Ncc模块匹配，示例中的代码
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# 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.template = image.Image("/2.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 = img.find_template(template, 0.7, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60)) if r: img.draw_rectangle(r) print(clock.fps())
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模板太大了，换成小尺寸的模板。