人脸3D网格
前言
人脸3D网格，在检测到人脸后用多个点描绘整个脸，从而把人脸轮廓像网格一样描绘出来，支持单个和多个人脸。
实验目的
将摄像头拍摄到的画面中的人脸描绘出3D网格效果。
实验讲解
本实验通过CanMV K230 AI视觉框架开发，详细说明参考 AI视觉开发框架 章节内容，这里不再重复。例程用到的模型已经存放在CanMV K230的文件系统，无需额外拷贝。
具体编程思路如下：
参考代码
'''
实验名称：人脸3D网格
实验平台：01Studio CanMV K230
教程：wiki.01studio.cc
'''
from libs.PipeLine import PipeLine, ScopedTiming
from libs.AIBase import AIBase
from libs.AI2D import Ai2d
import os
import ujson
from media.media import *
from time import *
import nncase_runtime as nn
import ulab.numpy as np
import time
import image
import aidemo
import random
import gc
import sys
# 自定义人脸检测任务类
class FaceDetApp(AIBase):
def __init__(self,kmodel_path,model_input_size,anchors,confidence_threshold=0.25,nms_threshold=0.3,rgb888p_size=[1280,720],display_size=[1920,1080],debug_mode=0):
super().__init__(kmodel_path,model_input_size,rgb888p_size,debug_mode)
# kmodel路径
self.kmodel_path=kmodel_path
# 检测模型输入分辨率
self.model_input_size=model_input_size
# 置信度阈值
self.confidence_threshold=confidence_threshold
# nms阈值
self.nms_threshold=nms_threshold
# 检测任务锚框
self.anchors=anchors
# sensor给到AI的图像分辨率，宽16字节对齐
self.rgb888p_size=[ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
# 视频输出VO分辨率，宽16字节对齐
self.display_size=[ALIGN_UP(display_size[0],16),display_size[1]]
# debug模式
self.debug_mode=debug_mode
# 实例化Ai2d，用于实现模型预处理
self.ai2d=Ai2d(debug_mode)
# 设置Ai2d的输入输出格式和类型
self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT,nn.ai2d_format.NCHW_FMT,np.uint8, np.uint8)
# 配置预处理操作，这里使用了pad和resize，Ai2d支持crop/shift/pad/resize/affine，具体代码请打开/sdcard/app/libs/AI2D.py查看
def config_preprocess(self,input_image_size=None):
with ScopedTiming("set preprocess config",self.debug_mode > 0):
# 初始化ai2d预处理配置，默认为sensor给到AI的尺寸，可以通过设置input_image_size自行修改输入尺寸
ai2d_input_size=input_image_size if input_image_size else self.rgb888p_size
# 设置padding预处理
self.ai2d.pad(self.get_pad_param(), 0, [104,117,123])
# 设置resize预处理
self.ai2d.resize(nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel)
# 构建预处理流程,参数为预处理输入tensor的shape和预处理输出的tensor的shape
self.ai2d.build([1,3,ai2d_input_size[1],ai2d_input_size[0]],[1,3,self.model_input_size[1],self.model_input_size[0]])
# 自定义后处理，results是模型推理输出的array列表，这里使用了aidemo库的face_det_post_process接口
def postprocess(self,results):
with ScopedTiming("postprocess",self.debug_mode > 0):
res = aidemo.face_det_post_process(self.confidence_threshold,self.nms_threshold,self.model_input_size[0],self.anchors,self.rgb888p_size,results)
if len(res)==0:
return res
else:
return res[0]
# padding参数计算
def get_pad_param(self):
dst_w = self.model_input_size[0]
dst_h = self.model_input_size[1]
# 计算最小的缩放比例，等比例缩放
ratio_w = dst_w / self.rgb888p_size[0]
ratio_h = dst_h / self.rgb888p_size[1]
if ratio_w < ratio_h:
ratio = ratio_w
else:
ratio = ratio_h
new_w = (int)(ratio * self.rgb888p_size[0])
new_h = (int)(ratio * self.rgb888p_size[1])
dw = (dst_w - new_w) / 2
dh = (dst_h - new_h) / 2
top = (int)(round(0))
bottom = (int)(round(dh * 2 + 0.1))
left = (int)(round(0))
right = (int)(round(dw * 2 - 0.1))
return [0,0,0,0,top, bottom, left, right]
# 自定义人脸网格任务类
class FaceMeshApp(AIBase):
def __init__(self,kmodel_path,model_input_size,rgb888p_size=[1920,1080],display_size=[1920,1080],debug_mode=0):
super().__init__(kmodel_path,model_input_size,rgb888p_size,debug_mode)
# kmodel路径
self.kmodel_path=kmodel_path
# 人脸网格模型输入分辨率
self.model_input_size=model_input_size
# sensor给到AI的图像分辨率，宽16字节对齐
self.rgb888p_size=[ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
# 视频输出VO分辨率，宽16字节对齐
self.display_size=[ALIGN_UP(display_size[0],16),display_size[1]]
# debug模式
self.debug_mode=debug_mode
# 人脸mesh参数均值
self.param_mean = np.array([0.0003492636315058917,2.52790130161884e-07,-6.875197868794203e-07,60.1679573059082,-6.295513230725192e-07,0.0005757200415246189,-5.085391239845194e-05,74.2781982421875,5.400917189035681e-07,6.574138387804851e-05,0.0003442012530285865,-66.67157745361328,-346603.6875,-67468.234375,46822.265625,-15262.046875,4350.5888671875,-54261.453125,-18328.033203125,-1584.328857421875,-84566.34375,3835.960693359375,-20811.361328125,38094.9296875,-19967.85546875,-9241.3701171875,-19600.71484375,13168.08984375,-5259.14404296875,1848.6478271484375,-13030.662109375,-2435.55615234375,-2254.20654296875,-14396.5615234375,-6176.3291015625,-25621.919921875,226.39447021484375,-6326.12353515625,-10867.2509765625,868.465087890625,-5831.14794921875,2705.123779296875,-3629.417724609375,2043.9901123046875,-2446.6162109375,3658.697021484375,-7645.98974609375,-6674.45263671875,116.38838958740234,7185.59716796875,-1429.48681640625,2617.366455078125,-1.2070955038070679,0.6690792441368103,-0.17760828137397766,0.056725528091192245,0.03967815637588501,-0.13586315512657166,-0.09223993122577667,-0.1726071834564209,-0.015804484486579895,-0.1416848599910736],dtype=np.float)
# 人脸mesh参数方差
self.param_std = np.array([0.00017632152594160289,6.737943476764485e-05,0.00044708489440381527,26.55023193359375,0.0001231376954820007,4.493021697271615e-05,7.923670636955649e-05,6.982563018798828,0.0004350444069132209,0.00012314890045672655,0.00017400001524947584,20.80303955078125,575421.125,277649.0625,258336.84375,255163.125,150994.375,160086.109375,111277.3046875,97311.78125,117198.453125,89317.3671875,88493.5546875,72229.9296875,71080.2109375,50013.953125,55968.58203125,47525.50390625,49515.06640625,38161.48046875,44872.05859375,46273.23828125,38116.76953125,28191.162109375,32191.4375,36006.171875,32559.892578125,25551.1171875,24267.509765625,27521.3984375,23166.53125,21101.576171875,19412.32421875,19452.203125,17454.984375,22537.623046875,16174.28125,14671.640625,15115.6884765625,13870.0732421875,13746.3125,12663.1337890625,1.5870834589004517,1.5077009201049805,0.5881357789039612,0.5889744758605957,0.21327851712703705,0.2630201280117035,0.2796429395675659,0.38030216097831726,0.16162841022014618,0.2559692859649658],dtype=np.float)
# 实例化Ai2d，用于实现模型预处理
self.ai2d=Ai2d(debug_mode)
# 设置Ai2d的输入输出格式和类型
self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT,nn.ai2d_format.NCHW_FMT,np.uint8, np.uint8)
# 配置预处理操作，这里使用了crop和resize，Ai2d支持crop/shift/pad/resize/affine，具体代码请打开/sdcard/app/libs/AI2D.py查看
def config_preprocess(self,det,input_image_size=None):
with ScopedTiming("set preprocess config",self.debug_mode > 0):
# 初始化ai2d预处理配置，默认为sensor给到AI的尺寸，可以通过设置input_image_size自行修改输入尺寸
ai2d_input_size=input_image_size if input_image_size else self.rgb888p_size
# 计算crop参数，并设置crop预处理
roi = self.parse_roi_box_from_bbox(det)
self.ai2d.crop(int(roi[0]),int(roi[1]),int(roi[2]),int(roi[3]))
# 设置resize预处理
self.ai2d.resize(nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel)
# 构建预处理流程,参数为预处理输入tensor的shape和预处理输出的tensor的shape
self.ai2d.build([1,3,ai2d_input_size[1],ai2d_input_size[0]],[1,3,self.model_input_size[1],self.model_input_size[0]])
return roi
# 自定义后处理，results是模型输出的array列表
def postprocess(self,results):
with ScopedTiming("postprocess",self.debug_mode > 0):
param = results[0] * self.param_std + self.param_mean
return param
def parse_roi_box_from_bbox(self,bbox):
# 获取人脸roi
x1, y1, w, h = map(lambda x: int(round(x, 0)), bbox[:4])
old_size = (w + h) / 2
center_x = x1 + w / 2
center_y = y1 + h / 2 + old_size * 0.14
size = int(old_size * 1.58)
x0 = center_x - float(size) / 2
y0 = center_y - float(size) / 2
x1 = x0 + size
y1 = y0 + size
x0 = max(0, min(x0, self.rgb888p_size[0]))
y0 = max(0, min(y0, self.rgb888p_size[1]))
x1 = max(0, min(x1, self.rgb888p_size[0]))
y1 = max(0, min(y1, self.rgb888p_size[1]))
roi = (x0, y0, x1 - x0, y1 - y0)
return roi
# 自定义人脸网格后处理任务类
class FaceMeshPostApp(AIBase):
def __init__(self,kmodel_path,model_input_size,rgb888p_size=[1920,1080],display_size=[1920,1080],debug_mode=0):
super().__init__(kmodel_path,model_input_size,rgb888p_size,debug_mode)
# kmodel路径
self.kmodel_path=kmodel_path
# 人脸网格模型输入分辨率
self.model_input_size=model_input_size
# sensor给到AI的图像分辨率，宽16字节对齐
self.rgb888p_size=[ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
# 视频输出VO分辨率，宽16字节对齐
self.display_size=[ALIGN_UP(display_size[0],16),display_size[1]]
# debug模式
self.debug_mode=debug_mode
# 实例化Ai2d，用于实现模型预处理
self.ai2d=Ai2d(debug_mode)
# 设置Ai2d的输入输出格式和类型
self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT,nn.ai2d_format.NCHW_FMT,np.uint8, np.uint8)
# 重写预处理函数preprocess，因为该模型的预处理不是单纯调用一个ai2d能实现的，返回模型输入的tensor列表
def preprocess(self,param):
with ScopedTiming("set preprocess config",self.debug_mode > 0):
# face mesh post模型预处理，param解析
param = param[0]
trans_dim, shape_dim, exp_dim = 12, 40, 10
R_ = param[:trans_dim].copy().reshape((3, -1))
R = R_[:, :3].copy()
offset = R_[:, 3].copy()
offset = offset.reshape((3, 1))
alpha_shp = param[trans_dim:trans_dim + shape_dim].copy().reshape((-1, 1))
alpha_exp = param[trans_dim + shape_dim:].copy().reshape((-1, 1))
R_tensor = nn.from_numpy(R)
offset_tensor = nn.from_numpy(offset)
alpha_shp_tensor = nn.from_numpy(alpha_shp)
alpha_exp_tensor = nn.from_numpy(alpha_exp)
return [R_tensor,offset_tensor,alpha_shp_tensor,alpha_exp_tensor]
# 自定义模型后处理，这里调用了aidemo的face_mesh_post_process接口
def postprocess(self,results,roi):
with ScopedTiming("postprocess",self.debug_mode > 0):
x, y, w, h = map(lambda x: int(round(x, 0)), roi[:4])
x = x * self.display_size[0] // self.rgb888p_size[0]
y = y * self.display_size[1] // self.rgb888p_size[1]
w = w * self.display_size[0] // self.rgb888p_size[0]
h = h * self.display_size[1] // self.rgb888p_size[1]
roi_array = np.array([x,y,w,h],dtype=np.float)
aidemo.face_mesh_post_process(roi_array,results[0])
return results[0]
# 3D人脸网格
class FaceMesh:
def __init__(self,face_det_kmodel,face_mesh_kmodel,mesh_post_kmodel,det_input_size,mesh_input_size,anchors,confidence_threshold=0.25,nms_threshold=0.3,rgb888p_size=[1920,1080],display_size=[1920,1080],debug_mode=0):
# 人脸检测模型路径
self.face_det_kmodel=face_det_kmodel
# 人脸3D网格模型路径
self.face_mesh_kmodel=face_mesh_kmodel
# 人脸3D网格后处理模型路径
self.mesh_post_kmodel=mesh_post_kmodel
# 人脸检测模型输入分辨率
self.det_input_size=det_input_size
# 人脸3D网格模型输入分辨率
self.mesh_input_size=mesh_input_size
# anchors
self.anchors=anchors
# 置信度阈值
self.confidence_threshold=confidence_threshold
# nms阈值
self.nms_threshold=nms_threshold
# sensor给到AI的图像分辨率，宽16字节对齐
self.rgb888p_size=[ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
# 视频输出VO分辨率，宽16字节对齐
self.display_size=[ALIGN_UP(display_size[0],16),display_size[1]]
# debug_mode模式
self.debug_mode=debug_mode
# 人脸检测实例
self.face_det=FaceDetApp(self.face_det_kmodel,model_input_size=self.det_input_size,anchors=self.anchors,confidence_threshold=self.confidence_threshold,nms_threshold=self.nms_threshold,rgb888p_size=self.rgb888p_size,display_size=self.display_size,debug_mode=0)
# 人脸网格实例
self.face_mesh=FaceMeshApp(self.face_mesh_kmodel,model_input_size=self.mesh_input_size,rgb888p_size=self.rgb888p_size,display_size=self.display_size)
# 人脸网格后处理实例
self.face_mesh_post=FaceMeshPostApp(self.mesh_post_kmodel,model_input_size=self.mesh_input_size,rgb888p_size=self.rgb888p_size,display_size=self.display_size)
# 人脸检测预处理配置
self.face_det.config_preprocess()
# run函数
def run(self,input_np):
# 执行人脸检测
det_boxes=self.face_det.run(input_np)
mesh_res=[]
for det_box in det_boxes:
# 对检测到的每一个人脸配置预处理，执行人脸网格和人脸网格后处理
roi=self.face_mesh.config_preprocess(det_box)
param=self.face_mesh.run(input_np)
tensors=self.face_mesh_post.preprocess(param)
results=self.face_mesh_post.inference(tensors)
res=self.face_mesh_post.postprocess(results,roi)
mesh_res.append(res)
return det_boxes,mesh_res
# 绘制人脸解析效果
def draw_result(self,pl,dets,mesh_res):
pl.osd_img.clear()
if dets:
draw_img_np = np.zeros((self.display_size[1],self.display_size[0],4),dtype=np.uint8)
draw_img = image.Image(self.display_size[0], self.display_size[1], image.ARGB8888, alloc=image.ALLOC_REF,data = draw_img_np)
for vertices in mesh_res:
aidemo.face_draw_mesh(draw_img_np, vertices)
pl.osd_img.copy_from(draw_img)
if __name__=="__main__":
# 显示模式，默认"hdmi",可以选择"hdmi"和"lcd"
display_mode="lcd"
if display_mode=="hdmi":
display_size=[1920,1080]
else:
display_size=[800,480]
# 人脸检测模型路径
face_det_kmodel_path="/sdcard/app/tests/kmodel/face_detection_320.kmodel"
# 人脸网格模型路径
face_mesh_kmodel_path="/sdcard/app/tests/kmodel/face_alignment.kmodel"
# 人脸网格后处理模型路径
face_mesh_post_kmodel_path="/sdcard/app/tests/kmodel/face_alignment_post.kmodel"
# 其他参数
anchors_path="/sdcard/app/tests/utils/prior_data_320.bin"
rgb888p_size=[1920,1080]
face_det_input_size=[320,320]
face_mesh_input_size=[120,120]
confidence_threshold=0.5
nms_threshold=0.2
anchor_len=4200
det_dim=4
anchors = np.fromfile(anchors_path, dtype=np.float)
anchors = anchors.reshape((anchor_len,det_dim))
# 初始化PipeLine，只关注传给AI的图像分辨率，显示的分辨率
pl=PipeLine(rgb888p_size=rgb888p_size,display_size=display_size,display_mode=display_mode)
pl.create()
fm=FaceMesh(face_det_kmodel_path,face_mesh_kmodel_path,face_mesh_post_kmodel_path,det_input_size=face_det_input_size,mesh_input_size=face_mesh_input_size,anchors=anchors,confidence_threshold=confidence_threshold,nms_threshold=nms_threshold,rgb888p_size=rgb888p_size,display_size=display_size)
clock = time.clock()
try:
while True:
os.exitpoint()
clock.tick()
img=pl.get_frame() # 获取当前帧
det_boxes,mesh_res=fm.run(img) # 推理当前帧
print(det_boxes,mesh_res) # 打印结果
fm.draw_result(pl,det_boxes,mesh_res) # 绘制推理结果
pl.show_image() # 显示推理效果
gc.collect()
print(clock.fps()) #打印帧率
except Exception as e:
sys.print_exception(e)
finally:
fm.face_det.deinit()
fm.face_mesh.deinit()
fm.face_mesh_post.deinit()
pl.destroy()
这里对关键代码进行讲解：
- 主函数代码：
可以看到使用默认配置后只使用了4行代码便实现了获取当前帧图像、AI推理、绘制结果、显示结果 的识别流程。
代码中det_boxes变量为人脸检测结果， mesh_res为网格点数据。
...
while True:
os.exitpoint()
clock.tick()
img=pl.get_frame() # 获取当前帧
det_boxes,mesh_res=fm.run(img) # 推理当前帧
print(det_boxes,mesh_res) # 打印结果
fm.draw_result(pl,det_boxes,mesh_res) # 绘制推理结果
pl.show_image() # 显示推理效果
gc.collect()
print(clock.fps()) #打印帧率
...
实验结果
运行代码，将摄像头正对下方人脸图检测。
原图：
识别结果：
