self.Motor1:
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GPIO.setup(self.Motor1[x], GPIO.OUT)
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GPIO.setup(self.Motor2[x], GPIO.OUT)
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# utilize PWM function, enable motors and frequency is 100Hz
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self.EN1 = GPIO.PWM(self.Motor1['EN'], 100)
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self.EN2 = GPIO.PWM(self.Motor2['EN'], 100)
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self.EN1.start(0)
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self.EN2.start(0)
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# stop signals for motors and alarm
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self.motorStop = False
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self.alarmStop = False
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def start_motor(self):
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while (not self.motorStop) or (not GPIO.input(5)): # break the loop when motor stop signal is detected
|
||||
|
||||
print("FORWARD MOTION")
|
||||
self.motorStop = self.stop_motor()
|
||||
|
||||
self.EN1.ChangeDutyCycle(50)
|
||||
self.EN2.ChangeDutyCycle(50)
|
||||
|
||||
GPIO.output(self.Motor1['input1'], GPIO.HIGH)
|
||||
GPIO.output(self.Motor1['input2'], GPIO.LOW)
|
||||
|
||||
GPIO.output(self.Motor2['input1'], GPIO.HIGH)
|
||||
GPIO.output(self.Motor2['input2'], GPIO.LOW)
|
||||
|
||||
GPIO.cleanup()
|
||||
|
||||
def stop_motor(self):
|
||||
|
||||
userStop = input("Stop the motor? choose between Y/N")
|
||||
|
||||
if (userStop == "Y") or (not GPIO.input(13)):
|
||||
print("stopping motor...")
|
||||
self.EN1.ChangeDutyCycle(0)
|
||||
self.EN2.ChangeDutyCycle(0)
|
||||
print("motor stops")
|
||||
return True
|
||||
elif userStop == "N":
|
||||
return False
|
||||
else:
|
||||
self.stop_motor(self)
|
||||
|
||||
def start_alarm(self):
|
||||
|
||||
while (not self.alarmStop) or (not GPIO.input(16)):
|
||||
self.alarmStop = self.stop_alarm()
|
||||
GPIO.output(26, True)
|
||||
|
||||
GPIO.cleanup()
|
||||
|
||||
def stop_alarm(self):
|
||||
|
||||
stopRequest = input("Turn off the alarm? choose between Y/N")
|
||||
if stopRequest == "Y":
|
||||
print("Alarm turning off...")
|
||||
GPIO.output(26, False)
|
||||
print("Alarm is off")
|
||||
return True
|
||||
elif stopRequest == "N":
|
||||
return False
|
||||
else:
|
||||
self.stop_alarm()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# print("Execute function...")
|
||||
|
||||
motor1 = Motor()
|
||||
# motor1.start_motor()
|
||||
motor1.start_alarm()
|
||||
BIN
Hardware/__pycache__/Motor.cpython-36.pyc
Normal file
BIN
Hardware/__pycache__/Motor.cpython-36.pyc
Normal file
Binary file not shown.
Binary file not shown.
|
|
@ -8,24 +8,23 @@ def start():
|
|||
count = 0
|
||||
users = DBHelper.db.child("Users").get()
|
||||
try:
|
||||
for user in users.each():
|
||||
for x in users.each():
|
||||
count = +1
|
||||
for x in range(20):
|
||||
for y in range(20):
|
||||
if not os.path.isdir("Facial_images/face_rec/train/User_" + str(count)):
|
||||
os.makedirs("Photos_of_Users/User_" + str(count))
|
||||
DBHelper.download_user_photo("User_" + str(count) + "/" + str(x) + ".jpg")
|
||||
DBHelper.download_user_photo("User_" + str(count) + "/" + str(y) + ".jpg")
|
||||
except:
|
||||
print("No Users are registered.")
|
||||
count = 0
|
||||
try:
|
||||
for user in users.each():
|
||||
for x in users.each():
|
||||
count = +1
|
||||
for x in range(20):
|
||||
for y in range(20):
|
||||
if not os.path.isdir("Photos_of_Thieves/Thief_" + str(count)):
|
||||
os.makedirs("Photos_of_Thieves/Thief_" + str(count))
|
||||
DBHelper.download_thief_photo("Thief_" + str(count) + "/" + str(x) + ".jpg")
|
||||
DBHelper.download_thief_photo("Thief_" + str(count) + "/" + str(y) + ".jpg")
|
||||
except:
|
||||
print("No Thieves for now.")
|
||||
Facial_Recognition_Wrapper.training_recognizer("Fisher")
|
||||
Facial_Recognition_Wrapper.face_recognition_inference("Fisher")
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue