fixed smoothing

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Prohurtz 2022-01-23 13:19:59 -08:00 committed by GitHub
parent 388e3e04a7
commit 202be04bc8
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@ -23,17 +23,17 @@ print(width, height)
def lefteye(): def lefteye():
x = 1 x = 1 #defining variables for smoothing
y = 1 y = 1
x1 = 1 x1 = 1
y1 = 1 y1 = 1
h = 1 #defines hight value so blink check doesnt error out h = 1 #defines hight value so blink check doesnt error out
def smooth(x, x1, y, y1): def smooth(x, x1, y, y1):
xsmooth = x1 + x / 2 xsmooth = (x1 + x) / 2
ysmooth = y1 + y / 2 ysmooth = (y1 + y) / 2
print('x filtered:', xsmooth, 'y filtered:', ysmooth) print('x filtered:', xsmooth, 'y filtered:', ysmooth, 'x raw:', x, 'y raw:', y)
print('x raw:', x, 'y raw:', y) #print('x raw:', x, 'y raw:', y)
while True: #loop for eye detection while True: #loop for eye detection
@ -104,22 +104,14 @@ def lefteye():
################################################ ################################################
# try:
try: # trys at set size if it errors it will revert to working size/ doesnt do what was orrigionally planed, it kinda helps, entire thing will eventually go into try and except later try: # trys at set size if it errors it will revert to working size/ doesnt do what was orrigionally planed, it kinda helps
roi = frame[int(vxl): int(float(vy)), int(vyl): int(float(vx))] roi = frame[int(vxl): int(float(vy)), int(vyl): int(float(vx))]
except: except:
roi = frame[100: 300, 200: 316] roi = frame[100: 300, 200: 316]
try: try:
if x == 1: x1 = x
print('no data to smooth') y1 = y
else:
x1 == x
if y == 1:
print('no data to smooth')
else:
y1 == y
rows, cols, _ = roi.shape rows, cols, _ = roi.shape
gray_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY) gray_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
gray_roi = cv2.GaussianBlur(gray_roi, (7, 7), 0) gray_roi = cv2.GaussianBlur(gray_roi, (7, 7), 0)
@ -136,6 +128,7 @@ def lefteye():
openes = 95 openes = 95
if h <= 25: #way to find if eye is closed and sets value (hopefully will train tensorflow model for correct openess detection) if h <= 25: #way to find if eye is closed and sets value (hopefully will train tensorflow model for correct openess detection)
openes = 0 openes = 0
smooth(x, x1, y, y1) smooth(x, x1, y, y1)
#print('Left: x:', x, 'y:', y, 'openess:', openes) #print('Left: x:', x, 'y:', y, 'openess:', openes)
#print('Right: x:', x, 'y:', y, 'openess:', openes) #print('Right: x:', x, 'y:', y, 'openess:', openes)