def _generate_mesh(geojson_filename, action, dataset):
msh_filename = os.path.splitext(geojson_filename)[0] + '.msh'
if action in ('update', 'download') or not os.path.exists(msh_filename):
geo_filename = os.path.splitext(geojson_filename)[0] + '.geo'
mesh.main(geojson_filename, geo_filename)
subprocess.call(['gmsh', '-2', '-format', 'msh2', geo_filename])
return msh_filename
def get_images():
clear_folder(datasetPath + "images")
clear_folder(datasetPath + "projections")
try:
uploaded_files = request.files.getlist("file[]")
for idx, file in enumerate(uploaded_files):
file.save(dst=datasetPath + "images/" + file.filename + ".jpg")
camera.main(datasetPath)
pmvs.main(datasetPath)
mesh.main("expansion_pointcloud.ply")
print('DONE')
except:
print(exc_info())
return jsonify({"message": "fail"})
return jsonify({"message": "done"})
def main():
mirror = False
cam = cv2.VideoCapture(0)
while True:
ret_val, img = cam.read()
if mirror:
img = cv2.flip(img, 1)
img = mesh.main("img", sys.argv[1], sys.argv[2])
cv2.imshow('web', img)
if cv2.waitKey(1) == 27:
break # esc to quit
cv2.destroyAllWindows()
def runn(mode, character, crop_mode):
if mode == 'vert':
out_vert.main(character)
if mode == 'repose':
repose.main(character)
if mode == 'mesh':
mesh.main(character)
if mode == 'crop':
extractor.main(character, crop_mode)
if mode == 'convert':
convert.main(character)
if mode == 'comp':
replacer.main(character)
if mode == 'swap':
fswap.main(character)
if mode == 'train':
train.main(character)
def vol_to_grids(vol_ffn, grid_config, vol_dbz_offset):
#refl cappi index
#5 = 2.5km
cappi_index = 5
#read radar
radar = pyart.aux_io.read_odim_h5(vol_ffn, file_field_names=True)
try:
#check if DBZH_CLEAN is present
refl_field = radar.fields['DBZH_CLEAN']['data']
except:
#run clutter filtering if DBZH_CLEAN is missing
refl_field, _ = clutter.main(radar, [], 'DBZH')
#run range filtering
R, A = np.meshgrid(radar.range['data']/1000, radar.azimuth['data'])
ma_refl_field = np.ma.masked_where(np.logical_or(R<10, R>120), refl_field.copy())
radar.add_field_like('DBZH', 'MA_DBZH_CLEAN', ma_refl_field, replace_existing=True)
#temp data <-- get from ACCESS-R archive
temp_data = mesh.temperature_profile_access(radar)
#grid
grid = pyart.map.grid_from_radars(
radar,
grid_shape = grid_config['GRID_SHAPE'],
grid_limits = grid_config['GRID_LIMITS'],
roi_func = 'constant',
constant_roi = grid_config['GRID_ROI'],
weighting_function = 'Barnes2',
fields = ['MA_DBZH_CLEAN'])
#extract data and apply refl offset
refl_grid = grid.fields['MA_DBZH_CLEAN']['data'] - vol_dbz_offset
alt_vec = grid.z['data']
#calculate refl field
REFL = refl_grid[cappi_index,:,:]
REFL_meta = {'units': 'dBZ', 'long_name': 'Horizontal Reflectivity Factor',
'standard_name': 'reflectivity', 'comments': 'calibrated reflectivity'}
#convert to mesh
MESH, MESH_meta = mesh.main(refl_grid, alt_vec, temp_data)
# fig = plt.figure(figsize=[10,10])
# plt.imshow(REFL)
#return 2D mesh grid
return MESH, MESH_meta, REFL, REFL_meta