自学教程：基于OpenMV的图像识别之数字识别功能

# 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, imagefrom image import SEARCH_EX, SEARCH_DS# Reset sensorsensor.reset()# Set sensor settingssensor.set_contrast(1)sensor.set_gainceiling(16)# Max resolution for template matching with SEARCH_EX is QQVGAsensor.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.template8 = image.Image("/8.pgm")template9 = image.Image("/9.pgm")clock = time.clock()# Run template matchingwhile (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 8= img.find_template(template8, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))    if r8:        img.draw_rectangle(r8)   r 9= img.find_template(template9, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))    if r9:        img.draw_rectangle(r9)    print(clock.fps())