# 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
# 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)
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QQVGA) # Set frame size to QVGA (320x240)
sensor.skip_frames(time = 2000) # Wait for settings take effect.
# Load template.
# Template should be a small (eg. 32x32 pixels) grayscale image.
template = image.Image("/666.pgm")
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, 10, step=4, search=SEARCH_EX)
if r:
img.draw_rectangle(r)
print(clock.fps())
