>>> 星瞳科技|OpenMV中国官方网站 <<<

>>> 星瞳科技店铺地址|OpenMV中国官方代理,全球九大代理商之一 <<<

>>> OpenMV3 Cam M7 官方高配版上市啦~戳我戳我戳我~ <<<

>>> 星瞳科技-OpenMV中文教程网 <<<

本例程为 09-Feathre_detection-template_matching
本例程的目标是用 NCC(归一化积相关算法)实现模版匹配。
模版匹配的具体操作请看OpenMV教程11-模版匹配

# 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
from image import SEARCH_EX, SEARCH_DS
#从imgae模块引入SEARCH_EX和SEARCH_DS。使用from import仅仅引入SEARCH_EX, 
#SEARCH_DS两个需要的部分,而不把image模块全部引入。

# 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("/template.pgm")
#加载模板图片

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))
    #find_template(template, threshold, [roi, step, search]),threshold中
    #的0.7是相似度阈值,roi是进行匹配的区域(左上顶点为(10,0),长80宽60的矩形),
    #注意roi的大小要比模板图片大,比frambuffer小。
    #把匹配到的图像标记出来
    if r:
        img.draw_rectangle(r)

    print(clock.fps())