def exp5():
""" Query visualization. """
dataset = 'imdbmulti'
model = 'astar'
concise = True
norms = [True, False]
dir = get_result_path() + '/{}/query_vis/{}'.format(dataset, model)
create_dir_if_not_exists(dir)
info_dict = {
# draw node config
'draw_node_size': 150 if dataset != 'linux' else 10,
'draw_node_label_enable': True,
'node_label_name': None if dataset == 'linux' else 'type',
'draw_node_label_font_size': 6,
'draw_node_color_map': TYPE_COLOR_MAP,
# draw edge config
'draw_edge_label_enable': False,
'edge_label_name': 'valence',
'draw_edge_label_font_size': 6,
# graph text info config
'each_graph_text_list': [],
'each_graph_text_font_size': 8,
'each_graph_text_pos': [0.5, 1.05],
# graph padding: value range: [0, 1]
'top_space': 0.20 if concise else 0.26, # out of whole graph
'bottom_space': 0.05,
'hbetween_space': 0.6 if concise else 1, # out of the subgraph
'wbetween_space': 0,
# plot config
'plot_dpi': 200,
'plot_save_path_eps': '',
'plot_save_path_png': ''
}
train_data = load_data(dataset, train=True)
test_data = load_data(dataset, train=False)
row_graphs = test_data.graphs
col_graphs = train_data.graphs
r = load_result(dataset, model, row_graphs=row_graphs, col_graphs=col_graphs)
tr = load_result(dataset, TRUE_MODEL, row_graphs=row_graphs, col_graphs=col_graphs)
for norm in norms:
ids = r.get_sort_id_mat(norm)
m, n = r.m_n()
num_vis = 10
for i in range(num_vis):
q = test_data.graphs[i]
gids = np.concatenate([ids[i][:3], [ids[i][int(n / 2)]], ids[i][-3:]])
gs = [train_data.graphs[j] for j in gids]
info_dict['each_graph_text_list'] = \
[get_text_label(dataset, r, tr, i, i, q, model, norm, True, concise)] + \
[get_text_label(dataset, r, tr, i, j,
train_data.graphs[j], model, norm, False, concise) \
for j in gids]
# print(info_dict['each_graph_text_list'])
info_dict['plot_save_path_png'] = '{}/query_vis_{}_{}_{}{}.{}'.format(
dir, dataset, model, i, get_norm_str(norm), 'png')
info_dict['plot_save_path_eps'] = '{}/query_vis_{}_{}_{}{}.{}'.format(
dir, dataset, model, i, get_norm_str(norm), 'eps')
vis(q, gs, info_dict)
def pics(ticker): #return send_file(vis(ticker), mimetype='image/png') #return send_file(BytesIO(vis(ticker)), mimetype='image/png') rv = cache.get(ticker) if rv: print 'cached' if rv is None: rv = BytesIO(vis(ticker)) cache.set(ticker, rv, timeout=5 * 60) return send_file(rv, mimetype='image/png')
def exp10():
# Query visualization.
dataset = 'aids10k'
model = 'beam80'
k = 5
info_dict = {
'draw_node_size': 10,
'draw_node_label_enable': True,
'draw_node_label_font_size': 8,
'draw_node_color_map': {
'C': 'red',
'O': 'blue',
'N': 'green'
},
'draw_edge_label_enable': True,
'draw_edge_label_font_size': 6,
'each_graph_text_list': [],
'each_graph_font_size': 10,
'plot_dpi': 200,
'plot_save_path': ''
}
r = load_result(dataset, model)
ged_mat = r.ged_mat()
time_mat = r.time_mat()
ids = r.ged_sort_id_mat()
m, n = ged_mat.shape
train_data = load_data(dataset, train=True)
test_data = load_data(dataset, train=False)
for i in range(m):
q = test_data.graphs[i]
gids = ids[i][:k]
gs = [train_data.graphs[j] for j in gids]
info_dict['each_graph_text_list'] = \
['query id: {}'.format(q.graph['gid'])] + \
[get_text_label(ged_mat, time_mat, i, j, \
train_data.graphs[j]) for j in gids]
info_dict['plot_save_path'] = \
get_root_path() + \
'/files/{}//query_vis/{}/query_vis_{}_{}_{}.png'.format( \
dataset, model, dataset, model, i)
vis(q, gs, info_dict)
def process(image):
global left_line, right_line, detected
undist = cal_undistort(image, objp, imgp)
thresh_s = (170, 255)
thresh_sx = (50, 255)
thresh_sy = (50, 255)
thresh_mag = (50, 255)
thresh_gdir = (0.7, 1.3)
com = cal_thresh_img(undist, thresh_s, thresh_sx, thresh_sy, thresh_mag, thresh_gdir)
warped = warper(com, src, dst)
if not detected:
fit = sliding_window_search(warped)
if fit != None:
left_line.add_fit(fit[0])
right_line.add_fit(fit[1])
detected = True
else:
fit = sliding_window_search_with_known(warped, left_line.get_fit(), right_line.get_fit())
if fit != None:
left_line.add_fit(fit[0])
right_line.add_fit(fit[1])
else:
detected = False
left_fit, right_fit = left_line.get_fit(), right_line.get_fit()
res = vis(undist, warped, left_fit, right_fit, src, dst)
left_c, right_c = cal_curvature(warped, left_fit, right_fit)
offset = cal_offset(undist, left_fit, right_fit)
avg_c = (left_c + right_c)/2
label_c = 'Radius of curvature: %.2f m' % (avg_c)
offset_c = 'Offset from the center of lane: %.2f m' % (offset)
cv2.putText(res, label_c, (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,0), 2)
cv2.putText(res, offset_c, (20, 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,0), 2)
cv2.imwrite('./res.jpg', res)
return res
def output_each_step():
images = glob.glob('./test_images/*.jpg')
objp, imgp = cal_calib_points('./camera_cal/calibration*.jpg')
output_path = './output_images/'
for img in images:
print('Processing' + img)
img_name = img.split('/')[-1]
image = cv2.imread(img)
undist = cal_undistort(image, objp, imgp)
cv2.imwrite(output_path+'undist_'+img_name, undist)
thresh_s = (170, 255)
thresh_sx = (30, 255)
thresh_sy = (30, 255)
thresh_mag = (50, 255)
thresh_gdir = (0.7, 1.3)
com = cal_thresh_img(undist, thresh_s, thresh_sx, thresh_sy, thresh_mag, thresh_gdir)
cv2.imwrite(output_path+'thresh_'+img_name, com*255)
src = np.float32([[200, 720], [1100, 720], [595, 450], [685, 450]])
dst = np.float32([[300, 720], [980, 720], [300, 0], [980, 0]])
warped = warper(com, src, dst)
cv2.imwrite(output_path+'warped_'+img_name, warped*255)
fit_name = output_path+'fit_'+img_name
left_fit, right_fit = sliding_window_search(warped, vis=True, file_name=fit_name)
out_img = np.dstack((warped, warped, warped))*255
ploty = np.linspace(0, warped.shape[0]-1, warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
res = vis(undist, warped, left_fit, right_fit, src, dst)
cv2.imwrite(output_path+'final_'+img_name,res)
print('total time: {0}'.format((end - start).total_seconds()))
robot_path = dict()
robot_pre_suf_time = dict()
for type_robot in robot_path_pre.keys():
robot_path[type_robot] = robot_path_pre[type_robot] + robot_path_suf[type_robot][1:]
if len(robot_path_suf[type_robot]) == 1:
robot_pre_suf_time[type_robot] = [robot_time_pre[type_robot][-1], robot_time_pre[type_robot][-1]]
else:
robot_pre_suf_time[type_robot] = [robot_time_suf[type_robot][1], robot_time_suf[type_robot][-1]]
# # only display robots that are assigned tasks
for robot, time in list(robot_pre_suf_time.items()):
if time[-1] == 0:
del robot_path[robot]
del robot_pre_suf_time[robot]
vis(workspace, robot_path, robot_pre_suf_time, task.ap)
for type_robot, waypoint in robot_waypoint_pre.items():
print(type_robot, " : ", waypoint)
print(type_robot, " : ", robot_time_pre[type_robot])
print(type_robot, " : ", robot_path_pre[type_robot])
print(type_robot, " : ", list(range(round(robot_time_pre[type_robot][-1])+1)))
for type_robot, waypoint in robot_waypoint_plus_suf.items():
print(type_robot, " : ", waypoint)
print(type_robot, " : ", robot_time_suf[type_robot])
print(type_robot, " : ", robot_path_suf[type_robot])
print(type_robot, " : ", list(range(round(robot_time_suf[type_robot][0]),
round(robot_time_suf[type_robot][-1]) + 1)))
help="""Write test data to OUTPUT_FILE
(defaults to PSYSH_DIR/test/fixtures)""")
args = argp.parse_args()
cp_range = RANGES['bmp'] if args.all else RANGES[args.range]
indent = 2 if args.format_output else None
if args.output_file:
OUTPUT_FILE = abspath(expanduser(args.output_file))
fixtures = []
# use SMALL_RANGE by default, it should be enough.
# use BMP_RANGE for a more complete smoke test
for codepoint in cp_range:
char = chr(codepoint)
encoded = vis(char, VIS_WHITE)
decoded = unvis(encoded)
fixtures.append((encoded, decoded))
# Add our own custom fixtures at the end,
# since they would fail anyway if one of the previous did.
for fixture in CUSTOM_FIXTURES:
encoded = vis(fixture, VIS_WHITE)
decoded = unvis(encoded)
fixtures.append((encoded, decoded))
with open(OUTPUT_FILE, 'w') as fp:
# dump as json to avoid backslashin and quotin nightmare
# between php and python
json.dump(fixtures, fp, indent=indent)
0,
'hbetween_space':
0.4, # out of the subgraph
'wbetween_space':
0.01,
# plot config
'plot_dpi':
200,
'plot_save_path':
get_root_path() + '/temp/test_vis.png'
}
test_data = load_data('aids10k', train=False)
train_data = load_data('aids10k', train=True)
q = test_data.graphs[0]
gs = []
for i in range(1, 5):
print(i)
gs.append(train_data.graphs[i])
vis(q, gs, info_dict)
'''
TODO:
1. node color [done]
2. support graph-level text [done]
3. plt.save in vis [done]
4. edge color
'''
help="""Write test data to OUTPUT_FILE
(defaults to PSYSH_DIR/test/fixtures)""")
args = argp.parse_args()
cp_range = RANGES['bmp'] if args.all else RANGES[args.range]
indent = 2 if args.format_output else None
if args.output_file:
OUTPUT_FILE = abspath(expanduser(args.output_file))
fixtures = []
# use SMALL_RANGE by default, it should be enough.
# use BMP_RANGE for a more complete smoke test
for codepoint in cp_range:
char = chr(codepoint)
encoded = vis(char, VIS_WHITE)
decoded = unvis(encoded)
fixtures.append((encoded, decoded))
# Add our own custom fixtures at the end,
# since they would fail anyway if one of the previous did.
for fixture in CUSTOM_FIXTURES:
encoded = vis(fixture, VIS_WHITE)
decoded = unvis(encoded)
fixtures.append((encoded, decoded))
with open(OUTPUT_FILE, 'w') as fp:
# dump as json to avoid backslashin and quotin nightmare
# between php and python
json.dump(fixtures, fp, indent=indent)