def main():
parser = build_parser()
options = parser.parse_args()
print(options.session)
# opens the user specified session
if options.session:
with open(os.path.join("Sessions", options.session), 'r') as f:
session = json.load(f)
# opens the default session
else:
with open(DEFAULT_SESSION, 'r') as f:
session = json.load(f)
# set variables from loaded session data
fam = session["crystal_family"]
provide_family = session["known_family"]
display_type = session["display_type"]
auto_calibrate = session["auto_calibrate"]
file_path = session["file_path"]
output_file = session["output_file"]
is_profile = session["is_profile"]
manual_peak_selection = session["manual_peak_selection"]
scale_bar = session["scale_bar"]
# Load calibration from specified file (json)
try:
print("Loading calibration from {}".format(
os.path.join("Calibrations", auto_calibrate)))
with open(os.path.join("Calibrations", auto_calibrate), 'r') as f:
calibration = json.load(f)
except:
print(
"No calibration could be loaded from {}\nPlease check the file exists and is formatted properly"
.format(auto_calibrate))
calibration = cf.set_calibration(is_profile)
print(calibration)
with open(SERVER_INFO, 'r') as f:
server_info = json.load(f)
if server_info['URL']:
URL = server_info['URL']
else:
raise ValueError('you need to have the server URL provided to you')
# Load user from provided path, [IN PROGRESS]
with open(USER_INFO) as f:
user_info = json.load(f)
# Determine if the path is a directory or a file
if os.path.isdir(file_path):
print("loading files from directory")
file_paths = []
for dirpath, dirnames, fpath in os.walk(file_path):
for path in fpath:
file_paths.append(os.path.join(dirpath, path))
print("found {} files to load.".format(len(file_paths)))
else:
file_paths = [file_path]
print(file_paths)
for f_path in file_paths:
# Load Data from specified file (DM3, TIFF, CSV etc....)
try:
print("loading data from {}".format(f_path))
image_data = ClientSide.Load_Image(f_path)
print("I successfully loaded the data")
except:
print(
"Invalid file path given ({}).\n Please enter filepath to your data"
.format(f_path))
options.fpath = input()
# Change the processing based on data type
if is_profile:
# Choose which profile if there are multiple
image_data, scale = cf.choose_profile(image_data)
else:
plt.imshow(np.log(image_data))
plt.show(block=False)
#plt.show()
# Change the Processing based on the type of data
if is_profile:
print("identifying from profile")
radial_profile = {"brightness": image_data, "pixel_range": scale}
else:
radial_profile = ClientSide.Extract_Profile(image_data)
peak_locs = ClientSide.Find_Peaks(radial_profile, calibration,
is_profile, display_type, scale_bar)
# Choose which peaks to classify on
if manual_peak_selection:
peak_locs = cf.choose_peaks(peak_locs, display_type)
if provide_family == "yes":
while fam is None:
temp_fam = input("What family does the Crystal belong to?\n")
if temp_fam in FAMILIES:
fam = temp_fam
else:
print("Invalid choice. choose from {}\n".format(
str(FAMILIES)[1:-1]))
elif provide_family == "no":
fam = None
classificated = ClientSide.Send_For_Classification(
peak_locs, user_info, URL, fam)
classificated["file_name"] = f_path
# update the user on the results before saving
print(classificated)
# write results out to the specified file
if not os.path.exists("Results"):
os.makedirs("Results")
cf.write_to_csv(os.path.join("Results", output_file), classificated)
def main():
parser = build_parser()
options = parser.parse_args()
print(options.session)
# opens the user specified session
if options.session:
with open(os.path.join("Sessions",options.session),'r') as f:
session = json.load(f)
# opens the default session
else:
with open(DEFAULT_SESSION,'r') as f:
session = json.load(f)
# set variables from loaded session data
fam = session["crystal_family"]
provide_family = session["known_family"]
display_type = session["display_type"]
auto_calibrate = session["auto_calibrate"]
file_path = session["file_path"]
output_file = session["output_file"]
is_profile = session["is_profile"]
manual_peak_selection = session["manual_peak_selection"]
scale_bar = session["scale_bar"]
# Load calibration from specified file (json)
try:
print("Loading calibration from {}".format(os.path.join("Calibrations",auto_calibrate)))
with open(os.path.join("Calibrations",auto_calibrate),'r') as f:
calibration = json.load(f)
except:
print("No calibration could be loaded from {}\nPlease check the file exists and is formatted properly".format(auto_calibrate))
calibration = cf.set_calibration(is_profile)
print(calibration)
with open(SERVER_INFO,'r') as f:
server_info = json.load(f)
if server_info['URL']:
URL = server_info['URL']
else:
raise ValueError('you need to have the server URL provided to you')
# Load user from provided path, [IN PROGRESS]
with open(USER_INFO) as f:
user_info = json.load(f)
if not os.path.exists(file_path):
print("The path provided could not be found. Please check your session file path")
return
# Determine if the path is a directory or a file
if os.path.isdir(file_path):
print("loading files from directory")
file_paths = []
for dirpath,dirnames,fpath in os.walk(file_path):
for path in fpath:
file_paths.append(os.path.join(dirpath,path))
print("found {} files to load.".format(len(file_paths)))
else:
file_paths = [file_path]
print(file_paths)
for f_path in file_paths:
# Load Data from specified file (DM3, TIFF, CSV etc....)
try:
print("loading data from {}".format(f_path))
image_data = ClientSide.Load_Image(f_path)
print("I successfully loaded the data")
except:
print("Invalid file path given ({}).\n Please enter filepath to your data".format(f_path))
options.fpath = input()
# Change the processing based on data type
if is_profile:
# Choose which profile if there are multiple
image_data,scale = cf.choose_profile(image_data)
else:
pass
#plt.imshow(np.log(image_data))
#plt.show(block=False)
#plt.show()
# Change the Processing based on the type of data
if is_profile:
print("identifying from profile")
radial_profile = {"brightness":image_data,
"pixel_range":scale}
else:
radial_profile = ClientSide.Extract_Profile(image_data)
peak_locs = ClientSide.Find_Peaks(radial_profile,calibration,is_profile,display_type,scale_bar)
print(peak_locs)
# Choose which peaks to classify on
if manual_peak_selection:
peak_locs = cf.choose_peaks(peak_locs,display_type)
if provide_family =="yes":
while fam is None:
temp_fam = input("What family does the Crystal belong to?\n")
if temp_fam in FAMILIES:
fam = temp_fam
else:
print("Invalid choice. choose from {}\n".format(str(FAMILIES)[1:-1]))
elif provide_family == "no":
fam = None
print(peak_locs)
with open(os.path.join("Results",f_path.split(os.sep)[-1][:-4]+".json"), "w") as o:
o.write(json.dumps(peak_locs))
lower_gen = SpGr.edges["genus"][fam][0]
upper_gen = SpGr.edges["genus"][fam][1]
fam_range = range(SpGr.edges["species"][lower_gen][0],1+SpGr.edges["species"][upper_gen][1])
common_peaks,guesses = combination_peaks(peak_locs,f_path.split(os.sep)[-1][:-4],user_info,URL,fam)
plt.figure(figsize=(len(fam_range)//2,4))
prev_histograms = []
plots = []
for rank in range(1,5):
histo = np.histogram([int(g) for g in guesses["species_{}".format(rank)]],bins=fam_range)
#histo[0] = histo[0]*(2-(rank/5.0))
if rank > 1:
plot = plt.bar(histo[1][:-1],histo[0],
bottom=np.sum(np.vstack(prev_histograms),axis=0),align="center")
else:
plot = plt.bar(histo[1][:-1],histo[0],align="center",color='red')
plots.append(plot)
plt.yticks(rotation='vertical')
plt.xticks(histo[1][:-1],rotation='vertical')
prev_histograms.append(histo[0])
plt.xlabel("Prediction",fontsize=10,rotation='vertical')
plt.ylabel("Counts",fontsize=10)
#plt.legend(plots,("species_1","species_2","species_3","species_4"))
if not os.path.exists("Results"):
os.makedirs("Results")
print("Results/"+f_path.split(os.sep)[-1][:-4]+"gen1.png")
plt.savefig("Results/" + f_path.split(os.sep)[-1][:-4] + "_gen1.png")
plt.show(block=False)
