基于Aidlux平台的人脸关键点检测以及换脸算法

简介: Face Mesh:468个人脸部关键点精确定位并支持多个人同时检测,支持关键点3D坐标。Face Swap:超好玩的换脸算法,把明星的脸融合到你的身体上,让你也有星范。

第一步:安装APP
手机应用市场下载AidLux
手机和电脑连接同一个Wifi
第二步:配置APP
赋予AidLux各种系统权限,包括:媒体和文件、相机、麦克风、后台弹窗
手机-设置-关于手机-点击操作系统版本号多次,打开开发者模式
重启AidLux,按照提示完成配置
第三步:获取手机IP地址
在手机上点击Cloud_ip蓝色云朵图标,获取IP地址。
第四步:电脑浏览器远程登录Aidlux桌面
在电脑浏览器中输入手机IP地址,远程登录Aidlux桌面
默认密码:aidlux
第五步:玩转Aidlux中的例子中心
运行Aidlux中examples的自带Demo:人脸、人体、手关键点检测、头发语义分割、人像语义分割、人脸检测、图像风格迁移、句子分类等,
以下展示的是人脸关键点检测以及换脸算法。
Face Mesh
468个人脸部关键点精确定位并支持多个人同时检测,支持关键点3D坐标。
目录位置:cd /home/examples-gpu/face
运行代码:python testmesh.py
image.png
```import cv2
import math

import tensorflow as tf

import sys
import numpy as np
from blazeface import
from cvs import

import aidlite_gpu
aidlite=aidlite_gpu.aidlite(1)

def preprocess_image_for_tflite32(image, model_image_size=192):
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (model_image_size, model_image_size))
image = np.expand_dims(image, axis=0)
image = (2.0 / 255.0) * image - 1.0
image = image.astype('float32')

return image

def preprocess_img_pad(img,image_size=128):

# fit the image into a 128x128 square
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
shape = np.r_[img.shape]
pad_all = (shape.max() - shape[:2]).astype('uint32')
pad = pad_all // 2
# print ('pad_all',pad_all)
img_pad_ori = np.pad(
    img,
    ((pad[0],pad_all[0]-pad[0]), (pad[1],pad_all[1]-pad[1]), (0,0)),
    mode='constant')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_pad = np.pad(
    img,
    ((pad[0],pad_all[0]-pad[0]), (pad[1],pad_all[1]-pad[1]), (0,0)),
    mode='constant')
img_small = cv2.resize(img_pad, (image_size, image_size))
img_small = np.expand_dims(img_small, axis=0)
# img_small = np.ascontiguousarray(img_small)
img_small = (2.0 / 255.0) * img_small - 1.0
img_small = img_small.astype('float32')
# img_norm = self._im_normalize(img_small)

return img_pad_ori, img_small, pad

def plot_detections(img, detections, with_keypoints=True):
output_img = img
print(img.shape)
x_min=0
x_max=0
y_min=0
y_max=0
print("Found %d faces" % len(detections))
for i in range(len(detections)):
ymin = detections[i][ 0] img.shape[0]
xmin = detections[i][ 1]
img.shape[1]
ymax = detections[i][ 2] img.shape[0]
xmax = detections[i][ 3]
img.shape[1]
w=int(xmax-xmin)
h=int(ymax-ymin)
h=max(w,h)
h=h*1.5

        x=(xmin+xmax)/2.
        y=(ymin+ymax)/2.

        xmin=x-h/2.
        xmax=x+h/2.
        # ymin=y-h/2.
        # ymax=y+h/2.
        ymin=y-h/2.-0.08*h
        ymax=y+h/2.-0.08*h

        # ymin-=0.08*h

        # xmin-=0.25*w
        # xmax=xmin+1.5*w;
        # ymax=ymin+1.0*h;

        # x=(xmin+xmax)/2.
        # y=(ymin+ymax)/2

        # xmin=x-h/2.
        # xmax=x+h/2.
        # ymin=y-h/2.
        # ymax=y+h/2.

        # if w<h:
        #     xmin=xmin-(h+0.08*h-w)/2
        #     xmax=xmax+(h+0.08*h-w)/2
        #     ymin-=0.08*h
        #     # ymax-=0.08*h
        # else :
        #     ymin=ymin-(w-h)/2
        #     ymax=ymax+(w-h)/2

        # h=int(ymax-ymin)
        # ymin-=0.08*h            
        # landmarks_xywh[:, 2:4] += (landmarks_xywh[:, 2:4] * pad_ratio).astype(np.int32) #adding some padding around detection for landmark detection step.
        # landmarks_xywh[:, 1:2] -= (landmarks_xywh[:, 3:4]*0.08).astype(np.int32)

        x_min=int(xmin)
        y_min=int(ymin)
        x_max=int(xmax)
        y_max=int(ymax)            
        p1 = (int(xmin),int(ymin))
        p2 = (int(xmax),int(ymax))
        # print(p1,p2)
        cv2.rectangle(output_img, p1, p2, (0,255,255),2,1)

        # cv2.putText(output_img, "Face found! ", (p1[0]+10, p2[1]-10),cv2.FONT_ITALIC, 1, (0, 255, 129), 2)

        # if with_keypoints:
        #     for k in range(6):
        #         kp_x = int(detections[i, 4 + k*2    ] * img.shape[1])
        #         kp_y = int(detections[i, 4 + k*2 + 1] * img.shape[0])
        #         cv2.circle(output_img,(kp_x,kp_y),4,(0,255,255),4)

    return x_min,y_min,x_max,y_max

def draw_mesh(image, mesh, mark_size=2, line_width=1):
"""Draw the mesh on an image"""

# The mesh are normalized which means we need to convert it back to fit
# the image size.
image_size = image.shape[0]
mesh = mesh * image_size
for point in mesh:
    cv2.circle(image, (point[0], point[1]),
               mark_size, (0, 255, 128), -1)

# Draw the contours.
# Eyes
left_eye_contour = np.array([mesh[33][0:2],
                             mesh[7][0:2],
                             mesh[163][0:2],
                             mesh[144][0:2],
                             mesh[145][0:2],
                             mesh[153][0:2],
                             mesh[154][0:2],
                             mesh[155][0:2],
                             mesh[133][0:2],
                             mesh[173][0:2],
                             mesh[157][0:2],
                             mesh[158][0:2],
                             mesh[159][0:2],
                             mesh[160][0:2],
                             mesh[161][0:2],
                             mesh[246][0:2], ]).astype(np.int32)
right_eye_contour = np.array([mesh[263][0:2],
                              mesh[249][0:2],
                              mesh[390][0:2],
                              mesh[373][0:2],
                              mesh[374][0:2],
                              mesh[380][0:2],
                              mesh[381][0:2],
                              mesh[382][0:2],
                              mesh[362][0:2],
                              mesh[398][0:2],
                              mesh[384][0:2],
                              mesh[385][0:2],
                              mesh[386][0:2],
                              mesh[387][0:2],
                              mesh[388][0:2],
                              mesh[466][0:2]]).astype(np.int32)
# Lips
cv2.polylines(image, [left_eye_contour, right_eye_contour], False,
              (255, 255, 255), line_width, cv2.LINE_AA)

def draw_landmarks(image, mesh):
image_size = image.shape[0]
mesh = mesh * image_size
landmark_point = []
for point in mesh:
landmark_point.append((int(point[0]),int(point[1])))

    # landmark_point.append((point[0],point[1]))
    cv2.circle(image, (int(point[0]),int( point[1])), 2, (255, 255, 0), -1)

if len(landmark_point) > 0:
    # 参考:https://github.com/tensorflow/tfjs-models/blob/master/facemesh/mesh_map.jpg

    # 左眉毛(55:内側、46:外側)
    cv2.line(image, landmark_point[55], landmark_point[65], (0, 0, 255), 2,-3)
    cv2.line(image, landmark_point[65], landmark_point[52], (0, 0, 255), 2,-3)
    cv2.line(image, landmark_point[52], landmark_point[53], (0, 0, 255), 2,-3)
    cv2.line(image, landmark_point[53], landmark_point[46],(0, 0, 255), 2,-3)

    # 右眉毛(285:内側、276:外側)
    cv2.line(image, landmark_point[285], landmark_point[295], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[295], landmark_point[282], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[282], landmark_point[283], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[283], landmark_point[276], (0, 0, 255),
            2)

    # 左目 (133:目頭、246:目尻)
    cv2.line(image, landmark_point[133], landmark_point[173], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[173], landmark_point[157], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[157], landmark_point[158], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[158], landmark_point[159], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[159], landmark_point[160], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[160], landmark_point[161], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[161], landmark_point[246], (0, 0, 255),
            2)

    cv2.line(image, landmark_point[246], landmark_point[163], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[163], landmark_point[144], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[144], landmark_point[145], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[145], landmark_point[153], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[153], landmark_point[154], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[154], landmark_point[155], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[155], landmark_point[133], (0, 0, 255),
            2)

    # 右目 (362:目頭、466:目尻)
    cv2.line(image, landmark_point[362], landmark_point[398], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[398], landmark_point[384], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[384], landmark_point[385], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[385], landmark_point[386], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[386], landmark_point[387], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[387], landmark_point[388], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[388], landmark_point[466], (0, 0, 255),
            2)

    cv2.line(image, landmark_point[466], landmark_point[390], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[390], landmark_point[373], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[373], landmark_point[374], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[374], landmark_point[380], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[380], landmark_point[381], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[381], landmark_point[382], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[382], landmark_point[362], (0, 0, 255),
            2)

    # 口 (308:右端、78:左端)
    cv2.line(image, landmark_point[308], landmark_point[415], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[415], landmark_point[310], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[310], landmark_point[311], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[311], landmark_point[312], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[312], landmark_point[13], (0, 0, 255), 2)
    cv2.line(image, landmark_point[13], landmark_point[82], (0, 0, 255), 2)
    cv2.line(image, landmark_point[82], landmark_point[81], (0, 0, 255), 2)
    cv2.line(image, landmark_point[81], landmark_point[80], (0, 0, 255), 2)
    cv2.line(image, landmark_point[80], landmark_point[191], (0, 0, 255), 2)
    cv2.line(image, landmark_point[191], landmark_point[78], (0, 0, 255), 2)

    cv2.line(image, landmark_point[78], landmark_point[95], (0, 0, 255), 2)
    cv2.line(image, landmark_point[95], landmark_point[88], (0, 0, 255), 2)
    cv2.line(image, landmark_point[88], landmark_point[178], (0, 0, 255), 2)
    cv2.line(image, landmark_point[178], landmark_point[87], (0, 0, 255), 2)
    cv2.line(image, landmark_point[87], landmark_point[14], (0, 0, 255), 2)
    cv2.line(image, landmark_point[14], landmark_point[317], (0, 0, 255), 2)
    cv2.line(image, landmark_point[317], landmark_point[402], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[402], landmark_point[318], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[318], landmark_point[324], (0, 0, 255),
            2)
    cv2.line(image, landmark_point[324], landmark_point[308], (0, 0, 255),
            2)

return image        

input_shape=[128,128]
inShape =[1 128 128 34,]
outShape= [1 896164,189614]
model_path="models/face_detection_front.tflite"
print('==========')
print('gpu:',aidlite.FAST_ANNModel(model_path,inShape,outShape,4,0))
print('=======fast end')
model_path="models/face_landmark.tflite"
aidlite.set_g_index(1)
inShape1 =[1 192 192 34,]
outShape1= [1 14044,1*4]
print('cpu:',aidlite.FAST_ANNModel(model_path,inShape1,outShape1,4,0))

anchors = np.load('models/anchors.npy').astype(np.float32)
camid=1
cap=cvs.VideoCapture(camid)
bFace=False
x_min,y_min,x_max,y_max=(0,0,0,0)
fface=0.0
while True:

frame=cvs.read()
if frame is None:
    continue
if camid==1:
    # frame=cv2.resize(frame,(640,480))
    frame=cv2.flip(frame,1)

start_time = time.time()    

# img = preprocess_image_for_tflite32(frame,128)
img_pad, img, pad = preprocess_img_pad(frame,128)


# interpreter.set_tensor(input_details[0]['index'], img[np.newaxis,:,:,:])
if bFace==False:
    aidlite.set_g_index(0)
    aidlite.setTensor_Fp32(img,input_shape[1],input_shape[1])

    aidlite.invoke()


    raw_boxes = aidlite.getTensor_Fp32(0)
    classificators = aidlite.getTensor_Fp32(1)

    detections = blazeface(raw_boxes, classificators, anchors)[0]


    if len(detections)>0 :
        bFace=True
if bFace:
    for i in range(len(detections)):
        ymin = detections[i][ 0] * img_pad.shape[0]
        xmin = detections[i][ 1] * img_pad.shape[1] 
        ymax = detections[i][ 2] * img_pad.shape[0]
        xmax = detections[i][ 3] * img_pad.shape[1] 
        w=int(xmax-xmin)
        h=int(ymax-ymin)
        h=max(w,h)
        h=h*1.5

        x=(xmin+xmax)/2.
        y=(ymin+ymax)/2.

        xmin=x-h/2.
        xmax=x+h/2.
        ymin=y-h/2.
        ymax=y+h/2.
        ymin=y-h/2.-0.08*h
        ymax=y+h/2.-0.08*h
        x_min=int(xmin)
        y_min=int(ymin)
        x_max=int(xmax)
        y_max=int(ymax)  

        x_min=max(0,x_min)
        y_min=max(0,y_min)
        x_max=min(img_pad.shape[1],x_max)
        y_max=min(img_pad.shape[0],y_max)
        roi_ori=img_pad[y_min:y_max, x_min:x_max]
        # cvs.imshow(roi)
        # roi_ori=roi_ori[:,:,::-1]
        roi =preprocess_image_for_tflite32(roi_ori,192)

        aidlite.set_g_index(1)
        aidlite.setTensor_Fp32(roi,192,192)
        # start_time = time.time()
        aidlite.invoke()
        mesh = aidlite.getTensor_Fp32(0)
        ffacetmp = aidlite.getTensor_Fp32(1)[0]
        print('fface:',abs(fface-ffacetmp))
        if abs(fface - ffacetmp) > 0.5:
            bFace=False
        fface=ffacetmp

        # print('mesh:',mesh.shape)
        mesh = mesh.reshape(468, 3) / 192
        draw_landmarks(roi_ori,mesh)

        shape=frame.shape
        x,y=img_pad.shape[0]/2,img_pad.shape[1]/2

        frame=img_pad[int(y-shape[0]/2):int(y+shape[0]/2), int(x-shape[1]/2):int(x+shape[1]/2)]



t = (time.time() - start_time)
# print('elapsed_ms invoke:',t*1000)
lbs = 'Fps: '+ str(int(100/t)/100.)+" ~~ Time:"+str(t*1000) +"ms"
cvs.setLbs(lbs) 

cvs.imshow(frame)
sleep(1)
Face Swap
超好玩的换脸算法,把明星的脸融合到你的身体上,让你也有星范。
![image.png](https://ucc.alicdn.com/pic/developer-ecology/4rwjyzfkmixqs_4272049d41324a54a9066fe671f265f0.png)
```import cv2
import math
import sys
import numpy as np
##############################################################################

back_img_path=('models/rs.jpeg','models/wy.jpeg','models/zyx.jpeg','models/monkey.jpg','models/star2.jpg','models/star1.jpg','models/star3.jpg','models/star4.jpg')

faceimg=cv2.imread(back_img_path[0])
mod=-1
bfirstframe=True

def readPoints(path) :
    # Create an array of points.
    points = [];

    # Read points
    with open(path) as file :
        for line in file :
            x, y = line.split()
            points.append((int(x), int(y)))


    return points

# 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


# 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[0] + rect[2] :
        return False
    elif point[1] > rect[1] + rect[3] :
        return False
    return True


#calculate delanauy triangle
def calculateDelaunayTriangles(rect, points):
    #create subdiv
    subdiv = cv2.Subdiv2D(rect);

    # Insert points into subdiv

    ttp=None
    for p in points:
        try:
            subdiv.insert(p)
            ttp=p
        except:
            subdiv.insert(ttp)
            continue

    triangleList = subdiv.getTriangleList();

    delaunayTri = []

    pt = []    

    for t in triangleList:        
        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):
            ind = []
            #Get face-points (from 68 face detector) by coordinates
            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)    
            # Three points form a triangle. Triangle array corresponds to the file tri.txt in FaceMorph 
            if len(ind) == 3:                                                
                delaunayTri.append((ind[0], ind[1], ind[2]))

        pt = []        


    return delaunayTri


# 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]]
    #img2Rect = np.zeros((r2[3], r2[2]), dtype = img1Rect.dtype)

    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 


def faceswap(points1,points2,img1,img2):



    # # Read images
    # filename1 ='sabina.jpg'
    # filename2 ='bid.jpg' 

    # img1 = cv2.imread(filename1);
    # img2 = cv2.imread(filename2);
    img1Warped = np.copy(img2);    

    # Read array of corresponding points
    # points1 = readPoints('sabina.txt')
    # points2 = readPoints('bid.txt')    

    # Find convex hull
    hull1 = []
    hull2 = []

    hullIndex = cv2.convexHull(np.array(points2), returnPoints = False)

    for i in range(0, len(hullIndex)):
        hull1.append(points1[int(hullIndex[i])])
        hull2.append(points2[int(hullIndex[i])])


    # Find delanauy traingulation for convex hull points
    sizeImg2 = img2.shape    
    rect = (0, 0, sizeImg2[1], sizeImg2[0])

    dt = calculateDelaunayTriangles(rect, hull2)

    if len(dt) == 0:
        quit()

    # Apply affine transformation to Delaunay triangles
    for i in range(0, len(dt)):
        t1 = []
        t2 = []

        #get points for img1, img2 corresponding to the triangles
        for j in range(0, 3):
            t1.append(hull1[dt[i][j]])
            t2.append(hull2[dt[i][j]])

        warpTriangle(img1, img1Warped, t1, t2)


    # Calculate Mask
    hull8U = []
    for i in range(0, len(hull2)):
        hull8U.append((hull2[i][0], hull2[i][1]))

    mask = np.zeros(img2.shape, dtype = img2.dtype)  

    cv2.fillConvexPoly(mask, np.int32(hull8U), (255, 255, 255))

    r = cv2.boundingRect(np.float32([hull2]))    

    center = ((r[0]+int(r[2]/2), r[1]+int(r[3]/2)))


    # Clone seamlessly.
    try :
        output = cv2.seamlessClone(np.uint8(img1Warped), img2, mask, center, cv2.NORMAL_CLONE)
    except:
        return None
    return output

    # cv2.imshow("Face Swapped", output)
    # cv2.waitKey(0)

    # cv2.destroyAllWindows()





#############################################################################

import sys
import numpy as np
from blazeface import *
from cvs import *
import aidlite_gpu
aidlite=aidlite_gpu.aidlite()

def preprocess_image_for_tflite32(image, model_image_size=192):
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image = cv2.resize(image, (model_image_size, model_image_size))
    image = np.expand_dims(image, axis=0)
    image = (2.0 / 255.0) * image - 1.0
    image = image.astype('float32')

    return image

def preprocess_img_pad(img,image_size=128):
    # fit the image into a 128x128 square
    # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    shape = np.r_[img.shape]
    pad_all = (shape.max() - shape[:2]).astype('uint32')
    pad = pad_all // 2
    # print ('pad_all',pad_all)
    img_pad_ori = np.pad(
        img,
        ((pad[0],pad_all[0]-pad[0]), (pad[1],pad_all[1]-pad[1]), (0,0)),
        mode='constant')
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img_pad = np.pad(
        img,
        ((pad[0],pad_all[0]-pad[0]), (pad[1],pad_all[1]-pad[1]), (0,0)),
        mode='constant')
    img_small = cv2.resize(img_pad, (image_size, image_size))
    img_small = np.expand_dims(img_small, axis=0)
    # img_small = np.ascontiguousarray(img_small)
    img_small = (2.0 / 255.0) * img_small - 1.0
    img_small = img_small.astype('float32')
    # img_norm = self._im_normalize(img_small)

    return img_pad_ori, img_small, pad

def plot_detections(img, detections, with_keypoints=True):
        output_img = img
        print(img.shape)
        x_min=0
        x_max=0
        y_min=0
        y_max=0
        print("Found %d faces" % len(detections))
        for i in range(len(detections)):
            ymin = detections[i][ 0] * img.shape[0]
            xmin = detections[i][ 1] * img.shape[1] 
            ymax = detections[i][ 2] * img.shape[0]
            xmax = detections[i][ 3] * img.shape[1] 
            w=int(xmax-xmin)
            h=int(ymax-ymin)
            h=max(w,h)
            h=h*1.5

            x=(xmin+xmax)/2.
            y=(ymin+ymax)/2.

            xmin=x-h/2.
            xmax=x+h/2.
            # ymin=y-h/2.
            # ymax=y+h/2.
            ymin=y-h/2.-0.08*h
            ymax=y+h/2.-0.08*h



            x_min=int(xmin)
            y_min=int(ymin)
            x_max=int(xmax)
            y_max=int(ymax)            
            p1 = (int(xmin),int(ymin))
            p2 = (int(xmax),int(ymax))
            # print(p1,p2)
            cv2.rectangle(output_img, p1, p2, (0,255,255),2,1)

            # cv2.putText(output_img, "Face found! ", (p1[0]+10, p2[1]-10),cv2.FONT_ITALIC, 1, (0, 255, 129), 2)

            # if with_keypoints:
            #     for k in range(6):
            #         kp_x = int(detections[i, 4 + k*2    ] * img.shape[1])
            #         kp_y = int(detections[i, 4 + k*2 + 1] * img.shape[0])
            #         cv2.circle(output_img,(kp_x,kp_y),4,(0,255,255),4)

        return x_min,y_min,x_max,y_max

def draw_mesh(image, mesh, mark_size=2, line_width=1):
    """Draw the mesh on an image"""
    # The mesh are normalized which means we need to convert it back to fit
    # the image size.
    image_size = image.shape[0]
    mesh = mesh * image_size
    for point in mesh:
        cv2.circle(image, (point[0], point[1]),
                   mark_size, (0, 255, 128), -1)

    # Draw the contours.
    # Eyes
    left_eye_contour = np.array([mesh[33][0:2],
                                 mesh[7][0:2],
                                 mesh[163][0:2],
                                 mesh[144][0:2],
                                 mesh[145][0:2],
                                 mesh[153][0:2],
                                 mesh[154][0:2],
                                 mesh[155][0:2],
                                 mesh[133][0:2],
                                 mesh[173][0:2],
                                 mesh[157][0:2],
                                 mesh[158][0:2],
                                 mesh[159][0:2],
                                 mesh[160][0:2],
                                 mesh[161][0:2],
                                 mesh[246][0:2], ]).astype(np.int32)
    right_eye_contour = np.array([mesh[263][0:2],
                                  mesh[249][0:2],
                                  mesh[390][0:2],
                                  mesh[373][0:2],
                                  mesh[374][0:2],
                                  mesh[380][0:2],
                                  mesh[381][0:2],
                                  mesh[382][0:2],
                                  mesh[362][0:2],
                                  mesh[398][0:2],
                                  mesh[384][0:2],
                                  mesh[385][0:2],
                                  mesh[386][0:2],
                                  mesh[387][0:2],
                                  mesh[388][0:2],
                                  mesh[466][0:2]]).astype(np.int32)
    # Lips
    cv2.polylines(image, [left_eye_contour, right_eye_contour], False,
                  (255, 255, 255), line_width, cv2.LINE_AA)

def getkeypoint(image, mesh,landmark_point):
    image_size = image.shape[0]
    mesh = mesh * image_size
    # landmark_point = []
    for point in mesh:
        landmark_point.append((point[0], point[1]))
    return image
        # cv2.circle(image, (point[0], point[1]), 2, (255, 255, 0), -1)

def draw_landmarks(image, mesh,landmark_point):
    image_size = image.shape[0]
    mesh = mesh * image_size
    # landmark_point = []
    for point in mesh:
        landmark_point.append((point[0], point[1]))
        cv2.circle(image, (point[0], point[1]), 2, (255, 255, 0), -1)



    if len(landmark_point) > 0:
        # 参考:https://github.com/tensorflow/tfjs-models/blob/master/facemesh/mesh_map.jpg

        # 左眉毛(55:内側、46:外側)
        cv2.line(image, landmark_point[55], landmark_point[65], (0, 0, 255), 2,-3)
        cv2.line(image, landmark_point[65], landmark_point[52], (0, 0, 255), 2,-3)
        cv2.line(image, landmark_point[52], landmark_point[53], (0, 0, 255), 2,-3)
        cv2.line(image, landmark_point[53], landmark_point[46],(0, 0, 255), 2,-3)

        # 右眉毛(285:内側、276:外側)
        cv2.line(image, landmark_point[285], landmark_point[295], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[295], landmark_point[282], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[282], landmark_point[283], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[283], landmark_point[276], (0, 0, 255),
                2)

        # 左目 (133:目頭、246:目尻)
        cv2.line(image, landmark_point[133], landmark_point[173], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[173], landmark_point[157], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[157], landmark_point[158], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[158], landmark_point[159], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[159], landmark_point[160], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[160], landmark_point[161], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[161], landmark_point[246], (0, 0, 255),
                2)

        cv2.line(image, landmark_point[246], landmark_point[163], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[163], landmark_point[144], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[144], landmark_point[145], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[145], landmark_point[153], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[153], landmark_point[154], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[154], landmark_point[155], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[155], landmark_point[133], (0, 0, 255),
                2)

        # 右目 (362:目頭、466:目尻)
        cv2.line(image, landmark_point[362], landmark_point[398], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[398], landmark_point[384], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[384], landmark_point[385], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[385], landmark_point[386], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[386], landmark_point[387], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[387], landmark_point[388], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[388], landmark_point[466], (0, 0, 255),
                2)

        cv2.line(image, landmark_point[466], landmark_point[390], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[390], landmark_point[373], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[373], landmark_point[374], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[374], landmark_point[380], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[380], landmark_point[381], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[381], landmark_point[382], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[382], landmark_point[362], (0, 0, 255),
                2)

        # 口 (308:右端、78:左端)
        cv2.line(image, landmark_point[308], landmark_point[415], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[415], landmark_point[310], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[310], landmark_point[311], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[311], landmark_point[312], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[312], landmark_point[13], (0, 0, 255), 2)
        cv2.line(image, landmark_point[13], landmark_point[82], (0, 0, 255), 2)
        cv2.line(image, landmark_point[82], landmark_point[81], (0, 0, 255), 2)
        cv2.line(image, landmark_point[81], landmark_point[80], (0, 0, 255), 2)
        cv2.line(image, landmark_point[80], landmark_point[191], (0, 0, 255), 2)
        cv2.line(image, landmark_point[191], landmark_point[78], (0, 0, 255), 2)

        cv2.line(image, landmark_point[78], landmark_point[95], (0, 0, 255), 2)
        cv2.line(image, landmark_point[95], landmark_point[88], (0, 0, 255), 2)
        cv2.line(image, landmark_point[88], landmark_point[178], (0, 0, 255), 2)
        cv2.line(image, landmark_point[178], landmark_point[87], (0, 0, 255), 2)
        cv2.line(image, landmark_point[87], landmark_point[14], (0, 0, 255), 2)
        cv2.line(image, landmark_point[14], landmark_point[317], (0, 0, 255), 2)
        cv2.line(image, landmark_point[317], landmark_point[402], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[402], landmark_point[318], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[318], landmark_point[324], (0, 0, 255),
                2)
        cv2.line(image, landmark_point[324], landmark_point[308], (0, 0, 255),
                2)

    return image        

class MyApp(App):

    def __init__(self, *args):
        super(MyApp, self).__init__(*args)

    def idle(self):
        self.aidcam0.update()

    def main(self):
        #creating a container VBox type, vertical (you can use also HBox or Widget)
        main_container = VBox(width=360, height=680, style={'margin':'0px auto'})

        self.aidcam0 = OpencvVideoWidget(self, width=340, height=400)
        self.aidcam0.style['margin'] = '10px'

        i=0
        exec("self.aidcam%(i)s = OpencvVideoWidget(self)" % {'i': i})
        exec("self.aidcam%(i)s.identifier = 'aidcam%(i)s'" % {'i': i})
        eval("main_container.append(self.aidcam%(i)s)" % {'i': i})

        # self.aidcam0.identifier="myimage_receiver"
        main_container.append(self.aidcam0)

        self.lbl = Label('点击图片选择你喜欢的明星脸:')
        main_container.append(self.lbl)

        bottom_container = HBox(width=360, height=130, style={'margin':'0px auto'})
        self.img1 = Image('/res:'+os.getcwd()+'/'+back_img_path[0], height=80, margin='10px')
        self.img1.onclick.do(self.on_img1_clicked)
        bottom_container.append(self.img1)

        self.img2 = Image('/res:'+os.getcwd()+'/'+back_img_path[1], height=80, margin='10px')
        self.img2.onclick.do(self.on_img2_clicked)
        bottom_container.append(self.img2)

        self.img3 = Image('/res:'+os.getcwd()+'/'+back_img_path[2], height=80, margin='10px')
        self.img3.onclick.do(self.on_img3_clicked)
        bottom_container.append(self.img3)

        self.img4 = Image('/res:'+os.getcwd()+'/'+back_img_path[3], height=80, margin='10px')
        self.img4.onclick.do(self.on_img4_clicked)
        bottom_container.append(self.img4)

        bt_container = HBox(width=360, height=130, style={'margin':'0px auto'})
        self.img11 = Image('/res:'+os.getcwd()+'/'+back_img_path[4], height=80, margin='10px')
        self.img11.onclick.do(self.on_img11_clicked)
        bt_container.append(self.img11)

        self.img22 = Image('/res:'+os.getcwd()+'/'+back_img_path[5], height=80, margin='10px')
        self.img22.onclick.do(self.on_img22_clicked)
        bt_container.append(self.img22)

        self.img33 = Image('/res:'+os.getcwd()+'/'+back_img_path[6], height=80, margin='10px')
        self.img33.onclick.do(self.on_img33_clicked)
        bt_container.append(self.img33)

        self.img44 = Image('/res:'+os.getcwd()+'/'+back_img_path[7], height=80, margin='10px')
        self.img44.onclick.do(self.on_img44_clicked)
        bt_container.append(self.img44)        

        # self.bt1 = Button('抠图模式', width=100, height=30, margin='10px')
        # self.bt1.onclick.do(self.on_button_pressed1)

        # self.bt2 = Button('渲染模式', width=100, height=30, margin='10px')
        # self.bt2.onclick.do(self.on_button_pressed2)        

        # self.bt3 = Button('着色模式', width=100, height=30, margin='10px')
        # self.bt3.onclick.do(self.on_button_pressed3) 

        main_container.append(bottom_container)

        main_container.append(bt_container)
        # main_container.append(self.bt1)
        # main_container.append(self.bt2)
        # main_container.append(self.bt3)


        return main_container

    def on_img1_clicked(self, widget):
        global faceimg
        bgnd=cv2.imread(back_img_path[0])
        faceimg=bgnd
        # global bfirstframe
        # bfirstframe=True
        global mod
        mod=0

    def on_img2_clicked(self, widget):
        global faceimg
        bgnd=cv2.imread(back_img_path[1])
        faceimg=bgnd
        # global bfirstframe
        # bfirstframe=True
        global mod
        mod=1

    def on_img3_clicked(self, widget):
        global faceimg
        bgnd=cv2.imread(back_img_path[2])       
        faceimg=bgnd
        # global bfirstframe
        # bfirstframe=True
        global mod
        mod=2

    def on_img4_clicked(self, widget):
        global faceimg
        bgnd=cv2.imread(back_img_path[3])       
        faceimg=bgnd
        # global bfirstframe
        # bfirstframe=True
        global mod
        mod=3        

    def on_img11_clicked(self, widget):
        global faceimg
        bgnd=cv2.imread(back_img_path[4])
        faceimg=bgnd
        # global bfirstframe
        # bfirstframe=True
        global mod
        mod=4

    def on_img22_clicked(self, widget):
        global faceimg
        bgnd=cv2.imread(back_img_path[5])
        faceimg=bgnd
        # global bfirstframe
        # bfirstframe=True
        global mod
        mod=5

    def on_img33_clicked(self, widget):
        global faceimg
        bgnd=cv2.imread(back_img_path[6])       
        faceimg=bgnd
        # global bfirstframe
        # bfirstframe=True
        global mod
        mod=6

    def on_img44_clicked(self, widget):
        global faceimg
        bgnd=cv2.imread(back_img_path[7])       
        faceimg=bgnd
        # global bfirstframe
        # bfirstframe=True
        global mod
        mod=7

    def on_button_pressed1(self, widget):
        global mod
        mod=0

    def on_button_pressed2(self, widget):
        global mod
        mod=1   

    def on_button_pressed3(self, widget):
        global mod
        mod=2


def process():

    cvs.setCustomUI()

    input_shape=[128,128]
    inShape =[1 * 128 * 128 *3*4,]
    outShape= [1 * 896*16*4,1*896*1*4]
    model_path="models/face_detection_front.tflite"
    print('gpu:',aidlite.ANNModel(model_path,inShape,outShape,4,0))
    model_path="models/face_landmark.tflite"
    aidlite.set_g_index(1)
    inShape1 =[1 * 192 * 192 *3*4,]
    outShape1= [1 * 1404*4,1*4]
    print('cpu:',aidlite.ANNModel(model_path,inShape1,outShape1,4,0))

    anchors = np.load('models/anchors.npy').astype(np.float32)
    camid=1
    cap=cvs.VideoCapture(camid)
    bFace=False
    x_min,y_min,x_max,y_max=(0,0,0,0)
    fface=0.0
    global bfirstframe
    bfirstframe=True
    facepath="Biden.jpeg"
    # facepath="rs.jpeg"
    # faceimg=bgnd_mat
    global faceimg
    faceimg=cv2.resize(faceimg,(256,256))
    # 
    roi_orifirst=faceimg
    padfaceimg=faceimg
    fpoints=[]
    spoints=[]
    # mcap=cv2.VideoCapture('test.mp4')
    global mod
    mod=-1

    while True:


        frame= cvs.read()

        # _,mframe=mcap.read()
        if frame is None:
            continue
        if camid==1:
            frame=cv2.flip(frame,1)

        if mod>-1 or bfirstframe:
            x_min,y_min,x_max,y_max=(0,0,0,0)
            faceimg=cv2.resize(faceimg,(256,256))
            frame=faceimg
            bFace=False
            roi_orifirst=faceimg
            padfaceimg=faceimg
            bfirstframe=True
            fpoints=[]
            spoints=[]


        start_time = time.time()    

        # img = preprocess_image_for_tflite32(frame,128)
        img_pad, img, pad = preprocess_img_pad(frame,128)


        # interpreter.set_tensor(input_details[0]['index'], img[np.newaxis,:,:,:])
        if bFace==False:
            aidlite.set_g_index(0)
            aidlite.setTensor_Fp32(img,input_shape[1],input_shape[1])

            aidlite.invoke()

            raw_boxes = aidlite.getTensor_Fp32(0)
            classificators = aidlite.getTensor_Fp32(1)

            detections = blazeface(raw_boxes, classificators, anchors)[0]

            if len(detections)>0 :
                bFace=True
        if bFace:
            for i in range(len(detections)):
                ymin = detections[i][ 0] * img_pad.shape[0]
                xmin = detections[i][ 1] * img_pad.shape[1] 
                ymax = detections[i][ 2] * img_pad.shape[0]
                xmax = detections[i][ 3] * img_pad.shape[1] 
                w=int(xmax-xmin)
                h=int(ymax-ymin)
                h=max(w,h)
                h=h*1.5

                x=(xmin+xmax)/2.
                y=(ymin+ymax)/2.

                xmin=x-h/2.
                xmax=x+h/2.
                ymin=y-h/2.
                ymax=y+h/2.
                ymin=y-h/2.-0.08*h
                ymax=y+h/2.-0.08*h
                x_min=int(xmin)
                y_min=int(ymin)
                x_max=int(xmax)
                y_max=int(ymax)  

                x_min=max(0,x_min)
                y_min=max(0,y_min)
                x_max=min(img_pad.shape[1],x_max)
                y_max=min(img_pad.shape[0],y_max)
                roi_ori=img_pad[y_min:y_max, x_min:x_max]
                # cvs.imshow(roi)
                # roi_ori=roi_ori[:,:,::-1]
                roi =preprocess_image_for_tflite32(roi_ori,192)

                aidlite.set_g_index(1)
                aidlite.setTensor_Fp32(roi,192,192)
                # start_time = time.time()
                aidlite.invoke()
                mesh = aidlite.getTensor_Fp32(0)
                ffacetmp = aidlite.getTensor_Fp32(1)[0]
                print('fface:',abs(fface-ffacetmp))
                if abs(fface - ffacetmp) > 0.5:
                    bFace=False
                fface=ffacetmp


                spoints=[]   
                # print('mesh:',mesh.shape)
                mesh = mesh.reshape(468, 3) / 192
                if bfirstframe :
                    getkeypoint(roi_ori,mesh,fpoints)
                    roi_orifirst=roi_ori.copy()
                    bfirstframe=False
                    mod=-1
                    # padfaceimg=img_pad
                else:
                    getkeypoint(roi_ori,mesh,spoints)
                    roi_ori=faceswap(fpoints,spoints,roi_orifirst,roi_ori)
                    if roi_ori is None:
                        continue
                    img_pad[y_min:y_max, x_min:x_max]=roi_ori

                shape=frame.shape
                x,y=img_pad.shape[0]/2,img_pad.shape[1]/2
                # frame=roi_ori
                frame=img_pad[int(y-shape[0]/2):int(y+shape[0]/2), int(x-shape[1]/2):int(x+shape[1]/2)]


        t = (time.time() - start_time)
        # print('elapsed_ms invoke:',t*1000)
        lbs = 'Fps: '+ str(int(100/t)/100.)+" ~~ Time:"+str(t*1000) +"ms"
        cvs.setLbs(lbs) 

        cvs.imshow(frame)
        sleep(1)

if __name__ == '__main__':

    initcv(startcv, MyApp)
    process()

效果演示视频:
人脸关键点检测:https://www.bilibili.com/video/BV1Zk4y137c8/
换脸算法:https://www.bilibili.com/video/BV1K14y1B7Jk/

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