def upload():
if request.method == 'POST':
sketch_src = request.form.get("sketchUpload")
upload_flag = request.form.get("uploadFlag")
sketch_src_2 = None
if upload_flag:
sketch_src_2 = request.files["uploadSketch"]
if sketch_src:
flag = 1
elif sketch_src_2:
flag = 2
else:
return render_template('canvas.html')
basepath = os.path.dirname(__file__)
upload_path = os.path.join(basepath, 'static/sketch_tmp', 'upload.png')
if flag == 1:
# base64 image decode
sketch = base64.b64decode(sketch_src[22:])
user_input = request.form.get("name")
file = open(upload_path, "wb")
file.write(sketch)
file.close()
elif flag == 2:
# upload sketch
sketch_src_2.save(upload_path)
user_input = request.form.get("name")
# for retrieval
retrieval_list, real_path = retrieval(retrieval_net, upload_path)
real_path = json.dumps(real_path)
return render_template('panel.html',
userinput=user_input,
val1=time.time(),
upload_src=sketch_src,
retrieval_list=retrieval_list,
json_info=real_path)
return render_template('canvas.html')
def get_recommendation(self, gender, occasion, images=None):
"""
Method for generating recommndations for a query
query = (gender, occasion, images)
Arguments:
---
gender: 'male' or 'female'
occasion: occasion string
images: (optional) set of images for preference modelling
Returns:
---
results: list of retrieved image IDs
"""
self.gender = gender
self.occasion = occasion
preference = None
if images != None:
images = preprocess(images)
bboxes, _ = self.bbm.get_boxes(images)
labels, probs = self.cap.get_labels(images, bboxes)
preference = []
for i, img in enumerate(images):
desc, conf = get_desc(bboxes[i], labels[i], probs[i])
preference.append(desc)
preference = pd.DataFrame(preference)
preference = preference.drop(columns=[
"colour_bottom", "colour_top", "full_body_bbox",
"upper_body_bbox", "lower_body_bbox"
])
preference = np.array(preference)
results = retrieval(self.dataset,
occasion,
gender,
recom_num=10,
pref_matrix=preference)
return results
from controller import *
from numpy import *
from math import *
from search import search
from retrieval import retrieval
from stagnation import stagnation
import epuck_basic
#from keras.models import Sequential
#from keras.layers.core import Dense, Dropout, Activation
#from keras.optimizers import RMSprop
import math
import time
Search = search()
Retrieval = retrieval()
Stagnation = stagnation(Search)
MIN_FEEDBACK = 1
MAX_FEEDBACK = 8
DISTANCE_TRESHOLD = 200
IR_TRESHOLD = 3500
# Here is the main class of your controller.
# This class defines how to initialize and how to run your controller.
# Note that this class derives Robot and so inherits all its functions
class _crabs (epuck_basic.EpuckBasic):
def initialization(self):
self.emitter = self.getEmitter('emitter')
def evaluateRetrievalAccuracy(self, feat, in_list_filename,
in_label_filename):
nn, map = retrieval.retrieval(feat, in_list_filename,
in_label_filename, "COS")
return map
# general configuration files
observations["cfg_filename"] = "../../../input_files/general/standard_output.cfg;"
observations["cfg_filename"] += "../../../input_files/general/water_refractive_index_segelstein.cfg;"
observations["cfg_filename"] += "../../../input_files/general/white1999_integral.cfg;"
observations["cfg_filename"] += "../../../input_files/general/atmosphere/US1976.cfg;"
observations["cfg_filename"] += "../../../input_files/general/instruments/tara.cfg;"
observations["cfg_filename"] += "../../../input_files/retrieval/radarfilter/standard.cfg;"
# observations['cfg_filename'] += "../../../input_files/retrieval/scatterers/rain.cfg;"
observations["cfg_filename"] += "../../../input_files/retrieval/algorithm/windvectors_lwm.cfg;"
observations["additional_output_filename"] = additional_output_filename
observations["measurements_filename"] = measurements_filename
ao = retrieval.retrieval(observations)
else:
ao = additional_output.ao_dct(additional_output_filename)
# from mpl_toolkits.basemap import Basemap, cm
# from matplotlib import rc
# rc('text',usetex=True)
fontsize0 = 16
matplotlib.rc("xtick", labelsize=fontsize0)
matplotlib.rc("ytick", labelsize=fontsize0)
for plot in ["dBZ_hh", "Doppler_velocity_hh_ms", "Doppler_spectral_width_hh_ms"]:
fig = plt.figure(figsize=(8, 8))
#if not os.path.exists(additional_output_filename):
if True:
observations = {}
#general configuration files
observations['cfg_filename'] = "../../../input_files/general/standard_output.cfg;"
observations['cfg_filename'] += "../../../input_files/general/water_refractive_index_segelstein.cfg;"
observations['cfg_filename'] += "../../../input_files/general/white1999_integral.cfg;"
observations['cfg_filename'] += "../../../input_files/general/atmosphere/US1976.cfg;"
observations['cfg_filename'] += "../../../input_files/general/instruments/tara.cfg;"
observations['cfg_filename'] += "../../../input_files/retrieval/algorithm/windvectors_fdvar_horizontal_hdir_solution.cfg;"
observations['additional_output_filename'] = additional_output_filename
observations['measurements_filename'] = measurements_filename
ao = retrieval.retrieval(observations)
else:
ao = additional_output.ao_dct(additional_output_filename)
opts = {
'Doppler_velocity_ms_min': -10.,
'Doppler_velocity_ms_max': 10.,
}
fun_plot_scanning.plot_scanning(additional_output_filename, 'fdvar_plots_scanning/scanning_'+myway+'_', opts)
# File: _crabs.py
# Date:
# Description:
# Author:
# Modifications:
# or to import the entire module. Ex:
# from controller import *
from controller import *
from search import search
from retrieval import retrieval
from stagnation import stagnation
import epuck_basic
Search = search()
Retrieval = retrieval()
Stagnation = stagnation(Search)
MIN_FEEDBACK = 1
MAX_FEEDBACK = 8
DISTANCE_TRESHOLD = 200
IR_TRESHOLD = 3500
# Here is the main class of your controller.
# This class defines how to initialize and how to run your controller.
# Note that this class derives Robot and so inherits all its functions
class _crabs (epuck_basic.EpuckBasic):
stagnation_count = 0
[-1,-1,-1,-1,-1,-1,-1,1],
[-1,-1,-1,-1,-1,-1,1,-1],
[-1,-1,-1,-1,-1,1,-1,-1],
[1,1,1,1,1,1,1,1],
[1,1,1,1,1,1,1,-1],
[1,1,1,1,1,1,-1,1],
[1,1,1,1,1,-1,1,1]])
b = np.array([[1,0],
[1,0],
[1,0],
[1,0],
[0,1],
[0,1],
[0,1],
[0,1]])
Q,C,info = retrieval(a,b,a,b,True)
print "map"
print Q["map"]
print "cm"
print Q["cm"]
print "querymap"
print Q["querymap"]
print "MAP"
print Q["MAP"]
print "cmmap"
print Q["cmmap"]
print "rprecision"
print Q["rprecision"]
print "pn"
print Q["pn"]
print "queryrprecision"
return spectype
###################################################################################################################################
def julian_day(YY, MM, DD, HR, Min, Sec):
return 367 * YY - (7 * (YY + (
(MM + 9) / 12)) / 4) + (275 * MM / 9) + DD + 1721013.5 - 0.5 * np.sign(
(100 * YY) + MM - 190002.5) + 0.5 + HR / 24.0 + Min / (
60.0 * 24.0) + Sec / (3600.0 * 24.0)
###################################################################################################################################
ret = retrieval.retrieval(folder)
ngas = ret.ngas
gases = ret.gas
nlayer = ret.nlevel
nfitparam = len(ret.fitparameter)
nfitparamraw = len(ret.fit_list_raw)
nmw = ret.par.nmw
ret.spec()
npt = int(0)
for mw in ret.mw:
npt = npt + int(mw.npt)
print 'npt', npt
###################################################################################################################################
print 'gases'
print ret.fit_list_raw