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查、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 ​​编辑​ `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。

文件位置：cd /home/examples-gpu/face/

运行代码：python faceswap_gui.py ​​编辑​ `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()

`

演示视频： ​

 人脸关键点检测：人脸关键点检测_哔哩哔哩_bilibili

换脸算法：换脸算法_哔哩哔哩_bilibili

​