ret, frame = webcam.read()
x = 0
while webcam.isOpened():
ret, frame = webcam.read()
frame_1080p = np.zeros((720, 1920, 3)).astype(np.uint8)
#Resize Frame BGR
inputBGR = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
#Convert to facial_recogntion encoding BGR -> RGB
rgb_small_frame = inputBGR[:, :, ::-1]
frame_count += 1
frames.append(rgb_small_frame)
if stopButton.read():
break
# Only process every other frame of video to save time
if x % 4 == 0:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(
rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings,
face_encoding)
immaginee = frame[110:180, 140:210]
b1, g1, r1 = cv2.split(immaginee)
tmp = cv2.matchTemplate(r1, kern, cv2.TM_CCORR_NORMED)
tmp = tmp * 254
minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(tmp)
y_quad = maxLoc[1] + 8
x_quad = maxLoc[0] + 8
cv2.circle(frame, (x_quad + 140, y_quad + 110), 1, (255, 0, 0), 2)
reset=Button(3)
#CALIBRATION
button = Button(1)
if button.read() and acq:
b,g,r = cv2.split(frame[110:180,140:210])
r=cv2.GaussianBlur(r,(5,5),0)
r=cv2.Laplacian(r,cv2.CV_8U)
r=cv2.medianBlur(r,3)
circles=cv2.HoughCircles(r,cv2.HOUGH_GRADIENT,2,400,200,10,20)
for j in circles[0,:]:
j[0]=j[0]+140
j[1]=j[1]+110
cv2.circle(frame,(j[0],j[1]),j[2],(0,0,255),)
cv2.circle(frame,(j[0],j[1]),1,(0,255,0),1)
c.append([j[0], j[1]])
dist_init_y = -y_quad + j[1]
logger.debug("Starting now")
while True:
time.sleep(1)
if run:
LED_1.on()
posted = False
counter += 1
if worker(
counter): # If we get a message successfully, reset timer
timer = 0
else: # otherwise, keep track of failed messages (eg when car is off)
timer += 1
LED_1.off()
elif not posted:
LED_POSTING.on()
posted = serve_logs()
# Stop running when we encounter too many failed messages
if timer >= STOP_COUNT:
LED_RUNNING.off()
logger.debug("Stopped running, serving now")
run = False
timer = 0
elif rst.read() == 1: # on Button press start running
LED_RUNNING.on()
LED_POSTING.off()
logger.debug("Started running")
reset()
run = True
counter = 0
# Leds&Button assignements
button = Button(0)
led1 = LED(0)
led2 = LED(1)
# We have 2 players -- i<2
while (i<2):
# Status Led: Ready to start
if(i==0):
led1.on()
else:
led2.on()
# Wait button push
while(button.read() == 0):
time.sleep(0.1)
# Check if correct
if(i==0):
led1.off()
else:
led2.off()
values = []
sampling_time = 5
start = time.time()
end = start
while(end - start < sampling_time):
value = pmod_adc.read(0)
cv2.rectangle(frame, (140, 110), (210, 180), (255, 0, 0), 2)
immaginee = frame[110:180, 140:210]
b1, g1, r1 = cv2.split(immaginee)
tmp = cv2.matchTemplate(r1, kern, cv2.TM_CCORR_NORMED)
tmp = tmp * 254
minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(tmp)
y_quad = maxLoc[1] + 8
x_quad = maxLoc[0] + 8
cv2.circle(frame, (x_quad + 140, y_quad + 110), 1, (255, 0, 0), 2)
reset = Button(3)
#CALIBRATION
button = Button(1)
if button.read() and acq:
b, g, r = cv2.split(frame[110:180, 140:210])
r = cv2.GaussianBlur(r, (5, 5), 0)
r = cv2.Laplacian(r, cv2.CV_8U)
r = cv2.medianBlur(r, 3)
circles = cv2.HoughCircles(r, cv2.HOUGH_GRADIENT, 2, 400, 200,
10, 20)
for j in circles[0, :]:
j[0] = j[0] + 140
j[1] = j[1] + 110
cv2.circle(
frame,
(j[0], j[1]),