def createWidgets(self, x, y):
self.p = params.FitParams(self.root,
Gs=10 * abs(mean(diff(x))),
xL=min(x),
xR=max(x),
row=0,
column=1)
self.p.bl.set(mean(y))
self.mpl = mpl.MPL(self.root, x, y, self.p, row=0, column=0, rowspan=3)
self.fit = fit.Fit(self.root, self.p, self.mpl, row=1, column=1)
def pipeline(input_img):
"""entire image processing pipeline to go from source image to annotated output image
:param input_img:
:return:
"""
global last_fit
curr_fit = fit.Fit()
# undistort image
undist = morph_img.undistort_image(input_img)
# threshold binary
binary_threshold = abs_sobel_thresh(undist)
# bird's eye view
birds_eye_view = morph_img.warp_to_birds_eye(binary_threshold)
# fit polynomial
left_fit, right_fit, left_fitx, right_fitx, ploty = fit_polynomial(birds_eye_view)
curr_fit.set_fits(left_fit, right_fit)
# calculate lane curvature
l_curve, r_curve = measure_curvature_real(left_fit, right_fit)
curr_fit.set_curves(l_curve, r_curve)
# calculate position of vehicle
left_peak, right_peak = get_peaks(birds_eye_view)
curr_fit.set_peaks(left_peak, right_peak)
vehicle_pos = position_of_vehicle(left_peak,
right_peak) # TODO fix this to take most recent left/right instead o hist
curr_fit.vehicle_pos = vehicle_pos
# draw the detected lane on the source image
lane_img = draw_lane_on_img(undist, left_fitx, right_fitx, ploty)
# annotate the image
final_img = add_info_to_img(lane_img, (l_curve + r_curve) / 2, vehicle_pos)
# if current found fit makes sense, use it for the next prediction
# if not, increase the age of the last fit, if too old, search from scratch
if curr_fit.is_sane():
last_fit = curr_fit
last_fit.age = 0
else:
try:
last_fit.age += 1
if last_fit.age >= 10:
last_fit = None
except AttributeError:
pass
return final_img
def fit_to_json(fd_in, fd_out):
data_fields = []
def stash_data(name, data):
data_fields.append({name: data})
f = fit.Fit(fd_in, data_field_disposition=stash_data)
data = {
'data_records': data_fields,
'unknown_messages': f.unknown_messages,
'unknown_fields': f.unknown_fields
}
json.dump(data, fd_out, indent=4, ensure_ascii=False, sort_keys=True)
#!/usr/local/bin/python
# -*- coding: utf-8 -*-
# Vimのアップデートを確認するプログラム.
import sys
import logging
import os
if sys.version_info < (2, 6, 0):
import fit
fit.Fit(sys.argv[0].rsplit("\\", 1)[0] + "\\").fit()
import pe32b as pe32
else:
import pe32
from updater import OnlineUpdater
def __detectArch(rootdir):
arch = pe32.ARCH_UNKNOWN
exe = os.path.join(rootdir, 'vim.exe')
if os.path.exists(exe):
arch = pe32.detectArch(exe)
else:
machtype = os.environ.get('PROCESSOR_ARCHITECTURE').upper()
if machtype == 'X86':
arch = pe32.ARCH_WIN32
elif machtype == 'AMD64':
arch = pe32.ARCH_WIN64
if arch != pe32.ARCH_WIN32 and arch != pe32.ARCH_WIN64:
logging.error('failed to detect CPU arch')
return arch
import argparse
import sys
import fit
import generate
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('mode', nargs='?', default='generate')
parser.add_argument('filename', nargs='?', default='test.txt')
args = parser.parse_args(sys.argv[1:])
if args.mode == 'fit':
curr = fit.Fit(args.filename)
data = curr.action()
curr.rewrite(data)
elif args.mode == 'generate':
curr = generate.Generate()
curr.action()
DEnsem_Top_List = []
Ensemble_Tuple_list = []
AEnsemble_SpecSum_Weight = []
AEnsemble_SpecProj_Weight = []
BEnsemble_SpecSum_Weight = []
BEnsemble_SpecProj_Weight = []
DEnsemble_SpecSum_Weight = []
DEnsemble_SpecProj_Weight = []
AM_sqr = []
BM_sqr = []
DM_sqr = []
a_r0_sqr = []
Spacing = []
ChiralContFit = ft.Fit("SpecSum Global Fit")
ChiralContFit_SP = ft.Fit("SpecProj Global Fit")
FitA = ft.Fit("SpecSum A Ensemble")
FitB = ft.Fit("SpecSum B Ensemble")
FitD = ft.Fit("SpecSum D Ensemble")
FitA_SP = ft.Fit("SpecProj A Ensemble")
FitB_SP = ft.Fit("SpecProj B Ensemble")
FitD_SP = ft.Fit("SpecProj D Ensemble")
for i in range(0, len(AEnsembleList), 1):
path = Full_Conf_Dir + AEnsembleList[i].EnsembleName
EigProd_List = df.Data_Loader(path)
AM_sqr.append(
M_sqr_ev_set[0] *
(pow(2 * AEnsembleList[i].Kappa * AEnsembleList[i].a / 197.32, 2)))
def setup_fit(self):
if self.downloaded:
self.fit = fit.Fit(self.full_path)
self.fit.connect('status-changed', self.fit_status_changed_cb)
self.status = Activity.Status.DOWNLOADED