def main():
app = QtWidgets.QApplication(sys.argv)
udp = UdpThread()
udp.connect()
udp.start()
Graph(rssi_listener=udp).show()
sys.exit(app.exec_())
def _generate_graph_embeddings(self):
'''
This function first generates an object tree using the ObjectTree Class
and then generates a graph using the Graph Class.
The output of this function is a feature array along with label array
(if any).
'''
self.tree = ObjectTree()
self.graph = Graph()
# Generate a graph dictionary
self.tree.read(self.object_map, self.image)
graph_dict, text_list, coords_arr = self.tree.connect(plot=False,
export_df=True)
# Generate the Adjacency Matrix (A) and the Feature Matrix (X)
A, X = self.graph.make_graph_data(graph_dict=graph_dict,
text_list=text_list,
coords_arr=coords_arr,
img_dims=self.img_dims)
# transform the feature matrix by muptiplying with the Adjacency Matrix
X_transformed = np.matmul(A, X)
# form new feature matrix
self.X = np.concatenate((X, X_transformed), axis=1)
def appStarted(mode):
try:
mode.bg = mode.loadImage("muslevelbg.jpg")
except:
mode.bg = mode.loadImage("https://i.ibb.co/8DwvThD/muslevelbg.jpg")
mode.Progress = Progress()
mode.gameState = "levels"
mode.game = "music"
(mode.marginX, mode.marginY) = (mode.app.width // 10,
mode.app.height // 9)
(mode.puzzleW, mode.puzzleH) = (mode.app.width - 2 * mode.marginX,
mode.app.height - 2 * mode.marginY)
mode.widthUnit = mode.app.width // 10
mode.heightUnit = mode.app.height // 9
mode.selected = [(-100, -100)]
mode.complete = False
mode.lastBeatTimer = 0
mode.beatTimer = 0
mode.dots = []
mode.createButtons()
mode.levelButtons = []
levelPageB = FakeButton(mode.widthUnit * 0.5, mode.heightUnit * 5.5,
mode.widthUnit * 0.8, mode.heightUnit * 0.6,
"Levels", mode.levelPageBCom, mode)
mode.buttons.append(levelPageB)
cusLevelB = FakeButton(mode.widthUnit * 0.5, mode.heightUnit * 1.5,
mode.widthUnit * 0.8, mode.heightUnit * 0.6,
" S ", mode.cusBCom, mode, " S ")
level1B = FakeButton(mode.widthUnit * 1.5, mode.heightUnit * 1.5,
mode.widthUnit * 0.8, mode.heightUnit * 0.6,
" 1 ", mode.levelBCom, mode, " 1 ")
mode.levelButtons.append(cusLevelB)
mode.levelButtons.append(level1B)
mode.fourierColors = "#FFC5C5"
mode.fourierGraph = Graph((mode.app.width, 0), mode.app.height,
mode.fourierColors, 1.5,
0.684 * MusicGameMode.fourierInterval, False)
mode.colorschemas = [
["#55efc4", "#81ecec", "#74b9ff", "#a29bfe", "#ffeaa7", "#fab1a0"],
["#fa983a", "#b71540", "#F8EFBA", "#1e3799", "#3c6382"],
["#00CCCD", "#1287A5", "#EA7773", "#2B2B52", "#F5BCBA"],
["#30336B", "#67E6DC", "#0A3D62", "#6A89CC"],
["#40407a", "#706fd3", "#f7f1e3", "#34ace0", "#33d9b2"]
]
mode.colors = mode.colorschemas[0]
mode.time = 0
mode.last = 0
mode.backgroundColor = "#FEFEFE"
mode.font = "Calibri"
mode.velx = 10
mode.vely = 10
mode.offset = 0
mode.lastBeat = int(round(time.time() * 1000))
json_file = name + str(i).zfill(5) + '.json'
image_file = name + str(i).zfill(5) + '.png'
receipt_csv_file = name + str(i).zfill(5) + '.csv'
print(json_file, image_file, receipt_csv_file)
receipt_csv = pd.read_csv(folderNameReceipt_csv + receipt_csv_file)
img = cv2.imread(folderName_image + image_file, 0)
# json_path = '/home/arjun/Gcn_paper/gcn/gcn/OneDrive_2020-11-13/spaCy NER Annotator output/Image_0006.json'
tree = ObjectTree()
tree.read(receipt_csv, img)
graph_dict, text_list = tree.connect(plot=True, export_df=True)
graph = Graph(max_nodes=len(text_list))
adj, features = graph.make_graph_data(graph_dict, text_list)
adj = sparse.csr_matrix(adj)
adj = normalize(adj + sp.eye(adj.shape[0]))
# adj = sparse_mx_to_torch_sparse_tensor(adj)
if first_count == 0:
features_merged = features
else:
features_merged = np.concatenate((features_merged, features),
axis=0)
# features = sparse.csr_matrix(features)
# features = torch.FloatTensor(np.array(features.todense()))
adj_array.append(adj.todense())
def init(data): #Initialize all of data's variables
global dati
dati = data
data.checkRadius = 400
data.targetRadii = []
data.leftScore = 0
data.rightScore = 0
data.leftDistance = 99999
data.rightDistance = 99999
data.lastBeatTimer = 0
data.beatTimer = 0
data.circWidth = 3
data.circColor = "black"
data.songPath = ""
data.dots = []
data.buttons = []
data.instant_energies = []
data.energy_averages = []
data.maxEnergy = 0
data.beats = []
data.fourier = []
data.fourier_colors = ["#FFC5C5", "#FFDFDF"]
data.colorschemas = [
["#BE1B38", "#01A98F", "#C9CF7E", "#E86644"],
["#354458", "#EB7260", "#29ABA4", "#E9E0D6", "#3A9AD9"],
["#FF0066", "#333366", "#3399CC", "#00CCCC", "#003399"],
["#a6dace", "#F7A04B", "#666699", "#EF8D24"],
["#05b3b2", "#2ca5dc", "#a5d3ea", "#f4cfbd", "#ff946c"]
]
data.colors = data.colorschemas[0]
data.targets = []
data.time = 0
data.last = 0
data.radius = 10
data.posx = data.width // 3
data.posy = data.height // 2
pickNewTarget(data)
data.timescale = 1.0
data.backgroundBase = "#FEFEFE"
data.font = "Calibri"
data.gamestate = 0
data.gamemode = "song"
data.offset = 0
data.score = 0
data.graphers = []
data.lastBeat = int(round(time.time() * 1000))
data.lastCallBack = time.time()
data.backgroundColor = data.backgroundBase
data.canvas = None
data.playerPhased = False
data.soundcloudActive = False
data.beats = []
data.velx = 10
data.vely = 10
data.p = None
data.volSum = 1841616
data.volCount = 1
data.max = 343
data.phaseJuice = 0
data.maxPhaseJuice = 100
data.boostJuice = 0
data.maxBoostJuice = 100
data.playerR = 370
data.playerPos = 6.28
data.playerVel = 0
data.playerAcel = 0.001
data.graphers += [BeatGrapher((1390, 10), 250, "#979797")]
data.graphers += [Graph((1390, 10), 250, "red")]
data.graphers += [Graph((1390, 10), 250, "green", 1)]
data.graphers += [Graph((1390, 10), 250, "blue", 2)]
data.graphers += [
Graph((1412, 260), 300, data.fourier_colors[0], 1.5,
0.684 * fourierInterval, False)
]
# Usual Fourier height = 625
makeBtns(data)
def keyPressed(event, data):
if (event.keysym == "space"): #Slow down time (or speed it back up)
if (data.timescale == 1):
data.backup = data.colors
data.timescale = 0.2
for dot in data.dots:
dot.fill = "#FF0051"
data.colors = ["#FF0051"]
data.backgroundColor = "#FFB9B9"
data.graphers[4] = Graph((1412, 260), 300, data.fourier_colors[1],
1.5, 0.684 * fourierInterval,
False) #Change the fourier graph's colors
if (data.gamemode == "song"):
remakeStream(songTimeScale)
else:
proont("-----")
data.timescale = 1.0
data.backgroundColor = data.backgroundBase
data.colors = data.backup
for dot in data.dots:
dot.fill = random.choice(data.colors)
data.graphers[4] = Graph((1412, 260), 300, data.fourier_colors[0],
1.5, 0.684 * fourierInterval,
False) #Change the fourier graph's colors
if (data.gamemode == "song"):
remakeStream(1)
elif (event.keysym == "r"): #Move the emitter back to the center
data.posx = data.width // 2
data.posy = data.height // 2
data.startx = data.posx
data.starty = data.posy
#Change some game modes
elif (event.keysym == "1"):
data.gamestate = 1
data.timer = 0
elif (event.keysym == "2"):
data.gamestate = 2
data.startx = data.posx
data.starty = data.posy
elif (event.keysym == "3"):
data.gamestate = 3
data.startx = data.posx
data.starty = data.posy
elif (event.keysym == "4"):
data.gamestate = 4
elif (event.keysym == "5"):
data.gamestate = 5
elif (event.keysym == "Escape"): #Go back to the menu screen and clean up
data.gamestate = 0
if (data.stream != None):
data.stream.stop_stream()
data.p = None
data.stream = None
data.maxEnergy = 0
data.timescale = 1
data.gamemode = "song"
data.fourier = []
data.instant_energies = []
data.energy_averages = []
data.beats = []
data.graphers[4].maxVal = 0
data.playerR = 370
data.playerPos = 6.28
data.playerVel = 0
data.leftScore = 0
data.rightScore = 0
data.targetRadii = []
elif (event.keysym == "0"): #Change the emitter's colors
changeColors(data)
if (not data.gamemode == "sandbox"
): #If we're playing a game, handle the players' key presses
if (event.keysym == "a"):
bestDist = 9999999
for test in data.targetRadii:
distance = abs(data.checkRadius - test)
if distance < bestDist:
bestDist = distance
data.leftDistance = bestDist
if (bestDist > 50
): #If there's no beat even somewhat close, they lose points
data.leftScore -= 1
else:
data.beatTimer = 2 #Wait a bit for the other player to make their move before we decide who gets the beat
elif (event.keysym == "l"):
bestDist = 9999999
for test in data.targetRadii:
distance = abs(data.checkRadius - test)
if distance < bestDist:
bestDist = distance
data.rightDistance = bestDist
if (bestDist > 50
): #If there's no beat even somewhat close, they lose points
data.rightScore -= 1
else:
data.beatTimer = 2 #Wait a bit for the other player to make their move before we decide who gets it
pass
def get_train_data(data_folder_path):
# ============= get paths for images and object maps ===========================
image_jpg_path_glob = data_folder_path + r'\*\*.jpg'
image_png_path_glob = data_folder_path + r'\*\*.png'
csv_path_glob = data_folder_path + r'\*\*.csv'
list_img_paths = glob(image_jpg_path_glob) + \
glob(image_png_path_glob)
list_csv_paths = glob(csv_path_glob)
# ------------------------------------------------------------------------------
# =============== initialize the ObjectTree and Graph Objects ==================
tree = ObjectTree()
graph = Graph()
# ------------------------------------------------------------------------------
# === generate graph embeddings for each document ==============================
data_df = pd.DataFrame(columns=['features', 'label'])
count, skip_count = 0, 0
for image_path, csv_path in zip(list_img_paths, list_csv_paths):
img = cv2.imread(image_path, 0)
object_map = pd.read_csv(csv_path)
# drop rows with nans
object_map.dropna(inplace=True, how='any')
try:
# generate object tree
tree.read(object_map, img)
graph_dict, text_list, coords_arr = tree.connect(plot=True,
export_df=True)
# make graph data
A, X = graph.make_graph_data(graph_dict, text_list, coords_arr,
img)
# transform the feature matrix by muptiplying with the Adjacency Matrix
X_transformed = np.matmul(A, X)
# form new feature matrix
X = np.concatenate((X, X_transformed), axis=1)
# get the labels
y = object_map.label.values
if count == 0:
X_train = X
y_train = y
else:
X_train = np.concatenate((X_train, X), axis=0)
y_train = np.concatenate((y_train, y), axis=0)
print('Finished processing image {} of {}...'.format(
count, len(list_img_paths)))
except Exception:
skip_count += 1
print('Skipping Graph Generation...')
count += 1
print('Finished generating Graph Dataset for {} documents. Skipped {}.'\
.format(count, skip_count))
return X_train, y_train
