def extract_bright(grey_img, histogram=False):
"""
Extracts brightest part of the image.
Expected to be the LEDs (provided that there is a dark background)
Returns a Thresholded image
histgram defines if we use the hist calculation to find the best margin
"""
## Searches for image maximum (brightest pixel)
# We expect the LEDs to be brighter than the rest of the image
[minVal, maxVal, minLoc, maxLoc] = cv2.minMaxLoc(grey_img)
# print "Brightest pixel val is %d" %(maxVal)
state = ""
if maxVal == 255:
state = "="
else:
state = "."
return state
# We retrieve only the brightest part of the image
# Here is use a fixed margin (80%), but you can use hist to enhance this one
if 0:
## Histogram may be used to wisely define the margin
# We expect a huge spike corresponding to the mean of the background
# and another smaller spike of bright values (the LEDs)
hist = grey_histogram(grey_img, nBins=64)
[hminValue, hmaxValue, hminIdx, hmaxIdx] = cv2.getMinMaxHistValue(hist)
margin = 0 # statistics to be calculated using hist data
else:
margin = 0.8
thresh = int(maxVal * margin) # in pix value to be extracted
# print "Threshold is defined as %d" %(thresh)
return thresh
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
hist = 0
maxVal = 0
thresh = 0
success, image = vidcap.read()
print('Read a new frame: ', success)
cv2.imwrite(os.path.join(Folder, "frame{:d}.jpg".format(count)),
image) # save frame as JPEG file
image1 = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
cv2.imwrite(os.path.join(Folder, "frameg{:d}.jpg".format(count)), image1)
#hist = cv2.calcHist([image1],[0],None,[255],[0,256])
[minVal, maxVal, minLoc, maxLoc] = cv2.minMaxLoc(image1)
print "Brightest pixel val is %d" % (maxVal)
if 0:
## Histogram may be used to wisely define the margin
# We expect a huge spike corresponding to the mean of the background
# and another smaller spike of bright values (the LEDs)
hist = cv2.calcHist([image1], [0], None, [64], [0, 256])
[hminValue, hmaxValue, hminIdx, hmaxIdx] = cv2.getMinMaxHistValue(hist)
margin = 0 # statistics to be calculated using hist data
else:
margin = 0.71
thresh = int(maxVal * margin)
print "Threshold is defined as %d" % (thresh)
_, thresh1 = cv2.threshold(image1, thresh, 255, cv2.THRESH_BINARY)
cv2.imwrite(os.path.join(Folder, "framet{:d}.jpg".format(count)), thresh1)
count += 1
time_end = time.time()
print time_end - time_start
print fps
