def main():
hist = []
capture = cv2.VideoCapture('http://192.168.1.36:4746/mjpegfeed?1000x1080')
# capture = cv2.VideoCapture(1)
count = 0
while True:
if cv2.waitKey(1) & 0xFF == ord('q'):
break
k = cv2.waitKey(10)
ret, frame = capture.read()
#frame = Zoom(frame, 1)
resize = cv2.resize(frame, (700, 800))
#cv2.imshow('Current',resize)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
image = Image.fromarray(gray)
width, height = image.size
zbar_image = zbar.Image(width, height, 'Y800', image.tobytes())
scanner = zbar.ImageScanner()
scanner.scan(zbar_image)
for decoded in zbar_image:
points = decoded.location
print(decoded.data)
if len(points) > 4:
hull = cv2.convexHull(
np.array([point for point in points], dtype=np.float32))
hull = list(map(tuple, np.squeeze(hull)))
else:
hull = points
n = len(hull)
for j in range(0, n):
cv2.line(frame, hull[j], hull[(j + 1) % n], (255, 0, 0), 3)
frame = cv2.resize(frame, (800, 800))
frame2 = cv2.flip(frame, 1)
blank_image = np.zeros((height, width, 3), np.uint8)
blank_image = cv2.resize(blank_image, (1500 - 200 - 500, 1000))
cv2.imshow("Results", frame2)
arr = []
for decoded in zbar_image:
points = decoded.location
ll = [point for point in points]
# x1=min(1500-200-500-ll[0][0],ll[1][0],ll[2][0],ll[3][0])#top-left pt. is the leftmost of the 4 points
# x2=max(1500-200-500-ll[0][0],ll[1][0],ll[2][0],ll[3][0])#bottom-right pt. is the rightmost of the 4 points
# y1=min(1500-200-500-ll[0][1],ll[1][1],ll[2][1],ll[3][1])#top-left pt. is the uppermost of the 4 points
# y2=max(1500-200-500-ll[0][1],ll[1][1],ll[2][1],ll[3][1])#bottom-right pt. is the lowermost of the 4 points
#print("coordinates")
#cv2.line(blank_image,(h1,h2),(1500-200-500-ll[0][0],ll[0][1]),(255,255,255),15)
cv2.line(blank_image, (1500 - 200 - 500 - ll[0][0], ll[0][1]),
(1500 - 200 - 500 - ll[1][0], ll[1][1]), (255, 255, 255),
10)
cv2.line(blank_image, (1500 - 200 - 500 - ll[1][0], ll[1][1]),
(1500 - 200 - 500 - ll[2][0], ll[2][1]), (255, 255, 255),
10)
cv2.line(blank_image, (1500 - 200 - 500 - ll[2][0], ll[2][1]),
(1500 - 200 - 500 - ll[3][0], ll[3][1]), (255, 255, 255),
10)
cv2.line(blank_image, (1500 - 200 - 500 - ll[3][0], ll[3][1]),
(1500 - 200 - 500 - ll[0][0], ll[0][1]), (255, 255, 255),
10)
cv2.line(blank_image, (1500 - 200 - 500 - ll[0][0], ll[0][1]),
(1500 - 200 - 500 - ll[2][0], ll[2][1]), (255, 255, 255),
10)
cv2.line(blank_image, (1500 - 200 - 500 - ll[2][0], ll[2][1]),
(1500 - 200 - 500 - ll[2][0], ll[2][1]), (0, 0, 255), 25)
xa = ll[0][0] + ll[1][0] + ll[2][0] + ll[3][0]
ya = ll[0][1] + ll[1][1] + ll[2][1] + ll[3][1]
xa /= 4
ya /= 4
lx, ly = xa, ya
lol = 1
for j in hist:
lol += 1
if lol <= 2:
lx = j[1]
ly = j[2]
continue
#print('lowllal',decoded.data,j[0])
if decoded.data == j[0]:
#print "yaaaa",j[1],j[2],
cv2.line(blank_image, (1500 - 200 - 500 - lx, ly),
(1500 - 200 - 500 - j[1], j[2]), (100, 30, 255),
10)
lx = j[1]
ly = j[2]
arr.append((xa, ya))
hist += [[decoded.data, xa, ya]]
print('lenggg', len(hist))
if len(hist) > 100:
del hist[0]
for j in arr:
cv2.line(blank_image, (1500 - 200 - 500 - j[0], j[1]),
(1500 - 200 - 500 - xa, ya), (0, 255, 255), 10)
dist = math.sqrt((xa - j[0])**2 + (ya - j[1])**2)
if dist > 0.1:
cv2.putText(blank_image, "{0}".format(round(dist, 2)),
(1500 - 200 - 500 - (xa + j[0]) / 2,
(ya + j[1]) / 2 + 5),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0))
if len(hist) > 100:
del hist[0]
cv2.imshow("Output", blank_image)
import math
import zbar
from PIL import Image
ser = serial.Serial(port="/dev/ttyAMA0",
baudrate=4800,
parity=serial.PARITY_NONE,
stopbits=serial.STOPBITS_ONE,
bytesize=serial.EIGHTBITS,
timeout=1)
# qr code vars
##################
camera = PiCamera()
scanner = zbar.ImageScanner()
scanner.parse_config('enable')
sizeQrCode = 22.0
# in cm de diagonaal van de qr
qrCodeDiag = 675.0
# the diagonal of the qr in px
distance = 50
# in cm in real life
distfac = (distance / qrCodeDiag)
# in cm/px de size moet ook in cm zijn
# lidar vars
#############################
def __init__(self):
import zbar
self.scanner = zbar.ImageScanner()
self.scanner.parse_config('enable')
def setUp(self):
self.scn = zbar.ImageScanner()
def main():
#note: multithreading is untested, if code no work, replace
capture = WebcamVideoStream(src=0).start()
prevID = 0
oldtime = 0
fixed_time = datetime.strptime("3:00:00.000000", "%H:%M:%S.%f")
fixed_time2 = datetime.strptime("4:00:00.000000", "%H:%M:%S.%f")
while True:
current_time = datetime.strptime(str(datetime.today().time()),
"%H:%M:%S.%f")
#logout at 3am
if (fixed_time < current_time and current_time > fixed_time2):
#data.logout()
pass
else:
pass
# Breaks down the video into frames
frame = capture.read()
# Converts image to grayscale.
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Uses PIL to convert the grayscale image into a ndary array that ZBar can understand.
image = Image.fromarray(gray)
width, height = image.size
zbar_image = zbar.Image(width, height, 'Y800', image.tobytes())
# Scans the zbar image.
scanner = zbar.ImageScanner()
scanner.scan(zbar_image)
# Prints data from image.
for decoded in zbar_image:
idnumber = decoded.data
#if idnumber is not prevID, login
if (idnumber != prevID):
data.login(idnumber)
oldtime = time.time()
else:
#if 5 minutes has passed, reset prevID
if (time.time() - oldtime > 299):
#reset variables
prevID = 0
idnumber = 0
else:
pass
prevID = idnumber
frame = cv2.flip(frame, 1)
cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.resizeWindow('image', 500, 390)
cv2.rectangle(frame, (175, 150), (450, 350), (255, 0, 0), 3)
cv2.imshow('image', frame)
if cv2.waitKey(1) == 27:
break # esc to quit
def __init__(self, **kwargs):
super(ZBarCam, self).__init__(**kwargs)
Clock.schedule_once(lambda dt: self._setup())
# creates a scanner used for detecting qrcode
self.scanner = zbar.ImageScanner()
def scanqr():
Orders.Orders().loadOrder(
) #Sincronizza Ordini da FoglioGoogle->orders.txt
timeout = 60 #Timeout time for QRCode scanning
timeout_start = time.time() #Avvio Timer
while time.time() < timeout_start + timeout:
RGB.RGBClass()
RGB.RGBClass.blue_on()
RGB.RGBClass.red_off()
RGB.RGBClass.green_off()
# crea uno stream dati in memoria
stream = io.BytesIO()
# visualizzazione dello streaming della camera
with picamera.PiCamera() as camera:
camera.exposure_mode = 'auto'
camera.preview_fullscreen = False
# ridimensiono il preview per poter interrompere in caso di problemi
camera.preview_window = (300, 200, 640, 480)
camera.start_preview()
sleep(1) # aumentare se si vuole inquadrare meglio
camera.capture(stream, format='jpeg')
# recupera tutto il flusso per creare l'immagine
stream.seek(0)
pil = Image.open(stream)
# inizializza lo scan dell'immagine
scanner = zbar.ImageScanner()
# configura il lettore
scanner.parse_config('enable')
pil = pil.convert('L')
width, height = pil.size
# raw = pil.tostring()
raw = pil.tobytes()
# wrap dati della immagine
image = zbar.Image(width, height, 'Y800', raw)
# cerca un QRcode
scanner.scan(image)
# se la password corrisponde ed e' esatta
for symbol in image:
# if hashlib.sha256(password).hexdigest() == symbol.data:
for order in OrderClassList.loadorders(
): #Carico e inizializzo classe da Orders.txt
'''
TO SHOW + CMD CLICK
'''
if (len(symbol.data) == 44):
if Crypter.decode(
symbol.data
) == order.SerialNumber or Crypter.decode(
symbol.data) == '000-0000000-0000001':
#Gestione SpecialKeyQRCode
if (Crypter.decode(
symbol.data) == '000-0000000-0000001'):
order = 1
RGB.RGBClass.blue_off()
RGB.RGBClass.green_on()
'''
print Crypter.decode(symbol.data)
print order.SerialNumber
print len(symbol.data)
'''
return order # strip Eliminato qui
else:
RGB.RGBClass.blue_off()
RGB.RGBClass.red_pulse()
else:
RGB.RGBClass.blue_off()
RGB.RGBClass.red_pulse()
# pulisci e cancella
del (image)
return None
def get_qr_content(with_gui=False, manual_detect=False):
detected = False
camera.init()
if not len(camera.list_cameras()):
print("No camera detected!")
sys.exit(-1)
cam = camera.Camera(camera.list_cameras()[config.camera_number - 1])
size = cam.get_size()
width, height = size
if not manual_detect:
sys.stdout.write("QR detection started, wait several seconds...")
sys.stdout.flush()
cam.start()
if with_gui:
screen = pygame.display.set_mode(cam.get_size())
pygame.display.set_caption("Check QR recognize")
else:
with_gui = True
print("QR detection through GUI, press any key when green line flash")
data = 0
while not detected:
try:
if manual_detect:
qr = QR()
qr.decode_webcam()
data = qr.data
else:
img = cam.get_image()
# we can use file buffer for recognition
# pygame.image.save(img, "file.jpg")
# pil_string_image = Image.open("file.jpg").convert('L').tostring()
pygame_img = pygame.image.tostring(img, 'RGB', False)
pil_string_image = Image.fromstring(
'RGB', size, pygame_img).convert('L').tostring()
if with_gui:
screen.blit(img, (0, 0))
pygame.display.flip() # display update
zbar_image = zbar.Image(width, height, 'Y800',
pil_string_image)
scanner = zbar.ImageScanner()
scanner.parse_config('enable')
data = scanner.scan(zbar_image)
sys.stdout.write('.')
sys.stdout.flush()
for qr in zbar_image:
if data:
"Additional QR recognized!"
data = qr.data
if data:
print("\nRecognized: `{}`".format(data))
detected = True
except Exception as e:
print("Error! " + str(e))
pass
finally:
time.sleep(config.qr_scan_waiting)
if not manual_detect:
pygame.display.quit()
cam.stop()
return 0 if data == "NULL" else data
def getQRPosition (path):
# create a reader
scanner = zbar.ImageScanner()
# configure the reader
scanner.parse_config('enable')
# obtain image data
img = cv2.imread(path)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
pil = Image.open(path).convert('L')
width, height = pil.size
raw = pil.tobytes()
# wrap image data
image = zbar.Image(width, height, 'Y800', raw)
# scan the image for barcodes
scanner.scan(image)
res=False
# extract results
for symbol in image:
# do something useful with results
print 'decoded', symbol.type, 'symbol', '"%s"' % symbol.data
QRData=symbol.data
topLeftCorners, bottomLeftCorners, bottomRightCorners, topRightCorners = [item for item in symbol.location]
topLeftCorner=[[topLeftCorners[0],topLeftCorners[1]]]
bottomLeftCorner=[[bottomLeftCorners[0],bottomLeftCorners[1]]]
topRightCorner=[[topRightCorners[0],topRightCorners[1]]]
bottomRightCorner=[[bottomRightCorners[0],bottomRightCorners[1]]]
corners = np.asarray([bottomLeftCorner,bottomRightCorner,topLeftCorner,topRightCorner],dtype=np.float32)
res=True
if (res==False):
print 'Not able to scan QR'
return 0,0,0
exit(1)
objp = np.zeros((2*2,3), np.float32)
objp[:,:2] = np.mgrid[0:2,0:2].T.reshape(-1,2)
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
objpoints.append(objp)
imgpoints.append(corners)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
retval,rvec,tvec = cv2.solvePnP(objp, corners, mtx, dist)
a=np.zeros((4,4),np.float32)
a[0:3,0:3],_ = cv2.Rodrigues(np.asarray(rvecs))
a[0:3,3]= np.transpose(np.asarray(tvecs)[0])[0]
a[3,3]=1
ai = np.linalg.inv(a)
return 1, QRData, ai[0:3,3]
#axis = np.float32([[1,0,0], [0,1,0], [0,0,-1]]).reshape(-1,3)
#def draw(img, corners, imgpts):
# corner = tuple(corners[0].ravel())
# img = cv2.line(img, corner, tuple(imgpts[0].ravel()), (255,0,0), 5)
# img = cv2.line(img, corner, tuple(imgpts[1].ravel()), (0,255,0), 5)
# img = cv2.line(img, corner, tuple(imgpts[2].ravel()), (0,0,255), 5)
# return img
#imgpts, jac = cv2.projectPoints(axis, rvec, tvec, mtx, dist)
#img = draw(img,corners,imgpts)
#cv2.imwrite('res.jpg', img)
# cv2.imwrite('res.jpg',img)
# clean up
del(image)
def detectQR(frame):
""" Detects QR Codes with ZBar.
Parameters
----------
frame : numpy.ndarray
image captured from the camera.
"""
# Convert frame to BW and get its size
frameGrey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# array with height, widht and channels
size = frameGrey.shape
# Convert image from OpenCV to ZBar
imageZBar = zbar.Image(size[1], size[0], 'Y800', frameGrey.tobytes())
# Construct an scan object and detect all QRCodes at frame
scan = zbar.ImageScanner()
scan.scan(imageZBar)
index = 1
for QRCode in imageZBar:
# Save corners and label attached to every QR Code
imageCorners = QRCode.location
tag = QRCode.data
if len(imageCorners) == 4:
# IMAGE VALUES
Qi_x = (float(imageCorners[0][0]+imageCorners[1][0]+imageCorners[2][0]+imageCorners[3][0]))/4.0
Qi_y = (float(imageCorners[0][1]+imageCorners[1][1]+imageCorners[2][1]+imageCorners[3][1]))/4.0
Oi = np.array([frame.shape[1]/2, frame.shape[0]/2])
Qi = np.array([Qi_x, Qi_y]) # center
Ai = np.array([imageCorners[0][0], imageCorners[0][1]])
Bi = np.array([imageCorners[1][0], imageCorners[1][1]])
Di = np.array([imageCorners[3][0], imageCorners[3][1]])
displayPoints(frame,Oi,Qi)
displayShape(frame,imageCorners)
# REAL VALUES
# Inicialite values for getting the coordinates from the tag
pri = 1
fin = 0
coor = np.zeros(2)
j = 0
for i in range(len(tag)):
if tag[i] == "," or tag[i] == ")":
fin = i
coor[j] = float(tag[pri:fin])
pri = i+1
j = j+1
# Tag is normalized for getting real coordinates of the points
Qr = np.array([coor[0]*0.01, coor[1]*0.01]) # center
# LOCATION
# 1. Calculate L
ax = Oi[0]-Qi[0]
ay = Oi[1]-Qi[1]
L = math.sqrt(ax**2 + ay**2)
# 2. Calculate vectors
q_v = np.array([(Ai[0]-Bi[0]),(Ai[1]-Bi[1])])
p_v = np.array([(Di[0]-Ai[0]),(Di[1]-Ai[1])])
a_v = np.array([ax,ay])
y_v = np.array([(Oi[0]-Oi[0]),(100-Oi[1])])
Y_v = np.array([((Qi[0]+q_v[0])-Qi[0]),((Qi[1]+q_v[1])-Qi[1])])
X_v = np.array([((Qi[0]+p_v[0])-Qi[0]),((Qi[1]+p_v[1])-Qi[1])])
displayVectors(frame, Oi, Qi, q_v, p_v)
# 3. Calculate alpha3
alpha3 = math.acos((np.dot(y_v, Y_v))/(mag(y_v)*mag(Y_v)))
# 4. Calculate alpha1
alpha1 = math.acos((np.dot(a_v, Y_v))/(mag(a_v)*mag(Y_v)))
# 5. Calculate alpha2
alpha2 = math.acos((np.dot(a_v, X_v))/(mag(a_v)*mag(X_v)))
# 6. Calculate unit
unitx = 1/fx
unity = 1/fy
# 7. Relative coordinates
xCam = L*unitx*math.cos(alpha2)
yCam = L*unity*math.cos(alpha1)
thetaCam = alpha3
# 8. Global coordinates - same axis
# xRob = xCam + Qr[0]*0.01
# yRob = yCam + Qr[1]*0.01
# thetaRob = thetaCam
#
# 8.Global coordinates - different axis
beta_x = 0 #coor[2]
beta_y = math.pi #coor[3]
Qr = np.append(Qr, beta_x)
Qr = np.append(Qr, beta_y)
rot = np.array([[math.cos(Qr[2]), math.sin(Qr[2]), 0],
[-math.sin(Qr[3]), math.cos(Qr[3]), 0],
[0, 0, 1]])
posCam = np.array([[xCam],[yCam],[thetaCam]])
posQr =np.array([Qr[0], Qr[1], Qr[3]])
posRob = np.transpose(np.dot(rot,posCam))[0] + posQr
print("-----Robot position QR {}-----------------------------------------------\n".format(index))
print("X: {} Y: {} theta: {}".format(posRob[0], posRob[1], posRob[2]))
print("-----------------------------------------------------------------------\n")
index += 1;
def scanQR():
# initialize camera interface
camera = picamera.PiCamera()
#
# MAX_IMAGE_RESOLUTION required so preview field of view matches capture one.
camera.resolution = camera.MAX_RESOLUTION
#
# position and size info
camera.rotation = 0
camera.preview_fullscreen = False
camera.preview_window = (0, 0, 323, 240) # x, y, width, height
#
# loop forever
while True:
todo = to_cam.get()
to_cam.task_done()
if todo == 'start':
# start writing camera image to screen
camera.start_preview()
#
loop = True
print "camera thread: camera started by command"
while loop:
# Create an in-memory stream
stream = io.BytesIO()
#
# jpeg to save memory, resize to make processing faster, use_video_port to not change modes
camera.capture(stream, format='jpeg', resize=(1024, 768), use_video_port=True)
#
# Rewind the stream for reading
stream.seek(0)
#
# create a reader
scanner = zbar.ImageScanner()
#
# configure the reader
scanner.parse_config('enable')
#
# obtain image data
pil = Image.open(stream).convert('L')
width, height = pil.size
raw = pil.tostring()
#
# wrap image data
image = zbar.Image(width, height, 'Y800', raw)
#
# scan the image for barcodes
scanner.scan(image)
#
# extract results, send them
for symbol in image.symbols:
if str(symbol.type) == 'QRCODE':
if from_cam.empty():
from_cam.put(str(symbol.data))
camera.stop_preview()
loop = False
print "camera thread: camera stopped by QR capture"
break
# stop if told
if not to_cam.empty():
todo = to_cam.get()
if todo == 'stop':
camera.stop_preview()
loop = False
print "camera stopped by command"
to_cam.task_done()
def scan(surface, matrix, x, y, width, height, btype="CODE128", kfill=False):
x0, y0 = matrix.transform_point(x, y)
x1, y1 = matrix.transform_point(x + width, y + height)
# Bounding box ...
x = min(x0, x1)
y = min(y0, y1)
width = max(x0, x1) - x
height = max(y0, y1) - y
x, y, width, height = int(x), int(y), int(width), int(height)
# Round the width to multiple of 4 pixel, so that the stride will
# be good ... hopefully
width = width - width % 4 + 4
# a1 surface for kfill algorithm
a1_surface = cairo.ImageSurface(cairo.FORMAT_A1, width, height)
cr = cairo.Context(a1_surface)
cr.set_operator(cairo.OPERATOR_SOURCE)
cr.set_source_surface(surface, -x, -y)
cr.paint()
if kfill:
pxpermm = (matrix[0] + matrix[3]) / 2
barwidth = pxpermm * defs.code128_barwidth
barwidth = int(round(barwidth))
if barwidth <= 3:
return
if barwidth > 6:
barwidth = 6
image.kfill_modified(a1_surface, barwidth)
# zbar does not understand A1, but it can handle 8bit greyscale ...
# We create an inverted A8 mask for zbar, which is the same as a greyscale
# image.
a8_surface = cairo.ImageSurface(cairo.FORMAT_A8, width, height)
cr = cairo.Context(a8_surface)
cr.set_source_rgba(1, 1, 1, 1)
cr.set_operator(cairo.OPERATOR_SOURCE)
cr.paint()
cr.set_source_rgba(0, 0, 0, 0)
cr.mask_surface(a1_surface, 0, 0)
del cr
a8_surface.flush()
# Now we have pixel data that can be passed to zbar
img = zbar.Image()
img.format = "Y800"
img.data = str(a8_surface.get_data())
img.height = height
# Use the stride, it should be the same as width, but if it is not, then
# it is saner to add a couple of junk pixels instead of getting weird
# offsets and missing data.
img.width = a8_surface.get_stride()
scanner = zbar.ImageScanner()
scanner.scan(img)
result = None
result_quality = -1
for symbol in scanner.results:
# Ignore barcodes of the wrong type
if str(symbol.type) != btype:
continue
# return the symbol with the highest quality
if symbol.quality > result_quality:
result = symbol.data
result_quality = symbol.quality
# The following can be used to look at the images
#rgb_surface = cairo.ImageSurface(cairo.FORMAT_RGB24, width, height)
#cr = cairo.Context(rgb_surface)
#cr.set_source_rgba(1, 1, 1, 1)
#cr.set_operator(cairo.OPERATOR_SOURCE)
#cr.paint()
#cr.set_source_rgba(0, 0, 0, 0)
#cr.mask_surface(a1_surface, 0, 0)
#rgb_surface.write_to_png("/tmp/barcode-%03i.png" % barcode)
#barcode += 1
return result
def __init__(self, pub, cube):
self.pub = pub
self.cube = cube
self.scanner = zbar.ImageScanner()
self.scanner.parse_config('enable')
self.symbols = []
def main():
hist = {}
q = []
count = 0
capture = cv2.VideoCapture('http://192.168.169.3:4745/mjpegfeed?1000x1080')
#capture = cv2.VideoCapture(1)
#capture = cv2.VideoCapture('./vid2.mp4')
count = 0
while True:
if cv2.waitKey(1) & 0xFF == ord('q'):
break
ret, frame = capture.read()
resize = cv2.resize(frame, (700, 800))
#cv2.imshow('Current',resize)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
image = Image.fromarray(gray)
width, height = image.size
zbar_image = zbar.Image(width, height, 'Y800', image.tobytes())
scanner = zbar.ImageScanner()
scanner.scan(zbar_image)
for decoded in zbar_image:
points = decoded.location
print(decoded.data)
if len(points) > 4:
hull = cv2.convexHull(
np.array([point for point in points], dtype=np.float32))
hull = list(map(tuple, np.squeeze(hull)))
else:
hull = points
n = len(hull)
for j in range(0, n):
cv2.line(frame, hull[j], hull[(j + 1) % n], (255, 0, 0), 3)
frame = cv2.resize(frame, (800, 800))
frame2 = cv2.flip(frame, 1)
blank_image = np.zeros((height, width, 3), np.uint8)
blank_image = cv2.resize(blank_image, (800, 1000))
cv2.imshow("Results", frame2)
arr = []
lll = {}
abra = False
for decoded in zbar_image:
abra = True
lll[decoded.data] = True
points = decoded.location
ll = [point for point in points]
# x1=min(1500-500-200-ll[0][0],ll[1][0],ll[2][0],ll[3][0])#top-left pt. is the leftmost of the 4 points
# x2=max(1500-500-200-ll[0][0],ll[1][0],ll[2][0],ll[3][0])#bottom-right pt. is the rightmost of the 4 points
# y1=min(1500-500-200-ll[0][1],ll[1][1],ll[2][1],ll[3][1])#top-left pt. is the uppermost of the 4 points
# y2=max(1500-500-200-ll[0][1],ll[1][1],ll[2][1],ll[3][1])#bottom-right pt. is the lowermost of the 4 points
#print("coordinates")
#cv2.line(blank_image,(h1,h2),(1500-500-200-ll[0][0],ll[0][1]),(255,255,255),15)
try:
cv2.line(blank_image, (1500 - 500 - 200 - ll[0][0], ll[0][1]),
(1500 - 500 - 200 - ll[1][0], ll[1][1]),
(255, 255, 255), 10)
cv2.line(blank_image, (1500 - 500 - 200 - ll[1][0], ll[1][1]),
(1500 - 500 - 200 - ll[2][0], ll[2][1]),
(255, 255, 255), 10)
cv2.line(blank_image, (1500 - 500 - 200 - ll[2][0], ll[2][1]),
(1500 - 500 - 200 - ll[3][0], ll[3][1]),
(255, 255, 255), 10)
cv2.line(blank_image, (1500 - 500 - 200 - ll[3][0], ll[3][1]),
(1500 - 500 - 200 - ll[0][0], ll[0][1]),
(255, 255, 255), 10)
cv2.line(blank_image, (1500 - 500 - 200 - ll[0][0], ll[0][1]),
(1500 - 500 - 200 - ll[2][0], ll[2][1]),
(255, 255, 255), 10)
cv2.line(blank_image, (1500 - 500 - 200 - ll[2][0], ll[2][1]),
(1500 - 500 - 200 - ll[2][0], ll[2][1]), (0, 0, 255),
25)
except:
continue
xa = ll[0][0] + ll[1][0] + ll[2][0] + ll[3][0]
ya = ll[0][1] + ll[1][1] + ll[2][1] + ll[3][1]
xa /= 4
ya /= 4
lx, ly = xa, ya
lol = 1
try:
#print(hist[decoded.data])
for j in range(2, len(hist[decoded.data])):
lol += 1
if lol <= 2:
lx = hist[decoded.data][j][0]
ly = hist[decoded.data][j][1]
continue
#print('lowllal',decoded.data,j[0])
#print "yaaaa",hist[decoded.data][j],hist[decoded.data][j+1],
cv2.line(blank_image, (1500 - 500 - 200 - lx, ly),
(1500 - 500 - 200 - hist[decoded.data][j][0],
hist[decoded.data][j][1]),
(100, (30 + hist[decoded.data][j][1] +
hist[decoded.data][j][0]) % 254, 255), 10)
lx = hist[decoded.data][j][0]
ly = hist[decoded.data][j][1]
#print 'lo', lx, ly
except:
#print 'la',lx, ly
cv2.line(blank_image, (1500 - 500 - 200 - lx, ly),
(1500 - 500 - 200 - lx, ly), (100, 30, 255), 10)
arr.append((xa, ya))
try:
hist[decoded.data].append([xa, ya])
hist[decoded.data][0] = 0
hist[decoded.data][1] = decoded
except:
hist[decoded.data] = [0, decoded, [xa, ya]]
#print('lenggg',len(hist))
if len(hist[decoded.data]) > 80:
del hist[decoded.data][2]
# for j in arr:
# cv2.line(blank_image, (1500-500-200 - j[0], j[1]), (1500-500-200 - xa, ya), (0, 255, 255), 10)
# dist=math.sqrt((xa-j[0])**2+(ya-j[1])**2)
# if dist>0.1:
# cv2.putText(blank_image, "{0}".format(round(dist,2)), (1500-500-200-(xa+j[0])/2, (ya+j[1])/2+5 ), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,0,0))
count += 1
for j in hist:
if hist[j][0] > 0:
q.append(hist[j][1])
for j in hist:
try:
hist[j][0] += 1
except:
hist[j][0] = 1
for decoded in q:
if abra == False:
continue
print('hissss', hist[decoded.data][0])
if hist[decoded.data][0] > 20:
continue
try:
if lll[decoded.data]:
continue
except:
pass
points = decoded.location
ll = [point for point in points]
# x1=min(1500-500-200-ll[0][0],ll[1][0],ll[2][0],ll[3][0])#top-left pt. is the leftmost of the 4 points
# x2=max(1500-500-200-ll[0][0],ll[1][0],ll[2][0],ll[3][0])#bottom-right pt. is the rightmost of the 4 points
# y1=min(1500-500-200-ll[0][1],ll[1][1],ll[2][1],ll[3][1])#top-left pt. is the uppermost of the 4 points
# y2=max(1500-500-200-ll[0][1],ll[1][1],ll[2][1],ll[3][1])#bottom-right pt. is the lowermost of the 4 points
#print("coordinates")
#cv2.line(blank_image,(h1,h2),(1500-500-200-ll[0][0],ll[0][1]),(255,255,255),15)
try:
cv2.line(blank_image, (1500 - 500 - 200 - ll[0][0], ll[0][1]),
(1500 - 500 - 200 - ll[1][0], ll[1][1]),
(255, 255, 255), 10)
cv2.line(blank_image, (1500 - 500 - 200 - ll[1][0], ll[1][1]),
(1500 - 500 - 200 - ll[2][0], ll[2][1]),
(255, 255, 255), 10)
cv2.line(blank_image, (1500 - 500 - 200 - ll[2][0], ll[2][1]),
(1500 - 500 - 200 - ll[3][0], ll[3][1]),
(255, 255, 255), 10)
cv2.line(blank_image, (1500 - 500 - 200 - ll[3][0], ll[3][1]),
(1500 - 500 - 200 - ll[0][0], ll[0][1]),
(255, 255, 255), 10)
cv2.line(blank_image, (1500 - 500 - 200 - ll[0][0], ll[0][1]),
(1500 - 500 - 200 - ll[2][0], ll[2][1]),
(255, 255, 255), 10)
cv2.line(blank_image, (1500 - 500 - 200 - ll[2][0], ll[2][1]),
(1500 - 500 - 200 - ll[2][0], ll[2][1]), (0, 0, 255),
25)
except:
continue
xa = ll[0][0] + ll[1][0] + ll[2][0] + ll[3][0]
ya = ll[0][1] + ll[1][1] + ll[2][1] + ll[3][1]
xa /= 4
ya /= 4
lx, ly = xa, ya
lol = 1
try:
#print(hist[decoded.data])
for j in range(2, len(hist[decoded.data])):
lol += 1
if lol <= 2:
lx = hist[decoded.data][j][0]
ly = hist[decoded.data][j][1]
continue
#print('lowllal',decoded.data,j[0])
#print "yaaaa",hist[decoded.data][j],hist[decoded.data][j+1],
cv2.line(blank_image, (1500 - 500 - 200 - lx, ly),
(1500 - 500 - 200 - hist[decoded.data][j][0],
hist[decoded.data][j][1]),
(100, (30 + hist[decoded.data][j][1] +
hist[decoded.data][j][0]) % 254, 255), 10)
lx = hist[decoded.data][j][0]
ly = hist[decoded.data][j][1]
#print 'lo', lx, ly
except:
#print 'la',lx, ly
cv2.line(blank_image, (1500 - 500 - 200 - lx, ly),
(1500 - 500 - 200 - lx, ly), (100, 30, 255), 10)
arr.append((xa, ya))
try:
hist[decoded.data].append([xa, ya])
hist[decoded.data][0] += 1
hist[decoded.data][1] = decoded
except:
hist[decoded.data] = [1, decoded, [xa, ya]]
#print('lenggg',len(hist))
if len(hist[decoded.data]) > 100:
del hist[decoded.data][2]
# for j in arr:
# cv2.line(blank_image, (1500-500-200 - j[0], j[1]), (1500-500-200 - xa, ya), (0, 255, 255), 10)
# dist=math.sqrt((xa-j[0])**2+(ya-j[1])**2)
# if dist>0.1:
# cv2.putText(blank_image, "{0}".format(round(dist,2)), (1500-500-200-(xa+j[0])/2, (ya+j[1])/2+5 ), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,0,0))
if count > 4:
q = []
count = 0
cv2.imshow("Output", blank_image)
def MealApp():
global QRCode
global isEaten
while True:
isEaten = 0
now = datetime.datetime.now()
if (now.hour >= 16 and now.hour < 18) or (
now.hour >= 0 and now.hour <= 5) or (now.hour >= 4
and now.hour < 8):
isQrDetected = False
frame = MC.readCamera()
cv2.rectangle(frame, (1200, 500), (500, 730), (100, 70, 0), -2)
cv2.putText(
frame, "Current Date : " + str(now.year) + "-" +
str(now.month) + "-" + str(now.day), (550, 550), MC.font, 1,
(70, 0, 100), 2)
cv2.putText(frame,
"BreakFast Eaten User : "******" Lunch Eaten User : "******" Dinner Eaten User : "******"Face Recognition Cafe Monitering System", frame)
cv2.waitKey(1)
if MC.isDetected == True:
i = 0
while i < 10:
frame = MC.readCamera()
cv2.imshow("Face Recognition Cafe Monitering System",
frame)
cv2.waitKey(1)
i = i + 1
blackImage = MC.blackImage
cv2.rectangle(blackImage, (1200, 500), (500, 730),
(100, 70, 0), -2)
cv2.putText(
frame, "Current Date : " + str(now.year) + "-" +
str(now.month) + "-" + str(now.day), (550, 550), MC.font,
1, (70, 0, 100), 2)
cv2.putText(blackImage,
"BreakFast Eaten User : "******" Lunch Eaten User : "******" Dinner Eaten User : "******":" + str(
now1.minute) + ":" + str(now1.second)
cv2.putText(blackImage, 'Position Your QRCode Correctly',
(750, 50), MC.font, 0.5, (70, 0, 100), 2)
cv2.putText(blackImage, CurrTime, (1250, 50), MC.font, 0.5,
(70, 0, 100), 2)
if QRDetect == True:
try:
if int(QRCode) <= 0 and int(QRCode) >= 9999:
QRDetect = False
engine.say(
'You Entered Invalid Meal Card Code')
engine.runAndWait()
else:
engine.say('Meal Card Detected')
engine.runAndWait()
except:
QRDetect = False
engine.say('You Entered Invalid Meal Card Code')
engine.runAndWait()
cv2.imshow("Face Recognition Cafe Monitering System",
blackImage)
cv2.waitKey(1)
cv2.imshow("Face Recognition Cafe Monitering System",
blackImage)
cv2.waitKey(1)
cv2.putText(blackImage,
'Position Your QRCode Correctly(' + QRCode + ')',
(750, 50), MC.font, 0.5, (70, 0, 100), 2)
isEaten = MC.checkUser(QRCode)
if isEaten == 1 or isEaten == 2:
cursor = db.cursor()
sql = "SELECT * FROM cafeuser WHERE QrNum=" + QRCode
cursor.execute(sql)
db.commit()
row = cursor.fetchone()
cursor.close()
mealType = MC.mealTypeCheck()
if isEaten == 1:
engine.say('Hello ' + row[1] + ' ' + row[2] +
' Have A Good ' + mealType)
engine.runAndWait()
else:
engine.say(row[1] + ' ' + row[2] + ' You Are Cheating')
engine.runAndWait()
elif isEaten == 3:
engine.say('You Have Stolen Other Persons Meal Card')
engine.runAndWait()
elif isEaten == 4:
engine.say('You Are Using Invalid Meal Card')
engine.runAndWait()
elif isEaten == 5:
engine.say(
'You Meal Card is Disabled Please ReEnabled it Again')
engine.runAndWait()
else:
ImageNotOpened = cv2.imread("BlackImage.jpg")
CurrTime = "Cafe is Closed "
cv2.putText(ImageNotOpened, CurrTime, (300, 400), MC.font, 2,
(25, 255, 255), 2)
CurrTime = "Current Time " + str(now.hour) + ":" + str(
now.minute) + ":" + str(now.second)
cv2.putText(ImageNotOpened, CurrTime, (400, 500), MC.font, 2,
(25, 255, 255), 2)
MC.readCamera()
if MC.isDetected == True:
engine.say('Dear User The Cafe is Still Closed')
engine.runAndWait()
cv2.imshow("Face Recognition Cafe Monitering System",
ImageNotOpened)
cv2.waitKey(1)
def main():
aff = 0
ok = False
#choisir ta cam pour filme
capture = cv2.VideoCapture(0)
while True:
# pour quite le porgramme apuis sur q
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# metre limage de la video sur frame
ret, frame = capture.read()
# couvrire limage au grayscale.
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Uses PIL to convert the grayscale image into a ndary array that ZBar can understand.
image = Image.fromarray(gray)
width, height = image.size
zbar_image = zbar.Image(width, height, 'Y800', image.tobytes())
#scanner le zbar image
scanner = zbar.ImageScanner()
n = scanner.scan(zbar_image)
if n > aff:
aff += 1
ok = True
elif n < aff:
aff -= 1
# scan le qr code (afiche la couleur ver sur les alentoure du qr code)
for decoded in zbar_image:
#desin le rectoncle pour affiche le dessin
p = numpy.array(decoded.location)
cv2.line(frame, (p[0][0], p[0][1]), (p[1][0], p[1][1]),
(0, 255, 0), 2, 8, 0)
cv2.line(frame, (p[1][0], p[1][1]), (p[2][0], p[2][1]),
(0, 255, 0), 2, 8, 0)
cv2.line(frame, (p[2][0], p[2][1]), (p[3][0], p[3][1]),
(0, 255, 0), 2, 8, 0)
cv2.line(frame, (p[3][0], p[3][1]), (p[0][0], p[0][1]),
(0, 255, 0), 2, 8, 0)
#envoie le ficher au serveur avec le threds + print sur la console
if ok:
thread2 = images(zbar_image, ok)
thread2.start()
thread2.join()
ok = False
#for decoded in zbar_image:
# if ok :
# ok=False
# data = decoded.data.split(' ')
# datas={'fname':data[0], 'lname':data[1]}
# thread=post2(datas)
# thread.start()
# thread.join()
# print(data[0])
# print(data[1])
# affiche la video on coure
cv2.imshow('Current', frame)
def __init__(self):
self.scanner = zbar.ImageScanner()
# configure the reader
self.scanner.parse_config('enable')
def __init__(self, region):
self.bar_region = region
# create a bar reader
self.scanner = zbar.ImageScanner()
# configure the reader
self.scanner.parse_config('enable')
def CheckPart():
#print "Scanning Start"
timeOut = 0
while True:
my_stream = io.BytesIO()
if timeOut >= 7:
return
with picamera.PiCamera() as camera:
camera.capture(my_stream, format='jpeg')
timeOut = timeOut + 1
scanner = zbar.ImageScanner()
scanner.parse_config('enable')
my_stream.seek(0)
pil = Image.open(my_stream).convert('L')
width, height = pil.size
raw = pil.tostring()
dcdImg = zbar.Image(width, height, 'Y800', raw)
scanner.scan(dcdImg)
#print "QR Code Sencing"
for symbol in dcdImg:
#print 'decoded', symbol.type, 'symbol', '"%s"' % symbol.data
#time.sleep(0.5)
if symbol.data != None:
try:
primary_key = symbol.data.split(',')[0]
user_email = symbol.data.split(',')[1]
qrock_key = symbol.data.split(',')[2]
except:
primary_key = ''
user_email = ''
qrock_key = ''
data = {}
data['qrock_pk'] = primary_key
data['serial_number'] = SERIALNUMBER
data['user_email'] = user_email
data['qrock_key'] = qrock_key
while True:
header = {
"Content-type": "application/x-www-form-urlencoded",
"x-access-token": tknValue[0]
}
#print header
r = request.post("http://qrock.cafe24app.com/iot/select",
headers=header,
data=data)
#print r.text
jsonData = json.loads(r.text)
#Exception Handling : success, fail
#print jsonData[0][u'code']
if jsonData[0][u'code'] == "success":
mtrAction(motOpen)
return
elif jsonData[0][u'code'] == "not-token":
#print "token refresh"
refreshTkn()
elif jsonData[0][u'code'] == "fail":
print "It's wrong code"
for i in range(3):
ledTurn([rgbR])
time.sleep(0.8)
ledTurn(())
time.sleep(0.8)
return
def main():
"""
A simple function that captures webcam video utilizing OpenCV. The video is then broken down into frames which
are constantly displayed. The frame is then converted to grayscale for better contrast. Afterwards, the image
is transformed into a numpy array using PIL. This is needed to create zbar image. This zbar image is then scanned
utilizing zbar's image scanner and will then print the decodeed message of any QR or bar code. To quit the program,
press "q".
:return:
"""
# Begin capturing video. You can modify what video source to use with VideoCapture's argument. It's currently set
# to be your webcam.
capture = cv2.VideoCapture(1)
f=open('Drink_Data.txt','r')
_items={
'drink0001':0,
'drink0002':0,
'drink0003':0,
'drink0004':0,
'drink0005':0
}
_items_time={
'drink0001':time.time(),
'drink0002':time.time(),
'drink0003':time.time(),
'drink0004':time.time(),
'drink0005':time.time()
}
_items_sold={
'drink0001':0,
'drink0002':0,
'drink0003':0,
'drink0004':0,
'drink0005':0
}
Sold_Number=0
_sold_data=f.readline()
print _sold_data
for i in range(5):
print _sold_data[i]
if _sold_data[i]=='1':
_items_sold['drink000'+chr(1+i+ord('0'))]=1
Sold_Number+=1
f.close()
while True:
# To quit this program press q.
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Breaks down the video into frames
ret, frame = capture.read()
# Displays the current frame
cv2.imshow('Current', frame)
# Converts image to grayscale.
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Uses PIL to convert the grayscale image into a ndary array that ZBar can understand.
image = Image.fromarray(gray)
width, height = image.size
zbar_image = zbar.Image(width, height, 'Y800', image.tobytes())
# Scans the zbar image.
scanner = zbar.ImageScanner()
scanner.scan(zbar_image)
# Prints data from image.
cnt=0
decoded_code=[]
for decoded in zbar_image:
cnt+=1
decoded_code.append(decoded.data)
#print(decoded.data)
#print(cnt)
# Set when does the program stops
End_Point=0
for _dcode in decoded_code:
for _ecode in End_Code:
if _dcode==_ecode:
End_Point=1
break
if End_Point==1:
break
if End_Point==1:
break
# Update dictionary _items
for i in _items:
if _items_sold[i]==1:
continue
_check=0
for _dcode in decoded_code:
if _items[i]>0 and i==_dcode:
_items_time[i]=time.time()
_check=1
continue
if _items[i]==0 and i==_dcode:
_items_time[i]=time.time()
_items[i]=1
_check=1
if _items[i]==0 and _check==0:
if time.time()-_items_time[i]>_sold_time:
print 'Item',i,'sold out!'
_items_sold[i]=1
Sold_Number+=1
f=open('Drink_Data.txt','w')
ch=''
for j in range(5):
ch+=chr(ord('0')+_items_sold['drink000'+chr(1+j+ord('0'))])
f.write(ch)
f.close()
if _items[i]>0 and _check==0:
_items[i]=0
if Sold_Number==len(_items_sold):
print 'All items were sold!'
break
for i in _items:
if _items_sold[i]==1:
continue
print i, _items[i]
def barcode(state): #����ͧ�������ҹ������
#scsound()
while True:
db = MySQLdb.connect(host='192.168.94.64',
user='******',
passwd='',
db='test') ############################
## ���ҧ��ͺ��㹡�����¡��ҹ������ ##
cur = db.cursor() ############################
cur.execute("SELECT trackid FROM ems2") #���͡�ҹ������㹡����ҹ������
cap = cv2.VideoCapture(0) #��ҹ�Ҿ�ҡ���ͧ�����
scanner = zbar.ImageScanner() #���ҧ��ͻ��㹡����ҹ������
scanner.parse_config('enable')
while (cap.isOpened()):
endp = True
end = 0
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #������
pil = Image.fromarray(gray)
width, height = pil.size
raw = pil.tobytes() #�ŧ�Ҿ���������͵�Ǩ�Ѻ������
image = zbar.Image(width, height, 'Y800', raw)
scanner.scan(image) #�ʡ��ç��ǹ����繺�����
for symbol in image:
print ' decoded', symbol.type, 'symbol', "'%s'" % symbol.data
for row in cur.fetchall(): #��ҹ���ʺ����鴨ҡ�ҹ�����ŷ�����
trackid = str(row[0])
if symbol.data == trackid: #���ʵç�Ѻ�ҹ������
print 'code is correct'
cur.execute("DELETE FROM ems2 WHERE trackid = '" +
trackid +
"'") #ź���ʷ��ç�Ѻ�ҹ�������͡�ҡ���ҧ�����ҹ��
cur.execute(
"UPDATE ems SET status=%s WHERE trackid=%s",
('recieved',
trackid)) #�ѻവ������ʷ��ç�Ѻ���������Ѻ����
db.commit()
end = 2
endp = False
break
if end == 0:
#scasound()
end = 1
break
if end == 1: #����������١��ͧ LED ��ᴧ�С�о�Ժ �����觢�ͤ�����ҹ�Ź�
msg = "package number is not correct"
a = test(msg)
a.send()
blink(redf)
endp = False
break
if end == 2: #������ʶ١��ͧ ��е٨��Դ�͡�������ͻԴ���觢�ͤ�����Ҿ�ʴص�������Ţ���Ѻ����
#crsound()
msg = "package number " + str(symbol.data) + ' received'
a = test(msg)
openLockf()
a.send()
break
cv2.imshow('img', img)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
cur.close()
db.close()
cap.release()
cv2.destroyAllWindows()
if endp == False:
break
GPIO.output(redf, GPIO.LOW)
state[0] = 1 #����¹��Ңͧ��������ͺ͡ʶҹ���Ҩ���������
def sendqrcode(lock):
"""
A simple function that captures webcam video utilizing OpenCV. The video is then broken down into frames which
are constantly displayed. The frame is then converted to grayscale for better contrast. Afterwards, the image
is transformed into a numpy array using PIL. This is needed to create zbar image. This zbar image is then scanned
utilizing zbar's image scanner and will then print the decodeed message of any QR or bar code. To quit the program,
press "q".
:return:
"""
ser = serial.Serial("/dev/ttyS0",
baudrate=115200,
parity=serial.PARITY_NONE,
stopbits=serial.STOPBITS_ONE,
bytesize=serial.EIGHTBITS,
writeTimeout=1,
timeout=10,
rtscts=False,
dsrdtr=False,
xonxoff=False)
# Begin capturing video. You can modify what video source to use with VideoCapture's argument. It's currently set
# to be your webcam.
# capture = cv2.VideoCapture(0)
while True:
lock.acquire()
try:
# Breaks down the video into frames
ret, img = video.read()
# print("2 lock")
finally:
lock.release()
# print("2 unlock")
# Breaks down the video into frames
# ret, frame = capture.read()
# Displays the current frame
# cv2.imshow('Current', frame)
frame = cv2.resize(img, (640, 360))
# Converts image to grayscale.
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Uses PIL to convert the grayscale image into a ndary array that ZBar can understand.
image = Image.fromarray(gray)
width, height = image.size
zbar_image = zbar.Image(width, height, 'Y800', image.tobytes())
# Scans the zbar image.
scanner = zbar.ImageScanner()
scanner.scan(zbar_image)
# Prints data from image.
for decoded in zbar_image:
print(decoded.data)
cmd = (decoded.data)
ser.write(cmd.encode('ascii'))
time.sleep(1)
# To quit this program press q.
if cv2.waitKey(1) & 0xFF == ord('q'):
break
ser.close()
def setUp(self):
self.scn = zbar.ImageScanner()
self.image = zbar.Image(size[0], size[1], 'Y800', data)
def detectQR(frame):
""" Detects QR Codes with ZBar.
Parameters
----------
frame : numpy.ndarray
image captured from the camera.
"""
# Convert frame to BW and get its size
frameGrey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# array with height, widht and channels
size = frameGrey.shape
# Convert image from OpenCV to ZBar
imageZBar = zbar.Image(size[1], size[0], 'Y800', frameGrey.tobytes())
# Construct an scan object and detect all QRCodes at frame
scan = zbar.ImageScanner()
scan.scan(imageZBar)
for QRCode in imageZBar:
# Save corners and label attached to every QR Code
imageCorners = QRCode.location
tag = QRCode.data
# Inicialite values for getting the coordinates from the tag
pri = 0
fin = 0
coor = [0] * 12
j = 0
for i in range(len(tag)):
if (tag[i] == ","):
fin = i
coor[j] = tag[pri:fin]
pri = i + 1
j = j + 1
# Tag is normalized for getting real coordinates of the points
x1 = float(coor[0])
y1 = float(coor[1])
z1 = float(coor[2])
x2 = float(coor[3])
y2 = float(coor[4])
z2 = float(coor[5])
x3 = float(coor[6])
y3 = float(coor[7])
z3 = float(coor[8])
x4 = float(coor[9])
y4 = float(coor[10])
z4 = float(coor[11])
imageCornersNp = np.array(imageCorners, np.float32)
realCorners = np.float32([[x1, y1, z1], [x2, y2, z2], [x3, y3, z3],
[x4, y4, z4]])
if len(imageCorners) == 4:
# Get corners and shape of the QR Code in the original frame
displayShape(frame, imageCorners)
# Get decodified data from the QR Code
displayLabel(frame, imageCorners, realCorners)
# Get angle of the QR Code
# getRotation(frame, imageCorners)
# Get distorsion angle of the QR Code
getAngles(frame, imageCornersNp, realCorners)
def main():
hist = {}
q = []
counter = 0
pts = deque(maxlen=args["buffer"])
#variables for QR_code Connection
centres = []
qr_conn = []
qr_conn1 = []
ccc = None
(dX, dY) = (0, 0)
direction = ""
conn = []
counti = 0
capture = cv2.VideoCapture(
'http://192.168.173.168:4745/mjpegfeed?1000x1080')
#capture = cv2.VideoCapture(1)
#capture = cv2.VideoCapture('vid2.mp4')
count = 0
vs = capture
while True:
if cv2.waitKey(1) & 0xFF == ord('q'):
break
ret, frame = capture.read()
resize = cv2.resize(frame, (700, 800))
# cv2.imshow('Current',resize)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
image = Image.fromarray(gray)
width, height = image.size
zbar_image = zbar.Image(width, height, 'Y800', image.tobytes())
scanner = zbar.ImageScanner()
scanner.scan(zbar_image)
if len(hist) < len(qr_conn):
qr_conn = qr_conn[len(qr_conn) - 1 -
(len(qr_conn) - len(hist)):len(qr_conn) - 1]
if len(conn) < 1:
cv2.line(frame, (0, 0), (0, 0), (30, 90, 235), 1)
for l in range(len(conn) - 1):
#print('lol ',tuple(hist[conn[l].data][-1]),tuple(hist[conn[l+1].data][-1]))
cv2.line(frame, tuple(hist[conn[l].data][-1]),
tuple(hist[conn[l + 1].data][-1]), (30, 90, 235), 5)
#cv2.line(frame, tuple(hist[conn[l].data][-1]),tuple(hist[conn[l+1].data][-1]) , (255, 255, 255), 5)
#if l==len(conn)-1:
#cv2.line(frame, tuple(hist[conn[l+1].data][-1]), tuple(hist[conn[0].data][-1]), (30, 90, 235), 5)
for decoded in zbar_image:
points = decoded.location
#print(decoded.data)
if len(points) > 4:
hull = cv2.convexHull(
np.array([point for point in points], dtype=np.float32))
hull = list(map(tuple, np.squeeze(hull)))
else:
hull = points
n = len(hull)
#print(hull)
try:
c1 = hull[0][0] + hull[1][0] + hull[2][0] + hull[3][0]
c2 = hull[0][1] + hull[1][1] + hull[2][1] + hull[3][1]
c1 /= 4
c2 /= 4
except:
continue
for j in range(0, n):
cv2.line(frame, hull[j], hull[(j + 1) % n], (255, 0, 0), 3)
cv2.line(frame, (c1, c2), (c1, c2), (255, 255, 0), 3)
# if (math.sqrt((xa - xb) ** 2 + (ya - yb) ** 2) < threshold):
# cv2.circle(blank_image, (xa, ya), 20, (0, 255, 0), -1)
# if (xa, ya) not in qr_conn:
# qr_conn.append((xa, ya))
#
# if (len(qr_conn) > 0):
# cv2.line(blank_image, qr_conn[len(qr_conn) - 1], (xb, yb), (0, 255, 255), 5)
# for i in range(0, len(qr_conn) - 1):
# cv2.line(blank_image, qr_conn[i], qr_conn[i + 1], (0, 255, 255), 5)
#Code For Connecting QR_Codes.
#print ("center:",centres)
#needs code for setting an adaptive threshold
threshold = 110 #set an appropriate threshold for distance between finger and QRcentre
if (ccc != None):
center = ccc
#print('yas')
#For connection in Blank image
xa, ya = c1, c2
ya = ya
xb, yb = center
xb = abs(980 - xb - 40)
yb = (yb)
cv2.circle(frame, (c1, c2), 5, (255, 0, 255), -1)
#cv2.circle(frame,(xa,ya),10,(0, 255,0 ), -1)
print('c2,c2', c1, " ", c2, "xb,yb", xb, " ", yb)
print("distance:", math.sqrt((xa - xb)**2 + (ya - yb)**2))
if (math.sqrt((xa - xb)**2 + (ya - yb)**2) < threshold):
cv2.circle(frame, (c1, c2), 20, (255, 0, 255), -1)
conn.append(decoded)
if (xa, ya) not in qr_conn:
qr_conn.append((xa, ya))
print(qr_conn)
# if(len(qr_conn)>0):
# cv2.line(frame, qr_conn[len(qr_conn)-1], (xb,yb) , (30, 90, 235), 5)
# for i in range(0,len(qr_conn)-1):
# cv2.line(frame, qr_conn[i], qr_conn[i+1] , (30, 90, 235), 5)
# #For connection in Real Time Video
# for point in arr:
# xa,ya=point
# xa=xa
# ya=ya
# xb,yb=center
#
# cv2.circle(fframe,(c1,c2),10,(0, 255,0 ), -1)
# #print("distance:",math.sqrt((xa-xb)**2+(ya-yb)**2))
# if(math.sqrt((xa-xb)**2+(ya-yb)**2)<threshold):
# cv2.circle(fframe,(xa,ya),20,(0, 255,0 ), -1)
# if (xa,ya) not in qr_conn1:
# qr_conn1.append((xa,ya))
#
#
#
# if(len(qr_conn1)>0):
# cv2.line(fframe, qr_conn1[len(qr_conn1)-1], center , (0, 255, 255), 5)
# for i in range(0,len(qr_conn1)-1):
# cv2.line(fframe, qr_conn1[i], qr_conn1[i+1] , (0, 255, 255), 5)
frame = cv2.resize(frame, (800, 800))
frame2 = frame
blank_image = np.zeros((height, width, 3), np.uint8)
blank_image = cv2.resize(blank_image, (800, 1000))
arr = []
lll = {}
abra = False
centresl = []
for decoded in zbar_image:
abra = True
lll[decoded.data] = True
points = decoded.location
#centresl.append(find_centre(points))
ll = [point for point in points]
# x1=min(1500-500-ll[0][0],ll[1][0],ll[2][0],ll[3][0])#top-left pt. is the leftmost of the 4 points
# x2=max(1500-500-ll[0][0],ll[1][0],ll[2][0],ll[3][0])#bottom-right pt. is the rightmost of the 4 points
# y1=min(1500-500-ll[0][1],ll[1][1],ll[2][1],ll[3][1])#top-left pt. is the uppermost of the 4 points
# y2=max(1500-500-ll[0][1],ll[1][1],ll[2][1],ll[3][1])#bottom-right pt. is the lowermost of the 4 points
# print("coordinates")
# cv2.line(blank_image,(h1,h2),(1500-500-ll[0][0],ll[0][1]),(255,255,255),15)
try:
cv2.line(blank_image, (1500 - 500 - ll[0][0], ll[0][1]),
(1500 - 500 - ll[1][0], ll[1][1]), (255, 255, 255),
10)
cv2.line(blank_image, (1500 - 500 - ll[1][0], ll[1][1]),
(1500 - 500 - ll[2][0], ll[2][1]), (255, 255, 255),
10)
cv2.line(blank_image, (1500 - 500 - ll[2][0], ll[2][1]),
(1500 - 500 - ll[3][0], ll[3][1]), (255, 255, 255),
10)
cv2.line(blank_image, (1500 - 500 - ll[3][0], ll[3][1]),
(1500 - 500 - ll[0][0], ll[0][1]), (255, 255, 255),
10)
cv2.line(blank_image, (1500 - 500 - ll[2][0], ll[2][1]),
(1500 - 500 - ll[2][0], ll[2][1]), (0, 0, 255), 25)
except:
continue
xa = ll[0][0] + ll[1][0] + ll[2][0] + ll[3][0]
ya = ll[0][1] + ll[1][1] + ll[2][1] + ll[3][1]
xa /= 4
ya /= 4
lx, ly = xa, ya
lol = 1
try:
# print(hist[decoded.data])
for j in range(2, len(hist[decoded.data])):
lol += 1
if lol <= 2:
lx = hist[decoded.data][j][0]
ly = hist[decoded.data][j][1]
continue
# print('lowllal',decoded.data,j[0])
# print "yaaaa",hist[decoded.data][j],hist[decoded.data][j+1],
cv2.line(blank_image, (1500 - 500 - lx, ly),
(1500 - 500 - hist[decoded.data][j][0],
hist[decoded.data][j][1]),
(100, (30 + hist[decoded.data][j][1] +
hist[decoded.data][j][0]) % 254, 255), 5)
lx = hist[decoded.data][j][0]
ly = hist[decoded.data][j][1]
# print 'lo', lx, ly
except:
# print 'la',lx, ly
cv2.line(blank_image, (1500 - 500 - lx, ly),
(1500 - 500 - lx, ly), (100, 30, 255), 10)
arr.append((xa, ya))
try:
hist[decoded.data].append([xa, ya])
hist[decoded.data][0] = 0
hist[decoded.data][1] = decoded
except:
hist[decoded.data] = [0, decoded, [xa, ya]]
# print('lenggg',len(hist))
if len(hist[decoded.data]) > 80:
del hist[decoded.data][2]
for j in arr:
cv2.line(blank_image, (1500 - 500 - j[0], j[1]),
(1500 - 500 - xa, ya), (0, 255, 255), 5)
dist = math.sqrt((xa - j[0])**2 + (ya - j[1])**2)
if dist > 0.1:
cv2.putText(blank_image, "{0}".format(round(dist, 2)),
(1500 - 500 - (xa + j[0]) / 2,
(ya + j[1]) / 2 + 5),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0))
#print("Centres:",centres)
count += 1
# if(len(arr)>=len(centres)):
#centres=arr
for j in hist:
if hist[j][0] > 0:
q.append(hist[j][1])
for j in hist:
try:
hist[j][0] += 1
except:
hist[j][0] = 1
for decoded in q:
if abra == False:
continue
#print('hissss', hist[decoded.data][0])
if hist[decoded.data][0] > 20:
continue
try:
if lll[decoded.data]:
continue
except:
pass
points = decoded.location
ll = [point for point in points]
# x1=min(1500-500-ll[0][0],ll[1][0],ll[2][0],ll[3][0])#top-left pt. is the leftmost of the 4 points
# x2=max(1500-500-ll[0][0],ll[1][0],ll[2][0],ll[3][0])#bottom-right pt. is the rightmost of the 4 points
# y1=min(1500-500-ll[0][1],ll[1][1],ll[2][1],ll[3][1])#top-left pt. is the uppermost of the 4 points
# y2=max(1500-500-ll[0][1],ll[1][1],ll[2][1],ll[3][1])#bottom-right pt. is the lowermost of the 4 points
# print("coordinates")
# cv2.line(blank_image,(h1,h2),(1500-500-ll[0][0],ll[0][1]),(255,255,255),15)
try:
cv2.line(blank_image, (1500 - 500 - ll[0][0], ll[0][1]),
(1500 - 500 - ll[1][0], ll[1][1]), (255, 255, 255),
10)
cv2.line(blank_image, (1500 - 500 - ll[1][0], ll[1][1]),
(1500 - 500 - ll[2][0], ll[2][1]), (255, 255, 255),
10)
cv2.line(blank_image, (1500 - 500 - ll[2][0], ll[2][1]),
(1500 - 500 - ll[3][0], ll[3][1]), (255, 255, 255),
10)
cv2.line(blank_image, (1500 - 500 - ll[3][0], ll[3][1]),
(1500 - 500 - ll[0][0], ll[0][1]), (255, 255, 255),
10)
cv2.line(blank_image, (1500 - 500 - ll[2][0], ll[2][1]),
(1500 - 500 - ll[2][0], ll[2][1]), (0, 0, 255), 25)
except:
continue
xa = ll[0][0] + ll[1][0] + ll[2][0] + ll[3][0]
ya = ll[0][1] + ll[1][1] + ll[2][1] + ll[3][1]
xa /= 4
ya /= 4
# font = cv2.FONT_HERSHEY_SIMPLEX
# cv2.putText(blank_image, decoded.data, (xa, ya), font, 2, (255, 255, 255), 2, cv2.LINE_AA)
lx, ly = xa, ya
lol = 1
try:
# print(hist[decoded.data])
for j in range(2, len(hist[decoded.data])):
lol += 1
if lol <= 2:
lx = hist[decoded.data][j][0]
ly = hist[decoded.data][j][1]
continue
# print('lowllal',decoded.data,j[0])
# print "yaaaa",hist[decoded.data][j],hist[decoded.data][j+1],
cv2.line(blank_image, (1500 - 500 - lx, ly),
(1500 - 500 - hist[decoded.data][j][0],
hist[decoded.data][j][1]),
(100, (30 + hist[decoded.data][j][1] +
hist[decoded.data][j][0]) % 254, 255), 7)
lx = hist[decoded.data][j][0]
ly = hist[decoded.data][j][1]
# print 'lo', lx, ly
except:
# print 'la',lx, ly
cv2.line(blank_image, (1500 - 500 - lx, ly),
(1500 - 500 - lx, ly), (100, 30, 255), 10)
arr.append((xa, ya))
try:
hist[decoded.data].append([xa, ya])
hist[decoded.data][0] += 1
hist[decoded.data][1] = decoded
except:
hist[decoded.data] = [1, decoded, [xa, ya]]
# print('lenggg',len(hist))
if len(hist[decoded.data]) > 100:
del hist[decoded.data][2]
for j in arr:
cv2.line(blank_image, (1500 - 500 - j[0], j[1]),
(1500 - 500 - xa, ya), (0, 255, 255), 5)
dist = math.sqrt((xa - j[0])**2 + (ya - j[1])**2)
if dist > 0.1:
cv2.putText(blank_image, "{0}".format(round(dist, 2)),
(1500 - 500 - (xa + j[0]) / 2,
(ya + j[1]) / 2 + 5),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0))
if count > 4:
q = []
count = 0
fframe = frame2
fframe = fframe[1] if args.get("video", False) else fframe
fframe = imutils.resize(fframe, width=800, height=1000)
blurred = cv2.GaussianBlur(fframe, (11, 11), 0)
hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)
hsv = cv2.flip(hsv, 1)
fframe = cv2.flip(fframe, 1)
mask = cv2.inRange(hsv, greenLower, greenUpper)
mask = cv2.erode(mask, None, iterations=2)
mask = cv2.dilate(mask, None, iterations=2)
# find contours in the mask and initialize the current
# (x, y) center of the ball
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
center = None
# only proceed if at least one contour was found
if len(cnts) > 0:
# find the largest contour in the mask, then use
# it to compute the minimum enclosing circle and
# centroid
c = max(cnts, key=cv2.contourArea)
((x, y), radius) = cv2.minEnclosingCircle(c)
M = cv2.moments(c)
center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
ccc = center
# only proceed if the radius meets a minimum size
if radius > 10:
# draw the circle and centroid on the fframe,
# then update the list of tracked points
cv2.circle(fframe, (int(x), int(y)), int(radius),
(0, 255, 255), 2)
cv2.circle(fframe, center, 5, (0, 0, 255), -1)
pts.appendleft(center)
#lololol
# loop over the set of tracked points
for i in np.arange(1, len(pts)):
# if either of the tracked points are None, ignore
# them
if pts[i - 1] is None or pts[i] is None:
continue
# check to see if enough points have been accumulated in
# the buffer
if counter >= 10 and i == 1 and pts[-1] is not None:
# compute the difference between the x and y
# coordinates and re-initialize the direction
# text variables
dX = pts[-1][0] - pts[i][0]
dY = pts[-1][1] - pts[i][1]
(dirX, dirY) = ("", "")
# ensure there is significant movement in the
# x-direction
if np.abs(dX) > 20:
dirX = "East" if np.sign(dX) == 1 else "West"
# ensure there is significant movement in the
# y-direction
if np.abs(dY) > 20:
dirY = "North" if np.sign(dY) == 1 else "South"
# handle when both directions are non-empty
if dirX != "" and dirY != "":
direction = "{}-{}".format(dirY, dirX)
# otherwise, only one direction is non-empty
else:
direction = dirX if dirX != "" else dirY
# otherwise, compute the thickness of the line and
# draw the connecting lines
thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 2.5)
cv2.line(fframe, pts[i - 1], pts[i], (0, 0, 255), thickness)
# show the movement deltas and the direction of movement on
# the fframe
cv2.putText(fframe, direction, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.65, (0, 0, 255), 3)
cv2.putText(fframe, "dx: {}, dy: {}".format(dX, dY),
(10, fframe.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35,
(0, 0, 255), 1)
# show the fframe to our screen and increment the fframe counter
counter += 1
cv2.imshow("input", fframe)
cv2.imshow("Output", blank_image)
def ProcessPage(paper):
answers = [] #contains answers
gray_paper = cv2.cvtColor(paper,
cv2.COLOR_BGR2GRAY) #convert image to grayscale
scanner = zbar.ImageScanner()
scanner.parse_config('enable')
pil = Image.fromarray(gray_paper).convert('L')
width, height = pil.size
try:
raw = pil.tobytes()
except AttributeError:
raw = pil.tostring()
image = zbar.Image(width, height, 'Y800', raw)
result = scanner.scan(image)
if result == 0:
codes = None
else:
for symbol in image:
pass
# clean up
del (image)
# Assuming data is encoded in utf8
data = symbol.data.decode(u'utf-8')
codes = data
corners = FindCorners(paper) #find the corners of the bubbled area
#if we can't find the markers, return an error
if corners is None:
return [-1], paper, [-1]
#calculate dimensions for scaling
dimensions = [corners[1][0] - corners[0][0], corners[2][1] - corners[0][1]]
#iterate over test questions
for k in range(0, 2): #columns
for i in range(0, 25): #rows
questions = []
for j in range(0, 5): #answers
#coordinates of the answer bubble
x1 = int((columns[k][0] + j * spacing[0] - radius * 1.5) *
dimensions[0] + corners[0][0])
y1 = int((columns[k][1] + i * spacing[1] - radius) *
dimensions[1] + corners[0][1])
x2 = int((columns[k][0] + j * spacing[0] + radius * 1.5) *
dimensions[0] + corners[0][0])
y2 = int((columns[k][1] + i * spacing[1] + radius) *
dimensions[1] + corners[0][1])
#draw rectangles around bubbles
cv2.rectangle(paper, (x1, y1), (x2, y2), (255, 0, 0),
thickness=1,
lineType=8,
shift=0)
#crop answer bubble
questions.append(gray_paper[y1:y2, x1:x2])
#find image means of the answer bubbles
means = []
#coordinates to draw detected answer
x1 = int((columns[k][0] - radius * 8) * dimensions[0] +
corners[0][0])
y1 = int((columns[k][1] + i * spacing[1] + 0.5 * radius) *
dimensions[1] + corners[0][1])
#calculate the image means for each bubble
for question in questions:
means.append(np.mean(question))
#sort by minimum mean; sort by the darkest bubble
min_arg = np.argmin(means)
min_val = means[min_arg]
#find the second smallest mean
means[min_arg] = 255
min_val2 = means[np.argmin(means)]
#check if the two smallest values are close in value
if min_val2 - min_val < test_sensitivity_epsilon:
#if so, then the question has been double bubbled and is invalid
min_arg = 5
#write the answer
cv2.putText(paper, answer_choices[min_arg], (x1, y1),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 150, 0), 1)
#append the answers to the array
answers.append(answer_choices[min_arg])
#draw the name if found from the QR code
if codes is not None:
cv2.putText(paper, str(codes),
(int(0.28 * dimensions[0]), int(0.125 * dimensions[1])),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 0), 1)
else:
codes = [-1]
return answers, paper, codes
def find_barcode(img, do_zlib=True, zxing_path=""):
"""
**SUMMARY**
This function requires zbar and the zbar python wrapper
to be installed or zxing and the zxing python library.
**ZBAR**
To install please visit:
http://zbar.sourceforge.net/
On Ubuntu Linux 12.04 or greater:
sudo apt-get install python-zbar
**ZXING**
If you have the python-zxing library installed, you can find 2d and 1d
barcodes in your image. These are returned as Barcode feature objects
in a FeatureSet. The single parameter is the ZXing_path along with
setting the do_zlib flag to False. You do not need the parameter if you
don't have the ZXING_LIBRARY env parameter set.
You can clone python-zxing at:
http://github.com/oostendo/python-zxing
**INSTALLING ZEBRA CROSSING**
* Download the latest version of zebra crossing from:
http://code.google.com/p/zxing/
* unpack the zip file where ever you see fit
>>> cd zxing-x.x, where x.x is the version number of zebra crossing
>>> ant -f core/build.xml
>>> ant -f javase/build.xml
This should build the library, but double check the readme
* Get our helper library
>>> git clone git://github.com/oostendo/python-zxing.git
>>> cd python-zxing
>>> python setup.py install
* Our library does not have a setup file. You will need to add
it to your path variables. On OSX/Linux use a text editor to modify
your shell file (e.g. .bashrc)
export ZXING_LIBRARY=<FULL PATH OF ZXING LIBRARY - (i.e. step 2)>
for example:
export ZXING_LIBRARY=/my/install/path/zxing-x.x/
On windows you will need to add these same variables to the system
variable, e.g.
http://www.computerhope.com/issues/ch000549.htm
* On OSX/Linux source your shell rc file (e.g. source .bashrc). Windows
users may need to restart.
* Go grab some barcodes!
.. Warning::
Users on OSX may see the following error:
RuntimeWarning: tmpnam is a potential security risk to your program
We are working to resolve this issue. For normal use this should not
be a problem.
**Returns**
A :py:class:`FeatureSet` of :py:class:`Barcode` objects. If no barcodes
are detected the method returns None.
**EXAMPLE**
>>> bc = cam.getImage()
>>> barcodes = img.findBarcodes()
>>> for b in barcodes:
>>> b.draw()
**SEE ALSO**
:py:class:`FeatureSet`
:py:class:`Barcode`
"""
if do_zlib:
try:
import zbar
except:
logger.warning('The zbar library is not installed, please '
'install to read barcodes')
return None
#configure zbar
scanner = zbar.ImageScanner()
scanner.parse_config('enable')
raw = img.get_pil().convert('L').tostring()
width = img.width
height = img.height
# wrap image data
image = zbar.Image(width, height, 'Y800', raw)
# scan the image for barcodes
scanner.scan(image)
barcode = None
# extract results
for symbol in image:
# do something useful with results
barcode = symbol
else:
if not ZXING_ENABLED:
logger.warn("Zebra Crossing (ZXing) Library not installed. "
"Please see the release notes.")
return None
global _barcode_reader
if not _barcode_reader:
if not zxing_path:
_barcode_reader = zxing.BarCodeReader()
else:
_barcode_reader = zxing.BarCodeReader(zxing_path)
tmp_filename = os.tmpnam() + ".png"
img.save(tmp_filename)
barcode = _barcode_reader.decode(tmp_filename)
os.unlink(tmp_filename)
if barcode:
f = Factory.Barcode(img, barcode)
return FeatureSet([f])
else:
return None
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 23 11:46:29 2019
@author: Srivatsan
"""
import time
import zbar
cv2.namedWindow("camera", 1)
capture = cv2.VideoCapture(0)
i = 0
while True:
set_zbar = zbar.ImageScanner()
cv2.imshow("camera", img)
cv2.imwrite('pic{:>05}.jpg'.format(i), img)
if cv2.WaitKey(10) == 27:
break
i += 1
def face_track():
with picamera.PiCamera() as camera:
camera.resolution = (640, 480)
#time.sleep(2)
first_image = True
print("Start Motion Tracking ....")
#frame_count = 0
#start_time = time.time()
while (True):
with picamera.array.PiRGBArray(camera) as stream:
camera.capture(stream, format='bgr')
camera.framerate = 30
camera.sharpness = 60
camera.contrast = 50
camera.brightness = 50
image = stream.array
if first_image:
face_cascade = cv2.CascadeClassifier(
'/home/pi/New/SelfieLibrary/faces.xml')
#Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
scanner = zbar.ImageScanner()
scanner.parse_config('enable')
#thresh = 70
#im_bw = cv2.threshold(gray, thresh, 255, cv2.THRESH_BINARY)[1]
#width, height = my_stream.size
raw = gray.tostring()
cv2.imwrite("bw.jpg", gray)
stream = zbar.Image(600, 400, 'Y800', raw)
scanner.scan(stream)
#Here connectivity of database
con = None
for symbol in stream:
BookID = symbol.data
#print 'decoded', symbol.type, 'symbol', '"%s"' % symbol.data
print 'BookID found', '"%s"' % BookID
#return Book
#BookID = 123456
try:
con = psycopg2.connect(database="testdb",
user="******",
password="******",
host="127.0.0.1",
port="5432")
cur = con.cursor()
cur.execute("SELECT * FROM BookD where Id = %s" %
(BookID))
rows = cur.fetchall()
for row in rows:
print row
#return row
#con.commit()
except psycopg2.DatabaseError, e:
if con:
con.rollback()
print 'Error %s' % e
sys.exit(1)
finally:
if con:
con.close()
#Look for faces in the image using the loaded cascade file
faces = face_cascade.detectMultiScale(gray, 1.1, 5)
distances = []
if window_on:
#Draw a rectangle around every found face
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x + w, y + h),
(255, 255, 0), 2)
#print "Found "+str(len(faces))+" face(s)"
#if len(faces)>0 :
cv2.imwrite(
'/home/pi/New/SelfieLibrary/result.jpg', gray)
print "Found " + str(len(faces)) + " face(s)"
img = cv2.imread(
'/home/pi/New/SelfieLibrary/result.jpg')
crop_img = img[y:y + h, x:x + w]
crop_img = cv2.resize(crop_img, (100, 100))
# NOTE: its img[y: y + h, x: x + w] and *not* img[x: x + w, y: y + h]
cv2.imwrite(
"/home/pi/New/SelfieLibrary/cropped/probe/cropped.png",
crop_img)
distances = learnsk.face_rec()
print min(distances)[0]
if int(min(distances)[0]) <= 1.77436551952e+58:
cv2.putText(image, str(min(distances)[1]),
(x - 100, y),
cv2.FONT_HERSHEY_PLAIN, 1,
(0, 0, 250), 1, 1)
if window_on:
cv2.imshow('Movement Status', image)
#Save the result image
# Close Window if q pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
#im = cv2.imread("/home/pi/New/cropped/cropped.jpg")
#print type(im)
cv2.destroyAllWindows()
break
def scan_image(filename):
ret = []
#print filename
# create a reader
# configure the reader
scanner = zbar.ImageScanner()
scanner.parse_config('enable')
# obtain image data
pil = Image.open(filename).convert('L')
def from_pil(scanner,pil):
ret = []
try:
#pil = pil.convert('L')
width, height = pil.size
raw = pil.tobytes()
# wrap image data
image = zbar.Image(width, height, 'Y800', raw)
# scan the image for barcodes
scanner.scan(image)
# extract results
for symbol in image:
# do something useful with results
ret.append((symbol.type, symbol.data))
# clean up
del image
del raw
except:
pass
return ret
ct = "[" + threading.currentThread().getName() + "]"
r = from_pil(scanner,pil)
if len(r) > 0:
ret += r
print ct,filename,"Found: Normal"
sharpen = True
if sharpen:
pil2 = pil
#for i in xrange(0,9):
pil2 = pil2.filter(ImageFilter.SHARPEN)
r = from_pil(scanner,pil2)
if len(r) > 0:
ret += r
print ct,filename,"Found: sharpen!"
del pil2
enhancer = True
if enhancer:
pil2 = image_enhancer(pil)
r = from_pil(scanner,pil2)
if len(r) > 0:
ret += r
print ct,filename,"Found: enhanced!"
del pil2
rotate = True
if rotate:
i = 0
for angle in xrange(1,181):
pil3 = pil.rotate(angle, resample=Image.BICUBIC, expand=0)
r = from_pil(scanner,pil3)
if len(r) >0:
ret += r
print ct,filename,"Found: Rotated: %d" % angle
i+=1
if i > 9:
break
del pil3
del scanner
del pil
return ret
