Face Detect 68LandMark - 人脸检测68点模型

import sensor, image, time, lcd
from maix import KPU
import gc

lcd.init()                          # 初始化LCD显示屏
sensor.reset()                      # 复位并初始化摄像头
sensor.set_pixformat(sensor.RGB565) # 设置摄像头输出格式为 RGB565
sensor.set_framesize(sensor.QVGA)   # 设置摄像头输出大小为 QVGA (320x240)
sensor.skip_frames(time = 500)      # 等待摄像头稳定
clock = time.clock()                # 创建一个clock对象，用来计算帧率

#人脸检测模型需要320*256图输入，这里初始化一个image
od_img = image.Image(size=(320,256), copy_to_fb=False)

anchor = (0.893, 1.463, 0.245, 0.389, 1.55, 2.58, 0.375, 0.594, 3.099, 5.038, 0.057, 0.090, 0.567, 0.904, 0.101, 0.160, 0.159, 0.255)
# 创建一个kpu对象，用于人脸检测
kpu = KPU()
print("ready load model")
# 加载模型
kpu.load_kmodel("/sd/KPU/face_detect_with_68landmark/face_detect.kmodel")
# yolo2初始化
kpu.init_yolo2(anchor, anchor_num=9, img_w=320, img_h=240, net_w=320 , net_h=256 ,layer_w=10 ,layer_h=8, threshold=0.7, nms_value=0.2, classes=1)

# 创建一个kpu对象，用于人脸68关键点检测
lm68_kpu = KPU()
print("ready load model")
# 加载模型
lm68_kpu.load_kmodel("/sd/KPU/face_detect_with_68landmark/landmark68.kmodel")

RATIO = 0.08
def extend_box(x, y, w, h, scale):
    x1_t = x - scale*w
    x2_t = x + w + scale*w
    y1_t = y - scale*h
    y2_t = y + h + scale*h
    x1 = int(x1_t) if x1_t>1 else 1
    x2 = int(x2_t) if x2_t<320 else 319
    y1 = int(y1_t) if y1_t>1 else 1
    y2 = int(y2_t) if y2_t<256 else 255
    cut_img_w = x2-x1+1
    cut_img_h = y2-y1+1
    return x1, y1, cut_img_w, cut_img_h

while 1:
    gc.collect()
    #print("mem free:",gc.mem_free())
    clock.tick()                            # 更新计算帧率的clock
    img = sensor.snapshot()                 # 拍照，获取一张图像
    a =  od_img.draw_image(img, 0,0)        # 将img图像写到od_img图像的坐标（0,0）位置处
    od_img.pix_to_ai()                      # 对rgb565的image生成ai运算需要的r8g8b8格式存储
    kpu.run_with_output(od_img)             # 对输入图像进行kpu运算
    dect = kpu.regionlayer_yolo2()          # yolo2后处理
    fps = clock.fps()                       # 获取帧率
    if len(dect) > 0:
        print("dect:",dect)
        for l in dect :
            x1, y1, cut_img_w, cut_img_h = extend_box(l[0], l[1], l[2], l[3], scale=RATIO) # 扩大人脸框
            face_cut = img.cut(x1, y1, cut_img_w, cut_img_h) # 从img中裁剪出人脸图
            a = img.draw_rectangle(l[0],l[1],l[2],l[3], color=(0, 255, 0))
            face_cut_128 = face_cut.resize(128, 128)
            face_cut_128.pix_to_ai()
            out = lm68_kpu.run_with_output(face_cut_128, getlist=True)
            #print("out:",len(out))
            for j in range(68):
                x = int(KPU.sigmoid(out[2 * j])*cut_img_w + x1)
                y = int(KPU.sigmoid(out[2 * j + 1])*cut_img_h + y1)
                #a = img.draw_cross(x, y, size=1, color=(0, 0, 255))
                a = img.draw_circle(x, y, 2, color=(0, 0, 255), fill=True)
            del (face_cut_128)
            del (face_cut)

    a = img.draw_string(0, 0, "%2.1ffps" %(fps), color=(0, 60, 255), scale=2.0)
    lcd.display(img)
    gc.collect()

# 创建的kpu对象去初始化，释放模型内存
kpu.deinit()
lm68_kpu.deinit()