def main(displayHistory=True):
#Window for past frames
framesDiffHistory = [(getBlankFrameDiff(), getBlankFrameDiff())
for i in range(framesInHistory)]
lastEyes = None
#Load model classifier
classifier = Classifier()
#Start thread to make predictions
classifier.startPredictions()
#Initialize webcam
vs = WebcamVideoStream(src=0).start()
#For FPS computation
t0 = -1
#Face/eyes detector
detector = Detector()
print "Starting eye recognition..."
while True:
#Compute FPS
dt = time.time() - t0
fps = 1 / dt
t0 = time.time()
#Limit FPS with wait
waitMs = 5
key = cv2.waitKey(waitMs) & 0xFF
#Get image from webcam, convert to grayscale and resize
fullFrame = vs.read()
fullFrame = cv2.cvtColor(fullFrame, cv2.COLOR_BGR2GRAY)
frame = imutils.resize(fullFrame, width=300)
#Find face
faceBB = detector.getFace(frame)
if faceBB is None:
#Invalidate eyes bounding box as all will change
lastEyes = None
detector.resetEyesBB()
continue
#Get low resolution face coordinates
x, y, w, h = faceBB
face = frame[y:y + h, x:x + w]
#Apply to high resolution frame
xScale = fullFrame.shape[1] / frame.shape[1]
yScale = fullFrame.shape[0] / frame.shape[0]
x, y, w, h = x * xScale, y * yScale, w * xScale, h * yScale
fullFace = fullFrame[y:y + h, x:x + w]
#Find eyes on high resolution face
eyes = detector.getEyes(fullFace)
if eyes is None:
#Reset last eyes
lastEyes = None
continue
eye0, eye1 = eyes
#Process (normalize, resize)
eye0 = process(eye0)
eye1 = process(eye1)
#Reshape for dataset
eye0 = np.reshape(eye0, [datasetImageSize, datasetImageSize, 1])
eye1 = np.reshape(eye1, [datasetImageSize, datasetImageSize, 1])
#We have a recent picture of the eyes
if lastEyes is not None:
#Load previous eyes
eye0previous, eye1previous = lastEyes
#Compute diffs
diff0 = getDifferenceFrame(eye0, eye0previous)
diff1 = getDifferenceFrame(eye1, eye1previous)
#Display/debug
displayDiff = False
if displayDiff:
displayCurrentDiff(eye0,
eye1,
eye0previous,
eye1previous,
stopFrame=False)
#Crop beginning then add new to end
framesDiffHistory = framesDiffHistory[1:]
framesDiffHistory.append([diff0, diff1])
#Keep current as last frame
lastEyes = [eye0, eye1]
#Note: this is not time consuming
if displayHistory:
displayHistoryDiffs(framesDiffHistory, fps)
#Extract each eyes
X0, X1 = zip(*framesDiffHistory)
#Reshape as a tensor (NbExamples,SerieLength,Width,Height,Channels)
X0 = np.reshape(X0, [
-1,
len(framesDiffHistory), datasetImageSize, datasetImageSize, 1
])
X1 = np.reshape(X1, [
-1,
len(framesDiffHistory), datasetImageSize, datasetImageSize, 1
])
#Save history to Classifier
classifier.X0 = X0
classifier.X1 = X1
