def BlobTest(thresholds, loopCnt=390, barLen=120):
sensor.reset()
sensor.set_framerate(1 << 11)
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.CIF)
#sensor.set_windowing((320,240))
sensor.set_auto_gain(True)
#sensor.set_auto_whitebal(True) # must be turned off for color tracking
clock = time.clock()
avg = 0.0
startTick = time.ticks()
while (True):
if time.ticks() - startTick > loopCnt:
break
clock.tick()
img = sensor.snapshot()
t0 = time.ticks()
blobSet = img.find_blobs(thresholds,
pixels_threshold=200,
area_threshold=200)
t1 = time.ticks() - t0
avg = avg * 0.95 + t1 * 0.05
lnLen = (barLen * (loopCnt - (time.ticks() - startTick))) // loopCnt
DrawPgsBar(img, barLen, loopCnt, startTick,
'red,green,blue blob detect')
for blob in blobSet:
img.draw_rectangle(blob.rect())
img.draw_cross(blob.cx(), blob.cy())
print('algo time cost : %.2f ms' % (avg))
def CIFAR10Test(loopCnt=600, isFull=False, barLen=105):
pyb.LED(1).off()
sensor.reset() # Reset and initialize the sensor.
sensor.set_contrast(3)
sensor.set_pixformat(
sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.VGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((192, 192)) # Set window
sensor.skip_frames(time=100) # Wait for settings take effect.
sensor.set_auto_gain(True)
sensor.set_framerate(0 << 9 | 1 << 12)
if isFull:
net = nn.load('/cifar10.network')
else:
net = nn.load('/cifar10_fast.network')
labels = [
'plane', 'auto', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship',
'truck'
]
clock = time.clock()
tAvg = 0.0
startTick = time.ticks()
while (True):
if time.ticks() - startTick > loopCnt:
break
clock.tick()
img = sensor.snapshot()
t0 = time.ticks()
lst = net.search(img, threshold=0.640, min_scale=1, scale_mul=0.8, \
x_overlap=-1, y_overlap=-1, contrast_threshold=0.5)
t1 = time.ticks() - t0
tAvg = tAvg * 0.9 + t1 * 0.1
img.draw_string(
4,
8,
'CIFAR-10: classify:\nplane,auto,cat,dog,\ndeer,horse,frog,ship,\ntruck,horse',
color=(0, 0, 0))
lnLen = (barLen * (loopCnt - (time.ticks() - startTick))) // loopCnt
DrawPgsBar(img, barLen, loopCnt, startTick)
for obj in lst:
print(' %s - Confidence %f%%' %
(labels[obj.index()], obj.value()))
rc = obj.rect()
#img.draw_rectangle(rc, color=(255,255,255))
img.draw_rectangle(barLen + 10,
1,
50,
8,
fill=True,
color=(0, 0, 0))
img.draw_string(barLen + 10, 0, labels[obj.index()])
print('algo time cost : %.2f ms' % (tAvg))
def QRCodeTest(loopCnt=120, barLen=120):
sensor.reset()
sensor.set_framerate(1 << 11)
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.VGA)
sensor.set_windowing((400, 272))
sensor.skip_frames(time=300)
sensor.set_auto_gain(False)
clock = time.clock()
avg = 0.0
startTick = time.ticks()
while (True):
if time.ticks() - startTick > loopCnt:
break
clock.tick()
img = sensor.snapshot()
#img.lens_corr(1.5) # strength of 1.8 is good for the 2.8mm lens.
t1 = time.ticks()
codeSet = img.find_qrcodes()
t2 = time.ticks() - t1
lnLen = (barLen * (loopCnt - (time.ticks() - startTick))) // loopCnt
DrawPgsBar(img, barLen, loopCnt, startTick, 'QR code scan')
avg = avg * 0.92 + t2 * 0.08
for code in codeSet:
rc = code.rect()
img.draw_rectangle(rc, thickness=2, color=(0, 191, 255))
#print(type(code))
#print(code.payload())
sPayload = code.payload()
#print(len(sPayload))
lnLen = len(sPayload) * 8
if rc[0] + lnLen >= 400:
x = 400 - lnLen
else:
x = rc[0]
img.draw_rectangle(x - 1,
rc[1] + 1,
lnLen + 2,
8,
color=(0, 0, 0),
fill=True)
img.draw_string(x, rc[1], sPayload)
print('algo time cost : %.2f ms' % (avg))
def LENETTest(loopCnt=1200, barLen=60):
sensor.reset() # Reset and initialize the sensor.
sensor.set_contrast(3)
sensor.set_pixformat(
sensor.GRAYSCALE) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.VGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((84, 84)) # Set 128x128 window.
sensor.skip_frames(time=1400) # Wait for settings take effect.
sensor.set_auto_gain(False)
sensor.set_framerate(2 << 2)
#sensor.set_auto_whitebal(False)
#sensor.set_auto_exposure(False)
net = nn.load('/lenet.network')
labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
clock = time.clock()
avg = 0.0
pyb.LED(1).on()
startTick = time.ticks()
while (True):
if time.ticks() - startTick > loopCnt:
break
clock.tick()
img = sensor.snapshot()
img.draw_string(3, 8, 'recg 0-9', color=(0, 0, 0))
t1 = time.ticks()
tmp_img = img.copy().binary([(120, 255)], invert=True)
lst = net.search(tmp_img, threshold=0.8, min_scale=1, scale_mul=0.8, \
x_overlap=-1, y_overlap=-1, contrast_threshold=0.5, softmax=False)
t2 = time.ticks() - t1
avg = avg * 0.95 + t2 * 0.05
lnLen = (barLen * (loopCnt - (time.ticks() - startTick))) // loopCnt
img.draw_rectangle(0, 2, barLen + 1, 3)
img.draw_rectangle(0, 3, lnLen, 1, fill=True)
for obj in lst:
print('Detected %s - Confidence %f%%' %
(labels[obj.index()], obj.value()))
img.draw_rectangle(obj.rect())
img.draw_string(barLen + 8,
2,
labels[obj.index()],
color=(0, 0, 0))
# print(clock.fps())
print('algo time cost : %.2f ms' % (avg))
import sensor, image, time, pyb import my_ips, my_file, my_uart, my_key sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framerate(2<<11) sensor.set_framesize(sensor.QQVGA)#160x120 my_ips.init() sensor.skip_frames(time = 500) sensor.set_auto_gain(False) sensor.set_auto_whitebal(False) sensor.skip_frames(time = 100) clock = time.clock() ledR = pyb.LED(1) ledG = pyb.LED(2) ledB = pyb.LED(3) ledB.on() ledR.on() ledG.on() colorxy = 0 img_cnt = 0 red_threshold = [[12, 80, 16, 73, -1, 56],[12, 80, 16, 73, -1, 56],[12, 80, 16, 73, -1, 56],[0, 70, 19, 90, -11, 35]] blue_threshold = [0, 50, -128, 127, -128, -5] black_threshold = [0, 15, -128, 127, -128, 127] white_threshold = [40, 100, -128, 127, -128, 127] red_ch = 0 roi_white = [(109,0,2,120),(113,0,2,120),(117,0,2,120),(121,0,2,120)] roi_white2 = [73,0,2,120]
# Himax motion detection example.
import sensor, image, time, pyb
from pyb import Pin, ExtInt
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time=2000)
# The sensor is less noisy with lower FPS.
sensor.set_framerate(15)
# Configure and enable motion detection
sensor.ioctl(sensor.IOCTL_HIMAX_MD_THRESHOLD, 0x01)
sensor.ioctl(sensor.IOCTL_HIMAX_MD_WINDOW, (0, 0, 320, 240))
sensor.ioctl(sensor.IOCTL_HIMAX_MD_CLEAR)
sensor.ioctl(sensor.IOCTL_HIMAX_MD_ENABLE, True)
motion_detected = False
def on_motion(line):
global motion_detected
motion_detected = True
led = pyb.LED(3)
# Configure external interrupt pin. When motion is detected, this pin is pulled high
ext = ExtInt(Pin("PC15"), ExtInt.IRQ_RISING, Pin.PULL_DOWN, on_motion)
# Reset sensor
sensor.reset()
# Sensor settings
sensor.set_auto_gain(True)
sensor.set_auto_exposure(True)
sensor.set_auto_whitebal(True)
sensor.set_brightness(2)
sensor.set_contrast(1)
sensor.set_gainceiling(16)
# HQVGA and GRAYSCALE are the best for face tracking.
sensor.set_framesize(sensor.CIF)
sensor.set_windowing((256, 192))
sensor.set_pixformat(sensor.RGB565)
sensor.set_framerate(0 << 9 | 1 << 11)
sensor.skip_frames(time=700) # Wait for settings take effect.
sensor.set_auto_gain(False)
# FPS clock
clock = time.clock()
nndemo.show(0, index=0, data=0)
while (True):
clock.tick()
nndemo.show(0, index=0, data=0)
#sensor.snapshot()
# Print FPS.
# Note: Actual FPS is higher, streaming the FB makes it slower.
print(clock.fps())