def run(self):
hist = cv2.createHist([180], cv2.CV_HIST_ARRAY, [(0,180)], 1 )
backproject_mode = True
while True:
frame = cv2.QueryFrame( self.capture )
# Convert to HSV and keep the hue
hsv = cv2.createImage(cv2.GetSize(frame), 8, 3)
cv2.cvtColor(frame, hsv, cv2.CV_BGR2HSV)
self.hue = cv2.createImage(cv2.GetSize(frame), 8, 1)
cv2.split(hsv, self.hue, None, None, None)
# Compute back projection
backproject = cv2.createImage(cv2.GetSize(frame), 8, 1)
cv2.calcArrBackProject( [self.hue], backproject, hist )
# Run the cam-shift (if the a window is set and != 0)
if self.track_window and is_rect_nonzero(self.track_window):
crit = ( cv2.CV_TERMCRIT_EPS | cv2.CV_TERMCRIT_ITER, 10, 1)
(iters, (area, value, rect), track_box) = cv2.camShift(backproject, self.track_window, crit) #Call the camshift !!
self.track_window = rect #Put the current rectangle as the tracked area
# If mouse is pressed, highlight the current selected rectangle and recompute histogram
if self.drag_start and is_rect_nonzero(self.selection):
sub = cv2.getSubRect(frame, self.selection) #Get specified area
#Make the effect of background shadow when selecting a window
save = cv2.cloneMat(sub)
cv2.convertScale(frame, frame, 0.5)
cv2.copy(save, sub)
#Draw temporary rectangle
x,y,w,h = self.selection
cv2.rectangle(frame, (x,y), (x+w,y+h), (255,255,255))
#Take the same area but in hue image to calculate histogram
sel = cv2.getSubRect(self.hue, self.selection )
cv2.calcArrHist( [sel], hist, 0)
#Used to rescale the histogram with the max value (to draw it later on)
(_, max_val, _, _) = cv2.getMinMaxHistValue( hist)
if max_val != 0:
cv2.convertScale(hist.bins, hist.bins, 255. / max_val)
elif self.track_window and is_rect_nonzero(self.track_window): #If window set draw an elipseBox
cv2.ellipseBox( frame, track_box, cv2.CV_RGB(255,0,0), 3, cv2.CV_AA, 0 )
cv2.showImage( "CamShiftDemo", frame )
cv2.showImage( "Backprojection", backproject)
cv2.showImage( "Histogram", self.hue_histogram_as_image(hist))
c = cv2.waitKey(7) % 0x100
if c == 27:
break
def OnFileButtonDownClick(self):
global p1
global p1max
global button1Val
global Nfile
global currentImg
global originalImg
print('file button down')
if Nfile > 0:
Nfile = Nfile - 1
print('Nfile:' + str(Nfile))
self.labelVariable.set(self.entryVariable.get() + " p1val=" +
str(filelist[Nfile]))
self.entry.focus_set()
self.entry.selection_range(0, Tkinter.END)
originalImg = cv2.imread(filelist[Nfile])
currentImg = cv2.copy(originalImg)
img = Image.open(filelist[Nfile])
photo2 = ImageTk.PhotoImage(img)
label1 = Tkinter.Label(self, image=photo2)
label1.image = photo2
label1.grid(row=Imrow, column=0, columnspan=Ncols, sticky=Tkinter.NW)
self.update()
def get_feed(self):
global a_time_to_die, feed_data, feed_width, feed_height, feed_ready
scanner = zbar.ImageScanner()
scanner.parse_config('enable')
shift = 1
while 1:
if a_time_to_die:
return
try:
HOST = '' # The remote host
PORT = 12345 # The same port as used by the server
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((HOST, PORT))
s.send('Yo!')
data = data = recvall(s)
s.close()
data = pickle.loads(data)
#print 'Received img'
height, width, depth = data.shape
if effect == smiley_face:
try:
data = cv2.flip(detect_faces(cv2.flip(data, 0)),0)
except:
print "face fail"
elif effect == target_face:
try:
data = cv2.flip(detect_faces_2(cv2.flip(data, 0)),0)
except:
print "face 2 fail"
elif effect == trippy_colours:
try:
shift += 1
data += shift % 256
except:
print "shift fail"
elif effect == trippy_colours_2:
try:
shift += 1
for x in xrange(shift % 3):
data2 = cv2.copy(data)
cv2.mixChannels(data2,data, [(0,1),(1,2),(2,1)])
except:
print "mario fail"
elif effect == vertical_hold:
try:
shift += 10
M = numpy.float32([[1,0,0],[0,1,shift%height]])
data1 = cv2.warpAffine(data,M,(width,height))
M = numpy.float32([[1,0,0],[0,1,(shift%height)-height]])
data2 = cv2.warpAffine(data,M,(width,height))
data = data1 + data2
M = numpy.float32([[1,0,shift%width],[0,1,0]])
data1 = cv2.warpAffine(data,M,(width,height))
M = numpy.float32([[1,0,(shift%width)-width],[0,1,0]])
data2 = cv2.warpAffine(data,M,(width,height))
data = data1 + data2
except:
print "vhold fail"
elif effect == colour_band:
try:
shift += 5
b = height / 20
top = shift % (width - b)
# band of colour
data_g = cv2.cvtColor(cv2.cvtColor(data, cv2.COLOR_BGR2GRAY), cv2.CV_GRAY2BGR)
data_g[0:width, top:top+b] = data[0:width, top:top+b]
data = data_g
# inversion
data_i = 255 - data
data[0:width, top:top+b] = data_i[0:width, top:top+b]
except:
print "vhold fail"
data = cv2.cvtColor(data,cv2.COLOR_BGR2RGB)
feed_data = data.tostring()
feed_width = width
feed_height = height
feed_ready = True
sleep(.001)
except:
s.close()
print("fail")
sleep(25)
# (3) an OEM value, in this case, 7 which implies that we are
# treating the ROI as a single line of text
config = ("-l eng --oem 1 --psm 7")
text = pytesseract.image_to_string(roi, config=config)
# add the bounding box coordinates and OCR'd text to the list
# of results
results.append(((startX, startY, endX, endY), text))
# sort the results bounding box coordinates from top to bottom
results = sorted(results, key=lambda r: r[0][1])
# loop over the results
for ((startX, startY, endX, endY), text) in results:
# display the text OCR'd by Tesseract
print("OCR TEXT")
print("========")
print("{}\n".format(text))
# strip out non-ASCII text so we can draw the text on the image
# using OpenCV, then draw the text and a bounding box surrounding
# the text region of the input image
text = "".join([c if ord(c) < 128 else "" for c in text]).strip()
output = cv2.copy()
cv2.rectangle(output, (startX, startY), (endX, endY), (0, 0, 255), 2)
cv2.putText(output, text, (startX, startY - 20), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 3)
# show the output image
cv2.imshow("Text Detection", output)
cv2.waitKey(0)
