openmv怎么打开已有视频进行人脸识别？

@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("/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())

@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("/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())

直接用的历程，运行出现这样的问题？

@yuan 尝试过了，谢谢

所以，还不能实现是么？有没有其他的好办法么？

求助，要计数AB两人的人脸出现的频率，如三秒内AB两人分别出现的次数？目前单人可以计次频率（基于人脸识别）。

分辨不同人脸，保存图片的时间都很奇怪。