# -*- coding: utf-8 -*-
from common import get_data_set, show_image, save_image
import numpy as np
data_set = get_data_set(0)
# get data
left = data_set['left']
right = data_set['right']
result = data_set['result']
my_result = np.zeros(left.shape)
def calculate_diff_naive(pixel_value, row_right, pixel_pos, d_max=10):
"""
:param pixel_value:左视图像素点
:param row_right:右视图的这一行
显而易见 右视图中的点在左视图左侧
:param pixel_pos:左视图像素点位置
:param d_max:最大深度
:return:视差值
"""
start_pos = (pixel_pos - d_max) if (pixel_pos - d_max) > 0 else 0
row_right = row_right[start_pos:pixel_pos]
diff = map(lambda value: abs(value - pixel_value), row_right)
diff = diff[::-1] # 逆序
data_min = 0
for depth in range(len(diff)):
if diff[data_min] == 0:
break
if diff[depth] < diff[data_min]:
# 其他区域赋值
for row_pos in np.arange(color_window.shape[0]):
# 记录行差异
diff_row = np.absolute(row_pos - row_start)
for column_pos in np.arange(color_window.shape[1]):
if result_window[row_pos][column_pos] != 0:
continue
diff_column = np.absolute(column_pos - column_start)
result_window[row_pos][column_pos] = (window_size - 1) / (
diff_column + diff_row)
return result_window
if __name__ == '__main__':
data_set = get_data_set(9)
# get data
left = data_set['left']
right = data_set['right']
result = data_set['result']
import time
window_size = 19
d_max = 64
tt = time.time()
stereo = StereoVisionBM2(left, right, window_size, d_max)
(low_texture_row, low_texture_column) = stereo.low_texture_detection()
data_set['low_texture_row'] = low_texture_row
data_set['low_texture_column'] = low_texture_column
self.right_low_cost_extend = self.make_border(self.low_texture_cost_lr, self.d_max)
for d in range(self.d_max):
# print 'compute_cost_d:', d
self.left_diff[d] = self.gaussian_filter(self.compute_cost_d(d))
return self.left_diff.copy()
def compute_cost_d(self, d):
return compute_cost_d_cpp(self.compute_cost_d_cpp_func, self.low_texture_cost_ll,
self.right_low_cost_extend[:,
self.d_max - d:self.column_length + self.d_max - d].copy())+StereoVisionBM2.compute_cost_d(self,d)
#return StereoVisionBM2.compute_cost_d(self,d)
if __name__ == '__main__':
data_set = get_data_set(8)
# get data
left = data_set['left']
right = data_set['right']
result = data_set['result']
import time
window_size = 21
d_max = 64
tt = time.time()
stereo = StereoVisionLowTexture_BT(left, right, window_size, d_max)
low_texture = stereo.low_texture_detection(0.2)[0]
(left_c, right_c) = stereo.get_low_cost_l()
stereo.compute_cost()
for d in np.arange(1, self.d_max):
if self.sad_result[min_sad][row][column] > self.sad_result[d][row][column]:
min_sad = d
self.my_result[row][column] = min_sad
return self.my_result
@staticmethod
def default_window_compare_method(window_1, window_2):
return np.sum(np.absolute(window_1 - window_2))
def set_window_compare_method(self, method):
self.used_window_compare_method = method
if __name__ == '__main__':
data_set = get_data_set(0)
# get data
left = data_set['left']
right = data_set['right']
result = data_set['result']
import time
window_size = 5
d_max = 15
tt = time.time()
stereo = StereoVisionBM1(left, right, window_size, d_max)
'''
stereo.get_sad_all()
my_result = stereo.get_result()
my_result = my_result * 255 / d_max
data_set['my_result_6'] = my_result
if self.sad_result[min_sad][row][column] > self.sad_result[
d][row][column]:
min_sad = d
self.my_result[row][column] = min_sad
return self.my_result
@staticmethod
def default_window_compare_method(window_1, window_2):
return np.sum(np.absolute(window_1 - window_2))
def set_window_compare_method(self, method):
self.used_window_compare_method = method
if __name__ == '__main__':
data_set = get_data_set(0)
# get data
left = data_set['left']
right = data_set['right']
result = data_set['result']
import time
window_size = 5
d_max = 15
tt = time.time()
stereo = StereoVisionBM1(left, right, window_size, d_max)
'''
stereo.get_sad_all()
my_result = stereo.get_result()
my_result = my_result * 255 / d_max
data_set['my_result_6'] = my_result
from common import get_data_set, show_image, save_image, get_compute_cost_bt_d_cpp_func
from stereo_test_dp import StereoVisionBM_DP
from stereo_test_bm import StereoVisionBM2
import numpy as np
class StereoVisionBM_BT(StereoVisionBM2):
def __init__(self, left, right, window_size=13, d_max=10, is_color=False):
StereoVisionBM2.__init__(self, left, right, window_size, d_max,
is_color)
self.compute_cost_d_cpp_func = get_compute_cost_bt_d_cpp_func(self.dll)
if __name__ == '__main__':
data_set = get_data_set(1)
# get data
left = data_set['left']
right = data_set['right']
result = data_set['result']
import time
window_size = 11
d_max = 20
tt = time.time()
stereo = StereoVisionBM_BT(left, right, window_size, d_max)
stereo.low_texture_detection()
stereo.compute_cost()
stereo.aggregate_cost()
my_result = stereo.get_result()
my_result = my_result * (255.0 / d_max / 16)
break
# 其他区域赋值
for row_pos in np.arange(color_window.shape[0]):
# 记录行差异
diff_row = np.absolute(row_pos - row_start)
for column_pos in np.arange(color_window.shape[1]):
if result_window[row_pos][column_pos] != 0:
continue
diff_column = np.absolute(column_pos - column_start)
result_window[row_pos][column_pos] = (window_size - 1) / (diff_column + diff_row)
return result_window
if __name__ == '__main__':
data_set = get_data_set(9)
# get data
left = data_set['left']
right = data_set['right']
result = data_set['result']
import time
window_size = 19
d_max = 64
tt = time.time()
stereo = StereoVisionBM2(left, right, window_size, d_max)
(low_texture_row, low_texture_column) = stereo.low_texture_detection()
data_set['low_texture_row'] = low_texture_row
data_set['low_texture_column'] = low_texture_column
