@kidswong999 这样吖，好的

@kidswong999 好的，这就试试

@kidswong999 我下载了github上的这个模型和代码

但是测试的结果依然不理想 咋回事捏

（上图为测试配戴口罩时的结果）

@kidswong999 好的，感谢

@kidswong999 带口罩与不带口罩2中分别各230张，一共400多张，应该够了吖（因为我看教程是各200多张）

我按照教程学习识别口罩目标检测 通过云端自动生成openmv的神经网络模型 判断是否佩戴口罩。 前面除了API Key 密钥无法使用 好在可以文件上传 （不知道会不会有影响），然后一切顺利，最后生成的3个文件复制到openmv里 运行py脚本 测试结果奇怪 图片中是否佩戴 它的2个识别概率都十分接近1（甚至还有超过1的，这比识别错了还难受 ）

网上的训练效果

生成的3个文件 添加至openmv的sd卡

代码

# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf

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 = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

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 tf.classify(net, 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())
        print(obj.output())
        # 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")

程序运行后的效果（2种情况的识别率很相近）

下面是未带口罩的返回值

Predictions at [x=0,y=0,w=240,h=240]
[1.05078, 0.949219]
face = 1.050781
mask = 0.949219
8.2121 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.03906, 0.960938]
face = 1.039063
mask = 0.960938
8.21203 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.01563, 0.984375]
face = 1.015625
mask = 0.984375
8.21228 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.03516, 0.964844]
face = 1.035156
mask = 0.964844
8.2122 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.00781, 0.992188]
face = 1.007813
mask = 0.992188
8.21213 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.01563, 0.984375]
face = 1.015625
mask = 0.984375
8.21238 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.01563, 0.984375]
face = 1.015625
mask = 0.984375
8.2123 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.01563, 0.984375]
face = 1.015625
mask = 0.984375
8.21223 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.01563, 0.984375]
face = 1.015625
mask = 0.984375
8.21247 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.00781, 0.992188]
face = 1.007813
mask = 0.992188
8.21239 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.01563, 0.984375]
face = 1.015625
mask = 0.984375
8.21233 fps

---

带上口罩后的返回值（识别率和不戴口罩差不多）

---

Predictions at [x=0,y=0,w=240,h=240]
[1.02344, 0.976563]
face = 1.023438
mask = 0.976563
8.21273 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.02344, 0.976563]
face = 1.023438
mask = 0.976563
8.21266 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.01172, 0.988281]
face = 1.011719
mask = 0.988281
8.21259 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.02344, 0.976563]
face = 1.023438
mask = 0.976563
8.21282 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.01563, 0.984375]
face = 1.015625
mask = 0.984375
8.21275 fps

---

Predictions at [x=0,y=0,w=240,h=240]
[1.01563, 0.984375]
face = 1.015625
mask = 0.984375
8.21268 fps