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toddler.py
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toddler.py
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# import the necessary packages
#!/usr/bin/env python
from __future__ import division
from __future__ import absolute_import
import numpy as np
import time
import imutils
import paho.mqtt.client as mqtt
import cv2
from itertools import izip
from picamera import PiCamera
import argparse
import glob
from enum import Enum
template = cv2.imread("/home/student/logo.jpg")
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
template = cv2.Canny(template, 50, 200)
(tH, tW) = template.shape[:2]
left_time=0
rigt_time=0
count=0
camera=PiCamera()
camera.resolution=(360,240)
location= np.zeros(7)
logo_number=0
def start_capture():
curt=time.time()
bf_wr =time.time()
camera.capture("image.jpg")
image= cv2.imread("/home/student/image.jpg")
cv2.imshow('Camera',image)
af_wr = time.time()
time.sleep(0.05)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
found = None
all_found = None
final_result = None
global location
location = np.zeros(7)
# loop over the scales of the image
for scale in np.linspace(0.1, 1.0, 30)[::-1]:
# resize the image according to the scale, and keep track
# of the ratio of the resizing
resized = imutils.resize(gray, width=int(gray.shape[1] * scale))
r = gray.shape[1] / float(resized.shape[1])
# if the resized image is smaller than the template, then break
# from the loop
if resized.shape[0] < tH or resized.shape[1] < tW:
break
# detect edges in the resized, grayscale image and apply template
# matching to find the template in the image
edged = cv2.Canny(resized, 50, 200)
result = cv2.matchTemplate(edged, template, cv2.TM_CCOEFF)
(_, maxVal, _, maxLoc) = cv2.minMaxLoc(result)
threshold = 0.20*maxVal
loc = np.where(result >= threshold)
# if we have found a new maximum correlation value, then update
# the bookkeeping variable
if found is None or maxVal > found[0]:
found = (maxVal, maxLoc, r)
all_found = loc
final_result = result
# unpack the bookkeeping variable and compute the (x, y) coordinates
# of the bounding box based on the resized ratio
previous = None # previous possible template place
ps_X = None
ps_Y = None
pe_X = None
pe_Y = None
p_logo = None
(_, maxLoc, r) = found
Y, X = all_found
indice = np.argsort(X)
all_found = (Y[indice], sorted(X))
total = len(all_found[0])
counter = 0
image3 = image.copy()
for pt in izip(*all_found[::-1]):
counter = counter + 1
(startX, startY) = (int(pt[0] * r), int(pt[1] * r))
(endX, endY) = (int((pt[0] + tW) * r), int((pt[1] + tH) * r))
# at corner
if (pt[0] <= 1 or pt[0] >= final_result.shape[1] - 1) or (pt[1] <= 1 or pt[1] >= final_result.shape[0] - 1):
continue
# only one logo
if total == 1:
detected, Logo = color_detection(image3[startY:endY, startX: endX])
if detected:
mark_logo(Logo, startX, endX, location)
continue
# get local infomation
img = final_result[pt[1]-1: pt[1] + 1, pt[0] - 1: pt[0] + 1]
if previous is None:
detected, Logo = color_detection(image3[startY:endY, startX: endX])
if detected:
previous = np.argmax(img)
(ps_X, ps_Y) = (startX, startY)
(pe_X, pe_Y) = (endX, endY)
p_logo = Logo
continue
continue
if ps_X - 2 <= startX <= ps_X + 2:
if counter < total:
continue
else:
detected, Logo = color_detection(image3[ps_Y:pe_Y, ps_X: pe_X])
if detected:
mark_logo( Logo, ps_X, pe_X, location)
continue
if ps_X - int(tW) * r <= startX <= ps_X + int(tW) * r:
current = np.argmax(img)
if current < previous:
if counter == total:
mark_logo(p_logo, ps_X, pe_X, location)
continue
else:
detected, Logo = color_detection(image3[startY:endY, startX: endX])
if detected:
if counter == total:
mark_logo(Logo, startX, endX, location)
continue
else:
previous = np.argmax(img)
(ps_X, ps_Y) = (startX, startY)
(pe_X, pe_Y) = (endX, endY)
p_logo = Logo
continue
else:
if counter == total:
detected, Logo = color_detection(image3[ps_Y:pe_Y, ps_X: pe_X])
if detected:
mark_logo(Logo, ps_X, pe_X, location)
else:
continue
# draw a bounding box around the detected result and display the image
detected, Logo = color_detection(image3[ps_Y:pe_Y, ps_X: pe_X])
if detected:
mark_logo(Logo, ps_X, pe_X, location)
detected, Logo = color_detection(image3[startY:endY, startX: endX])
if detected:
previous = np.argmax(img)
(ps_X, ps_Y) = (startX, startY)
(pe_X, pe_Y) = (endX, endY)
p_logo = Logo
else:
previous = None
if counter == total:
detected, Logo = color_detection(image3[startY:endY, startX: endX])
if detected:
mark_logo(Logo, startX, startY, location)
timexx=time.time()
print(af_wr-bf_wr)
print(timexx-curt)
print(location)
return (location)
def vision():
while(1):
start_capture()
def color_detection(image):
img_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
x = image.shape[0]
y = image.shape[1]
e = 5
d = 1
k = None
lower_blue = np.array([75, 50, 50])
upper_blue = np.array([130, 255, 255])
maskb = cv2.inRange(img_hsv, lower_blue, upper_blue)
maskb = cv2.dilate(maskb, kernel=k, iterations=d)
maskb = cv2.erode(maskb, kernel=k, iterations=e)
(contours, _) = cv2.findContours(maskb, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
B = len(contours)
lower_yellow = np.array([22, 50, 50])
upper_yellow = np.array([38, 255, 255])
masky = cv2.inRange(img_hsv, lower_yellow, upper_yellow)
masky = cv2.dilate(masky, kernel=k, iterations=d)
masky = cv2.erode(masky, kernel=k, iterations=e)
(contours, _) = cv2.findContours(masky, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
if len(contours) != 1:
return False, -1
cx = None
cy = None
for c in contours:
# compute the center of the contour
m = cv2.moments(c)
if m[u"m00"] == 0:
return False, -1
cx = int(m[u"m10"] / m[u"m00"])
cy = int(m[u"m01"] / m[u"m00"])
x_r, y_r = cx / x, cy / y
if x_r <= 0.28 or x_r >= 0.72 or y_r >= 0.72 or y_r <= 0.28:
return False, -1
# upper mask (170-180)
lower_red = np.array([170, 50, 50])
upper_red = np.array([180, 255, 255])
mask1 = cv2.inRange(img_hsv, lower_red, upper_red)
lower_red = np.array([0, 50, 50])
upper_red = np.array([10, 255, 255])
mask2 = cv2.inRange(img_hsv, lower_red, upper_red)
maskr = mask1 + mask2
maskr = cv2.dilate(maskr, kernel=k, iterations=d)
maskr = cv2.erode(maskr, kernel=k, iterations=e)
(contours, _) = cv2.findContours(maskr, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
R = len(contours)
if (R + B) != 6:
return False, -1
if len(contours) == 1:
return True, 1
if len(contours) == 2:
return True, 2
if len(contours) == 3:
return True, 3
if len(contours) == 4:
return True, 4
if len(contours) == 5:
return True, 5
if len(contours) == 6:
return True, 6
if len(contours) == 0:
return True, 7
return False, -1
def mark_logo(Logo, startX, endX, location):
if Logo == 1:
location[0] = (startX + endX)/2
if Logo == 2:
location[1] = (startX + endX)/2
if Logo == 3:
location[2] = (startX + endX)/2
if Logo == 4:
location[3] = (startX + endX)/2
if Logo == 5:
location[4] = (startX + endX)/2
if Logo == 6:
location[5] = (startX + endX)/2
if Logo == 7:
location[6] = (startX + endX)/2
def setup_mqtt():
client = mqtt.Client("Pi")
client.on_connect=onConnect
client.on_message=onMessage
client.connect("129.215.3.65")
return client
def onConnect(client,userdata,flags,rc):
print("connected with result code %i" % rc)
client.subscribe("pi-finish-instruction")
def onMessage(client,userdata,msg):
global location
global logo_number
if msg.topic=="pi-finish-instruction":
if(location[logo_number]==0 | location[logo_number]<130 | location[logo_number]>190):
send_message()
else:
stop()
def send_message():
global logo_number
global count
global left_time
global rigt_time
if (location[logo_number]==0):
count = count +1
if count % 2 == 0 :
left_time = right_time+2
client.publish("pi-start-instruction","rt,l,"+str(left_time),qos=2)
else:
rigt_time = left_time+2
client.publish("pi-start-instruction","rt,r,"+str(rigt_time),qos=2)
else:
if (location[logo_number]<130):
client.publish("pi-start-instruction","rc,r",qos=2)
if (location[logo_number]>190):
client.publish("pi-start-instruction","rc,l",qos=2)
def stop():
return 0
client = setup_mqtt()
location=start_capture()
send_message()
vision()