def hog_features(im,
orients=8,
cell_size=8,
block_size=2,
vector=True,
show=False):
if show == True:
features, im_hog = hog(im,
orientations=orients,
pixels_per_cell=(cell_size, ) * 2,
cells_per_block=(block_size, ) * 2,
feature_vector=vector,
transform_sqrt=True,
visualise=show)
show_image([im, im_hog],
ncols=2,
window_title='HOG',
titles=['original', 'hog'],
cmaps=['gray', 'gray'])
else:
features = hog(im,
orientations=orients,
pixels_per_cell=(cell_size, ) * 2,
cells_per_block=(block_size, ) * 2,
feature_vector=vector,
transform_sqrt=True,
visualise=show)
return features
def binspatial_features(im, dst_size=32, show=False):
features = cv.resize(im, (dst_size, ) * 2)
if show == True:
show_image([im, features],
ncols=2,
window_title='Bin Spatial',
titles=['original', 'resized'])
return features.ravel()
def single_image(filename, model_path='./data/model.p'):
if not clb.initialized():
clb.find_pictures(directory='./camera_cal/')
clb.calibrate_camera(9, 6)
im = common.load_image(filename, color='RGB')
common.show_image(im)
m = model.CarModel()
m.load(filename=model_path)
t = track.FrameVehiclePipeline(m, shape=im.shape[:2])
t.process(im, show=True)
def _draw_car_boxes(self, im, show=False):
n = self._labels[1] + 1
for car in range(1, n):
## x and y coordinates
y, x = (self._labels[0] == car).nonzero()
nw, se = (np.min(x), np.min(y)), (np.max(x), np.max(y))
cv.rectangle(im, nw, se, (255, 255, 0), 2)
if show == True:
common.show_image(im,
window_title='Cars Heat Map',
titles='Detected cars')
return im
def _find_cars_heatmap(self, im, show=False):
shape = self._model.input_shape
#_show = show if show == True else False
_show = False
for nw, se in self._slicer.wins:
ys, ye = nw[1], se[1]
xs, xe = nw[0], se[0]
#print(nw, se)
car = self._model.predict(cv.resize(im[ys:ye, xs:xe, :],
shape[:2]),
show=_show)
#_show = False
if car == 1:
self._heatmap[ys:ye, xs:xe] += 1
if show == True:
cv.rectangle(im, nw, se, (0, 0, 255), 2)
#common.show_image(im[ys:ye,xs:xe,:], titles='resized-car')
if show == True:
common.show_image([im, self._heatmap],
ncols=2,
window_title='Cars Heat Map',
titles=['original', 'heatmap'])
data_set['low_texture_row'] = low_texture_row
data_set['low_texture_column'] = low_texture_column
stereo.compute_cost()
t_diff = stereo.aggregate_cost()
my_result = stereo.get_result()
my_result = my_result * (255.0 / d_max / 16)
data_set['my_result_7'] = my_result
diff_result = stereo.left_right_check()
diff_result = diff_result * (255.0 / d_max / 16)
data_set['diff_result'] = diff_result
post_result = stereo.post_processing()
post_result = post_result * (255.0 / d_max / 16)
data_set['post_result'] = post_result
post_result2 = stereo.fix_low_texture()
post_result2 = post_result2 * (255.0 / d_max / 16)
post_result2 = filters.median_filter(post_result2, 5)
data_set['post_result2'] = post_result2
print time.time() - tt
show_image(data_set)
save_image(diff_result, 'diff_result')
save_image(post_result, 'post_result')
save_image(post_result2, 'post_result_2')
save_image(low_texture_column, 'low_texture_column')
save_image(low_texture_row, 'low_texture_row')
save_image(my_result, 'window method 7')
: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]:
data_min = depth
return data_min
# 扫描像素
for row_pos in range(len(left)):
row_left = left[row_pos]
row_right = right[row_pos]
for pixel_pos in range(len(row_left)):
pixel = row_left[pixel_pos]
depth = calculate_diff_naive(pixel, row_right, pixel_pos)
my_result[row_pos][pixel_pos] = depth * 255 / 10
data_set['my_result'] = my_result
show_image(data_set)
save_image(my_result, 'pixel naive method')
if __name__ == '__main__':
pass
#sobel sobel_x = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]]) sobel_y = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) output_1 = cv2.filter2D(after_lap, -1, sobel_x) output_2 = cv2.filter2D(after_lap, -1, sobel_y) output_1 = np.abs(output_1) output_2 = np.abs(output_2) output = output_1 + output_2 output = co.shrink(output, 255, 6) #co.show_image(output) output = after_lap - output #qu zao dian co.show_image(output) output = cv2.medianBlur(output, 5) #co.show_image(output) imsave(dic + "sobel.png", output) #kai yun suan image = morphology.opening(output[:, :, 0], morphology.disk(12)) image_g = co.gray_rgb(image) imsave(dic + "open_grey.png", image_g) #laplacian image_g = cv.imread(dic + "open_grey.png") gray_lap = np.abs(cv2.Laplacian(image_g, cv2.CV_16S, ksize=3)) gray_lap = co.shrink(gray_lap, 255, 5) #co.show_image(gray_lap) after_lap = image_g + gray_lap
from skimage import morphology from skimage.segmentation import random_walker import matplotlib.pyplot as plt from scipy import ndimage from skimage import io from matplotlib.image import imsave import cv2 import cv2.cv2 as cv from skimage.filters import thresholding, _rank_order import time from skimage.morphology import rectangle import common as co dic = "data/tree/" pic = "img_close.png" ''' img_contrasted = cv.imread(dic + "DJI_0330.JPG") print img_contrasted.shape #img_contrasted = co.green_digree(img_contrasted) #img_contrasted = img_contrasted[:,:,0] img_contrasted = np.array(cv2.cvtColor(img_contrasted, cv2.COLOR_BGR2GRAY)) img_contrasted = co.get_derode(img_contrasted) print img_contrasted.shape img_close = morphology.closing(img_contrasted, morphology.disk(20)) #close co.show_image(img_close) ''' img_close = cv.imread(dic + pic) img_close = img_close[:, :, 0] #define region of interset
item_path = self.imagesTop[idx]
elif self.view == 'Side':
item_path = self.imagesSide[idx]
elif self.view == 'Front':
item_path = self.imagesFront[idx]
data_input = Image.open(item_path).convert('L')
# data augmentation
if self.mode == 'train':
data_input = self.augment_data(data_input)
else:
data_input = self.pre_process(data_input)
data_input = 1. - data_input / 255.
return (data_input, data_truth)
if __name__ == '__main__':
loader = DataLoader('../../../../Courses_data/LeChairs/chairs-data/',
mode='train',
view='Top')
train_data_loader = torch.utils.data.DataLoader(loader,
batch_size=1,
shuffle=True,
num_workers=0)
idx, (image, label) = next(enumerate(train_data_loader))
show_image(image, is_tensor=True)
from common import import_pics, show_image
# 1.2
# import an image
im = import_pics("imk01765.tiff")
# a)
# add white noise using different variances
for i in (100, 1000, 5000, 10000, 30000):
# generate white noise
whiten = np.random.normal(0, i, (1020, 1532))
# add the noise to the image
imnoise = im + whiten
# show image
show_image(imnoise, "Noise added (sigma=%d)" % i)
# b)
# generate white noise
whiten = np.random.normal(0, 15000, (1020, 1532))
kernels = (3, 5, 9, 15, 29)
for n in kernels:
# create smooth white noise by convolving white noise with a rectangular window
tmp = convolve2d(whiten, np.ones((n, n)) / (n * n), mode="same", boundary="symm")
show_image(im + tmp, "Noise kernel (%d,%d)" % (n, n))
# first manual approach (with 1-d vector)
# for i in range(1532*1020):
# tmp = 0
# for j in range(-n/2,n/2):
import pylab as mpl
from common import import_pics, show_image
# (see common.py for the implementation of the functions used below)
# import an image from the database
im = import_pics("imk01765.tiff")
# show it
show_image(im, "Original")
mpl.show()
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
print time.time() - tt
save_image(my_result, 'window method 6')
show_image(data_set)'''
data_set = get_data_set(0, is_color=True)
left = data_set['left']
right = data_set['right']
result = data_set['result']
stereo = StereoVisionBM1(left, right, window_size, d_max, is_color=True)
stereo.get_sad_all()
my_result = stereo.get_result()
my_result = my_result * 255 / d_max
data_set['my_result_6'] = my_result
show_image(data_set,is_color=True)
save_image(my_result, 'window method 6')
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
print time.time() - tt
save_image(my_result, 'window method 6')
show_image(data_set)'''
data_set = get_data_set(0, is_color=True)
left = data_set['left']
right = data_set['right']
result = data_set['result']
stereo = StereoVisionBM1(left, right, window_size, d_max, is_color=True)
stereo.get_sad_all()
my_result = stereo.get_result()
my_result = my_result * 255 / d_max
data_set['my_result_6'] = my_result
show_image(data_set, is_color=True)
save_image(my_result, 'window method 6')
dic = "data/clip1/"
pic = "dsm_clip1.tif"
dsm = misc.imread(dic + pic) #tif
print dsm.shape
a = 3929 / 2268.0
b = 978 / 564.0
print a, b
#dsm = cv2.imread(dic + pic)
#dsm = dsm[:,:,0].astype(np.float32)
print type(dsm[0][0])
print dsm.shape
co.show_image(dsm)
'''
maxn = np.max(dsm)
dsm[dsm == np.min(dsm)] = maxn + 1
co.show_image(dsm)
'''
def slide_window_grey(image, px):
#resize image
width = int(math.ceil(float(image.shape[0]) / px) * px)
length = int(math.ceil(float(image.shape[1]) / px) * px)
img_resize = cv2.resize(image, (length, width))
print img_resize.shape
#divide windows
wds = []
import numpy as np from scipy import misc from PIL import Image from matplotlib import pyplot as plt from matplotlib.image import imsave import common as co import math import cv2 dic = "data/dsm/" dsm_100 = cv2.imread(dic + "dsm_grey_sw100.png") dsm_200 = cv2.imread(dic + "dsm_grey_sw200.png") dsm_100200 = (dsm_100 + dsm_200) / 2 co.show_image(dsm_100200) imsave(dic + "dsm_grey_sw1_2.png", dsm_100200)