From a6d4f6582afa7be7e25e7d0034e3de36b3830847 Mon Sep 17 00:00:00 2001 From: Feier Zhang Date: Mon, 5 Apr 2021 17:22:31 -0400 Subject: [PATCH] Add augmentation --- .vscode/settings.json | 3 + Facial_Image_Augmentation.py | 57 ++++ Facial_Recognition_Registration.py | 15 +- Facial_Recognition_Render(abandoned).py | 298 ------------------ Facial_Recognition_Wrapper(abandoned).py | 290 ----------------- Facial_images/face_rec/train/User_1/0.jpg | Bin 77040 -> 77291 bytes .../face_rec/train/User_1/0_aug0.jpg | Bin 0 -> 71417 bytes .../face_rec/train/User_1/0_aug1.jpg | Bin 0 -> 65618 bytes .../face_rec/train/User_1/0_aug2.jpg | Bin 0 -> 67508 bytes .../face_rec/train/User_1/0_aug3.jpg | Bin 0 -> 66577 bytes .../face_rec/train/User_1/0_aug4.jpg | Bin 0 -> 64017 bytes Facial_images/face_rec/train/User_1/1.jpg | Bin 82861 -> 81440 bytes Facial_images/face_rec/train/User_1/10.jpg | Bin 88776 -> 0 bytes Facial_images/face_rec/train/User_1/11.jpg | Bin 88335 -> 0 bytes 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__pycache__/Facial_Recognition_Registration.cpython-37.pyc create mode 100644 __pycache__/Facial_Recognition_Thief_Registration.cpython-37.pyc create mode 100644 __pycache__/Remove_Face.cpython-37.pyc create mode 100644 __pycache__/Update.cpython-37.pyc create mode 100644 __pycache__/Update.cpython-38.pyc create mode 100644 __pycache__/Upload_Face.cpython-37.pyc create mode 100644 __pycache__/Upload_Thief.cpython-37.pyc create mode 100644 __pycache__/start_engine.cpython-37.pyc create mode 100644 workspace.code-workspace diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 000000000..72d821a5e --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "python.pythonPath": "C:\\Users\\fayer\\anaconda3\\envs\\cv-env\\python.exe" +} \ No newline at end of file diff --git a/Facial_Image_Augmentation.py b/Facial_Image_Augmentation.py new file mode 100644 index 000000000..3133d46de --- /dev/null +++ b/Facial_Image_Augmentation.py @@ -0,0 +1,57 @@ +import sys +import cv2 +import os +import numpy as np +import math +import random + + +def facial_recognition_augmentation(label): + aug = 5 + image_path = sys.path[0] + '/Facial_images/face_rec/train/' + label + '/' + imagelist = os.listdir(image_path) + + for file_name in imagelist: + (nameWithoutExtention, extention) = os.path.splitext(os.path.basename(file_name)) + + if (extention==".jpg"): + + #print(image_path+file_name) + inputImage = cv2.imread(image_path+file_name) + + for i in range(aug): + rotate = random.randint(-8,8) + brightness = random.randint(-30, +30) + clahe = random.uniform(0.0, 1.0) + + choice = random.randint(0, 10) + + (h, w) = inputImage.shape[:2] #10 + center = (w // 2, h // 2) #11 + + M = cv2.getRotationMatrix2D(center, rotate, 1.0) #12 + inputImage = cv2.warpAffine(inputImage, M, (w, h)) #13 + + if (choice%3 == 1): + clahe = cv2.createCLAHE(clipLimit=clahe, tileGridSize=(8,8)) + inputImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2LAB) # convert from BGR to LAB color space + l, a, b = cv2.split(inputImage) # split on 3 different channels + l2 = clahe.apply(l) # apply CLAHE to the L-channel + inputImage = cv2.merge((l2,a,b)) # merge channels + inputImage = cv2.cvtColor(inputImage, cv2.COLOR_LAB2BGR) # convert from LAB to BGR + elif(choice%3 == 2): + fI = inputImage/255.0 + gamma = random.uniform(0.2, 0.4) + #inputImage = np.power(fI, gamma) + else: + cv2.addWeighted( inputImage, 0.5, inputImage, 0.5, brightness, inputImage) + + choice_1 = random.randint(0, 10) + if (choice_1%2 == 1): + gauss = random.randint(1, 2)*2-1 + + inputImage = cv2.GaussianBlur(inputImage, (gauss,gauss), 0) + + cv2.imwrite(image_path+nameWithoutExtention+"_aug"+str(i)+".jpg", inputImage) + + diff --git a/Facial_Recognition_Registration.py b/Facial_Recognition_Registration.py index d2872016d..805d0dae6 100644 --- a/Facial_Recognition_Registration.py +++ b/Facial_Recognition_Registration.py @@ -3,10 +3,11 @@ import os import math import cv2 import Facial_Recognition_Enrollment +import Facial_Image_Augmentation def register_your_face(label): - num_cap = 50 + num_cap = 10 path = sys.path[0] + '/Facial_images/face_rec/train/' + label @@ -30,14 +31,4 @@ def register_your_face(label): cap.release() cv2.destroyAllWindows() - -if __name__ == "__main__": - print("Face registration start...") - print() - label = input('Pleas enter your Name/Label: ') - register_your_face(label) - print("Data saved! Starting enrollment...") - print() - Facial_Recognition_Enrollment.enroll_face_dataset() # Need discuss and modify after intergrate with database. - print("Face registration completed!") - print() + Facial_Image_Augmentation.facial_recognition_augmentation(label) diff --git a/Facial_Recognition_Render(abandoned).py b/Facial_Recognition_Render(abandoned).py deleted file mode 100644 index 004c9d620..000000000 --- a/Facial_Recognition_Render(abandoned).py +++ /dev/null @@ -1,298 +0,0 @@ -import cv2 -import dlib -import numpy as np -import math - -# Returns 8 points on the boundary of a rectangle -def getEightBoundaryPoints(h, w): - boundaryPts = [] - boundaryPts.append((0,0)) - boundaryPts.append((w/2, 0)) - boundaryPts.append((w-1,0)) - boundaryPts.append((w-1, h/2)) - boundaryPts.append((w-1, h-1)) - boundaryPts.append((w/2, h-1)) - boundaryPts.append((0, h-1)) - boundaryPts.append((0, h/2)) - return np.array(boundaryPts, dtype=np.float) - - -# Constrains points to be inside boundary -def constrainPoint(p, w, h): - p = (min(max(p[0], 0), w - 1), min(max(p[1], 0), h - 1)) - return p - -# convert Dlib shape detector object to list of tuples -def dlibLandmarksToPoints(shape): - points = [] - for p in shape.parts(): - pt = (p.x, p.y) - points.append(pt) - return points - -# Compute similarity transform given two sets of two points. -# OpenCV requires 3 pairs of corresponding points. -# We are faking the third one. -def similarityTransform(inPoints, outPoints): - s60 = math.sin(60*math.pi/180) - c60 = math.cos(60*math.pi/180) - - inPts = np.copy(inPoints).tolist() - outPts = np.copy(outPoints).tolist() - - # The third point is calculated so that the three points make an equilateral triangle - xin = c60*(inPts[0][0] - inPts[1][0]) - s60*(inPts[0][1] - inPts[1][1]) + inPts[1][0] - yin = s60*(inPts[0][0] - inPts[1][0]) + c60*(inPts[0][1] - inPts[1][1]) + inPts[1][1] - - inPts.append([np.int(xin), np.int(yin)]) - - xout = c60*(outPts[0][0] - outPts[1][0]) - s60*(outPts[0][1] - outPts[1][1]) + outPts[1][0] - yout = s60*(outPts[0][0] - outPts[1][0]) + c60*(outPts[0][1] - outPts[1][1]) + outPts[1][1] - - outPts.append([np.int(xout), np.int(yout)]) - # Now we can use estimateRigidTransform for calculating the similarity transform. - tform = cv2.estimateAffinePartial2D(np.array([inPts]), np.array([outPts])) - return tform[0] - -# Normalizes a facial image to a standard size given by outSize. -# Normalization is done based on Dlib's landmark points passed as pointsIn -# After normalization, left corner of the left eye is at (0.3 * w, h/3 ) -# and right corner of the right eye is at ( 0.7 * w, h / 3) where w and h -# are the width and height of outSize. -def normalizeImagesAndLandmarks(outSize, imIn, pointsIn): - h, w = outSize - - # Corners of the eye in input image - if len(pointsIn) == 68: - eyecornerSrc = [pointsIn[36], pointsIn[45]] - elif len(pointsIn) == 5: - eyecornerSrc = [pointsIn[2], pointsIn[0]] - - # Corners of the eye in normalized image - eyecornerDst = [(np.int(0.3 * w), np.int(h/3)), - (np.int(0.7 * w), np.int(h/3))] - - # Calculate similarity transform - tform = similarityTransform(eyecornerSrc, eyecornerDst) - imOut = np.zeros(imIn.shape, dtype=imIn.dtype) - - # Apply similarity transform to input image - imOut = cv2.warpAffine(imIn, tform, (w, h)) - - # reshape pointsIn from numLandmarks x 2 to numLandmarks x 1 x 2 - points2 = np.reshape(pointsIn, (pointsIn.shape[0], 1, pointsIn.shape[1])) - - # Apply similarity transform to landmarks - pointsOut = cv2.transform(points2, tform) - - # reshape pointsOut to numLandmarks x 2 - pointsOut = np.reshape(pointsOut, (pointsIn.shape[0], pointsIn.shape[1])) - - return imOut, pointsOut - -def alignFace(imIn, faceRect, landmarkDetector, outSize): - # Corners of the eye in input image - (w, h) = outSize - landmarks = landmarkDetector(cv2.cvtColor(imIn, cv2.COLOR_BGR2RGB), faceRect) - pointsIn = np.array(dlibLandmarksToPoints(landmarks)) - - eyecornerSrc = [pointsIn[2], pointsIn[0]] - - # Corners of the eye in normalized image - eyecornerDst = [(np.int(0.2 * w), np.int(h/3)), - (np.int(0.8 * w), np.int(h/3))] - - # Calculate similarity transform - tform = similarityTransform(eyecornerSrc, eyecornerDst) - - imIn = np.float32(imIn)/255.0 - imOut = np.zeros(imIn.shape, dtype=imIn.dtype) - - # Apply similarity transform to input image - imOut = cv2.warpAffine(imIn, tform, outSize) - - imOut = np.uint8(imOut*255) - - return imOut - - -# find the point closest to an array of points -# pointsArray is a Nx2 and point is 1x2 ndarray -def findIndex(pointsArray, point): - dist = np.linalg.norm(pointsArray-point, axis=1) - minIndex = np.argmin(dist) - return minIndex - - -# Check if a point is inside a rectangle -def rectContains(rect, point): - if point[0] < rect[0]: - return False - elif point[1] < rect[1]: - return False - elif point[0] > rect[2]: - return False - elif point[1] > rect[3]: - return False - return True - - -# Calculate Delaunay triangles for set of points -# Returns the vector of indices of 3 points for each triangle -def calculateDelaunayTriangles(rect, points): - - # Create an instance of Subdiv2D - subdiv = cv2.Subdiv2D(rect) - - # Insert points into subdiv - for p in points: - subdiv.insert((p[0], p[1])) - - # Get Delaunay triangulation - triangleList = subdiv.getTriangleList() - - # Find the indices of triangles in the points array - delaunayTri = [] - - for t in triangleList: - # The triangle returned by getTriangleList is - # a list of 6 coordinates of the 3 points in - # x1, y1, x2, y2, x3, y3 format. - # Store triangle as a list of three points - pt = [] - pt.append((t[0], t[1])) - pt.append((t[2], t[3])) - pt.append((t[4], t[5])) - - pt1 = (t[0], t[1]) - pt2 = (t[2], t[3]) - pt3 = (t[4], t[5]) - - if rectContains(rect, pt1) and rectContains(rect, pt2) and rectContains(rect, pt3): - # Variable to store a triangle as indices from list of points - ind = [] - # Find the index of each vertex in the points list - for j in range(0, 3): - for k in range(0, len(points)): - if(abs(pt[j][0] - points[k][0]) < 1.0 and abs(pt[j][1] - points[k][1]) < 1.0): - ind.append(k) - # Store triangulation as a list of indices - if len(ind) == 3: - delaunayTri.append((ind[0], ind[1], ind[2])) - - return delaunayTri - -# Apply affine transform calculated using srcTri and dstTri to src and -# output an image of size. -def applyAffineTransform(src, srcTri, dstTri, size): - - # Given a pair of triangles, find the affine transform. - warpMat = cv2.getAffineTransform(np.float32(srcTri), np.float32(dstTri)) - - # Apply the Affine Transform just found to the src image - dst = cv2.warpAffine(src, warpMat, (size[0], size[1]), None, - flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101) - - return dst - -# Warps and alpha blends triangular regions from img1 and img2 to img -def warpTriangle(img1, img2, t1, t2): - # Find bounding rectangle for each triangle - r1 = cv2.boundingRect(np.float32([t1])) - r2 = cv2.boundingRect(np.float32([t2])) - - # Offset points by left top corner of the respective rectangles - t1Rect = [] - t2Rect = [] - t2RectInt = [] - - for i in range(0, 3): - t1Rect.append(((t1[i][0] - r1[0]), (t1[i][1] - r1[1]))) - t2Rect.append(((t2[i][0] - r2[0]), (t2[i][1] - r2[1]))) - t2RectInt.append(((t2[i][0] - r2[0]), (t2[i][1] - r2[1]))) - - # Get mask by filling triangle - mask = np.zeros((r2[3], r2[2], 3), dtype=np.float32) - cv2.fillConvexPoly(mask, np.int32(t2RectInt), (1.0, 1.0, 1.0), 16, 0) - - # Apply warpImage to small rectangular patches - img1Rect = img1[r1[1]:r1[1] + r1[3], r1[0]:r1[0] + r1[2]] - - size = (r2[2], r2[3]) - - img2Rect = applyAffineTransform(img1Rect, t1Rect, t2Rect, size) - - img2Rect = img2Rect * mask - - # Copy triangular region of the rectangular patch to the output image - img2[r2[1]:r2[1]+r2[3], r2[0]:r2[0]+r2[2]] = img2[r2[1]:r2[1]+r2[3], r2[0]:r2[0]+r2[2]] * ((1.0, 1.0, 1.0) - mask) - img2[r2[1]:r2[1]+r2[3], r2[0]:r2[0]+r2[2]] = img2[r2[1]:r2[1]+r2[3], r2[0]:r2[0]+r2[2]] + img2Rect - -# detect facial landmarks in image -def getLandmarks(faceDetector, landmarkDetector, im, FACE_DOWNSAMPLE_RATIO = 1): - points = [] - imSmall = cv2.resize(im,None, - fx=1.0/FACE_DOWNSAMPLE_RATIO, - fy=1.0/FACE_DOWNSAMPLE_RATIO, - interpolation = cv2.INTER_LINEAR) - - faceRects = faceDetector(imSmall, 0) - - if len(faceRects) > 0: - maxArea = 0 - maxRect = None - # TODO: test on images with multiple faces - for face in faceRects: - if face.area() > maxArea: - maxArea = face.area() - maxRect = [face.left(), - face.top(), - face.right(), - face.bottom() - ] - - rect = dlib.rectangle(*maxRect) - scaledRect = dlib.rectangle(int(rect.left()*FACE_DOWNSAMPLE_RATIO), - int(rect.top()*FACE_DOWNSAMPLE_RATIO), - int(rect.right()*FACE_DOWNSAMPLE_RATIO), - int(rect.bottom()*FACE_DOWNSAMPLE_RATIO)) - - landmarks = landmarkDetector(im, scaledRect) - points = dlibLandmarksToPoints(landmarks) - return points - -# Warps an image in a piecewise affine manner. -# The warp is defined by the movement of landmark points specified by pointsIn -# to a new location specified by pointsOut. The triangulation beween points is specified -# by their indices in delaunayTri. -def warpImage(imIn, pointsIn, pointsOut, delaunayTri): - h, w, ch = imIn.shape - # Output image - imOut = np.zeros(imIn.shape, dtype=imIn.dtype) - - # Warp each input triangle to output triangle. - # The triangulation is specified by delaunayTri - for j in range(0, len(delaunayTri)): - # Input and output points corresponding to jth triangle - tin = [] - tout = [] - - for k in range(0, 3): - # Extract a vertex of input triangle - pIn = pointsIn[delaunayTri[j][k]] - # Make sure the vertex is inside the image. - pIn = constrainPoint(pIn, w, h) - - # Extract a vertex of the output triangle - pOut = pointsOut[delaunayTri[j][k]] - # Make sure the vertex is inside the image. - pOut = constrainPoint(pOut, w, h) - - # Push the input vertex into input triangle - tin.append(pIn) - # Push the output vertex into output triangle - tout.append(pOut) - - # Warp pixels inside input triangle to output triangle. - warpTriangle(imIn, imOut, tin, tout) - return imOut diff --git a/Facial_Recognition_Wrapper(abandoned).py b/Facial_Recognition_Wrapper(abandoned).py deleted file mode 100644 index 2a0bce283..000000000 --- a/Facial_Recognition_Wrapper(abandoned).py +++ /dev/null @@ -1,290 +0,0 @@ -import sys -import os -import math -import cv2 -import dlib -import numpy as np -import Facial_Recognition_Render as fr -import _pickle as cPickle -import glob -import DBHelper - -faceWidth = 320 -faceHeight = 320 -SKIP_FRAMES = 1 - - -def alignFace(imFace, landmarks): - l_x = landmarks[39][0] - l_y = landmarks[39][1] - r_x = landmarks[42][0] - r_y = landmarks[42][1] - dy = r_y - l_y - dx = r_x - l_x - # Convert from radians to degrees - angle = math.atan2(dy, dx) * 180.0 / math.pi - - eyesCenter = ((l_x + r_x) * 0.5, (l_y + r_y) * 0.5) - rotMatrix = cv2.getRotationMatrix2D(eyesCenter, angle, 1) - alignedImFace = np.zeros(imFace.shape, dtype=np.uint8) - alignedImFace = cv2.warpAffine(imFace, rotMatrix, (imFace.shape[1], imFace.shape[0])) - return alignedImFace - - -def face_detector_haarcascade(image): - grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - - resize_fx = 1 - resize_fy = 1 - grey = cv2.resize(grey, dsize=None, fx=resize_fx, fy=resize_fy, interpolation=cv2.INTER_AREA) - - pwd = sys.path[0] - classfier = cv2.CascadeClassifier(pwd + "/Facial_models/haarcascade_frontalface_alt2.xml") - - faceRects = classfier.detectMultiScale(grey, scaleFactor=1.2, minNeighbors=1, minSize=(16, 16)) - - if len(faceRects) > 0: - for faceRect in faceRects: - x, y, w, h = faceRect - x = int(x / resize_fx) - y = int(y / resize_fy) - w = int(w / resize_fx) - h = int(h / resize_fy) - cv2.rectangle(image, (x - 10, y - 10), (x + w + 10, y + h + 10), (0, 255, 0), 5) - - return image - - -def face_detector_ssd(image): - pwd = sys.path[0] - net = cv2.dnn.readNetFromCaffe(pwd + "/Facial_models/deploy.prototxt", - pwd + "/Facial_models/res10_300x300_ssd_iter_140000_fp16.caffemodel") - - resize = (300, 300) - confidence_thres = 0.65 - - blob = cv2.dnn.blobFromImage(cv2.resize(image, dsize=resize), 1.0, resize, (104.0, 177.0, 123.0)) - # blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0)) - - net.setInput(blob) - - detections = net.forward() - - h, w, c = image.shape - - for i in range(0, detections.shape[2]): - confidence = detections[0, 0, i, 2] - if confidence > confidence_thres: - box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) - (startX, startY, endX, endY) = box.astype("int") - text = "{:.2f}%".format(confidence * 100) - y = startY - 10 if startY - 10 > 10 else startY + 10 - cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 5) - cv2.putText(image, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 1.00, (0, 255, 0), 3) - - return image - - -def training_data_loader(): - imagesFolder = sys.path[0] + "/Facial_images/face_rec/train/" - subfolders = [] - - for x in os.listdir(imagesFolder): - xpath = os.path.join(imagesFolder, x) - if os.path.isdir(xpath): - subfolders.append(xpath) - - imagePaths = [] - labels = [] - labelsMap = {} - labelsMap[-1] = "unknown" - - for i, subfolder in enumerate(subfolders): - labelsMap[i] = DBHelper.get_firstname(os.path.basename(subfolder)) + "_" + DBHelper.get_lastname( - os.path.basename(subfolder)) - for x in os.listdir(subfolder): - xpath = os.path.join(subfolder, x) - if x.endswith('jpg') or x.endswith('pgm'): - imagePaths.append(xpath) - labels.append(i) - - imagesFaceTrain = [] - labelsFaceTrain = [] - - faceDetector = dlib.get_frontal_face_detector() - landmarkDetector = dlib.shape_predictor(sys.path[0] + "/Facial_models/shape_predictor_68_face_landmarks.dat") - - for j, imagePath in enumerate(imagePaths): - im = cv2.imread(imagePath, 0) - imHeight, imWidth = im.shape[:2] - - landmarks = fr.getLandmarks(faceDetector, landmarkDetector, im) - - landmarks = np.array(landmarks) - - if len(landmarks) == 68: - x1Limit = landmarks[0][0] - (landmarks[36][0] - landmarks[0][0]) - x2Limit = landmarks[16][0] + (landmarks[16][0] - landmarks[45][0]) - y1Limit = landmarks[27][1] - 3 * (landmarks[30][1] - landmarks[27][1]) - y2Limit = landmarks[8][1] + (landmarks[30][1] - landmarks[29][1]) - - x1 = max(x1Limit, 0) - x2 = min(x2Limit, imWidth) - y1 = max(y1Limit, 0) - y2 = min(y2Limit, imHeight) - imFace = im[y1:y2, x1:x2] - - alignedFace = alignFace(imFace, landmarks) - alignedFace = cv2.resize(alignedFace, (faceHeight, faceWidth)) - - imagesFaceTrain.append(np.float32(alignedFace) / 255.0) - labelsFaceTrain.append(labels[j]) - - return imagesFaceTrain, labelsFaceTrain, labelsMap - - -def training_recognizer(rec_type): - imagesFaceTrain, labelsFaceTrain, labelsMap = training_data_loader() - - if (rec_type == 'LBPH'): - faceRecognizer = cv2.face.LBPHFaceRecognizer_create() - print("Training using LBPH Faces") - elif (rec_type == 'Eigen'): - faceRecognizer = cv2.face.EigenFaceRecognizer_create() - print("Training using Eigen Faces") - elif (rec_type == 'Fisher'): - faceRecognizer = cv2.face.FisherFaceRecognizer_create() - print("Training using Fisher Faces") - - faceRecognizer.train(imagesFaceTrain, np.array(labelsFaceTrain)) - faceRecognizer.write(sys.path[0] + '/Facial_models/face_rec_model.yml') - - with open(sys.path[0] + '/Facial_models/labels_map.pkl', 'wb') as f: - cPickle.dump(labelsMap, f) - - -def face_recognition_inference(rec_type): - # testFiles = glob.glob(sys.path[0]+'/Facial_test_images/face_rec/test/*.jpg') - # testFiles.sort() - i = 0 - correct = 0 - error = 0 - faceDetector = dlib.get_frontal_face_detector() - print(sys.path[0]) - landmarkDetector = dlib.shape_predictor(sys.path[0] + '/Facial_models/shape_predictor_68_face_landmarks.dat') - - if rec_type == 'LBPH': - faceRecognizer = cv2.face.LBPHFaceRecognizer_create() - print("Test using LBPH Faces") - elif rec_type == 'Eigen': - faceRecognizer = cv2.face.EigenFaceRecognizer_create() - print("Test using Eigen Faces") - elif rec_type == 'Fisher': - faceRecognizer = cv2.face.FisherFaceRecognizer_create() - print("Test using Fisher Faces") - - faceRecognizer.read(sys.path[0] + '/Facial_models/face_rec_model.yml') - labelsMap = np.load(sys.path[0] + '/Facial_models/labels_map.pkl', allow_pickle=True) - - cam = cv2.VideoCapture(0) - - while DBHelper.get_power() == "on": - # imagePath = testFiles[i] - success, original = cam.read() - im = cv2.resize(original, (640, 480)) - i += 1 - - im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) - imHeight, imWidth = im.shape[:2] - - landmarks = fr.getLandmarks(faceDetector, landmarkDetector, im) - landmarks = np.array(landmarks) - - cond = False - cond2 = False - - if len(landmarks) == 68: - x1Limit = landmarks[0][0] - (landmarks[36][0] - landmarks[0][0]) - x2Limit = landmarks[16][0] + (landmarks[16][0] - landmarks[45][0]) - y1Limit = landmarks[27][1] - 3 * (landmarks[30][1] - landmarks[27][1]) - y2Limit = landmarks[8][1] + (landmarks[30][1] - landmarks[29][1]) - - x1 = max(x1Limit, 0) - x2 = min(x2Limit, imWidth) - y1 = max(y1Limit, 0) - y2 = min(y2Limit, imHeight) - imFace = im[y1:y2, x1:x2] - - alignedFace = alignFace(imFace, landmarks) - alignedFace = cv2.resize(alignedFace, (faceHeight, faceWidth)) - imFaceFloat = np.float32(alignedFace) / 255.0 - - predictedLabel = -1 - predictedLabel, score = faceRecognizer.predict(imFaceFloat) - center = (int((x1 + x2) / 2), int((y1 + y2) / 2)) - radius = int((y2 - y1) / 2.0) - text = '{} {}%'.format(labelsMap[predictedLabel], round(score, 5)) - cv2.rectangle(original, (x1, y1), (x2, y2), (0, 255, 0), 5) - cv2.putText(original, text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 3) - cond = True - - if cond: - DBHelper.set_motor("on") - DBHelper.set_alarm("off") - elif not cond: - DBHelper.set_motor("off") - DBHelper.set_alarm("on") - - cv2.imshow('Face Recognition Demo', original) - - k = cv2.waitKey(10) - - DBHelper.set_alarm("off") - DBHelper.set_motor("off") - cam.release() - cv2.destroyAllWindows() - - -if __name__ == "__main__": - mode = 'test' - rec_type = 'LBPH' # 'LBPH' 'Fisher' 'Eigen' - - if mode == 'train': - training_recognizer(rec_type) - elif mode == 'test': - face_recognition_inference(rec_type) - - # video process (keep it in case if needed) - ''' - cameraCapture = cv2.VideoCapture(1) - success, frame = cameraCapture.read() - - while success and cv2.waitKey(1) == -1: - success, frame = cameraCapture.read() - face_detector_ssd(frame) - cv2.imshow("video", frame) - - cameraCapture.release() - cv2.destroyAllWindows() - ''' - - # image process (keep it in case if needed) - ''' - image_name = "8.jpg" - split_name = image_name.split(".") - - image_read_path = sys.path[0]+"/Facial_test_images/"+image_name - image_save_path = sys.path[0]+"/Facial_test_images/output/"+split_name[0]+"_result."+split_name[1] - - image = cv2.imread(image_read_path) - - image = face_detector_ssd(image) - image = face_detector_haarcascade(image) - - print(image_save_path) - - cv2.imwrite(image_save_path, image) - cv2.imshow("result", image) - cv2.waitKey() - cv2.destroyAllWindows() - ''' diff --git a/Facial_images/face_rec/train/User_1/0.jpg b/Facial_images/face_rec/train/User_1/0.jpg index 5d83ee1399bbf44dbe825285b8ce014b5e4387ca..48231d21089e46bb5ed4cada1cf83127b706f18d 100644 GIT binary patch literal 77291 zcmbTdWl&sC)IK-_8Ju7Vt^))K!5s!ku;A_xB)GeKut5fg5ZpDmTW}|MkT5VvaEHPF z{NA@}_sf3R-M-abee3q^dY+ckefpe!UV4TBUdv0%Ndu6O005+y2k^WCkOI6y`Y-&K zzWOhqApe)5qM#t7prN9n{Z9m9prZjXfM{swSm+p-|Am(vHWnuKe=q-4^1r{nLPbVK z#RQ@O|EI|Rr{uW@fRBy@dliI?LiPe6eg1Ajx zLNGAjyd@zeV_;-@$IQaR%f~MuC?xgqleCPioV>b*rk1vjuAZ5>#aBx!Ya3TLcMnf5 zZ=cYx@NW^3QPGJ>$tkI6=^2^%1%*Y$C8cHMb@dI6P0cN>ZNK{Z2L^|RM@DC6=jIm{ zm;NkoY{Iv;cXt2n?Vp`rTwYz@Aa3veHi}y{1;xY{_6waf4q=hdA>ws zd=yk#PBa1uRp1wALOQNsbRtP;UTrT1J-6B^v8l^%%r_vO4TiJ-sQnMK|98Yf{(mw1 zzliR((pH$AaSc2`UKWu zF48~&O4e$Zkna#m=+2;8vInKjWE#sau2u&;SHrl}-?x#)VAt?!>1AHR63~&rmfKD{ zHD37}O-m8nLeH|W-0zA8o^#hZe=z?^-OG3pTmoKs6^|zd9^^g)uCx!SD##u)CN(Tb 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lastname, email, phone) + print("Data saved! Starting enrollment...") Facial_Recognition_Enrollment.enroll_face_dataset() print("Face registration completed!") print("Success.") + except: print("Face registration start...") + Facial_Recognition_Registration.register_your_face("User_1") + Parallel(n_jobs=multiprocessing.cpu_count())( - delayed(upload_parallel_user_photo)(i) for i in range(50)) + delayed(upload_parallel_user_photo)(i) for i in range(10)) DBHelper.upload_data("User_1", firstname, lastname, email, phone) + print("Data saved! Starting enrollment...") 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