def start_video_stream(host):
if len(sys.argv) > 1:
host = sys.argv[1]
hoststr = 'http://' + host + '/video?x.mjpeg'
print('Streaming ' + hoststr)
stream = urllib.request.urlopen(hoststr)
template = cv2.imread('test_images\\template_new.png')
bytes_from_stream = bytes()
i = 0
while True:
bytes_from_stream += stream.read(1024)
a = bytes_from_stream.find(b'\xff\xd8')
b = bytes_from_stream.find(b'\xff\xd9')
if a != -1 and b != -1:
jpg = bytes_from_stream[a:b + 2]
bytes_from_stream = bytes_from_stream[b + 2:]
frame = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8), cv2.IMREAD_COLOR)
cv2.imshow(host, frame)
if i % 30 == 0:
adjusted_image = get_scorecard_sift(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), template)
if adjusted_image is not None:
cv2.imshow("image", adjusted_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
all_digits, digit_flags = get_digits_from_scorecard(adjusted_image)
predictions, prediction_flags = predict_digits(all_digits, digit_flags)
comp_id = construct_id(predictions[0:3])
times = construct_times(predictions[3:])
print("Comp ID:", comp_id)
for time in times:
print(time)
print(prediction_flags)
prediction_flags_formatted = []
# Handle ID
for i in range(0, 2):
prediction_flags_formatted.append([])
if prediction_flags[i] == 1:
prediction_flags_formatted[0].append(str(i))
# Handle the 5 rounds
for rounds in range(0, 4):
prediction_flags_formatted.append([])
for i in range(0, 6):
if prediction_flags[3 + i + 7*rounds] == 1:
prediction_flags_formatted[rounds + 1].append(str(i))
print(prediction_flags_formatted)
addInfoToDatabase(comp_id, times, prediction_flags_formatted)
getWinners()
# Press escape to close
if cv2.waitKey(1) == 27:
exit(0)
i += 1
def start_video_stream(host):
if len(sys.argv) > 1:
host = sys.argv[1]
hoststr = 'http://' + host + '/video?x.mjpeg'
print('Streaming ' + hoststr)
stream = urllib.request.urlopen(hoststr)
template = cv2.imread('test_images\\template_new.png')
bytes_from_stream = bytes()
i = 0
while True:
bytes_from_stream += stream.read(1024)
a = bytes_from_stream.find(b'\xff\xd8')
b = bytes_from_stream.find(b'\xff\xd9')
if a != -1 and b != -1:
jpg = bytes_from_stream[a:b + 2]
bytes_from_stream = bytes_from_stream[b + 2:]
frame = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8),
cv2.IMREAD_COLOR)
cv2.imshow(host, frame)
if i % 30 == 0:
adjusted_image = get_scorecard_sift(
cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), template)
if adjusted_image is not None:
cv2.imshow("image", adjusted_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
all_flags = []
all_times = []
id_digits = get_id_from_scorecard(adjusted_image)
comp_ip, flags = construct_id(id_digits)
all_flags.append(flags)
print("Competitor id:", comp_ip)
for row in range(1, 6):
row_of_digits = get_row_of_digits_from_scorecard(
adjusted_image, row)
constructed_time, flags = construct_time(row_of_digits)
all_flags.append(flags)
all_times.append(constructed_time)
print(constructed_time)
for flag in all_flags:
print(flag)
addInfoToDatabase(comp_ip, all_times, all_flags)
getWinners()
# Press escape to close
if cv2.waitKey(1) == 27:
exit(0)
i += 1
# Extract the digits
all_digits, digit_flags = get_digits_from_scorecard(adjusted_image)
# Run all the extracted digits through the neural network
predictions, prediction_flags = predict_digits(all_digits, digit_flags)
# Construct the competitor IDs and the solve times for each round
comp_id = construct_id(predictions[0:3])
times = construct_times(predictions[3:])
# Print our results
print("Comp ID:", comp_id)
for time in times:
print(time)
print(prediction_flags)
# Send the competitor ID and the solve times off to the database
# We must format our 'bad digit' flags for the database
prediction_flags_formatted = []
# Handle ID
prediction_flags_formatted.append([])
for i in range(0, 2):
if prediction_flags[i] == 1:
prediction_flags_formatted[0].append(str(i))
# Handle the 5 rounds
for rounds in range(0, 5):
prediction_flags_formatted.append([])
for i in range(0, 7):
if prediction_flags[3 + i + 7 * rounds] == 1:
prediction_flags_formatted[rounds + 1].append(str(i))
print(prediction_flags_formatted)
addInfoToDatabase(comp_id, times, prediction_flags_formatted)
getWinners()