r_r = (87,22,20,25) #right half of ROI
left_angle = 70 # these values are the angles of the largest line-segment ... with 90degrees
right_angle = 110 # being "straight down from the top of the frame". It was found empirically ...
#***************************************************************************************************
# Input from Speedy Packer
#***************************************************************************************************
# Pull Pin 0 LOW when about to dispense foam
# pin0 = Pin('P0', Pin.IN, Pin.PULL_UP) # IO Placeholder ...
#***************************************************************************************************
# Recorded Image
#***************************************************************************************************
img_reader = None if snapshot_source else image.ImageReader("/FU_IR_33_9V.bin")
#***************************************************************************************************
while(True):
clock.tick()
# if pin0.value() == FALSE :
# Do the rest of the loop ... you'll need to indent everything below.
img = sensor.snapshot() if snapshot_source else img_reader.next_frame(copy_to_fb=True, loop=True)
#img.draw_rectangle(l_r, color = 155 ) #commented out unless needed by user
#img.draw_rectangle(r_r, color = 155 )
# histogram of ROI
##hist = img.get_histogram(roi = r)
# this is the mean brightness of the ROI and was found to be approximately the "low end of the
# brightness" of the IR LEDs as seen through the bag
# Image Reader Example
#
# USE THIS EXAMPLE WITH A USD CARD!
#
# This example shows how to use the Image Reader object to replay snapshots of what your
# OpenMV Cam saw saved by the Image Writer object for testing machine vision algorithms.
import sensor, image, time
snapshot_source = False # Set to true once finished to pull data from sensor.
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()
img_reader = None if snapshot_source else image.ImageReader("/stream.bin")
while(True):
clock.tick()
img = sensor.snapshot() if snapshot_source else img_reader.next_frame(copy_to_fb=True, loop=True)
# Do machine vision algorithms on the image here.
print(clock.fps())
print("Loading model")
person_cascade = image.HaarCascade(
"/PeopleCounting/people_counting_cascade.cascade", stages=25)
height = None
width = None
count = 0
direction = 0
isCounted = False
i = 1
j = 1
b_boxes = []
vid = image.ImageReader("/PeopleCounting/walking.bin")
while (True):
clock.tick()
#img = image.Image("/PeopleCounting/person.bmp", copy_to_fb = True)
img = vid.next_frame(copy_to_fb=True)
if not img:
print("video end")
break
height = img.height()
width = img.width()
line_x1 = width // 2 #0
line_y1 = 0 #height//2