def ordInvFHWTForNumItersGivenNumFwdIters(signal, num_fwd_iters, num_inv_iters):
print("num_fwd_iters = %d" % num_fwd_iters)
print("num_inv_iters = %d" % num_inv_iters)
sig_len = len(signal)
if sig_len < 2 or not NumUtils.isPowOf2(sig_len):
raise Exception('sig_len < 2 or not isPowOf2')
num_avail_iters = NumUtils.intLog2(sig_len)
if num_inv_iters > num_fwd_iters:
raise Exception('ordInvFHWTForNumItersGivenNumFwdIters: num_inv_iters > num_fwd_iters')
a0, a1 = 0, 0
restored_vals = None
gap = int(math.pow(2, num_avail_iters - num_fwd_iters))
print "num_avail_iters", str(num_avail_iters)
print "num_fwd_iters", str(num_fwd_iters)
print "gap", str(gap)
j = 0
for scale in xrange(1, num_inv_iters+1):
restored_vals = []
restored_vals = [0 for i in xrange(2*gap)]
for i in xrange(gap):
a0 = signal[i] + signal[gap+i]
a1 = signal[i] - signal[gap+i]
restored_vals[2*i] = a0
restored_vals[2*i+1] = a1
for i in xrange(2*gap):
signal[i] = restored_vals[i]
gap *= 2
def ordFHWT(signal):
sig_len = len(signal)
if not NumUtils.isPowOf2(sig_len):
raise Exception('sig_len is not PowOf2')
num_sweeps = NumUtils.intLog2(sig_len)
if num_sweeps == 1:
acoeff = (signal[0] + signal[1]) / 2.0
ccoeff = (signal[0] - signal[1]) / 2.0
signal[0] = acoeff
signal[1] = ccoeff
return
acoeffs = [] ## step coeffs
ccoeffs = [] ## wavelet coeffs
for sweep_num in xrange(1, num_sweeps):
size = int(math.pow(2, num_sweeps - sweep_num))
acoeffs = [0.0 for i in xrange(size)]
ccoeffs = [0.0 for i in xrange(size)]
ai = 0
ci = 0
end = int(math.pow(2, num_sweeps - sweep_num + 1)) - 1
for i in xrange(0, end + 1, 2):
acoeffs[ai] = (signal[i] + signal[i + 1]) / 2.0
ccoeffs[ci] = (signal[i] - signal[i + 1]) / 2.0
ai += 1
ci += 1
for i in xrange(0, size):
signal[i] = acoeffs[i]
signal[i + size] = ccoeffs[i]
acoeff = (signal[0] + signal[1]) / 2.0
ccoeff = (signal[0] - signal[1]) / 2.0
signal[0] = acoeff
signal[1] = ccoeff
def ordFHWTForNumIters(signal, num_iters):
sig_len = len(signal)
if not NumUtils.isPowOf2(sig_len):
return
num_sweeps = NumUtils.intLog2(sig_len)
if num_iters > num_sweeps:
raise Exception('ordFHWTForNumIters: num_iters > num_sweeps')
acoeff = 0
ccoeff = 0
if num_sweeps == 1:
acoeff = (signal[0] + signal[1])/2.0
ccoeff = (signal[0] - signal[1])/2.0
signal[0] = acoeff
signal[1] = ccoeff
return
acoeffs = []
ccoeffs = []
for sweep_num in xrange(1, num_iters+1):
size = int(math.pow(2, num_sweeps - sweep_num))
acoeffs = [0.0 for i in xrange(size)]
ccoeffs = [0.0 for i in xrange(size)]
ai = 0
ci = 0
end = int(math.pow(2.0, num_sweeps - sweep_num + 1)) - 1
for i in xrange(0, end+1, 2):
acoeffs[ai] = (signal[i] + signal[i+1])/2.0
ccoeffs[ci] = (signal[i] - signal[i+1])/2.0
ai += 1
ci += 1
for i in xrange(size):
signal[i] = acoeffs[i]
signal[i + size] = ccoeffs[i]
def ordInvFHWTForNumItersGivenNumFwdIters(signal, num_fwd_iters, num_inv_iters):
print("num_fwd_iters = %d" % num_fwd_iters)
print("num_inv_iters = %d" % num_inv_iters)
sig_len = len(signal)
if sig_len < 2 or not NumUtils.isPowOf2(sig_len):
raise Exception('sig_len < 2 or not isPowOf2')
num_avail_iters = NumUtils.intLog2(sig_len)
if num_inv_iters > num_fwd_iters:
raise Exception('ordInvFHWTForNumItersGivenNumFwdIters: num_inv_iters > num_fwd_iters')
a0, a1 = 0, 0
restored_vals = None
gap = int(math.pow(2, num_avail_iters - num_fwd_iters))
print "num_avail_iters", str(num_avail_iters)
print "num_fwd_iters", str(num_fwd_iters)
print "gap", str(gap)
j = 0
for scale in xrange(1, num_inv_iters + 1):
restored_vals = []
restored_vals = [0 for i in xrange(2 * gap)]
for i in xrange(gap):
a0 = signal[i] + signal[gap + i]
a1 = signal[i] - signal[gap + i]
restored_vals[2 * i] = a0
restored_vals[2 * i + 1] = a1
for i in xrange(2 * gap):
signal[i] = restored_vals[i]
gap *= 2
def ordFHWTForNumIters(signal, num_iters):
sig_len = len(signal)
if not NumUtils.isPowOf2(sig_len):
return
num_sweeps = NumUtils.intLog2(sig_len)
if num_iters > num_sweeps:
raise Exception('ordFHWTForNumIters: num_iters > num_sweeps')
acoeff = 0
ccoeff = 0
if num_sweeps == 1:
acoeff = (signal[0] + signal[1]) / 2.0
ccoeff = (signal[0] - signal[1]) / 2.0
signal[0] = acoeff
signal[1] = ccoeff
return
acoeffs = []
ccoeffs = []
for sweep_num in xrange(1, num_iters + 1):
size = int(math.pow(2, num_sweeps - sweep_num))
acoeffs = [0.0 for i in xrange(size)]
ccoeffs = [0.0 for i in xrange(size)]
ai = 0
ci = 0
end = int(math.pow(2.0, num_sweeps - sweep_num + 1)) - 1
for i in xrange(0, end + 1, 2):
acoeffs[ai] = (signal[i] + signal[i + 1]) / 2.0
ccoeffs[ci] = (signal[i] - signal[i + 1]) / 2.0
ai += 1
ci += 1
for i in xrange(size):
signal[i] = acoeffs[i]
signal[i + size] = ccoeffs[i]
def process_image(image):
image2 = image[:]
image = OneDHWTLaneDetectorV2.crop_image(image)
thresh = OneDHWTLaneDetectorV2.pre_process(image)
ll_points = OneDHWTLaneDetectorV2.get_ll_spike_positions(thresh,
signal_row_start=Params.ROI_HEIGHT - 1, signal_row_end=0,
signal_col_start=0, signal_col_end=64,
row_thresh=40)
rl_points = OneDHWTLaneDetectorV2.get_rl_spike_positions(thresh,
signal_row_start=Params.ROI_HEIGHT - 1, signal_row_end=0,
signal_col_start=(Params.ROI_WIDTH - 64),
signal_col_end=Params.ROI_WIDTH,
row_thresh=40)
ll_points, rl_points = OneDHWTLaneDetectorV2.filter_cross_points(ll_points, rl_points)
updated_ll_points = [tuple((a + Params.ROI_START_COL, b + Params.ROI_START_ROW)) for a, b in ll_points]
updated_rl_points = [tuple((a + Params.ROI_START_COL, b + Params.ROI_START_ROW)) for a, b in rl_points]
NumUtils.draw_lanes(image2, updated_ll_points, -60, -30, color=(0, 255, 0), thickness=2)
NumUtils.draw_lanes(image2, updated_rl_points, 30, 60, color=(0, 0, 255), thickness=2)
cv2.rectangle(image2, (Params.ROI_START_COL, Params.ROI_START_ROW), (Params.ROI_END_COL, Params.ROI_END_ROW),
(255, 255, 0))
return image2
def ordFHWT(signal):
sig_len = len(signal)
if not NumUtils.isPowOf2(sig_len):
raise Exception('sig_len is not PowOf2')
num_sweeps = NumUtils.intLog2(sig_len)
if num_sweeps == 1:
acoeff = (signal[0] + signal[1]) / 2.0
ccoeff = (signal[0] - signal[1]) / 2.0
signal[0] = acoeff
signal[1] = ccoeff
return
acoeffs = []
ccoeffs = []
for sweep_num in xrange(1, num_sweeps):
size = int(math.pow(2, num_sweeps - sweep_num))
## print("size = %d" % size)
acoeffs = [0.0 for i in xrange(size)]
ccoeffs = [0.0 for i in xrange(size)]
ai = 0
ci = 0
end = int(math.pow(2, num_sweeps - sweep_num + 1)) - 1
for i in xrange(0, end + 1, 2):
acoeffs[ai] = (signal[i] + signal[i + 1]) / 2.0
ccoeffs[ci] = (signal[i] - signal[i + 1]) / 2.0
ai += 1
ci += 1
for i in xrange(0, size):
signal[i] = acoeffs[i]
signal[i + size] = ccoeffs[i]
acoeff = (signal[0] + signal[1]) / 2.0
ccoeff = (signal[0] - signal[1]) / 2.0
signal[0] = acoeff
signal[1] = ccoeff
def run(image_path, output_path):
image = cv2.imread(image_path)
image2 = image[:]
image = OneDHWTLaneDetectorV2.crop_image(image)
thresh = OneDHWTLaneDetectorV2.pre_process(image)
row_thresh = int(image.shape[1]/2)
ll_points = OneDHWTLaneDetectorV2.get_ll_spike_positions(thresh,
signal_row_start=Params.ROI_HEIGHT - 1, signal_row_end=0,
signal_col_start=0, signal_col_end=64,
dbg=debug, row_thresh=row_thresh, image=image2)
rl_points = OneDHWTLaneDetectorV2.get_rl_spike_positions(thresh,
signal_row_start=Params.ROI_HEIGHT - 1, signal_row_end=0,
signal_col_start=(Params.ROI_WIDTH - 64),
signal_col_end=Params.ROI_WIDTH,
dbg=debug, row_thresh=row_thresh, image=image2)
ll_points, rl_points = OneDHWTLaneDetectorV2.filter_cross_points(ll_points, rl_points)
# filtered_ll_points = OneDHWTLaneDetectorV2.filter_lines(ll_points, -60, -30, dbg=debug)
# filtered_rl_points = OneDHWTLaneDetectorV2.filter_lines(rl_points, 30, 60, dbg=debug)
updated_ll_points = [tuple((a + Params.ROI_START_COL, b + Params.ROI_START_ROW)) for a, b in ll_points]
updated_rl_points = [tuple((a + Params.ROI_START_COL, b + Params.ROI_START_ROW)) for a, b in rl_points]
if debug:
print("Filtered ll points: " + str(ll_points))
print("Filtered rl points: " + str(rl_points))
print("Updated ll points: " + str(updated_ll_points))
print("Updated rl points: " + str(updated_rl_points))
# NumUtils.draw_line(image2, updated_ll_points, (0, 255, 0))
# NumUtils.draw_line(image2, updated_rl_points, (0, 0, 255))
# NumUtils.draw_line(image2, filtered_ll_points, (0, 255, 0))
# NumUtils.draw_line(image2, filtered_rl_points, (0, 0, 255))
# NumUtils.draw_lanes(image, ll_points, -60, -30, color=(0, 255, 0), thickness=2)
# NumUtils.draw_lanes(image, rl_points, 30, 60, color=(0, 0, 255), thickness=2)
# NumUtils.draw_lanes(image2, updated_ll_points, -60, -30, color=(0, 255, 0), thickness=2)
# NumUtils.draw_lanes(image2, updated_rl_points, 30, 60, color=(0, 0, 255), thickness=2)
NumUtils.fit_lanes(image2, updated_ll_points, -60, -30, color=(0, 255, 0), thickness=2)
NumUtils.fit_lanes(image2, updated_rl_points, 30, 60, color=(0, 0, 255), thickness=2)
cv2.rectangle(image2, (Params.ROI_START_COL, Params.ROI_START_ROW), (Params.ROI_END_COL, Params.ROI_END_ROW), (255, 255, 0))
cv2.imwrite(output_path, image2)
del image2
del image
del thresh
del ll_points
del rl_points
def testOrdInvFHWT(signal):
n = NumUtils.intLog2(len(signal))
orig_signal = copy.deepcopy(signal)
print('n = %d' % n)
print('signal: %s' % str(signal))
OneDHWT.ordFHWT(signal)
print('OneDHWT: %s' % str(signal))
OneDHWT.ordInvFHWT(signal)
print('OneInvDHWT: %s' % str(signal))
rslt = NumUtils.areEqualLists(signal, orig_signal)
print('signal equality: %s' % str(rslt))
return rslt
def ordInvFHWT(signal):
sig_len = len(signal)
if sig_len < 2 or not NumUtils.isPowOf2(sig_len):
raise Exception('sig_len < 2 or not isPowOf2')
num_iters = NumUtils.intLog2(sig_len)
a0 = 0
a1 = 0
restored_vals = None
gap = 0
for scale in xrange(1, num_iters + 1):
gap = int(math.pow(2, scale - 1))
restored_vals = []
restored_vals = [0.0 for i in xrange(2 * gap)]
for i in xrange(gap):
a0 = signal[i] + signal[gap + i]
a1 = signal[i] - signal[gap + i]
restored_vals[2 * i] = a0
restored_vals[2 * i + 1] = a1
for i in xrange(2 * gap):
signal[i] = restored_vals[i]
def testOrdInvFHWTForNumItersGivenNumFwdIters(signal, num_fwd_iters,
num_inv_iters):
if num_inv_iters > num_fwd_iters:
raise Exception('num_inv_iters > num_fwd_iters')
num_avail_iters = NumUtils.intLog2(len(signal))
if num_inv_iters > num_avail_iters:
raise Exception('num_inv_iters > num_avail_iters')
orig_signal = copy.deepcopy(signal)
sig_copy = copy.deepcopy(signal)
print('num_fwd_iters = %d' % num_fwd_iters)
print('num_inv_iters = %d' % num_inv_iters)
OneDHWT.ordFHWTForNumIters(signal, num_fwd_iters)
OneDHWT.ordFHWTForNumIters(sig_copy, num_fwd_iters - num_inv_iters)
OneDHWT.ordInvFHWTForNumItersGivenNumFwdIters(signal, num_fwd_iters,
num_inv_iters)
rslt = NumUtils.areEqualLists(signal, sig_copy)
print(signal)
print(sig_copy)
print('signal equality: %s' % str(rslt))
return rslt
def ordInvFHWT(signal):
sig_len = len(signal)
if sig_len < 2 or not NumUtils.isPowOf2(sig_len):
raise Exception('sig_len < 2 or not isPowOf2')
num_iters = NumUtils.intLog2(sig_len)
a0 = 0
a1 = 0
restored_vals = None
gap = 0
for scale in xrange(1, num_iters+1):
gap = int(math.pow(2, scale-1))
restored_vals = []
restored_vals = [0.0 for i in xrange(2*gap)]
for i in xrange(gap):
a0 = signal[i] + signal[gap+i]
a1 = signal[i] - signal[gap+i]
restored_vals[2*i] = a0
restored_vals[2*i + 1] = a1
for i in xrange(2*gap):
signal[i] = restored_vals[i]
def ordInvFHWTForNumIters(signal, num_iters):
sig_len = len(signal)
if sig_len < 2 or not NumUtils.isPowOf2(sig_len):
return
num_avail_iters = NumUtils.intLog2(sig_len)
if num_iters > num_avail_iters:
return
a0, a1 = 0, 0
restored_vals = None
gap = 1
j = 0
for scale in xrange(1, num_iters + 1):
restored_vals = []
restored_vals = [0 for i in xrange(2 * gap)]
for i in xrange(gap):
a0 = signal[i] + signal[gap + i]
a1 = signal[i] - signal[gap + i]
restored_vals[2 * i] = a0
restored_vals[2 * i + 1] = a1
for i in xrange(2 * gap):
signal[i] = restored_vals[i]
gap *= 2
def filter_lines(points, angle_low_thresh, angle_high_thresh, dbg=False):
filtered_lines = []
for i in xrange(0, len(points), 2):
for j in xrange(i+1, len(points), 2):
if dbg:
print("OneDHWTLaneDetectorV2.filter_points:" + str(i) + "\t" + str(j))
angle = NumUtils.get_angle(points[i], points[j])
if angle_low_thresh <= angle <= angle_high_thresh:
filtered_lines.append((points[i], points[j]))
if dbg:
print("OneDHWTLaneDetectorV2.filter_points:\n" + str(points[i]) + "\t" + str(points[j]) +
"\tAngle: " + str(angle) + "\t" + str(angle_low_thresh <= angle <= angle_high_thresh))
return filtered_lines
def ordInvFHWTForNumIters(signal, num_iters):
sig_len = len(signal)
if sig_len < 2 or not NumUtils.isPowOf2(sig_len):
return
num_avail_iters = NumUtils.intLog2(sig_len)
if num_iters > num_avail_iters:
return
a0, a1 = 0, 0
restored_vals = None
gap = 1
j = 0
for scale in xrange(1, num_iters+1):
restored_vals = []
restored_vals = [0 for i in xrange(2*gap)]
for i in xrange(gap):
a0 = signal[i] + signal[gap+i]
a1 = signal[i] - signal[gap+i]
restored_vals[2*i] = a0
restored_vals[2*i+1] = a1
for i in xrange(2*gap):
signal[i] = restored_vals[i]
gap *= 2
def testOrdFHWTForNumIters(signal):
n = NumUtils.intLog2(len(signal))
print("n = %d" % n)
copy_signal = copy.deepcopy(signal)
for i in xrange(1, n):
print("num iters %d" % i)
print("Original: %s" % str(signal))
OneDHWT.ordFHWTForNumIters(signal, i)
print("Transformed: %s" % str(signal))
OneDHWT.orderedInvFHWTForNumIters(signal, i)
print("Reversed: %s" % str(signal))
for j in xrange(len(signal)):
if signal[j] != copy_signal[j]:
print("FALSE")
return
print("TRUE")
def filter_lines(points, angle_low_thresh, angle_high_thresh, dbg=False):
filtered_lines = []
for i in xrange(0, len(points), 2):
for j in xrange(i + 1, len(points), 2):
if dbg:
print("OneDHWTLaneDetectorV2.filter_points:" + str(i) +
"\t" + str(j))
angle = NumUtils.get_angle(points[i], points[j])
if angle_low_thresh <= angle <= angle_high_thresh:
filtered_lines.append((points[i], points[j]))
if dbg:
print("OneDHWTLaneDetectorV2.filter_points:\n" +
str(points[i]) + "\t" + str(points[j]) +
"\tAngle: " + str(angle) + "\t" +
str(angle_low_thresh <= angle <= angle_high_thresh))
return filtered_lines
def testOrdFHWTForNumIters(signal):
n = NumUtils.intLog2(len(signal))
print('n = %d' % n)
copy_signal = copy.deepcopy(signal)
for i in xrange(1, n):
print('num iters %d' % i)
print('Original: %s' % str(signal))
OneDHWT.ordFHWTForNumIters(signal, i)
print('Transformed: %s' % str(signal))
OneDHWT.orderedInvFHWTForNumIters(signal, i)
print('Reversed: %s' % str(signal))
for j in xrange(len(signal)):
if signal[j] != copy_signal[j]:
print('FALSE')
return
print('TRUE')
#OneDHWTTests.ex_1_15_p10()
#OneDHWTTests.ex_1_3_2_4_p11()
#OneDHWTTests.ex_1_12_p19()
#OneDHWTTests.ex_1_16_p20()
#OneDHWTTests.ex_1_19_p21()
#OneDHWTTests.testOrdFHWTForNumIters([1, 7])
#OneDHWTTests.testOrdInvFHWT([8, 6, 7, 3, 1, 1, 2, 4])
#for i in xrange(20):
# rslt = OneDHWTTests.testOrdInvFHWT(NumUtils.genListOfRandomInts(0, 200, 128))
# if rslt == False:
# break
OneDHWTTests.testOrdInvFHWTForNumItersGivenNumFwdIters(
[1, 2, 3, 4, 5, 6, 7, 8], 3, 1)
OneDHWTTests.testOrdInvFHWTForNumItersGivenNumFwdIters(
[1, 2, 3, 4, 5, 6, 7, 8], 3, 2)
OneDHWTTests.testOrdInvFHWTForNumItersGivenNumFwdIters(
[1, 2, 3, 4, 5, 6, 7, 8], 3, 3)
for i in xrange(20):
for num_fwd_iters in xrange(1, 6):
signal = NumUtils.genListOfRandomInts(0, 200, 32)
for num_inv_iters in xrange(1, num_fwd_iters + 1):
rslt = OneDHWTTests.testOrdInvFHWTForNumItersGivenNumFwdIters(
signal, num_fwd_iters, num_inv_iters)
if rslt == False:
break
def get_ll_spike_positions(thresh,
num_of_iterations=2,
signal_row_start=235,
signal_row_end=120,
signal_col_start=0,
signal_col_end=64,
row_thresh=150,
dbg=False,
image=None):
if dbg:
print(
" ########################## LEFT LANE ########################## "
)
spike_positions = []
no_spike_rows_count = 0
for signal_row_number in xrange(signal_row_start, signal_row_end, -2):
row = NumUtils.getImageRow(thresh, signal_row_number)
ud_spikes = OneDHWTLaneDetectorV2.get_up_down_spikes(
row, signal_col_start, signal_col_end, num_of_iterations,
signal_row_number)
# If no spikes gets detected the scan line rotated by 1 pixel,
# the last (64th) pixel is the average of 62nd and 63rd pixels
if len(ud_spikes) == 0:
temp_row = row[1:]
temp_row.append((temp_row[-1] + temp_row[-2]) / 2)
ud_spikes = OneDHWTLaneDetectorV2.get_up_down_spikes(
temp_row, signal_col_start, signal_col_end,
num_of_iterations, signal_row_number)
# If there are no spikes detected, incrementing the counter
if len(ud_spikes) == 0:
no_spike_rows_count += 1
# If no spikes gets detected for consecutive 5 rows, then
# scan line will be shifted by 7 pixels and tries to detect spikes
if len(ud_spikes) == 0 and no_spike_rows_count >= 5:
temp_signal_col_start = signal_col_start
temp_signal_col_end = signal_col_end
# The shifting continues until it reaches row_thresh
while temp_signal_col_end < row_thresh:
temp_signal_col_start += 7
temp_signal_col_end += 7
row = NumUtils.getImageRow(thresh, signal_row_number)
ud_spikes = OneDHWTLaneDetectorV2.get_up_down_spikes(
row, temp_signal_col_start, temp_signal_col_end,
num_of_iterations, signal_row_number)
if len(ud_spikes) > 0:
signal_col_start = temp_signal_col_start
signal_col_end = temp_signal_col_end
# drawing buffer lines
if dbg:
cv2.line(image,
(temp_signal_col_start, signal_row_number),
(temp_signal_col_end, signal_row_number),
(255, 0, 0), 1)
if len(ud_spikes) > 0:
no_spike_rows_count = 0
if dbg:
print("LL: detected spikes: " + str(len(ud_spikes)))
for spike in ud_spikes:
print spike
# Needs improvement
spike_position = (
OneDHWTLaneDetectorV2.optimum_spike_x(ud_spikes),
signal_row_number)
if dbg:
print("LL: Spike position: " + str(spike_position))
'''
spike_position = ((ud_spikes[0].getSigStartOfUpRun() + ud_spikes[0].getSigLenOfUpRun() / 2),
signal_row_number)
'''
# setting previous spike position as mid point as the scan line
delta = spike_position[0] - (signal_col_start +
signal_col_end) / 2
if delta > 0:
signal_col_start += delta
signal_col_end += delta
spike_positions.append(spike_position)
# drawing the scan lines
if dbg:
cv2.line(image, (signal_col_start, signal_row_number),
(signal_col_end, signal_row_number), (255, 255, 255),
1)
print("LL: " + str(signal_row_number) + " , " +
str(len(ud_spikes)))
NumUtils.draw_spikes(image, ud_spikes, signal_row_number)
return spike_positions
def get_ll_spike_positions(thresh, num_of_iterations=2, signal_row_start=235,
signal_row_end=120, signal_col_start=0, signal_col_end=64,
row_thresh=150, dbg=False, image=None):
if dbg:
print(" ########################## LEFT LANE ########################## ")
spike_positions = []
no_spike_rows_count = 0
for signal_row_number in xrange(signal_row_start, signal_row_end, -2):
row = NumUtils.getImageRow(thresh, signal_row_number)
ud_spikes = OneDHWTLaneDetectorV2.get_up_down_spikes(row, signal_col_start, signal_col_end,
num_of_iterations, signal_row_number)
# If no spikes gets detected the scan line rotated by 1 pixel,
# the last (64th) pixel is the average of 62nd and 63rd pixels
if len(ud_spikes) == 0:
temp_row = row[1:]
temp_row.append((temp_row[-1] + temp_row[-2])/2)
ud_spikes = OneDHWTLaneDetectorV2.get_up_down_spikes(temp_row, signal_col_start,
signal_col_end,
num_of_iterations, signal_row_number)
# If there are no spikes detected, incrementing the counter
if len(ud_spikes) == 0:
no_spike_rows_count += 1
# If no spikes gets detected for consecutive 5 rows, then
# scan line will be shifted by 7 pixels and tries to detect spikes
if len(ud_spikes) == 0 and no_spike_rows_count >= 5:
temp_signal_col_start = signal_col_start
temp_signal_col_end = signal_col_end
# The shifting continues until it reaches row_thresh
while temp_signal_col_end < row_thresh:
temp_signal_col_start += 7
temp_signal_col_end += 7
row = NumUtils.getImageRow(thresh, signal_row_number)
ud_spikes = OneDHWTLaneDetectorV2.get_up_down_spikes(row, temp_signal_col_start,
temp_signal_col_end,
num_of_iterations, signal_row_number)
if len(ud_spikes) > 0:
signal_col_start = temp_signal_col_start
signal_col_end = temp_signal_col_end
# drawing buffer lines
if dbg:
cv2.line(image, (temp_signal_col_start, signal_row_number),
(temp_signal_col_end, signal_row_number), (255, 0, 0), 1)
if len(ud_spikes) > 0:
no_spike_rows_count = 0
if dbg:
print("LL: detected spikes: " + str(len(ud_spikes)))
for spike in ud_spikes:
print spike
# Needs improvement
spike_position = (OneDHWTLaneDetectorV2.optimum_spike_x(ud_spikes), signal_row_number)
if dbg:
print("LL: Spike position: " + str(spike_position))
'''
spike_position = ((ud_spikes[0].getSigStartOfUpRun() + ud_spikes[0].getSigLenOfUpRun() / 2),
signal_row_number)
'''
# setting previous spike position as mid point as the scan line
delta = spike_position[0] - (signal_col_start + signal_col_end) / 2
if delta > 0:
signal_col_start += delta
signal_col_end += delta
spike_positions.append(spike_position)
# drawing the scan lines
if dbg:
cv2.line(image, (signal_col_start, signal_row_number),
(signal_col_end, signal_row_number), (255, 255, 255), 1)
print("LL: " + str(signal_row_number) + " , " + str(len(ud_spikes)))
NumUtils.draw_spikes(image, ud_spikes, signal_row_number)
return spike_positions
