# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, uos, gc
from pyb import UART

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.
uart = UART(3,9600)

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())
        b = obj.output()   # a[(),(),()];print(a[0])
        c = b.index(max(b))
        if c == 0 or c == 1:
           uart.write('n'+'\n')
           print(c)
           print("cihua")
           
           time.sleep_ms(500)
           
         
        else :
           uart.write('y'+'\n')
           print(c)
           print("xionghua")
           time.sleep_ms(500# Edge Impulse - OpenMV Image Classification Example
           
           
           
           import sensor, image, time, os, tf, uos, gc
           
           from pyb import UART
           
           
           
           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.
           
           uart = UART(3,9600)
           
           
           
           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()
           
           
           
              
           
               for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
           
                   
           
                   img.draw_rectangle(obj.rect())
           
                   b = obj.output()   # a[(),(),()];print(a[0])
           
                   c = b.index(max(b))
           
                   if c == 0 or c == 1:
           
                      uart.write('n'+'\n')
           
                      print(c)
           
                      print("cihua")
           
                      
           
                      time.sleep_ms(500)
           
                      
           
                    
           
                   else :
           
                      uart.write('y'+'\n')
           
                      print(c)
           
                      print("xionghua")
           
                      time.sleep_ms(500)                                       

11a7-image.png](https://fcdn.singtown.com/81070053-2ba3-4deb-8fbd-c2b3e077dd0c.png)