def work(img):
#tmp = image.Image(size=(160, 160))
#tmp = tmp.to_grayscale()
ui.canvas = ui.canvas.to_grayscale()
bak = img.cut(100, 60, 120, 120).to_grayscale()
#img.draw_image(tmp, (0, 0))
ui.canvas.draw_image(bak, (320 + 20, 0 + 20))
#img1 = tmp
#img1 = img1.to_grayscale(1)
#tmp = img.resize(224, 224)
#img1 = img.to_grayscale(1) # convert to gray
#tmp = img1.cut(20, 20, 120, 120) # resize to Minist input 28x28
#ui.canvas.draw_image(tmp, (320 + 20, 160 + 20))
a = bak.invert() # invert picture as Minist need
a = bak.strech_char(1) # preprocessing pictures, eliminate dark corner
img.draw_image(bak.resize(80, 80),
(0, 0)) # display small 28x28 picture # alpha=25
bak = bak.resize(28, 28)
a = bak.pix_to_ai() # generate data for ai
fmap = kpu.forward(Minist.task, bak) # run neural network model
plist = fmap[:] # get result (10 digit's probability)
pmax = max(plist) # get max probability
try:
max_index = plist.index(pmax) # get the digit
except Exception as e:
print(e)
Minist.score = pmax
Minist.result = max_index
#ui.canvas.draw_string(0, 300, "%d: %.3f" % (max_index, pmax), scale=2) # show result
img.draw_rectangle(80, 40, 160, 160, thickness=2, color=(0, 255, 0))
img.draw_rectangle(80 + 20,
40 + 20,
120,
120,
thickness=2,
color=(255, 0, 0))
if ui.weight > 240:
size = 28 * 2
x, y = 5, int(240) + 5 - size
#watch conv0
a = kpu.set_layers(Minist.task, 1)
fmap = kpu.forward(Minist.task, bak) # run neural network model
for i in range(0, 16):
tmp = kpu.fmap(fmap, i)
tmpx2 = tmp.resize(14 * 2, 14 * 2) # scale to display
a = ui.canvas.draw_image(tmpx2, (i % 8) * 14 * 2 + x,
y + size + 14 * 2 * int(i / 8))
x, y = x + 10 + size * 4, y + 5 - size
#watch conv1
a = kpu.set_layers(Minist.task, 2)
fmap = kpu.forward(Minist.task, bak) # run neural network model
for i in range(0, 32):
tmp = kpu.fmap(fmap, i)
tmpx2 = tmp.resize(7 * 2, 7 * 2) # scale to display
a = ui.canvas.draw_image(
tmpx2, (i % 16) * 7 * 2 + x,
y + size + 14 * 2 * 2 + 7 * 2 * int(i / 16))
x, y = x + 15, y + 10
#watch conv2
a = kpu.set_layers(Minist.task, 8)
fmap = kpu.forward(Minist.task, bak) # run neural network model
for i in range(0, 10):
tmp = kpu.fmap(fmap, i)
tmpx2 = tmp.resize(4 * 4, 4 * 4) # scale to display
a = ui.canvas.draw_image(tmpx2, i * 4 * 4 + x + i * 5,
y + size + 14 * 2 * 2 + 7 * 2 * 2)
#watch softmax
a = kpu.set_layers(Minist.task, 11)
fmap = kpu.forward(Minist.task, bak)
plist = fmap[:]
for i in range(0, 10):
cc = int(plist[i] * 256)
ui.canvas.draw_string(i * 4 * 4 + x + 3 + i * 5,
y + 20 + size + 14 * 2 * 2 + 7 * 2 * 2,
'%.0f' % (plist[i] * 100),
scale=1,
color=(255, 255, 255))
#print(i, cc)
del bak
return img
img_lcd = img_lcd.to_grayscale()
while True:
img2 = sensor.snapshot()
#lcd.display(img,oft=(0,0)) #display large picture
img2=img2.to_grayscale() #convert to gray
img2=img2.resize(28,28) #resize to mnist input 28x28
a=img2.invert() #invert picture as mnist need
# a=img2.strech_char(1) #preprocessing pictures, eliminate dark corner
img2x2=img2.resize(28*2,28*2) #scale to display
a = img_lcd.draw_image(img2x2,0,0)#display small 28x28 picture
a=img2.pix_to_ai(); #generate data for ai
#watch conv0
a=kpu.set_layers(task, 1)
fmap=kpu.forward(task,img2) #run neural network model
for i in range(0,16):
tmp=kpu.fmap(fmap,i)
tmpx2=tmp.resize(14*2,14*2) #scale to display
a=img_lcd.draw_image(tmpx2,(i%8)*14*2,28*2+14*2*int(i/8))
#watch conv1
a=kpu.set_layers(task, 2)
fmap=kpu.forward(task,img2) #run neural network model
for i in range(0,32):
tmp=kpu.fmap(fmap,i)
tmpx2=tmp.resize(7*2,7*2) #scale to display
a=img_lcd.draw_image(tmpx2,(i%16)*7*2,28*2+14*2*2+7*2*int(i/16))
#watch conv2
a=kpu.set_layers(task, 8)
fmap=kpu.forward(task,img2) #run neural network model
for i in range(0,10):
tmp=kpu.fmap(fmap,i)
tmpx2=tmp.resize(4*2,4*2) #scale to display