import sensor, image, time, os, ml, math, uos, gc,pyb
from ulab import numpy as np
from machine import UART
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QVGA)
sensor.set_windowing((120, 120))
sensor.skip_frames(time=2000)
threshold = (100, 255)
net = None
labels = None
min_confidence = 0.5
try:
net = ml.Model("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as 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) + ')')
colors = [
(255, 0, 0),
( 0, 255, 0),
(255, 255, 0),
( 0, 0, 255),
(255, 0, 255),
( 0, 255, 255),
(255, 255, 255),
]
threshold_list = [(math.ceil(min_confidence * 255), 255)]
def fomo_post_process(model, inputs, outputs):
ob, oh, ow, oc = model.output_shape[0]
x_scale = inputs[0].roi[2] / ow
y_scale = inputs[0].roi[3] / oh
scale = min(x_scale, y_scale)
x_offset = ((inputs[0].roi[2] - (ow * scale)) / 2) + inputs[0].roi[0]
y_offset = ((inputs[0].roi[3] - (ow * scale)) / 2) + inputs[0].roi[1]
l = [[] for i in range(oc)]
for i in range(oc):
img = image.Image(outputs[0][0, :, :, i] * 255)
blobs = img.find_blobs(
threshold_list, x_stride=1, y_stride=1, area_threshold=1, pixels_threshold=1
)
for b in blobs:
rect = b.rect()
x, y, w, h = rect
score = (
img.get_statistics(thresholds=threshold_list, roi=rect).l_mean() / 255.0
)
x = int((x * scale) + x_offset)
y = int((y * scale) + y_offset)
w = int(w * scale)
h = int(h * scale)
l[i].append((x, y, w, h, score))
return l
clock = time.clock()
led = pyb.LED(1)
uart = UART(3, 9600)
while(True):
led.on()
clock.tick()
img = sensor.snapshot()
img.binary([threshold])
for i, detection_list in enumerate(net.predict([img], callback=fomo_post_process)):
if i == 0: continue
if len(detection_list) == 0: continue
print("********** %s **********" % labels[i])
result_str = str(labels[i])
uart.write(result_str.encode())
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aehu
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4.5.8的固件,神经网络训练的代码,通过USB链接电脑供电可以脱机运行,但是通过5v供电不可以实现