一段代码可以实现灰度图人脸识别，一段代码可以实现口罩检测，请问如何合二为一？

[fggx]

两段代码都可以运行成功，想要实现既可以用矩形框框出人脸，又可以在终端显示带口罩的概率，跪求大神一助

[fggx]

@fggx # Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, uos, gc

sensor.reset() # Reset and initialize the sensor.
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_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.

net = None
labels = None

try:
# load the model, alloc the model file on the heap if we have at least 64K free after loading
net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
print(e)
raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

try:
labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

clock = time.clock()
while(True):
clock.tick()

img = sensor.snapshot()

# default settings just do one detection... change them to search the image...
for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
    print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
    img.draw_rectangle(obj.rect())
    # This combines the labels and confidence values into a list of tuples
    predictions_list = list(zip(labels, obj.output()))

    for i in range(len(predictions_list)):
        print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))

print(clock.fps(), "fps")

口罩识别的

导入相应的库

import sensor, image, time

初始化摄像头

sensor.reset()

设置相机图像的对比度为1

sensor.set_contrast(1)

设置相机的增益上限为16

sensor.set_gainceiling(16)

设置采集到照片的大小

sensor.set_framesize(sensor.HQVGA)

设置采集到照片的格式：灰色图像

sensor.set_pixformat(sensor.GRAYSCALE)

加载Haar Cascade 模型

默认使用25个步骤，减少步骤会加快速度但会影响识别成功率

face_cascade = image.HaarCascade("frontalface", stage = 25)
print(face_cascade)

创建一个时钟来计算摄像头每秒采集的帧数FPS

clock = time.clock()

while(True):
# 更新FPS时钟
clock.tick()

# 拍摄图片并返回img
img = sensor.snapshot()

# 寻找人脸对象
# threshold和scale_factor两个参数控制着识别的速度和准确性
objects = img.find_features(face_cascade, threshold=0.75, scale_factor=1.25)

# 用矩形将人脸画出来
for r in objects:
	img.draw_rectangle(r)

# 串口打印FPS参数
# print(clock.fps())

人脸灰度识别的

[fggx]

这是口罩检测的代码：
# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, uos, gc

sensor.reset()                         # Reset and initialize the sensor.
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_windowing((240, 240))       # Set 240x240 window.
sensor.skip_frames(time=2000)          # Let the camera adjust.

net = None
labels = None

try:
    # load the model, alloc the model file on the heap if we have at least 64K free after loading
    net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
    print(e)
    raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

try:
    labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
    raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

clock = time.clock()
while(True):
    clock.tick()

    img = sensor.snapshot()

    # default settings just do one detection... change them to search the image...
    for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
        print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
        img.draw_rectangle(obj.rect())
        # This combines the labels and confidence values into a list of tuples
        predictions_list = list(zip(labels, obj.output()))

        for i in range(len(predictions_list)):
            print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))

    print(clock.fps(), "fps")











这是人脸识别的代码：
import sensor, image, time
sensor.reset()
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)
face_cascade = image.HaarCascade("frontalface", stage = 25)
print(face_cascade)
clock = time.clock()
while(True):
	clock.tick()
	img = sensor.snapshot()
	objects = img.find_features(face_cascade, threshold=0.75, scale_factor=1.25)
	for r in objects:
		img.draw_rectangle(r)
	print(clock.fps())

[kidswong999]

你直接神经网络里加一个“无人”的分类得了。

如果要组合代码，可以参考 https://singtown.com/learn/50029/

[fggx]

人脸检测+口罩识别：
使用impluse edge 和haar，需要添加到U盘里生成的两个文件，可以直接复制运行。

以下代码可以成功运行，矩形框标出人脸，并显示出mask yes 或者mask no，并实现了在lcd上显示。欢迎大家参考，有问题可以留言。

import sensor, image, time, os, tf, uos, gc, lcd

sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((240, 240))
sensor.skip_frames(time=2000)
face_cascade = image.HaarCascade("frontalface", stage=25)
net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
labels = [line.rstrip('\n') for line in open("labels.txt")]
clock = time.clock()


lcd.init()
lcd.clear()

while True:
    clock.tick()

    
    img = sensor.snapshot()

    
    objects = img.find_features(face_cascade, threshold=0.75, scale_factor=1.25)

    
    for r in objects:
        img.draw_rectangle(r)

   
    for r in objects:
        
        face_img = img.crop(r)
        
        mask = net.classify(face_img)[0].output()[0]
        
        if mask > 0.1:
            img.draw_rectangle(r, color=(0, 255, 0))
            img.draw_string(r[0], r[1], "Mask", color=(0, 255, 0))
        
        else:
            img.draw_rectangle(r, color=(255, 0, 0))
            img.draw_string(r[0], r[1], "No Mask", color=(255, 0, 0))

    
    lcd.display(img)

    
    print(clock.fps())