@kidswong999 !

![0_1716554234285_NI0~~OZW8Q1T$VG]$I((FCL.png](正在上传 0%) @kidswong999 

# 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("/image/e-5.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, 0.60, 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())

这是模板匹配的代码

import sensor, image

# 初始化摄像头
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time=2000)

# 加载模板图像
template = image.Image("/image/e-5.pgm", copy_to_fb=True)

# 定义模板匹配的阈值
threshold = 0.7

while True:
    # 拍摄一张图像
    img = sensor.snapshot()
    
    # 在整个图像中搜索模板
    res = img.find_template(template, threshold, roi=(0, 0, img.width(), img.height()))
    
    # 如果找到了匹配
    if res:
        # 获取匹配位置的中心点
        cx = res[0] + res[2] // 2
        
        # 计算图像中心点与模板匹配中心点的横坐标差距
        img_center_x = img.width() // 2
        distance_x = img_center_x - cx
        
        # 打印横坐标差距到串行终端
        print("Horizontal Distance:", distance_x)

@kidswong999

你好大佬，我的问题是这样的
我想问下为什么这里总是显示没有未压缩的图片，但是我截取的图片采用的就是openmv官网给的代码，openmv进行模板匹配的图片必须是pgm格式，但是模板截取不能直接生成pgm格式，要转换格式必须进行压缩，这不是自相矛盾吗

dddddddd

在线等，挺急的:

rt，我在使用模板匹配时将roi的值省略，而且模板图片截取也是完全没有压缩，直接保存为pgm格式，路径名也检查过了完全没有问题，但是依然显示ValueError: Expected an uncompressed image错误，请问为什么