def _initme(self, userdata=None):
#the main classes
self.m = Model(self)
self.capture = Capture(self)
self.ui = UI(self)
return False
def __init__(self,
world,
filename=None,
simulator=None,
once=False,
headless=False):
logging.info('Initialising vision')
if simulator:
self.capture = SimCapture(simulator)
else:
self.capture = Capture(self.rawSize, filename, once)
self.headless = headless
self.threshold = threshold.AltRaw()
self.pre = Preprocessor(self.rawSize, self.threshold, simulator)
self.featureEx = FeatureExtraction(self.pre.cropSize)
self.interpreter = Interpreter()
self.world = world
self.gui = GUI(world, self.pre.cropSize, self.threshold)
self.histogram = Histogram(self.pre.cropSize)
self.times = []
self.N = 0
#debug.thresholdValues(self.threshold.Tblue, self.gui)
logging.debug('Vision initialised')
def setEmotion(self,emotion):
self.emotion = emotion
self.frameArea.focus_set()
if not self.captureStarted:
self.captureStarted = True
self.cap = Capture(self.frameArea,self.clf)
self.cap.capture(self.emotion)
def test_print_if():
class Print_If(Cell):
def __init__(self):
super().__init__()
self.print = P.Print()
def construct(self, x, y):
self.print("input_x before:", x, "input_y before:", y)
if x < y:
self.print("input_x after:", x, "input_y after:", y)
x = x + 1
return x
cap = Capture()
with capture(cap):
input_x = Tensor(3, dtype=ms.int32)
input_y = Tensor(4, dtype=ms.int32)
expect = Tensor(4, dtype=ms.int32)
net = Print_If()
out = net(input_x, input_y)
time.sleep(0.1)
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
patterns = {
'input_x before:\nTensor(shape=[], dtype=Int32, value=3)\n'
'input_y before:\nTensor(shape=[], dtype=Int32, value=4)',
'input_x after:\nTensor(shape=[], dtype=Int32, value=3)\n'
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)'
}
check_output(cap.output, patterns)
def test_print_assign_add():
class Print_Assign_Add(Cell):
def __init__(self):
super().__init__()
self.print = P.Print()
self.add = P.Add()
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
def construct(self, x, y):
self.print("before:", self.para)
self.para = x
self.print("after:", self.para)
x = self.add(self.para, y)
return x
cap = Capture()
with capture(cap):
input_x = Tensor(3, dtype=ms.int32)
input_y = Tensor(4, dtype=ms.int32)
expect = Tensor(7, dtype=ms.int32)
net = Print_Assign_Add()
out = net(input_x, input_y)
time.sleep(0.1)
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
patterns = {
'before:\nTensor(shape=[], dtype=Int32, value=1)',
'after:\nTensor(shape=[], dtype=Int32, value=3)'
}
check_output(cap.output, patterns)
def test_print_for():
class Print_For(Cell):
def __init__(self):
super().__init__()
self.print = P.Print()
def construct(self, x, y):
y = x + y
self.print("input_x before:", x, "input_y before:", y)
for _ in range(3):
y = y + 1
self.print("input_x after:", x, "input_y after:", y)
return y
cap = Capture()
with capture(cap):
input_x = Tensor(2, dtype=ms.int32)
input_y = Tensor(4, dtype=ms.int32)
expect = Tensor(9, dtype=ms.int32)
net = Print_For()
out = net(input_x, input_y)
time.sleep(0.1)
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
patterns = {
'input_x before:\nTensor(shape=[], dtype=Int32, value=2)\n'
'input_y before:\nTensor(shape=[], dtype=Int32, value=6)',
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
'input_y after:\nTensor(shape=[], dtype=Int32, value=7)',
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
'input_y after:\nTensor(shape=[], dtype=Int32, value=8)',
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
'input_y after:\nTensor(shape=[], dtype=Int32, value=9)'
}
check_output(cap.output, patterns)
def test_print_add():
class Print_Add(Cell):
def __init__(self):
super().__init__()
self.print = P.Print()
self.add = P.Add()
def construct(self, x, y):
x = self.add(x, y)
self.print("input_x:", x, "input_y:", y)
return x
cap = Capture()
with capture(cap):
input_x = Tensor(3, dtype=ms.int32)
input_y = Tensor(4, dtype=ms.int32)
expect = Tensor(7, dtype=ms.int32)
net = Print_Add()
out = net(input_x, input_y)
time.sleep(0.1)
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
patterns = {
'input_x:\nTensor(shape=[], dtype=Int32, value=7)\n'
'input_y:\nTensor(shape=[], dtype=Int32, value=4)'
}
check_output(cap.output, patterns)
def __init__(self):
self.logger = logger
self.sqlite = Sqlite()
self.cap = Capture()
self.report_available_yuming = []
self.logger.debug('init over')
def __init__(self):
# get the logger
self.logger = logger
self.logger.debug("logger test ok")
# init sqlite
self.sqlite = sqlite
self.cap = Capture()
self.logger.debug("init over")
async def init_app():
loop = asyncio.get_event_loop()
app = web.Application(loop=loop)
capture = Capture()
app['capture'] = capture
app['loop'] = loop
app.on_shutdown.append(on_shutdown)
app.router.add_post('/capture', handle)
return app
def __init__(self):
self.names = utils.get_names()
self.actions = {j:i for i,j in self.names.items()}
# special case for stopping the car when a stop or red light signal is detected
self.actions[-1] = 'stop'
# parts
self.sender = Sender()
self.decisor = Decisor()
self.capture = Capture()
def __init__(self, window, class_file, running_mode):
self.classes = classes_from_file(class_file)
self.window_x = window[0]
self.window_y = window[1]
self.window_width = window[2]
self.window_height = window[3]
self.capture = Capture(self.window_x, self.window_y, self.window_width,
self.window_height)
self.fast_rcnn = self.build_faster_rcnn_session()
def started_feed(message):
print(message)
re_initialise()
sockets[request.sid].capture = Capture(Check_Mask())
currentSocketId = request.sid
folder = os.path.join(os.getcwd(), "static")
sockets[request.sid].dirpath = os.path.join(folder, currentSocketId)
if not os.path.isdir(sockets[request.sid].dirpath):
os.mkdir(sockets[request.sid].dirpath)
def __init__(self):
# get the logger
self.logger = Logger(log_path=Config.logdir + '/Report.log',
log_level='debug',
log_name='Report')
self.logger.debug("logger test ok")
# init sqlite
self.sqlite = Sqlite()
self.cap = Capture()
self.logger.debug("init over")
def __init__(self, parent = None):
super().__init__(parent)
self.setGeometry(100, 100, 600, 600) # 起始位置，宽高
self.setWindowTitle("VideoStreamApp") # 设置标题
#self.setWindowIcon(QtGui.QIcon('opencvlogo.png'))
self.capture = Capture() # 设置摄像头组件
self.video_widget = VideoWidget() # 设置视频组件
image_window = self.video_widget.image_window # 获取播放器组件的播放窗口
self.capture.image_data.connect(image_window) # 将系统消息绑定到播放器窗口
self.add_buttons() # 窗口初始化时设置按钮
self.set_layout() # 窗口初始化时设置布局
def send_captured_data(ws):
capture = Capture(sys.argv[1], dataSize=480, isInvert=True)
capture.run()
# 初期値を設定
max_distance = 0
max_intensity = 0
max_elapsed_time = 0
sequence_id = 0
preTime = time.time()
while True:
if capture.dataObtained:
# 排他制御開始
capture.lock.acquire()
# データを取得
theta = list(capture.theta)
distance = list(capture.distance)
intensity = list(capture.intensity)
# データを取得したのでデータ取得フラグを下ろす
capture.dataObtained = False
# 排他制御終了
capture.lock.release()
# 現在設定されている最大値を取得
max_distance = max([max_distance] + distance)
max_intensity = max([max_intensity] + intensity)
# 送信するデータを作成
payload = json.dumps(
dict(header=dict(cmd="relay"),
contents=dict(sequenceId=sequence_id,
theta=theta,
distance=distance,
intensity=intensity,
maxDistance=max_distance,
maxIntensity=max_intensity)))
ws.send(payload) # データを送信
elapsedTime = time.time() - preTime
preTime = time.time()
if max_elapsed_time < elapsedTime:
max_elapsed_time = elapsedTime
print(
"[Log] Sequence ID: %d, Elapsed time: %f, Max elapsed time: %f"
% (sequence_id, elapsedTime, max_elapsed_time))
sequence_id += 1
else:
time.sleep(0.01)
def eval_fc(fname_pickle, model_path):
cam_props, recordings = pickle.load(open(fname_pickle, 'rb'))
speeds = np.zeros(len(recordings))
hits = np.zeros(len(recordings))
extras = np.zeros(len(recordings))
fname_avi = os.path.splitext(fname_pickle)[0] + '.avi'
model = keras.models.load_model(model_path)
cap = Capture(fname_avi, CapType.VIDEO)
ret, first_frame = cap.read()
cnn_input = CnnInput(first_frame)
run_processor = RunProcessor(cnn_input)
sticky_tolerance = StickyTolerance()
action = None
frame_num = 0
rec_i = 1
while cap.is_opened():
ret, frame = cap.read()
if not ret:
break
if cam_props.side == CamSide.LEFT:
frame = cv2.flip(frame, 1)
cnn_input.update(frame)
cnn_input_4d = np.expand_dims(cnn_input.frame, 0)
prediction = model.predict(cnn_input_4d)[0]
class_id = np.argmax(prediction)
class_label = dataset.id_to_gesture[class_id]
class_label, direction = run_processor.process(class_label)
action = sticky_tolerance.process(class_label, prediction[class_id],
action)
if direction is not None and action in ['walk', 'run']:
action = action + direction
if recordings[rec_i].label in ['jumpb', 'jumps']:
recordings[rec_i].label = 'jump'
target = recordings[rec_i].label
if action == target and hits[rec_i] == 0:
hits[rec_i] = 1
speeds[rec_i] = frame_num - recordings[rec_i].frame
frame_num += 1
if rec_i + 1 < len(recordings) and frame_num >= recordings[rec_i +
1].frame:
if hits[rec_i] == 0:
speeds[rec_i] = frame_num - recordings[rec_i].frame
rec_i += 1
return recordings, hits, speeds, extras
def main():
cap = Capture('./capture')
# load net
#net = caffe.Net('voc-fcn8s/deploy.prototxt', 'voc-fcn8s/fcn8s-heavy-pascal.caffemodel', caffe.TEST)
net = caffe.Net('voc-fcn-alexnet/deploy.prototxt',
'voc-fcn-alexnet/train_iter_16000.caffemodel', caffe.TEST)
for i in xrange(500):
imgfile = cap.capture()
run(net, imgfile, 'out/', '%d.jpg' % i)
def test_localize_map_all():
#use map of the buildings for localization
building_filename = os.path.join(root, 'flash', 'fft2', 'export', 'frames',
'DefineSprite_551', '1.png')
mapper = LocalizeMap(building_filename)
filename = os.path.join(root, 'flash', 'fft2', 'processed', 'level1_start.png')
c = Capture(filename)
while True:
template = c.snap_gray()
print mapper.localize_all(template)
def __init__(self):
self.model_name = "models/30epoch_depthrgb.hdf5"
self.throttle = 0.0
self.image_size = 250
self.throttle = 0.0
self.steering = 0.0
self.comms = CommServerS()
self.capture = Capture(self.model_name)
print("[INFO] AI Neural Net is firing!")
def test_localization():
filename = os.path.join(root, 'flash', 'fft2', 'processed', 'level1_start.png')
c = Capture(filename)
template = c.snap_gray()
w, h = template.shape[::-1]
res = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
print (max_loc[0], max_loc[1], max_loc[0] + w, max_loc[1] + h)
return (max_loc[0], max_loc[1], max_loc[0] + w, max_loc[1] + h)
def main():
capture = Capture(schema='camara_v1')
# capture data with this
capture.capture_data(
url=
'http://www.camara.leg.br/SitCamaraWS/Deputados.asmx/ObterPartidosCD')
data_list = capture.data['partidos']['partido']
data_list = capture.to_default_dict(data_list)
data_list = from_api_to_db_deputados(data_list, capture.url)
capture.insert_data(data_list,
table_name='partidos',
if_exists='pass',
key='id_partido')
def main():
capture = Capture(schema='camara_v2')
# capture data with this
base_url = 'https://dadosabertos.camara.leg.br/api/v2/proposicoes/{}/tramitacoes'
urls = urls_generator(capture, base_url)
for url, id_proposicao in urls:
print(url)
capture.capture_data(url, content_type='json')
data_list = capture.data['dados']
data_list = capture.to_default_dict(data_list)
data_list = from_api_to_db(data_list, url, id_proposicao)
capture.insert_data(data_list, table_name='tramitacao')
def main():
capture = Capture(schema='camara_v1', )
base_url = 'http://www.camara.leg.br/SitCamaraWS/Proposicoes.asmx/ObterVotacaoProposicao?tipo={tipo}&numero={numero}&ano={ano}'
url_and_ids, numero_captura = urls_generator(capture, base_url)
for url, id_proposicao in url_and_ids:
print('----')
print(url)
# capture data with this
try:
capture.capture_data(url)
data_proposicao = capture.data['proposicao']
data_generic = data_proposicao['Votacoes']['Votacao']
for data_votacao in force_list(data_generic):
# orientacao
try:
print()
print('orientacao')
data_list = data_votacao['orientacaoBancada']['bancada']
data_list = capture.to_default_dict(data_list)
data_list = from_api_to_db_votacao_orientacao(
data_list, url, data_proposicao, data_votacao,
id_proposicao, numero_captura)
capture.insert_data(
data_list, table_name='votacao_proposicao_orientacao')
except KeyError:
pass
# deputados
print()
print('deputados')
data_list = data_votacao['votos']['Deputado']
data_list = capture.to_default_dict(data_list)
data_list = from_api_to_db_votacao_deputado(
data_list, url, data_proposicao, data_votacao,
id_proposicao)
capture.insert_data(data_list, table_name='votacao_proposicao')
except:
continue
def __init__(self,
world,
filenames=None,
simulator=None,
once=False,
headless=False):
logging.info('Initialising vision')
self.headless = headless
self.capture = Capture(self.rawSize, filenames, once)
self.threshold = threshold.AltRaw()
self.threshold = threshold.PrimaryRaw()
self.world = world
self.simulator = simulator
self.initComponents()
self.times = []
self.N = 0
logging.debug('Vision initialised')
def main():
capture = Capture(schema='camara_v1', )
capture_number = get_capture_number(capture)
print('Numero Captura', capture_number)
capture.capture_data(
url='http://www.camara.leg.br/SitCamaraWS/Deputados.asmx/ObterDeputados'
)
data_list = capture.data['deputados']['deputado']
data_list = capture.to_default_dict(data_list)
data_list = from_api_to_db_deputados(data_list, capture.url,
capture_number)
capture.insert_data(data_list,
table_name='deputados',
if_exists='pass',
key='ide_cadastro')
def __init__(self, connec, *args, **kwargs):
self.connec = connec
#C:\local_tools\experimental\pyfire\flash\fft2\export\frames\DefineSprite_772_fla.maps.Map_02
#building_filename = os.path.join(root, 'flash', 'fft2', 'export', 'frames',
# 'DefineSprite_551', '1.png')
#map_filename = os.path.join(root, 'flash', 'fft2', 'export', 'frames',
# 'DefineSprite_772_fla.maps.Map_02', '1.png')
map_filename = os.path.join(root, 'flash', 'fft2', 'processed', 'aligned_localization_data_map.png')
self.mapper = LocalizeMap(map_filename)
filename = os.path.join(root, 'flash', 'fft2', 'processed', 'level1_start.png')
self.c = Capture(filename)
Process.__init__(self, *args, **kwargs)
def __init__(self):
super(Meeting, self).__init__()
self.ui = Ui_MainWindow()
self.resize(490, 326)
#self.setFixedSize(self.width(), self.height())
self.capture = Capture() # 设置摄像头组件
self.video_widget = VideoWidget() # 设置视频组件
# image_window = self.video_widget.image_window # 获取播放器组件的播放窗口
# self.capture.image_data.connect(image_window) # 播放器线程， 接收到摄像头数据时执行，在该函数中执行人脸检测及识别
self.capture.image_data.connect(
self.face_recognition) # 播放器线程， 接收到摄像头数据时执行，在该函数中执行人脸检测及识别
self.ui.setupUi(self)
self.setCapture()
self.setButtom()
self.meeting_id = None
#self.sensor = MLX90614()
self.tem = 36.9
self.fps = self.capture.get()
def test_print_assign_while():
class Print_Assign_While(Cell):
def __init__(self):
super().__init__()
self.print = P.Print()
self.para = Parameter(Tensor(0, dtype=ms.int32), name='para')
def construct(self, x, y):
self.print("input_x before:", x, "input_y before:", y,
"para before:", self.para)
while x < y:
self.para = x
x = self.para + 1
self.print("input_x after:", x, "input_y after:", y,
"para after:", self.para)
return x
cap = Capture()
with capture(cap):
input_x = Tensor(1, dtype=ms.int32)
input_y = Tensor(4, dtype=ms.int32)
expect = Tensor(4, dtype=ms.int32)
net = Print_Assign_While()
out = net(input_x, input_y)
time.sleep(0.1)
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
patterns = {
'input_x before:\nTensor(shape=[], dtype=Int32, value=1)\n'
'input_y before:\nTensor(shape=[], dtype=Int32, value=4)\n'
'para before:\nTensor(shape=[], dtype=Int32, value=0)',
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)\n'
'para after:\nTensor(shape=[], dtype=Int32, value=1)',
'input_x after:\nTensor(shape=[], dtype=Int32, value=3)\n'
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)\n'
'para after:\nTensor(shape=[], dtype=Int32, value=2)',
'input_x after:\nTensor(shape=[], dtype=Int32, value=4)\n'
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)\n'
'para after:\nTensor(shape=[], dtype=Int32, value=3)'
}
check_output(cap.output, patterns)
def main():
capture = Capture(schema='camara_v1', )
# capture data with this
capture.capture_data(
url='http://www.camara.leg.br/SitCamaraWS/Deputados.asmx/ObterDeputados'
)
# get the list of dict for this table
data_list = capture.data['deputados']['deputado']
#
data_list = capture.to_default_dict(data_list)
# make it rigth
data_list = from_api_to_db_deputados(data_list, capture.url)
# insert it!
capture.insert_data(data_list, table='deputados')
