def tweet(a):
pc.unlock()
pc.move(xy.app(3, 2))
pc.tap() # open twitter
pc.movexy(2850, 3000)
pc.lightTap() # hit tweet button
sleep(4) # wait for keyboard to come up
pc.type(a)
pc.movexy(2800, 6350) # hit tweet button
pc.tap()
pc.getOutTheWay()
sleep(5)
pc.homeButton()
pc.zero()
sleep(1)
pc.onOff()
def lookForHearts():
#Create a memory stream so photos doesn't need to be saved in a file
stream = io.BytesIO()
camW = 1280
camH = 960
#Get the picture (low resolution, so it should be quite fast)
#Here you can also specify other parameters (e.g.:rotate the image)
with picamera.PiCamera() as camera:
camera.resolution = (camW, camH) # 4:3 1280:960 also works
camera.capture(stream, format='jpeg')
#Convert the picture into a numpy array
buff = numpy.fromstring(stream.getvalue(), dtype=numpy.uint8)
#Now creates an OpenCV image
image = cv2.imdecode(buff, 1)
#crops in on image to search for hearts only in the left bit of the phone
roiX = 640
roiY = 300
roiYMax = 800
roi = image[roiY:roiYMax, 640:750] # height range, width range (weird)
#Load a cascade file for detecting faces
heart_cascade = cv2.CascadeClassifier('camera/heart_cascade_level_16.xml')
#Convert to grayscale
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY) # look for hearts within roi
#Look for faces in the image using the loaded cascade file
hearts = heart_cascade.detectMultiScale(
roi) # previously looked at gray instead of cropped roi
numHearts = len(hearts)
print "Found " + str(numHearts) + " heart(s)"
if numHearts == 1:
print "one heart"
#Draw a rectangle around every found heart
for (x, y, w, h) in hearts:
heartX = roiX + x + w / 2 # around the heart plus the distance from the edge to the roi
heartY = roiY + y + h / 2
cv2.rectangle(image, (x + roiX, y + roiY),
(x + w + roiX, y + h + roiY), (255, 0, 0), 2)
# draw a point at the center of the heart
cv2.line(image, (heartX, heartY), (heartX, heartY), (0, 0, 255), 5)
cv2.imwrite('result.jpg', image) # save image
print "heartX, heartY = (%d, %d)" % (heartX, heartY)
toY = mapValues.myMap(heartY)
print "toY = %d" % toY
pc.movexy(900, toY)
pc.lightTap()
pc.checkStatus()
b = random.randint(1, 100)
#tapped the heart, now decide whether to comment or not
print "random number = %d" % b
if b <= 20:
pc.x(1100) # move over to the comment button
pc.tap()
pc.type("nice")
pc.movexy(2800, 4300) #post comment
pc.tap()
sleep(2)
pc.movexy(900, 6300) # back to stream
pc.tap()
if b > 20 and b <= 40:
pc.x(1100) # move over to the comment button
pc.tap()
pc.type('great pic')
pc.movexy(2800, 4300) #post comment
pc.tap()
sleep(2)
pc.movexy(900, 6300) #back to stream
pc.tap()
return (toY)
else:
print "no hearts found"
def lookForLikes():
#Create a memory stream so photos doesn't need to be saved in a file
stream = io.BytesIO()
camW = 1280
camH = 960
#Get the picture (low resolution, so it should be quite fast)
#Here you can also specify other parameters (e.g.:rotate the image)
with picamera.PiCamera() as camera:
camera.resolution = (camW,camH) # 4:3 1280:960 also works
camera.capture(stream, format='jpeg')
#Convert the picture into a numpy array
buff = numpy.fromstring(stream.getvalue(), dtype=numpy.uint8)
#Now creates an OpenCV image
image = cv2.imdecode(buff, 1)
#crops in on image to search for hearts only in the left bit of the phone
roiX = 670
roiXMax = 830
roiY = 300
roiYMax = 1000
minsize = 60
maxsize = 69
roi = image[roiY:roiYMax,roiX:roiXMax] # height range, width range (weird)
#Load a cascade file for detecting faces
the_cascade = cv2.CascadeClassifier('camera/cascades/fb_like5.xml')
#Convert to grayscale
gray = cv2.cvtColor(roi,cv2.COLOR_BGR2GRAY) # look for hearts within roi
bluramt = 2
blur = cv2.blur(gray,(bluramt,bluramt))
#Look for faces in the image using the loaded cascade file
hearts = the_cascade.detectMultiScale(blur,1.05,20,minSize=(minsize,minsize),maxSize=(maxsize,maxsize)) # previously looked at gray instead of cropped roi
numHearts = len(hearts)
print "Found "+str(numHearts)+" heart(s)"
if numHearts == 1:
print "one heart"
#Draw a rectangle around every found heart
for (x,y,w,h) in hearts:
heartX = roiX + x + w/2 # around the heart plus the distance from the edge to the roi
heartY = roiY + y + h/2
cv2.rectangle(image,(x + roiX,y + roiY),(x+w + roiX,y+h + roiY),(255,0,0),2)
# draw a point at the center of the heart
cv2.line(image,(heartX,heartY),(heartX,heartY),(0,0,255),5)
cv2.imwrite('result.jpg',image) # save image
print "heartX, heartY = (%d, %d)" % (heartX, heartY)
toY = mapValues.myMap(heartY)
print "toY = %d" % toY
pc.movexy(1200,toY)
pc.lightTap()
pc.checkStatus()
b = random.randint(1,100)
return(toY)
else:
print "no hearts found"
def followBack():
#Create a memory stream so photos doesn't need to be saved in a file
stream = io.BytesIO()
camW = 1280
camH = 960
#Get the picture (low resolution, so it should be quite fast)
#Here you can also specify other parameters (e.g.:rotate the image)
with picamera.PiCamera() as camera:
camera.resolution = (camW, camH) # 4:3 1280:960 also works
camera.capture(stream, format='jpeg')
#Convert the picture into a numpy array
buff = numpy.fromstring(stream.getvalue(), dtype=numpy.uint8)
#Now creates an OpenCV image
image = cv2.imdecode(buff, 1)
#crops in on image to search for hearts only in the left bit of the phone
roiX = 955
roiXMax = 1255
roiY = 50
roiYMax = 850
roi = image[roiY:roiYMax,
roiX:roiXMax] # height range, width range (weird)
cv2.rectangle(image, (roiX, roiY), (roiXMax, roiYMax), (255, 255, 255),
2) # rect around roi
#Load a cascade file for detecting follow button
heart_cascade = cv2.CascadeClassifier(
'camera/follow_button_cascade_level_9.xml')
#Convert to grayscale
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY) # look for hearts within roi
bluramt = 4
blur = cv2.blur(gray, (bluramt, bluramt))
#Look for faces in the image using the loaded cascade file
hearts = heart_cascade.detectMultiScale(blur,
1.3,
4,
minSize=(120, 120),
maxSize=(130, 130))
# hearts = heart_cascade.detectMultiScale(roi,1.6,10, minSize=(100,100), maxSize=(160,160)) # previously looked at gray instead of cropped roi
numHearts = len(hearts)
print "Found " + str(numHearts) + " follow(s)"
if numHearts > 0:
#Draw a rectangle around every found heart
for (x, y, w, h) in hearts:
heartX = roiX + x + w / 2 # around the heart plus the distance from the edge to the roi
heartY = roiY + y + h / 2
cv2.rectangle(image, (x + roiX, y + roiY),
(x + w + roiX, y + h + roiY), (0, 0, 0), 2)
# draw a point at the center of the heart
cv2.line(image, (heartX, heartY), (heartX, heartY),
(255, 255, 255), 5)
''' for whatever reason this haar cascade finds both the blue "follow" and the
white "following" buttons. The following code determines average color and looks
for detected buttons that are primarily blue
'''
thisHeart = roi[y:y + h, x:x + h]
# cv2.imwrite('thisHeart.jpg',thisHeart)
avg_color_per_row = numpy.average(thisHeart, axis=0)
avg_color = numpy.average(avg_color_per_row, axis=0)
print(avg_color) # gives average color of the heart in (B, G, R)
avg_r_g = (avg_color[1] + avg_color[2]) / 2
print "avg_r_g = %r" % avg_r_g # the average red and green values
blue_ratio = avg_color[0] / avg_r_g
print "blue_ratio = %r" % blue_ratio
if (blue_ratio > 1): # previously set to 1.15
print "gotta follow this guy"
cv2.line(image, (heartX, heartY), (heartX, heartY),
(0, 0, 255), 5)
print "heartX, heartY = (%d, %d)" % (heartX, heartY)
print "heartW, heartH = (%d, %d)" % (w, h)
toY = mapValues.myMap(heartY)
print "toY = %d" % toY
pc.movexy(2650, toY)
sleep(1)
pc.lightTap()
pc.checkStatus()