diff --git a/Facial_Recognition_Registration.py b/Facial_Recognition_Registration.py new file mode 100644 index 000000000..0aabf845e --- /dev/null +++ b/Facial_Recognition_Registration.py @@ -0,0 +1,33 @@ +import sys +import os +import math +import cv2 + + +label = "vince" +num_cap = 12 + +path = sys.path[0]+'/Facial_images/face_rec/train/'+label + + +folder = os.path.exists(path) + +if not folder: + os.makedirs(path) + + +cap = cv2.VideoCapture(1) +c=0 +while(c < num_cap): + + ret, frame = cap.read() + + cv2.imshow("capture", frame) + + cv2.imwrite(path+'/'+str(c) + '.jpg', frame) + + c=c+1 + cv2.waitKey(500) + +cap.release() +cv2.destroyAllWindows() \ No newline at end of file diff --git a/Facial_Recognition_Render.py b/Facial_Recognition_Render.py new file mode 100644 index 000000000..895be0fc7 --- /dev/null +++ b/Facial_Recognition_Render.py @@ -0,0 +1,310 @@ +# Copyright 2017 BIG VISION LLC ALL RIGHTS RESERVED +# +# This code is made available to the students of +# the online course titled "Computer Vision for Faces" +# by Satya Mallick for personal non-commercial use. +# +# Sharing this code is strictly prohibited without written +# permission from Big Vision LLC. +# +# For licensing and other inquiries, please email +# spmallick@bigvisionllc.com +# +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_Software.py b/Facial_Recognition_Software.py deleted file mode 100644 index 70235dbf8..000000000 --- a/Facial_Recognition_Software.py +++ /dev/null @@ -1,103 +0,0 @@ -import sys -import os - -import cv2 -import numpy as np - -from glob import glob -from skimage import io - - -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 - - - -if __name__=="__main__": - - 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_Recognition_Wrapper.py b/Facial_Recognition_Wrapper.py new file mode 100644 index 000000000..d789f1bbd --- /dev/null +++ b/Facial_Recognition_Wrapper.py @@ -0,0 +1,279 @@ +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 + +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] = 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() + 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(1) + + while(True): + #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) + + 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) + + cv2.imshow('Face Recognition Demo', original) + + k = cv2.waitKey(10) + + + cam.release() + cv2.destroyAllWindows() + + +if __name__=="__main__": + mode = 'test' + rec_type = 'Fisher' # '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_test_images/output/3_result.jpg b/Facial_images/output/3_result.jpg similarity index 100% rename from Facial_test_images/output/3_result.jpg rename to Facial_images/output/3_result.jpg diff --git a/Facial_test_images/output/3s_result.jpg b/Facial_images/output/3s_result.jpg similarity index 100% rename from Facial_test_images/output/3s_result.jpg rename to Facial_images/output/3s_result.jpg diff --git a/Facial_test_images/output/8_result.jpg b/Facial_images/output/8_result.jpg similarity index 100% rename from Facial_test_images/output/8_result.jpg rename to Facial_images/output/8_result.jpg diff --git a/Facial_test_images/output/8s_result.jpg b/Facial_images/output/8s_result.jpg similarity index 100% rename from Facial_test_images/output/8s_result.jpg rename to Facial_images/output/8s_result.jpg diff --git a/Facial_test_images/output/9_result.jpg b/Facial_images/output/9_result.jpg similarity index 100% rename from Facial_test_images/output/9_result.jpg rename to Facial_images/output/9_result.jpg diff --git a/Facial_test_images/output/9s_result.jpg b/Facial_images/output/9s_result.jpg similarity index 100% rename from Facial_test_images/output/9s_result.jpg rename to Facial_images/output/9s_result.jpg diff --git a/Facial_models/face_rec_model.yml b/Facial_models/face_rec_model.yml new file mode 100644 index 000000000..de471ac20 --- /dev/null +++ b/Facial_models/face_rec_model.yml @@ -0,0 +1,102557 @@ +%YAML:1.0 +--- +opencv_fisherfaces: + threshold: 1.7976931348623157e+308 + num_components: 1 + mean: !!opencv-matrix + rows: 1 + cols: 102400 + dt: d + data: [ 7.3152340375460118e-02, 7.2850681268251866e-02, + 7.2850681268251866e-02, 7.3152340375460118e-02, + 7.7677227270144686e-02, 8.9140274203740641e-02, + 9.6983412137398362e-02, 9.7435900798210739e-02, + 9.7586730351814865e-02, 1.0120663992487468e-01, + 1.0542986771235099e-01, 1.0920060716139583e-01, + 1.1191553937701079e-01, 1.2111614271998405e-01, + 1.2865762125987273e-01, 1.3453997449519542e-01, + 1.4102564580165425e-01, 1.4675716941173261e-01, + 1.4675716941173261e-01, 1.4660633985812849e-01, + 1.4690799896533674e-01, 1.4705882851894087e-01, + 1.4705882851894087e-01, 1.4675716941173261e-01, + 1.4630468075092024e-01, 1.4660633985812849e-01, + 1.4675716941173261e-01, 1.4675716941173261e-01, + 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