识别模版并闪光 目前常量求解决方案

Template Matching Example - Normalized Cross Correlation (NCC)

This example shows off how to use the NCC feature of your OpenMV Cam to match

image patches to parts of an image... expect for extremely controlled enviorments

NCC is not all to useful.

WARNING: NCC supports needs to be reworked! As of right now this feature needs

a lot of work to be made into somethin useful. This script will reamin to show

that the functionality exists, but, in its current state is inadequate.

import time, sensor, image,pyb
from image import SEARCH_EX, SEARCH_DS

Reset sensor

sensor.reset()

Set sensor settings

sensor.set_contrast(1)
sensor.set_gainceiling(16)

Max resolution for template matching with SEARCH_EX is QQVGA

sensor.set_framesize(sensor.QQVGA)

You can set windowing to reduce the search image.

#sensor.set_windowing(((640-80)//2, (480-60)//2, 80, 60))
sensor.set_pixformat(sensor.GRAYSCALE)

Load template.

Template should be a small (eg. 32x32 pixels) grayscale image.

template = image.Image("/sb1.pgm")

clock = time.clock()

Run template matching

while (True):
clock.tick()
img = sensor.snapshot()

# find_template(template, threshold, [roi, step, search])
# ROI: The region of interest tuple (x, y, w, h).
# Step: The loop step used (y+=step, x+=step) use a bigger step to make it faster.
# Search is either image.SEARCH_EX for exhaustive search or image.SEARCH_DS for diamond search
#
# Note1: ROI has to be smaller than the image and bigger than the template.
# Note2: In diamond search, step and ROI are both ignored.
r = img.find_template(template, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
led = pyb.LED(3) # Red LED = 1, Green LED = 2, Blue LED = 3, IR LEDs = 4.
usb = pyb.USB_VCP()

led.on()
time.sleep(150)
led.off()
time.sleep(100)
led.on()
time.sleep(150)
led.off()
time.sleep(600)
if r:
    img.draw_rectangle(r)

print(clock.fps())