def get_image_array(time_shift, size):
time_offset = get_time_offset() + time_shift
time_stamps = radar_data['time_stamp']
scan_data = radar_data['scan_data']
range_bins = radar_data['range_bins']
scan_data = np.array(scan_data).astype(float)
#optional rcs need button for this
'''
for elements in range(len(scan_data)):
for elements2 in range(int(len(scan_data[0])*0.75)):
scan_data[elements][elements2] = scan_data[elements][elements2] * ((range_bins[elements2] - range_offset) **4.0)
'''
plat_pos = extract_platform_position()
#plt.figure()
#plt.imshow(20 * np.log10(np.abs(scan_data)),extent=[range_bins[0],range_bins[-1],(time_stamps[-1]-time_stamps[0])/1000,0],aspect = 'auto')
#plt.figure()
#plt.plot(plat_pos)
plat_pos = linear_interp_nan(extract_time_stamp(),plat_pos)[1]
refl_pos = extract_given_object()
range_to_refl = np.sqrt((plat_pos[:,0] - refl_pos[0])**2 + (plat_pos[:,1] - refl_pos[1])**2 + (plat_pos[:,2] - refl_pos[2])**2)
#plt.plot(range_to_refl, np.transpose(extract_time_stamp()))
#plt.figure()
#plt.imshow(20 * np.log10(np.abs(scan_data)),extent=[range_bins[0],range_bins[-1],(time_stamps[-1]-time_stamps[0])/1000,0],aspect = 'auto')
#plt.colorbar()
#plt.clim(60, 90)
#plt.plot(range_to_refl, np.transpose(extract_time_stamp()-time_offset))
center = extract_given_object()
#interpolation (aligning radar data and position values)
new_x_pos = np.interp((time_stamps-time_stamps[0])/1000,np.transpose(extract_time_stamp()-time_offset).flatten(),plat_pos[:,0])
new_y_pos = np.interp((time_stamps-time_stamps[0])/1000,np.transpose(extract_time_stamp()-time_offset).flatten(),plat_pos[:,1])
new_z_pos = np.interp((time_stamps-time_stamps[0])/1000,np.transpose(extract_time_stamp()-time_offset).flatten(),plat_pos[:,2])
range_to_refl = np.sqrt((new_x_pos[:] - refl_pos[0])**2 + (new_y_pos[:] - refl_pos[1])**2 + (new_z_pos[:] - refl_pos[2])**2)
#plt.figure()
#plt.imshow(20 * np.log10(np.abs(scan_data)),extent=[range_bins[0]-range_offset,range_bins[-1]-range_offset,(time_stamps[-1]-time_stamps[0])/1000,0],aspect = 'auto')
#plt.plot(range_to_refl, (time_stamps-time_stamps[0])/1000)
interp_plat_pos = list()
for elements in range(len(new_x_pos)):
interp_plat_pos.append([new_x_pos[elements],new_z_pos[elements],new_y_pos[elements]])
interp_plat_pos = np.array(interp_plat_pos).squeeze()
x_vec = np.linspace(-meters,meters,size)
y_vec = np.linspace(-meters,meters,size)
scan_data_final = scan_data[eyeballing_start_time:eyeballing_end_time,:]
interp_plat_pos_final = interp_plat_pos[eyeballing_start_time:eyeballing_end_time,:]
x_vec_new = x_vec+center[1]
y_vec_new = y_vec+center[2]
sar_image = interp_approach(scan_data_final, range_bins-range_offset, interp_plat_pos_final, x_vec_new, y_vec_new)
#plt.figure()
#plt.imshow((np.abs(sar_image)),extent=[x_vec_new[0], x_vec_new[-1], y_vec_new[-1], y_vec_new[0]],aspect = 'auto')
return sar_image
#value: the value you are trying to find the nearest index to
#
#return: index of closest value in array to value
def find_nearest(self,array,value):
idx = np.searchsorted(array, value, side="left")
if idx > 0 and (idx == len(array) or math.fabs(value - array[idx-1]) < math.fabs(value - array[idx])):
return idx-1
else:
return idx
if __name__ == "__main__":
from plotRTI import plotRTI
from backprojection import interp_approach
radar = RadarData()
target = CornerReflector(.33,radar.radarWavelength,[0,5,0])
targets = [target]
scans = []
totalRadarPos = []
for i in np.arange(0,2,.001):
totalRadarPos.append([0,0,i])
radar.radarPos = [i,0,0]
scans.append(radar.get_scan(targets))
plotRTI(scans)
radar_data = [np.array(scans),totalRadarPos,radar.rangebins]
interp_approach(radar_data,radar_data,[-3,3],[0,6],.2)
def main_func(self, param4, param5, param6, param7, param8, param9):
self.meters = param4
self.size = param5
self.eyeballing_start_time = param6
self.eyeballing_end_time = param7
self.time_offset = param8
self.range_offset = param9
time_stamps = self.radar_data['time_stamp']
scan_data = self.radar_data['scan_data']
range_bins = self.radar_data['range_bins']
scan_data = np.array(scan_data).astype(float)
#optional rcs need button for this
'''
for elements in range(len(scan_data)):
for elements2 in range(int(len(scan_data[0])*0.75)):
scan_data[elements][elements2] = scan_data[elements][elements2] * ((range_bins[elements2] - self.range_offset) **4.0)
'''
plat_pos = self.extract_platform_position()
#plt.figure()
#plt.imshow(20 * np.log10(np.abs(scan_data)),extent=[range_bins[0],range_bins[-1],(time_stamps[-1]-time_stamps[0])/1000,0],aspect = 'auto')
#plt.figure()
#plt.plot(plat_pos)
plat_pos = self.linear_interp_nan(self.extract_time_stamp(),
plat_pos)[1]
refl_pos = self.extract_given_object()
range_to_refl = np.sqrt((plat_pos[:, 0] - refl_pos[0])**2 +
(plat_pos[:, 1] - refl_pos[1])**2 +
(plat_pos[:, 2] - refl_pos[2])**2)
#plt.plot(range_to_refl, np.transpose(extract_time_stamp()))
#plt.figure()
#plt.imshow(20 * np.log10(np.abs(scan_data)),extent=[range_bins[0],range_bins[-1],(time_stamps[-1]-time_stamps[0])/1000,0],aspect = 'auto')
#plt.colorbar()
#plt.clim(60, 90)
#plt.plot(range_to_refl, np.transpose(extract_time_stamp()-time_offset))
center = self.extract_given_object()
print(center)
#interpolation (aligning radar data and position values)
new_x_pos = np.interp((time_stamps - time_stamps[0]) / 1000,
np.transpose(self.extract_time_stamp() -
self.time_offset).flatten(),
plat_pos[:, 0])
new_y_pos = np.interp((time_stamps - time_stamps[0]) / 1000,
np.transpose(self.extract_time_stamp() -
self.time_offset).flatten(),
plat_pos[:, 1])
new_z_pos = np.interp((time_stamps - time_stamps[0]) / 1000,
np.transpose(self.extract_time_stamp() -
self.time_offset).flatten(),
plat_pos[:, 2])
range_to_refl = np.sqrt((new_x_pos[:] - refl_pos[0])**2 +
(new_y_pos[:] - refl_pos[1])**2 +
(new_z_pos[:] - refl_pos[2])**2)
#plt.figure()
#plt.imshow(20 * np.log10(np.abs(scan_data)),extent=[range_bins[0]-range_offset,range_bins[-1]-range_offset,(time_stamps[-1]-time_stamps[0])/1000,0],aspect = 'auto')
#plt.plot(range_to_refl, (time_stamps-time_stamps[0])/1000)
interp_plat_pos = list()
for elements in range(len(new_x_pos)):
interp_plat_pos.append([
new_x_pos[elements], new_z_pos[elements], new_y_pos[elements]
])
interp_plat_pos = np.array(interp_plat_pos).squeeze()
x_vec = np.linspace(-self.meters, self.meters, self.size)
y_vec = np.linspace(-self.meters, self.meters, self.size)
scan_data_final = scan_data[
self.eyeballing_start_time:self.eyeballing_end_time, :]
interp_plat_pos_final = interp_plat_pos[
self.eyeballing_start_time:self.eyeballing_end_time, :]
x_vec_new = x_vec + center[1]
y_vec_new = y_vec + center[2]
sar_image = interp_approach(scan_data_final,
range_bins - self.range_offset,
interp_plat_pos_final, x_vec_new,
y_vec_new)
f = Figure(figsize=(5, 5), dpi=100)
ax = f.add_subplot(111)
img = ax.imshow(
20 * np.log10((np.abs(sar_image))),
extent=[x_vec_new[0], x_vec_new[-1], y_vec_new[-1], y_vec_new[0]],
aspect='auto')
return (f, ax, img)
motion_end = 29545
#Aligns data, currently using frames given in function definition
aligned_data = align_data(radar_data, motion_data, radar_start, motion_start,
motion_end, 100, 3500)
WrongRadarPosition = aligned_data[1] #Actual position of radar in 3D space
PulseData = aligned_data[0] #Data of all pulses in file
RangeBins = radar_data[
2] #Distance in meters between the sampling rate of PulseData
#Plots aligned graph
AlignedGraph(aligned_data, radar_data)
#Calculates and plots BackProjected Image
IntensityList = BackProjection(aligned_data, radar_data, [-4, 0], [4, 4], 0.01)
#IntensityList = read_intensity('../Raw_Data/intensity2.csv')
#Deconvolutes image and plots images
deconvolute(IntensityList, IterationNumber=3, PercentageMin=1 / 5.5)
'''
#Currently unused code
#Calculates backprojected image using Ramu's algorithm
interp_approach(aligned_data,radar_data,[-3,3],[-3,3],.1)
#Reads in an intensity list and saves it as an intensity list
IntensityList = read_intensity('../Raw_Data/intensity2.csv')
'''
plt.figure()
plt.imshow(20 * np.log10(np.abs(scan_data)),extent=[range_bins[0],range_bins[-1],(time_stamps[-1]-time_stamps[0])/1000,0],aspect = 'auto')
plt.colorbar()
plt.clim(60, 90)
plt.plot(range_to_refl, np.transpose(extract_time_stamp()-time_offset))
center = extract_given_object()
new_x_pos = np.interp((time_stamps-time_stamps[0])/1000,np.transpose(extract_time_stamp()-time_offset).flatten(),plat_pos[:,0])
new_y_pos = np.interp((time_stamps-time_stamps[0])/1000,np.transpose(extract_time_stamp()-time_offset).flatten(),plat_pos[:,1])
new_z_pos = np.interp((time_stamps-time_stamps[0])/1000,np.transpose(extract_time_stamp()-time_offset).flatten(),plat_pos[:,2])
range_to_refl = np.sqrt((new_x_pos[:] - refl_pos[0])**2 + (new_y_pos[:] - refl_pos[1])**2 + (new_z_pos[:] - refl_pos[2])**2)
plt.figure()
plt.imshow(20 * np.log10(np.abs(scan_data)),extent=[range_bins[0]-range_offset,range_bins[-1]-range_offset,(time_stamps[-1]-time_stamps[0])/1000,0],aspect = 'auto')
plt.plot(range_to_refl, (time_stamps-time_stamps[0])/1000)
interp_plat_pos = list()
for elements in range(len(new_x_pos)):
interp_plat_pos.append([new_x_pos[elements],new_z_pos[elements],new_y_pos[elements]])
interp_plat_pos = np.array(interp_plat_pos).squeeze()
x_vec = np.linspace(-meters,meters,size)
y_vec = np.linspace(-meters,meters,size)
scan_data_final = scan_data[eyeballing_start_time:eyeballing_end_time,:]
interp_plat_pos_final = interp_plat_pos[eyeballing_start_time:eyeballing_end_time,:]
x_vec_new = x_vec+center[1]
y_vec_new = y_vec+center[2]
sar_image = interp_approach(scan_data_final, range_bins-range_offset, interp_plat_pos_final, x_vec_new, y_vec_new)
plt.figure()
plt.imshow((np.abs(sar_image)),extent=[x_vec_new[0], x_vec_new[-1], y_vec_new[-1], y_vec_new[0]],aspect = 'auto')
