KPU特征识别

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我们来简单介绍一下
K210
的
KPU
。
KPU
是
K210
内部一个神经网络处理器，

它可以在低功耗的情况下实现卷积神经网络计算，实时获取被检测目标的大小、

坐标和种类，对人脸或者物体进行检测和分类。

KPU
具备以下几个特点：

➢
支持主流训练框架按照特定限制规则训练出来的定点化模型

➢
对网络层数无直接限制，支持每层卷积神经网络参数单独配置，包括输

入输出通道数目、输入输 出行宽列高

➢
支持两种卷积内核
1×1
和
3×3

➢
支持任意形式的激活函数

➢
实时工作时最大支持神经网络参数大小为
5.5MiB
到
5.9MiB

➢
非实时工作时最大支持网络参数大小为（
Flash
容量
–
软件体积）

简单来说就是
KPU
能加载和运行不同的模型，实现各种机器视觉等功能。

目前
K210
的
KPU
上主要跑
YOLO
（
You Only Look Once
）框架的目标检测算

法来实现。通过
micropython
的封装让我们可以不关心底层实现原理，非常方便

的使用起来。我们来看一下通过
KPU
运行
Yolo
的对象用法

1：人脸检测 

#实验名称：人脸检测 #翻译和注释：01Studio #导入相关模块 import sensor, image, time, lcd, gc from maix import KPU #lcd初始化 lcd.init() #摄像头初始化 sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QVGA) sensor.set_vflip(True) #摄像头后置 sensor.skip_frames(time = 1000) clock = time.clock() od_img = image.Image(size=(320,256)) #构建KPU对象 kpu = KPU() kpu.load_kmodel("/sd/yolo_face_detect.kmodel") #加载KPU模型，放在SD卡根目录 #kpu.load_kmodel(0x, ) #需要将kmdel通过固件下载工具下载到0x的位置（3M偏移） 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) 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=0.3, classes=1) while True: #print("mem free:",gc.mem_free()) #查询内存余量 clock.tick() img = sensor.snapshot() a = od_img.draw_image(img, 0,0) od_img.pix_to_ai() #将摄像头采集图片输送到KPU和yolo模型运算。 kpu.run_with_output(od_img) dect = kpu.regionlayer_yolo2() fps = clock.fps() if len(dect) > 0: #识别到人脸 print("dect:",dect) for l in dect : #画矩形 a = img.draw_rectangle(l[0],l[1],l[2],l[3], color=(0, 255, 0)) a = img.draw_string(0, 0, "%2.1ffps" %(fps), color=(0, 60, 128), scale=2.0) #显示屏显示FPS lcd.display(img) gc.collect()#内存回收 kpu.deinit() 

并且可在串口终端看见人脸位置。可用于面容跟踪

2：人手识别

#实验名称：人手检测 #翻译和注释：01Studio #导入相关模块 import sensor, image, time, lcd from maix import KPU import gc lcd.init() sensor.reset() # Reset and initialize the sensor. It will # run automatically, call sensor.run(0) to stop sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE) sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240) sensor.set_vflip(True) #摄像头后置 sensor.skip_frames(time = 1000) # Wait for settings take effect. clock = time.clock() # Create a clock object to track the FPS. od_img = image.Image(size=(320,256)) #构建KPU对象 anchor = (0.8125, 0.4556, 1.1328, 1.2667, 1.8594, 1.4889, 1.4844, 2.2000, 2.6484, 2.9333) kpu = KPU() print("ready load model") #加载KPU模型，放在SD卡根目录 kpu.load_kmodel("/sd/hand_detect.kmodel") #需要将kmdel通过固件下载工具下载到0x的位置（3M偏移） #kpu.load_kmodel(0x, ) kpu.init_yolo2(anchor, anchor_num=5, img_w=320, img_h=240, net_w=320 , net_h=256 ,layer_w=10 ,layer_h=8, threshold=0.7, nms_value=0.3, classes=1) while True: gc.collect() clock.tick() # Update the FPS clock. img = sensor.snapshot() a = od_img.draw_image(img, 0,0) od_img.pix_to_ai() #将摄像头采集图片输送到KPU和yolo模型运算。 kpu.run_with_output(od_img) dect = kpu.regionlayer_yolo2() fps = clock.fps() if len(dect) > 0: #识别到人手 print("dect:",dect) for l in dect :#画矩形 a = img.draw_rectangle(l[0],l[1],l[2],l[3], color=(0, 255, 0)) a = img.draw_string(0, 0, "%2.1ffps" %(fps), color=(0, 60, 128), scale=2.0) lcd.display(img) kpu.deinit() 

3:口罩识别

#实验名称：口罩检测 #翻译和注释：01Studio #导入相关模块 import sensor, image, time, lcd from maix import KPU import gc lcd.init() sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QVGA) sensor.set_vflip(True) #摄像头后置 sensor.skip_frames(time = 1000) clock = time.clock() od_img = image.Image(size=(320,256), copy_to_fb=False) #构建KPU对象 anchor = (0., 0., 0., 0., 0., 0., 1., 1., 3., 3.94000) kpu = KPU() print("ready load model") #加载KPU模型，放在SD卡根目录 kpu.load_kmodel("/sd/detect_5.kmodel") #需要将kmdel通过固件下载工具下载到0x的位置（3M偏移） #kpu.load_kmodel(0x, ) kpu.init_yolo2(anchor, anchor_num=5, img_w=320, img_h=240, net_w=320 , net_h=256 ,layer_w=10 ,layer_h=8, threshold=0.7, nms_value=0.4, classes=2) while True: #print("mem free:",gc.mem_free()) clock.tick() # Update the FPS clock. img = sensor.snapshot() a = od_img.draw_image(img, 0,0) od_img.pix_to_ai() #将摄像头采集图片输送到KPU和yolo模型运算。 kpu.run_with_output(od_img) dect = kpu.regionlayer_yolo2() fps = clock.fps() if len(dect) > 0: #识别到人脸 print("dect:",dect) for l in dect : if l[4] : #有带口罩 a = img.draw_rectangle(l[0],l[1],l[2],l[3], color=(0, 255, 0)) a = img.draw_string(l[0],l[1]-24, "with mask", color=(0, 255, 0), scale=2) else: #没有戴口罩 a = img.draw_rectangle(l[0],l[1],l[2],l[3], color=(255, 0, 0)) a = img.draw_string(l[0],l[1]-24, "without mask", color=(255, 0, 0), scale=2) a = img.draw_string(0, 0, "%2.1ffps" %(fps), color=(0, 60, 128), scale=2.0) lcd.display(img) gc.collect() kpu.deinit() 

4：人脸特征

具体编程思路如下：

#实验名称：人脸特征 #翻译和注释：01Studio #导入相关模块 import sensor, image, time, lcd from maix import KPU import gc lcd.init() sensor.reset() # Reset and initialize the sensor. It will # run automatically, call sensor.run(0) to stop sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE) sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240) sensor.skip_frames(time = 1000) # Wait for settings take effect. sensor.set_vflip(1) #摄像头后置模式 clock = time.clock() # Create a clock object to track the FPS. #构建KPU对象和配置模型参数信息 #导入3个模型，放在SD卡根目录 anchor = (0.1075, 0., 0., 0.175, 0., 0., 0., 0.25375, 0., 0., 0., 0., 0., 0., 0., 1.099687, 2., 2.) kpu = KPU() kpu.load_kmodel("/sd/face_detect_320x240.kmodel") kpu.init_yolo2(anchor, anchor_num=9, img_w=320, img_h=240, net_w=320 , net_h=240 ,layer_w=10 ,layer_h=8, threshold=0.5, nms_value=0.2, classes=1) ld5_kpu = KPU() print("ready load model") ld5_kpu.load_kmodel("/sd/ld5.kmodel") fac_kpu = KPU() print("ready load model") fac_kpu.load_kmodel("/sd/fac.kmodel") #人脸特征：性别、嘴巴是否张开、是否笑、是否戴眼镜 4个特征 pos_face_attr = ["Male ", "Mouth Open ", "Smiling ", "Glasses"] neg_face_attr = ["Female ", "Mouth Closed", "No Smile", "No Glasses"] # standard face key point position FACE_PIC_SIZE = 128 dst_point =[(int(38.2946 * FACE_PIC_SIZE / 112), int(51.6963 * FACE_PIC_SIZE / 112)), (int(73.5318 * FACE_PIC_SIZE / 112), int(51.5014 * FACE_PIC_SIZE / 112)), (int(56.0252 * FACE_PIC_SIZE / 112), int(71.7366 * FACE_PIC_SIZE / 112)), (int(41.5493 * FACE_PIC_SIZE / 112), int(92.3655 * FACE_PIC_SIZE / 112)), (int(70.7299 * FACE_PIC_SIZE / 112), int(92.2041 * FACE_PIC_SIZE / 112)) ] 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<240 else 239 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() # Update the FPS clock. img = sensor.snapshot() kpu.run_with_output(img) dect = kpu.regionlayer_yolo2() fps = clock.fps() #识别出人脸 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) 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() #五点人脸识别确认人脸完整 out = ld5_kpu.run_with_output(face_cut_128, getlist=True) #print("out:",len(out)) face_key_point = [] for j in range(5): 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=5, color=(0, 0, 255)) face_key_point.append((x,y)) T = image.get_affine_transform(face_key_point, dst_point) a = image.warp_affine_ai(img, face_cut_128, T) # face_cut_128.ai_to_pix() # img.draw_image(face_cut_128, 0,0) out2 = fac_kpu.run_with_output(face_cut_128, getlist=True) del face_key_point #判断特征数据 if out2 is not None: for i in range(4): th = KPU.sigmoid(out2[i]) if th >= 0.7: a = img.draw_string(l[0]+l[2], l[1]+i*16, "%s" %(pos_face_attr[i]), color=(255, 0, 0), scale=1.5) else: a = img.draw_string(l[0]+l[2], l[1]+i*16, "%s" %(neg_face_attr[i]), color=(0, 0, 255), scale=1.5) 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) kpu.deinit() ld5_kpu.deinit() fac_kpu.deinit() 

 5：68关键点

别是人脸检测模型和人脸 68 个关键点模型。层层递进，具体编程思路如下：

#实验名称：人脸检测_68关键点 #翻译和注释：01Studio #导入相关模块 import sensor, image, time, lcd from maix import KPU import gc lcd.init() sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QVGA) sensor.set_vflip(True) #摄像头后置 sensor.skip_frames(time = 500) clock = time.clock() od_img = image.Image(size=(320,256), copy_to_fb=False) #构建KPU对象 #需要导入2个模型，分别是人脸检测模型和68关键点检测模型 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) kpu = KPU() print("ready load model") kpu.load_kmodel("/sd/face_detect.kmodel") 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) lm68_kpu = KPU() print("ready load model") lm68_kpu.load_kmodel("/sd/landmark68.kmodel") RATIO = 0.08 while 1: gc.collect() #print("mem free:",gc.mem_free()) clock.tick() # Update the FPS clock. img = sensor.snapshot() a = od_img.draw_image(img, 0,0) od_img.pix_to_ai() kpu.run_with_output(od_img) dect = kpu.regionlayer_yolo2() fps = clock.fps() #识别到人脸 if len(dect) > 0: print("dect:",dect) for l in dect : a = img.draw_rectangle(l[0],l[1],l[2],l[3], color=(0, 255, 0)) x1_t = l[0] - RATIO*l[2] x2_t = l[0]+l[2] + RATIO*l[2] y1_t = l[1] - RATIO*l[3] y2_t = l[1]+l[3] + RATIO*l[3] 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 #print("cut img ",x1, y1, cut_img_w, cut_img_h) face_cut = img.cut(x1,y1,cut_img_w,cut_img_h) face_cut_128 = face_cut.resize(128, 128) face_cut_128.pix_to_ai() #识别68个关键点 out = lm68_kpu.run_with_output(face_cut_128, getlist=True) if out is not None: #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() kpu.deinit() lm68_kpu.deinit() 

初步视觉使用介绍就到此结束了，拜拜