import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import cv2
import glob
import time
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from skimage.feature import hog
from lesson_functions import *
import pickle
# NOTE: the next import is only valid for scikit-learn version <= 0.17
# for scikit-learn >= 0.18 use:
from sklearn.model_selection import train_test_split
from scipy.ndimage.measurements import label
%matplotlib inline
from pathlib import Path
folder_path_cars = Path('C:/Users/laljarus/Documents/GitHub/TrainingData/vehicles/')
vehicles_path = [x for x in folder_path_cars.iterdir() if x.is_dir()]
cars = []
for path in vehicles_path:
images = path.glob('*.png')
for image in images:
cars.append(str(image))
folder_path_noncars = Path('C:/Users/laljarus/Documents/GitHub/TrainingData/non-vehicles/')
non_vehicles_path = [x for x in folder_path_noncars.iterdir() if x.is_dir()]
notcars = []
for path in non_vehicles_path:
images = path.glob('*.png')
for image in images:
notcars.append(str(image))
# Reduce the sample size because
# The quiz evaluator times out after 13s of CPU time
sample_size = 8792
random_idx = np.random.randint(0,len(cars),sample_size)
cars = np.array(cars)[random_idx]
notcars = np.array(notcars)[0:sample_size]
### TODO: Tweak these parameters and see how the results change.
color_space = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 9 # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = "gray" # Can be 0, 1, 2, or "ALL" or "gray"
spatial_size = (16, 16) # Spatial binning dimensions
hist_bins = 32 # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off
y_start_stop = [350, 720] # Min and max in y to search in slide_window()
car_features = extract_features(cars, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
notcar_features = extract_features(notcars, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
X = np.vstack((car_features, notcar_features)).astype(np.float64)
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
scaled_X = X_scaler.transform(X)
# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))
# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(
scaled_X, y, test_size=0.2, random_state=rand_state)
print('Using:',orient,'orientations',pix_per_cell,
'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))
# Use a linear SVC
svc = LinearSVC()
# Check the training time for the SVC
t3=time.time()
svc.fit(X_train, y_train)
t4 = time.time()
print(round(t4-t3, 2), 'Seconds to train SVC...')
# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
# Check the prediction time for a single sample
t=time.time()
with open('svc_pickle.p','wb') as f:
dictPickle = {'svc':svc,'scaler':X_scaler,'orient':orient,'pix_per_cell':pix_per_cell,
'cell_per_block':cell_per_block,'spatial_size':spatial_size,'hist_bins':hist_bins}
pickle.dump(dictPickle,f)
car_ind = np.random.randint(0,len(cars))
notcar_ind = np.random.randint(0,len(notcars))
car_image = cv2.imread(cars[car_ind])
notcar_image = cv2.imread(notcars[notcar_ind])
car_spatial_feat = bin_spatial(car_image,size=spatial_size)
notcar_spatial_feat = bin_spatial(notcar_image,size=spatial_size)
car_img_yrcb = cv2.cvtColor(car_image,cv2.COLOR_BGR2YCrCb)
notcar_img_yrcb = cv2.cvtColor(notcar_image,cv2.COLOR_BGR2YCrCb)
car_hist_feat = color_hist(car_img_yrcb,hist_bins)
notcar_hist_feat = color_hist(notcar_img_yrcb,hist_bins)
car_hog_feat,car_hog_img = get_hog_features(cv2.cvtColor(car_image,cv2.COLOR_BGR2GRAY),
orient,pix_per_cell,cell_per_block,vis = True)
notcar_hog_feat,notcar_hog_img = get_hog_features(cv2.cvtColor(notcar_image,cv2.COLOR_BGR2GRAY),
orient,pix_per_cell,cell_per_block,vis = True)
vis_images = [car_image,car_img_yrcb,car_hog_img,notcar_image,notcar_img_yrcb,notcar_hog_img]
vis_titles = ['car image','car image Ycrcb','car hog image','notcar image','notcar image Ycrcb','notcar hog image']
f = plt.figure(figsize=(15,10))
visualize(f,2,3,vis_images,vis_titles)
fig, (ax1,ax2) = plt.subplots(1, 2, figsize=(15, 5), sharex=True, sharey=True)
ax1.plot(car_spatial_feat)
ax1.set_title('car spatial features')
ax1.set_xlim(0,len(car_spatial_feat))
ax1.set_ylim(0,max(car_spatial_feat))
ax2.plot(notcar_spatial_feat)
ax2.set_title('notcar spatial features')
ax2.set_xlim(0,len(notcar_spatial_feat))
ax2.set_ylim(0,max(notcar_spatial_feat))
fig, (ax3,ax4) = plt.subplots(1, 2, figsize=(15, 5), sharex=True, sharey=True)
ax3.bar(range(len(car_hist_feat)),car_hist_feat)
ax3.set_title('car color histogam features')
ax3.set_xlim(0,len(car_hist_feat))
ax3.set_ylim(0,max(car_hist_feat))
ax4.bar(range(len(notcar_hist_feat)),notcar_hist_feat)
ax4.set_title('notcar color histogam features')
ax4.set_xlim(0,len(notcar_hist_feat))
ax4.set_ylim(0,max(notcar_hist_feat))
def find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block,
spatial_size, hist_bins,cells_per_step = 2,hog_gray = False):
draw_img = np.copy(img)
img = img.astype(np.float32)/255
heatmap = np.zeros_like(img[:,:,0]).astype(np.float)
img_tosearch = img[ystart:ystop,:,:]
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YCrCb)
if scale != 1:
imshape = ctrans_tosearch.shape
ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
ch1 = ctrans_tosearch[:,:,0]
ch2 = ctrans_tosearch[:,:,1]
ch3 = ctrans_tosearch[:,:,2]
# Define blocks and steps as above
nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1
nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1
#nxblocks = (ch1.shape[1] // pix_per_cell) + 1
#nyblocks = (ch1.shape[0] // pix_per_cell) + 1
nfeat_per_block = orient*cell_per_block**2
# 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
window = 64
nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1
#nblocks_per_window = (window // pix_per_cell)+ 1
#cells_per_step = 2 # Instead of overlap, define how many cells to step
nxsteps = (nxblocks - nblocks_per_window) // cells_per_step + 1
nysteps = (nyblocks - nblocks_per_window) // cells_per_step + 1
if hog_gray == True:
ctrans_tosearch_rgb = cv2.cvtColor(ctrans_tosearch,cv2.COLOR_YCrCb2RGB)
ctrans_tosearch_gray = cv2.cvtColor(ctrans_tosearch,cv2.COLOR_RGB2GRAY)
hog = get_hog_features(ctrans_tosearch_gray, orient, pix_per_cell, cell_per_block, feature_vec=False)
else:
# Compute individual channel HOG features for the entire image
hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
hot_windows = []
windows = []
for xb in range(nxsteps):
for yb in range(nysteps):
ypos = yb*cells_per_step
xpos = xb*cells_per_step
if hog_gray == True:
hog_features = hog[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
else:
# Extract HOG for this patch
hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
xleft = xpos*pix_per_cell
ytop = ypos*pix_per_cell
# Extract the image patch
subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))
# Get color features
spatial_features = bin_spatial(subimg, size=spatial_size)
hist_features = color_hist(subimg, nbins=hist_bins)
# Scale features and make a prediction
test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))
#test_features = X_scaler.transform(np.hstack((shape_feat, hist_feat)).reshape(1, -1))
test_prediction = svc.predict(test_features)
xbox_left = np.int(xleft*scale)
ytop_draw = np.int(ytop*scale)
win_draw = np.int(window*scale)
windows.append([(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart)])
if test_prediction == 1:
hot_windows.append([(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart)])
#cv2.rectangle(draw_img,(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart),(0,0,255),6)
heatmap = add_heat(heatmap,hot_windows)
return hot_windows,heatmap,windows
dist_pickle = pickle.load( open("svc_pickle.p", "rb" ) )
svc = dist_pickle["svc"]
X_scaler = dist_pickle["scaler"]
orient = dist_pickle["orient"]
pix_per_cell = dist_pickle["pix_per_cell"]
cell_per_block = dist_pickle["cell_per_block"]
spatial_size = dist_pickle["spatial_size"]
hist_bins = dist_pickle["hist_bins"]
img_names = glob.glob('./test_images/*.jpg')
#img = mpimg.imread('./test_images/test5.jpg')
#print('pix_per_cell:',pix_per_cell)
#print('cell_per_block:',cell_per_block)
#print(img.shape)
out_images = []
titles = []
scales = [1.2,1.4,2.2]
ystart = [400,400,464]
ystop = [528,656,720]
for img_name in img_names:
t1 = time.time()
img = cv2.imread(img_name)
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
heatmap_sum = np.zeros_like(img[:,:,0]).astype(np.float)
n_windows = 0
for i in range(len(scales)):
hot_windows,heatmap,windows = find_cars(img, ystart[i], ystop[i], scales[i], svc, X_scaler,
orient, pix_per_cell, cell_per_block, spatial_size, hist_bins,hog_gray=True)
heatmap_sum = np.add(heatmap_sum,heatmap)
n_windows += len(windows)
#for window in hot_windows:
# cv2.rectangle(img,window[0],window[1],(0,0,255),6)
#out_images.append(img)
#titles.append(img_name.split('\\')[1])
out_images.append(heatmap_sum)
titles.append(img_name.split('\\')[1]+' heatmap')
heatmap = apply_threshold(heatmap_sum,2)
labels = label(heatmap_sum)
img_labeled = draw_labeled_bboxes(np.copy(img),labels)
out_images.append(img_labeled)
titles.append(img_name.split('\\')[1])
t2 = time.time()
time_diff = t2-t1
fname = './output_images/'+img_name.split('\\')[1]
cv2.imwrite(fname,cv2.cvtColor(img_labeled,cv2.COLOR_BGR2RGB))
print(time_diff," seconds is required to detect cars in the image ",img_name)
print(n_windows, ' windows are used')
fig = plt.figure(figsize=(15,70))
visualize(fig,16,2,out_images,titles)
img = cv2.imread('./test_images/test5.jpg')
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
hot_windows,heatmap_1,windows_1 = find_cars(img, ystart[0], ystop[0], scales[0], svc, X_scaler,orient, pix_per_cell,
cell_per_block, spatial_size, hist_bins,cells_per_step = 2,hog_gray = True)
hot_windows,heatmap_2,windows_2 = find_cars(img, ystart[1], ystop[1],scales[1], svc, X_scaler,orient, pix_per_cell,
cell_per_block, spatial_size, hist_bins,cells_per_step = 2,hog_gray = True)
hot_windows,heatmap_3,windows_3 = find_cars(img, ystart[2], ystop[2], scales[2], svc, X_scaler,orient, pix_per_cell,
cell_per_block, spatial_size, hist_bins,cells_per_step = 2,hog_gray = True)
windows = windows_1+windows_2+windows_3
#out_windows = draw_boxes(img,windows_1,color = (255,0,0),thick = 2)
#out_windows = draw_boxes(img,windows_2,color = (0,255,0),thick = 2)
out_windows = draw_boxes(img,windows,color = (0,0,255),thick = 2)
fig1 = plt.figure(figsize=(15,30))
#visualize(fig1,3,1,[out_windows_1,out_windows_2,out_windows_3],['small windows','medium_windows','large_windows'])
plt.imshow(out_windows)
class Test:
def __init__(self):
# counter to count the frame number in the video
self.counter = 1
# counter to count and reset n frames
self.arr_heatmap_frames = []
with open('CameraCalibration.p','rb') as f:
data = pickle.load(f)
mtx = data[1]
dist = data[2]
TestObj = Test()
def process_image(image):
global mtx
global dist
global TestObj
# Undistorting image:
undist = cv2.undistort(image, mtx, dist, None)
dist_pickle = pickle.load( open("svc_pickle.p", "rb" ) )
svc = dist_pickle["svc"]
X_scaler = dist_pickle["scaler"]
orient = dist_pickle["orient"]
pix_per_cell = dist_pickle["pix_per_cell"]
cell_per_block = dist_pickle["cell_per_block"]
spatial_size = dist_pickle["spatial_size"]
hist_bins = dist_pickle["hist_bins"]
scales = [1.2,1.6,2.2]
ystart = [400,400,464]
ystop = [528,656,720]
heatmap_frame = np.zeros_like(undist[:,:,0]).astype(np.float)
#arr_heatmap_frames = []
n_windows = 0
for i in range(len(scales)):
hot_windows,heatmap,windows = find_cars(undist, ystart[i], ystop[i], scales[i], svc, X_scaler,
orient, pix_per_cell, cell_per_block, spatial_size, hist_bins,hog_gray = True)
heatmap_frame = np.add(heatmap_frame,heatmap)
n_windows += len(windows)
if TestObj.counter<=10:
threshold = 2*(TestObj.counter)
TestObj.arr_heatmap_frames.append(heatmap_frame)
TestObj.counter +=1
else:
threshold = 20
del TestObj.arr_heatmap_frames[0]
TestObj.arr_heatmap_frames.append(heatmap_frame)
heatmap_frames = sum(TestObj.arr_heatmap_frames)
heatmap_thresholded = apply_threshold(heatmap_frames,threshold)
labels = label(heatmap_thresholded)
output_image = draw_labeled_bboxes(np.copy(undist),labels,thickness=2,area_thres =900)
return output_image
from moviepy.editor import VideoFileClip
from IPython.display import HTML
write_output = 'project_video_output_1.mp4'
clip1 = VideoFileClip('project_video.mp4')
#clip1 = VideoFileClip('test_video.mp4')
write_clip = clip1.fl_image(process_image)
%time write_clip.write_videofile(write_output, audio=False)
t1 = time.time()
image = mpimg.imread(img_names[5])
out_test_img = process_image(image)
plt.imshow(out_test_img)
t2 = time.time()
print(t2-t1,' time taken to process the image')


| ���������� | ������˵�� |
|---|---|
int |
calculateChecksum()
Calculates a checksum using protocol specification for a
header.
|
int |
checksum()
Retrieves the header's checksum.
|
boolean |
checksum(int crc)
Setter method to set a new checksum on the header.
|
boolean |
isChecksumValid()
Validates the header's data against the stored checksum.
|
boolean |
recalculateChecksum()
Method which recomputes the checksum and sets the new computed
value in
checksum field.
|
int calculateChecksum()
int checksum()
boolean checksum(int crc)
crc
- new value of the header. Can be 8, 16 or 32-bit checksum,
even if
in signed form.
boolean isChecksumValid()
boolean recalculateChecksum()


Channel Island Search Engine. Onourdoorstep to Search Jersey, Guernsey, Alderney, Sark and Herm.
Onourdoorstep also provides a large database of C.I Jobs from local recruitment agencies.
Use the Directory to find specific Channel Island Companies in Business & Finance, Property, Government & Education, Travel & Tourism.


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@inlinable public var isCurrent: Bool { get }


You are viewing an historical archive of past issues. Please
report new issues to the appropriate project issue tracker on
GitHub.
Issues
| # | Summary | Product | Status | Kind | When |
|---|---|---|---|---|---|
| 2198 |
Use git as datastore |
wincent.com | open | feature request | |
| 2196 |
'sidescroll' and probably other settings not being restored if leaving via `<C-w C-q>` |
Command-T | closed | bug | Created , updated |
| 2194 |
Deal with new deprecation warnings |
wincent.com | open | bug | |
| 2193 |
Use hash in front of tag references in the UI |
wincent.com | closed | feature request | |
| 2192 |
Autolinkify hash tags |
wincent.com | closed | feature request | Created , updated |
| 2191 |
CommandTMaxHeight not taken into account |
Command-T | closed | bug | Created , updated |
| 2190 |
Rename CommandTTraverseSCM's "pwd" to "cwd" |
Command-T | closed | feature request | Created , updated |
| 2189 |
Update to Rails 4.2.1 |
wincent.com | closed | feature request | Created , updated |
| 2188 |
Investigate Docker |
wincent.com | open | feature request | |
| 2186 |
Long URLs break responsive layout |
wincent.com | closed | bug |


The optimization results page displays parameter charts to show the relationship between the parameter value(s) selected by the optimizer and the value of several objectives. If your optimization job has one parameter, the result page displays a scatter plot for each objective. If your optimization job has two parameters, the result page displays a heat map for each objective.
Zones in the heatmap where the color of adjacent cells are relatively consistent represent areas where the objectives are stable. In these areas, the value of the objectives is not significantly influenced by the parameter values. The following image shows the parameter chart of an optimization job. The highlighted area identifies combinations of parameter values that stabilize the objective function.
You can add parameter charts for the following objectives:
Follow these steps to add a parameter chart to the optimization results page:
The optimization results page displays the new chart.


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Pedro Garzon
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Rayvanny has blocked me says Willy Paul
by The Citizen Reporter Dar es SalaamAll is not well between Kenyan singer Willy Paul and WCB’s Rayvanny as the two are reportedly not on talking terms and WCB signed artiste has since blocked the Kenyan.
According to Willy Paul the rift started with the recording of the song ‘Chuchuma’ which was his and he intended to have Rayvanny in another collaboration which would have seen the two work together after the success of hit single’ Mmmh’.
“I recorded the song and sent it to him in Dar es Salaam to have his input and the first edition was just okay but then in a rather bizarre turn he sent me another version which was completely different from what we had agreed,” says Willy Paul.
He says he was straight with his counterpart and he expressed his disappointment but in response the Pochi Nene singer blocked him.
“I don’t have a problem with Rayvanny because he is a good artiste but sometimes these things have to be said just the way it is,” says Willy.
Rayvanny has since gone on to release his other version of Chuchuma which has done relatively well on Youtube.
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Full-strength Qatar raring to have a go at Iraq in opener
Asian champions Qatar have welcomed back key defender Abdulkarim Hassan back in training as the home team - under coach Felix Sanchez - prepare for the opening clash of the Gulf Cup against Iraq on Tuesday.
Hassan, last year’s Asian Player of the Year winner, did not make the trip to Tajikistan last week where Qatar beat Afghanistan 2-0 in a 2022 FIFA World Cup and 2023 Asian Cup joint qualifying match in Dushanbe.
Sanchez, who guided the team to a spectacular Asian Cup title win early this year, did not take four others for the match in Dushanbe in a bid to give recovery time to his players for the eight-team Gulf Cup from November 26 to December 8.
The Qatar coach is set to announce the squad tomorrow, just a day ahead of the region’s most popular national team event.
Key strikers Hassan Al Haydos, Akram Afif and Almoez Ali - the stars from the Asian Cup title victory - are also fit for the tournament.
Hassan, who was on the pitch for a record time last year, is a certainty in the squad after he trained with his team-mates at Aspire Academy.
Qatar’s first match against Iraq on Tuesday at the iconic Khalifa International Stadium has Al Annabi’s central defender and defensive midfielder Karim Boudiaf excited.
“We are excited to be hosting the event and we are getting ready for a big tournament and that too on home soil,” Boudiaf, 29, said in an interview after the training yesterday.
“However, the Gulf Cup is undoubtedly a difficult assignment,” the Al Duhail player added.
“Our group is difficult because it includes strong teams like the UAE and Iraq,” Boudiaf said.
“Although we surpassed them in the last Asian Cup, but since then a lot of new developments have taken place,” Boudiaf said about his team’s 4-0 win over the UAE in the semi-finals of the Asian Cup earlier this year.
“First of all, we are facing Iraq in the opening clash and that is a tough side. It will not be easy in this tournament,” he warned.
“Playing well in the opening match is important,” he added.
“Of course, we will go step by step,” he said.
“We expect to receive great support from our fans, who will follow us in this competitive tournament. We are playing in an event on home soil for the first time since we won the Asian Cup,” Bouadiaf said. “We hope to have the necessary support from our fans,” he added.
The players returned to training after being given 48 hours rest by coach Sanchez. Qatar have played an astonishing 16 matches this year.
Meanwhile Yemen coach Sami Hasan Al Nash praised Qatar for giving his side the ‘best of facilities to train and for the matches’
Qatar will host the Gulf Cup matches at the Khalifa International Stadium and Al Duhail Stadium.
“Qatar is wonderful news for football teams and fans. The atmosphere here is distinctive,” Sami Hassan said in an interview.
“We are in a lovely country. You can feel there is great organisation and the hosts want a big success out of the tournament,” Sami Hassan added.
The coach added: “As you know, we were the first team that arrived in Doha in preparation for the Gulf Cup matches. We want to optimise our chances of doing well. We are ready for the tournament.”
“Yemen has recently improved its technical level for the best during the West Asian Championship and in the qualifiers for the World Cup Qatar 2022 and Asian Cup China 2023. We have a young group of players.
There is widespread praise for our courage but our ambition will not stop at this level. We hope to continue the good performances,” he added.
The coach said: “We are not at the level of other teams in the tournament but we are ready to give ourselves the chances to do well and compete well with the strong sides.”
When asked about his team’s opening clash against the UAE on Tuesday at Al Duhail Stadium, Sami Hassan said: “We have seen the results of the UAE in the recent period. Our task before them will not be easy. The UAE team has top players. And those players play well collectively and at individual levels.”
Coach Sami Hassan said the increase in the number of teams is ‘a positive step’ for the promotion of Gulf Cup.
“It is a very positive step because the more games the young players play, the better they get. We hope to gain experience in the tournament in three matches we play,” he said.
Regarding his expectation at the 24th Gulf Cup, Sami Hassan said: “Gulf Cup always throws up many surprises. The closest one in the last tournament was when Oman won the championship by beating the UAE in the final (in Kuwait). That was a big surprise.”
Read also
Read More
Arabian Gulf Cup: Oman aim to retain title
The Red Warriors are looking to win their second successive and third overall title at the 24th edition of the tournament which will take place from November 26 to December 8 in Doha.
Read More
QFA launches initiative to support team at Arabian Gulf Cup
Read More
Ooredoo Cup: Al Ahli down Al Sadd as Al Rayyan and Umm Salal play out exciting 2-2 draw


Barham Fire breaks out in Los Angeles near Hollywood film studios
by WABCLOS ANGELES -- A small brush fire broke out in the Hollywood Hills area of Los Angeles Saturday afternoon, sending plumes of smoke that towered over the Hollywood sign and could be seen for several miles.
The Los Angeles Fire Department said firefighters on the air and on the ground had stopped the forward progress of the 34-acre Barham Fire Saturday.
Though the fire broke out near the Warner Brothers studio lot, the fire department said on Twitter that there have been "no structures damaged and none imminently threatened."
Officials said wind was not a factor in the Barham Fire, which was slowly moving away from structures and toward Griffith Park. Some production companies and film crews evacuated out of an abundance of caution.

The cause of the fire is under investigation.
The Associated Press contributed to this report.


Haskell XML Toolbox %VERSION%
Contents
Introduction
The Haskell XML Toolbox is a collection of tools for
processing XML with
Haskell.
It is purely written in Haskell.
The Haskell XML Toolbox is a project of the
University of Applied Sciences Wedel,
The main design goal of the Haskell XML Toolbox
is the support of various XML standards including
Extensible Markup
Language (XML) 1.0 (Second Edition) with DTD
processing and Validation,
Namespaces in XML
1.0 (Second Edition),
XML Path Language
(XPath),
XSL Transformations
(XSLT),
RELAX NG Specification,
as well as HTML/XHTML processing.
Description
The Haskell XML Toolbox bases on the ideas of
HaXml and
HXML,
but introduces a more general and flexible approach for processing XML with
Haskell.
The Haskell XML Toolbox uses a generic data model for
representing XML documents,
including the DTD subset and the document subset, in Haskell.
This data model makes it possible to use filter functions
as a uniform design of XML processing applications.
The processing filters are implemented as
arrows.
This is more flexible than the filter approach from HXML and
HaXml,
but all filter applications can easily be transformed into
arrows.
Since version 5.2 HXT works with arrows instead of filters.
The filter part has been separated from this library and is
available in an extra package (see HXT with Filters)
There is a cookbook for using this arrow interface
to build (nontrivial) applications. Manuel Ohlendorf
has developed examples for processing RDF and has documented the
development in his master thesis: A Cookbook for the Haskell
XML Toolbox with Examples for Processing RDF Documents
(the thesis as PDF)
Features:
- Unicode and UTF-8, US-ASCII and ISO-Laitin-1 support
- http: and file: protocol support
- http access via proxy
- wellformed document parsing, validation
- namespace support: namespace propagation and checking
- XPath support for selection of document parts
- liberal HTML parser for interpreting any text containing <
... > as HTML/XML
- liberal and lasy lightweight HTML/XML parser based on
tagsoup
- Relax NG schema
validator
- integrated XSLT transformer
- easy conversion between user defined data structures and XML
by the use of pickler functions
Documentation
The HXT API Documentation generated
with Haddock.
A (somewhat) gentle introduction to HXT is avalable in the
Haskell
Wiki.
There's also a page about
HXT: Conversion of Haskell data from/to XML with picklers.
The XSLT transformer has been developed by Tim Walkenhorst in
this master thesis: Implementing an XSLT
processor for the Haskell XML Toolbox. It's a
rather complete implementation, but it's of course not a
substitute for Xalan or other advanced XSLT systems. The XSLT
module consists of less than 2000 lines of code. Compared with
the more than 300,000 lines of Java for Xalan, this Haskell code
can be viewed as one of the first formal specifications for XSLT.
Manuel Ohlendorfs master thesis, describing the arrow interface
of the toolbox: A Cookbook for the Haskell
XML Toolbox with Examples for Processing RDF Documents
(the thesis as PDF).
The source code of the example application is included in the
doc/cookbook directory of the distribution.
The master's thesis
"Design and Implementation of a validating XML parser in
Haskell"
by Martin Schmidt describes the design and motivation of the
Haskell XML Toolbox
(the thesis as HTML
or PDF) and the development of the DTD
validator module.
The documentation in the thesis is a bit out of date, the modules
and module names and some function names have been changed. For details the online
haddock documentation should be used.
The description of the development of the XPath modules
is described (in german) in Konzeption und Implementierung
eines XPath-Moduls für die Haskell XML Toolbox
(PDF-document).
The description of the internals of the Relax NG validator
modules is described (in german) in Design und Entwicklung
eines Relax NG Schema Validators auf Basis der Haskell XML
Toolbox (PDF-document).
Requirements
It is recommended to install the versions available from
Hackage.
HXT Downloads
hxt-8.5.0.tar.gz
hxt-xpath-8.5.0.tar.gz
hxt-xslt-8.5.0.tar.gz
hxt-filter-8.4.0.tar.gz
extension modules
- This version works with ghc 6.12.1 with cabal 1.8.
For ghc 6.10.* please use HXT 8.3.*.
- This version does not include the filter variant of hxt.
These modules are separated into an extra packages
hxt-filter.
Also
the XPath and XSLT modules are separated into packages
hxt-xpath and hxt-xslt.
- Includes sources for building a ghc package
hxt
with Cabal.
This package contains a Haskell DOM, an XML parser, a HTML parser based on parsec,
a lightweight HTML/XML parser based on tagsoup, a
DTD validator, namespace processing functions, and a Relax
NG validator and the serialization/deserialisation of data to/from XML.
- HTTP access is done via the curl binding.
When installing curl (under Linux) be aware to first install the
source package of the curl library.
- Includes various examples, e.g. in example dir
examples/arrows/hparser/
a validating parser, which can be used as a starting point for
a HXT command line application.
- Includes an arrow interface with type classes and
overloading for a more flexible use of the filter technique.
A git repository is available under
http://git.fh-wedel.de/repos/hxt.git.
This repository also contains the other hxt-packages.
Installation
Before installing this version, install the curl and tagsoup modules.
For a quick install and access to hackage use the cabal installation
program.
A quick test of the example programs unpack the tar files and move into the example
subdirectories.
cd <hxt-package>/examples
make all
make test
Change History
- In Version 8.5.0
-
XPath module has been internally refactored and optimized
for speed. Runtime deficiencies for complex and ambigious
XPath expressions have been removed.
- XPath modules are separated into extra package hxt-xpath
- In Version 8.4.0
- Changes for ghc-6.12. This version requires
ghc-6.12. There is a new option a_text_mode for
text output of documents.
- XSLT part separated into extra package hxt-xslt
- In Version 8.3.2
This is a bug fix release.
-
New output option a_output_xhtmlfor writing XHTML.
-
New output option a_no_empty_elem_for for precise
control, which empty elements shall not be emitted in
short form <name .../>.
- New output option a_add_default_dtd for easy
adding a Document Type Declaration.
-
writeDocumentToString changed, such that it is a pure
arrow and does not need to run in the IO monad.
-
Dealing with URIs containing unescaped chars changed.
When URIs can't be parsed (with Network.URI), then the
not allowed chars will be escaped in %XX format and
URI parsing is retried. This enables normal file names to contain blanks and
other chars without explicit escaping.
- In Version 8.3.1
- Additional input option "a_accept_mimetypes" for setting a list of allowed
mime types when using readDocument.
- In Version 8.3.1
This is only a bug fix release.
-
Interface and option handling for libcurl reworked.
New input option "a_no_redirect" for preventing autmatic
redirect added.
-
Encoding of none XML/HTML text data done with the same
encoding routines as for XML/HTML. This enables easy processing
of other text documents.
- In Version 8.3.0
-
New output option "a_no_empty_elements" for preventing
the XML short format "<name/>" for HTML elements,
like "script", "p", and others. Especially a script tag
of the form "<script href="..."/>" does not work in
firefox.
Turning on this option gives the form "<script href="..."></script>".
-
An input option "a_strict_input" for bytestring input of
files added. Lazy input, especially when using the tagsoup
parser, can lead to error messages like "too many open
files" when processing a whole bunsh of documents.
- Internal representation of qualified names changed to gain
more space efficency.
- In Version 8.2.0
-
Modifications to work with ghc-6.10.
-
A new module Data.Atom for dealing with names like LISP atoms
and for sharing the memory for these values. When using names as keys
in tables, trees or maps, it becomes much more efficient to represent these
names as atoms than as strings. Equality check on atoms is constant in time and really fast, and all occurences
of an atom share the same internal value.
-
Implementation of strictA changed. strictA is marked deprecated.
The implementation is not longer done with a DeepSeq function but with
Control.Parallel.Strategies, the NFData class and rnf.
There is a new combinator rnfA for complete deep evaluation of an
arrow result.
-
Further functions for working with W3C XML Schema Regular expressions
in module Text.XML.HXT.RelaxNG.XmlSchema.RegexMatch,
especially for tokenizing and sed like editing of text.
- In Version 8.1.1
- In Version 8.1.0
-
HTTP interface changed to work with libcurl
via curl bindings with package curl.
So the HTTP package is not longer needed, also the old
and somewhat inefficent interface to curl by starting an external process and
communicate via a pipe is not longer needed.
When installing the curl bindings, be aware that the
libcurl development packages including the C header files
must be installed. Otherwise the Setup.hs will complain of
missing files.
-
New input option for ignoring none XML/HTML contents
when reading documents (useful for crawler like applications).
-
Mime type support for the file: protocol. Mime type mapping
can be controlled by a config file in the format of
/etc/mime.types on some linux systems.
A few more picklers for de-serialization from/to XML, e.g. for maps.
-
A new option for ignoring decoding errors when reading XML documents.
This may be useful for crawler like applications.
In Version 8.0.0
-
Old filter interface separated from the hxt package and moved
to an extra package hxt-filter.
-
Version numbers added in hxt.cabal for required package versions.
-
DTD validation and XPath modules refactored to work with arrows instead of
filters.
This is done for separating the old hxt filter library
from the actively developed and maintained arrow part.
Known problems and limitations
The parser has been tested with the XML Validation Suite form the
W3C. The following problems have been encountered:
- Line numbers in XML parser do not always point to the
correct position of the syntax error.
- Line numbers are not yet reported for validation constraint
errors.
- The standalone document check is not yet implemented.
- The XSLT module does not support the complete XSLT standard.
Portability
Portability to Windows based systems has not been tested very
intensively, but did work on an XP system with the Cygwin tools installed.
Development was done under Linux with GHC 6.12 with the -Wall
flag. No warnings were issued, when compiling the sources.
HXT with Filters
For older applications using the filter functionality,
there is an extra package hxt-filter.
This package must be installed on top of hxt.
The filter package will not be actively developed any more.
Please move to the arrow version for long term projects.
Installation works with cabal in the usual way.
Download archive is hxt-filter-8.4.0.tar.gz,
HXT Filter API Documentation
with source links is availabe as well as a
darcs repository under
http://git.fh-wedel.de/repos/hxt.git.
Extension Packages
There is a small package
hxt-binary-0.0.1.tar.gz
for serializing and deserializing documents represented as
XmlTrees. This package contains Binary instances for the HXT
data types representing XML data.
The above package is used for a package implementing a simple
cache system for parsed XML data
hxt-cache-0.0.1.tar.gz.
The cache system may be useful for the development of crawlers
or for preprocessing templates for web frameworks.
Related work
- Malcolm Wallace and Colin Runciman wrote
HaXml,
a collection of utilities for using Haskell and XML together.
The Haskell XML Toolbox is based on their idea
of using filter combinators for processing XML with Haskell.
- Joe English wrote
HXML
- a non-validating XML parser in Haskell.
His
idea
of validating XML by using derivatives of regular expressions
was
implemented in the validation functions of this software.
Also his ideas and sources for navigateble trees are used
in the hxpath modules.
Feedback
We are interested in hearing your feedback
on our Haskell XML Toolbox, suggestions
for improvements, comments and criticisms.
Mail address is
<EMAIL_ADDRESS>|
The Haskell XML Toolbox
is distributed under the
MIT License.
|
|
| Last modified: %PUBDATE% | |


In the early months of the COVID-19 pandemic I worked on a tool for comparing genomes in C++. The project is named “vdiff”, after virus diff (like the unix command line tool “diff”).
The vdiff command line tool provides information about two genomes, including the number of matching strings within two genomes, and the percentage of the genomes that are matching when aligned. This information is output via command line logging, as well as optional CSV match output for graphing.
This is a relatively long term review of “Dance Pants” as a piece of backpacking gear.  They cost under $20, which is a much better deal than the current high end ultralight offerings. They weigh in around 3oz on my scale, but this will vary with size. The pants seem to be popular with dancers for warming up before dancing, but they are simply thin ripstop nylon pants.
I am going to keep this review simple, just like this product. I have been using these pants for several months and hundreds of miles and they do their job perfectly. They do not have pockets, or really any features at all. They are held up with an elastic waist, and held down at the ankles by more elastic. They are very flexible in terms of stretch and layering. I have worn them with just underwear underneath and have been comfortable, but have also worn the pants with underwear, shorts and a base layer underneath in cold conditions and had no issues. It does not feel like you are wearing an additional waistband when you layer these pants because they are so light and the waistband is so stretchy. I forget that I am wearing these pants a lot of the time while hiking or sleeping, but they do their job of adding some warmth and reducing wind well.
This post contains everything you might want to know about gear I use when backpacking, as well as some of my thoughts on gear choices. I have gotten some emails and messages about some items, so this is an effort to create something I can respond with. I am going to use this post as a place to link people to from other things such as future videos or posts as the one stop shop for my gear info. It will also contain some ramblings (more to come in the future) about gear and Ultralight ideas.
Like many, I follow most of the principals of Ultralight Backpacking. You will see this in some of my gear choices, but admittedly not all. I try to keep my base weight as low as possible, but it often varies between trips. Generally my base weight is under 10lbs, often much lower if conditions permit. I live in Vancouver, Canada, so weather and conditions are constantly changing. When I am out around home, I am often switching things up or taking more or less gear to suit the conditions. If you live somewhere where you can be certain it isn’t going to rain for the next 5 days, then you can adapt your gear accordingly!
This week my friend Riley and I hiked up to Dennett Lake, on Burke Mountain. The 21km hike took a total of about five and a half hours, but could be knocked down to four and a half if you were really on a mission.
We followed the same route to Munro Lake as detailed in my last post, so I won’t make a new map for this post. If you want the exact route, you can check out my gps track. Once you are at Munro lake, you should see a bare cliff on the other side of the lake. Head north (towards the cliff) from the area that the trail enters the lake, across the dried up section, and follow the edge of the lake counter clockwise until you see the trail on your left. There are some blue blazes on the first kilometer or so of this area.
This post contains information about “The Rospe Trail” named after my friend Rospe. It mostly uses pre existing trails in Burke Mountain, and can be used to get to Munro and Dennett lakes.
If you want to hike this trail you can park at the top of harper road. There is room for about 20-30 cars, and any time I have gone it has never been full. If it is, you could probably try parking at the gun range, but I’ve never tried that.
I am putting this post up to force myself to update it as I explore Burke Mountain. I hope to fill this post with all of the information that I can on long routes in Burke Mountain, cool places to get to, and good camp spots.
I will do my best to upload images, and of course will include maps of all of my routes.
With the recent Bancor Initial Coin Offering (ICO) raking in over 150 million USD in ETH, it is undeniable that ICO’s are huge. They are hyped throughout the community, and they often sell out within hours, minutes, or even blocks.
With “The DAO” incident still fresh in the memories of many, ICO’s can be a little worrying. This post will include the common concerns relating to the rapid ICO growth, and my opinion on their validity.
I originally wrote this post for an open source project I worked on called TF2 Stadium. The post was originally available at http://blog.tf2stadium.com. I am re posting it now since the blog has been shut down. It is mostly semi technical information that was released as teaser/promotional content in the early stages of development. TF2Stadium is a Team Fortress 2 lobby website where competitive players are matched with other players for practice matches.
Original post date: 03 SEP 2015
Hello, and welcome to the first actual entry of the TF2Stadium developer blog. In this entry, we will summarize some of the technology that our site uses, as well as some of our design goals.
The programming of TF2Stadium–as well as most other sites–is split into two major groups: “frontend” and “backend”.


--- title: Accessability subtitle: STRAPLESS - BUILD icon: fa-universal-access ---
"Accessibility is like a blueberry muffin - you can't push the berries in there afterward" - CordeliaDillon
"The most basic a11y (accessibility) win is using proper elements and ARIA roles where necessary." a11y For The Masses
This is our job as the authors of the code.
This is where semantics come into play. Label content with the correct html tags, and you'll automatically remove most accessability issues.
While it may seem daunting to correctly markup every single line of code, it's a relatively simple task on its own. Therefore, code with accessability and the meaning of the content in mind right from the start, for every element you code --- this upfront investment of time pays off hugely in the middle and end of a project (plus it's especially helpful for passing an ADA scan).
The way to cut down on dev time is a modular approach --- use the available components as a template, which have much of the initial accessability best practices already added to their markup.
"another pillar of accessibility is providing translatable (read: in-code) text" a11y For The Masses
"A big part of accessibility is language choice. Writing in a clear, concise tone." a11y For The Masses
Use clear, concise vocabulary.
This will make it easier for translators, in addition to improving the general clarity and readability of the text.
"An accessible website does not rely on the mouse; it provides all functionality via a keyboard." w3.org
Open Color - https://yeun.github.io/open-color/
Accessibility - https://www.w3.org/standards/webdesign/accessibility Web Accessibility Initiative (WAI) - https://www.w3.org/WAI/ Introduction to Web Accessibility - https://www.w3.org/WAI/intro/accessibility.php Contrast Rebellion - http://contrastrebellion.com/


Introducing Fast Gaussian Accumulator¶
Fast Gaussian Accumulator¶
A Gaussian Sphere Accumulator refers to the notion of discretizing the surface of the unit sphere (a gaussian surface) into buckets/cells. One can then integrate/accumulate a list of points (aka unit normals) into these buckets. The end result is then a histogram of the sphere. There are many choices for the discretization process, however this library uses equilateral triangles because each cell will have the exact same area and shape. This process is done by refining an icosahedron. The following image shows our discretization strategy. The first object discretizes a sphere with uniform spacing of phi/theta (note small cells at poles), the second object is an icosahedron, the third object is the first level of refinement for an icosahedron, the last object is the second level of refinement of an icosahedron.
Once a level of refinement is chosen, one can then integrate point vectors into the cells/buckets. For example integrating the normals of a level four (4) icosahedron would look like the image below. Bright yellow indicates more counts for the triangle cells. This is basically showing that the floor [0, 0, 1] and walls [0, +/-1, 0] are common.
Finding the Cell¶
To do this one must find the cell that corresponds to the point. This is a search process that has been implemented in several fashions in this repo. The main ways are as follows:
3D KD Tree - Do a nearest neighbor search using a binary tree.
GaussianAccumulatorKDPY
- One implementation using scipy kdtree.GaussianAccumulatorKD
One implementation uses C++ nanoflann.
Global Index and Local Search - A 3D point is transformed to a unique integer id. The unique ids have the property that ids close to each other will be close to each other in 3D space. The closest id is found corresponding to a triangle cell. A local search of triangle neighbors is performed to find closest triangle cell to the point.
GaussianAccumulatorOpt
- Works good on only on the top hemisphere. Projects 3D point to plane using Azimuth Equal Area projection. Convert 2D point to int32 index using Hilbert Curve. This implementation is severely limited and is not recommended.GaussianAccumulatorS2
- Works on full sphere! Uses Googles S2 space filling curve (uint64). 3D point is projected to unit cube, assigned to a face of the cube, and then a Hilbert curve index is found for that cube face. This is recommended, and what I use.
Peak Detection¶
There are two (2) peak detection methods of used within this repository. The user can choose which one best suite there needs.
2D Image Peak Detection¶
This method basically unwraps the icosahedron as a 2D image in a very particular way as described by Gauge Equivariant Convolutional Networks and the Icosahedral CNN. This unwrapping is hardcoded and fixed once a refinement level is chosen so its very fast. The library then uses a 2D peak detector algorithm followed up with agglomerative hierarchial clustering (AHC) to group similar peaks. All of this is user configurable.
1D Signal Peak Detection¶
This performs peak detection on the 1D thread following the hilbert curve. This produces more peaks which are actually near each other on S2 and are then grouped with AHC. This actually works pretty well, but I recommend to use the 2D Image Peak Detector.
Installation¶
Installation is entirely through CMake now. You must have CMake 3.14 or higher installed and a C++ compiler with C++ 14 or higher. No built binaries are included currently.
Build Project Library¶
mkdir cmake-build && cd cmake-build
. - create build folder directorycmake ../ -DCMAKE_BUILD_TYPE=Release -DWERROR=0
. For windows also add-DCMAKE_GENERATOR_PLATFORM=x64
cmake --build . -j$(nproc)
- Build FastGA
Build and Install Python Extension¶
Install conda or create a python virtual envrionment (Why?). I recommend conda for Windows users.
cd cmake-build && cmake --build . --target python-package --config Release -j4
cd lib/python_package && pip install -e .
If you want to run the examples then you need to install the following (from main directory):
pip install -r requirements-dev.txt
Documentation¶
Please see documentation website for more details.


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בחורה מדהימה
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| Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday |
|---|---|---|---|---|---|---|
| 9:00am | 9:00am | 9:00am | 9:00am | 9:00am | 9:00am | 9:00am |
| 10:00am | 10:00am | 10:00am | 10:00am | 10:00am | 10:00am | 10:00am |
| 11:00am | 11:00am | 11:00am | 11:00am | 11:00am | 11:00am | 11:00am |
| 12:00pm | 12:00pm | 12:00pm | 12:00pm | 12:00pm | 12:00pm | 12:00pm |
| 1:00pm | 1:00pm | 1:00pm | 1:00pm | 1:00pm | 1:00pm | 1:00pm |
| 2:00pm | 2:00pm | 2:00pm | 2:00pm | 2:00pm | 2:00pm | 2:00pm |
| 3:00pm | 3:00pm | 3:00pm | 3:00pm | 3:00pm | 3:00pm | 3:00pm |
| 4:00pm | 4:00pm | 4:00pm | 4:00pm | 4:00pm | 4:00pm | 4:00pm |
| 5:00pm | 5:00pm | 5:00pm | 5:00pm | 5:00pm | 5:00pm | 5:00pm |
| 6:00pm | 6:00pm | 6:00pm | 6:00pm | 6:00pm | 6:00pm | 6:00pm |


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Carpe Datum
Data Science for Life’s Big Questions
Preface
“[T]he most important questions of life are for the most part only problems of probability. It may even be said, strictly speaking, that almost all our knowledge is only probabilistic.”
— Pierre-Simon Laplace
Caveat
This book is an incomplete draft of a work in progress being developed as lecture notes for an online course. Content is provisional, contingent, and possibly wrong, but always well intended.
Guiding principles in this book
Question-driven
Because the presentation of topics in this book is question-driven rather than method-driven, this coursebook has some idiosyncracies. Some topics that might be considered rather basic may be omitted, while some topics that are typically considered as advanced will get (a simplified) treatment.
No proofs
As a mathematical subject, statistics is often taught with derivation and proof using definitions, simple assumptions, and the logic of algebra and calculus. Mathematical formulas are the standard language of statistics. This approach to learning is powerful if the math supports rather than gets in the way of understanding. However, for many learners, the math obscures rather than clarifies, and another way–using demonstrations and simulations–might enable understanding, as Johnson & Johnson once said, without tears.
Now, a demonstration is not a proof. That said, repeated experiments can be convincing even in the absence of proof. For example, I can prove to you that if you take any whole number (e.g., 1, 2, 3, 7, 21, 118, 8675309), multiply it by 9, and then sum the individual digits of that resulting product, that the sum itself will be a multiple of 9
Example:
7 * 9 = 63; 6 + 3 = 9.
21 * 9 = 189; 1 + 8 + 9 = 18.
An elegant and simple proof can be constructed (hint: by induction), but if you try it out yourself enough times, you won’t need the proof to be convinced. Twice, by the way, might not be enough. You could, however, write a computer program to test this equality a thousand times using a thousand random whole numbers. This is sometimes called a computer simulation.
Now problems like this one are often used to teach proof technique rather than to encode cute number-facts in memory. And indeed, for training statisticians, a rigorous mathematical presentation is important. So, for that matter, is computer simulation. For most users of this book, intuition and understanding are the priority, and the ability to derive formulas is not necessary. We will, in due course, bring out some computer simulations.


Who and Why
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Quick to grasp the brand and express it consistently with your business culture and values and then competently convey your brief to the chosen digital platform.
Efficient, effective, collaborative and innovative.
I'm skilled in all the major programming languages, in particular:


The people detector operation interface (Dedicated for all third-party cameras).
More...
#include <TopsensPeople.hpp>
|
| | People () |
| | Default constructor. More...
|
| |
| | ~People () |
| | Destructor. More...
|
| |
| Error | Initialize (uint32_t width, uint32_t height, float fx, float fy, float cx, float cy) |
| | Initializes people detector. More...
|
| |
| Error | Initialize (uint32_t width, uint32_t height) |
| | Initializes people detector with preconfigured identity. More...
|
| |
| void | Uninitialize () |
| | Uninitializes people detector instance. More...
|
| |
| Error | SetOrientation (Orientation orientation) |
| | Sets depth image orientation. More...
|
| |
| Orientation | GetOrientation () const |
| | Gets depth image orientation. More...
|
| |
| Error | SetImageFlipped (bool flipped) |
| | Sets whether the depth image is flipped. More...
|
| |
| bool | IsImageFlipped () const |
| | Checks whether the depth image is flipped. More...
|
| |
| Error | SetMaskEnabled (bool enabled) |
| | Sets whether to enable user mask. More...
|
| |
| bool | IsMaskEnabled () const |
| | Checks whether user mask is enabled. More...
|
| |
| Error | SetConfig (Config config) |
| | Sets preconfigured identity. More...
|
| |
| Error | Detect (const uint16_t *depth, uint32_t width, uint32_t height, int64_t timestamp, UsersFrame &users) |
| | Processes depth data. More...
|
| |
|
| static Error | Preload (const uint32_t *resources, uint32_t count) |
| | Preloads resource data to minimize sensor startup delay. More...
|
| |
| static Error | Preload (const std::initializer_list< uint32_t > &list) |
| | Preloads resource data to minimize sensor startup delay. More...
|
| |
The people detector operation interface (Dedicated for all third-party cameras).
Definition at line 11 of file TopsensPeople.hpp.
◆ People()
◆ ~People()
◆ Detect()
| Error Detect |
( |
const uint16_t * |
depth, |
|
|
uint32_t |
width, |
|
|
uint32_t |
height, |
|
|
int64_t |
timestamp, |
|
|
UsersFrame & |
users |
|
) |
| |
|
inline |
Processes depth data.
- Parameters
-
| depth | Depth frame data. |
| width | Depth frame width. |
| height | Depth frame height. |
| timestamp | Depth frame timestamp. |
| users | The generated users frame. |
- Returns
- Error::Ok if the call succeeds, otherwise refer to Error for the specific meaning.
Definition at line 186 of file TopsensPeople.hpp.
◆ GetOrientation()
◆ Initialize() [1/2]
| Error Initialize |
( |
uint32_t |
width, |
|
|
uint32_t |
height |
|
) |
| |
|
inline |
Initializes people detector with preconfigured identity.
- Parameters
-
| width | Depth frame width. |
| height | Depth frame height. |
- Returns
- Error::Ok if the call succeeds, otherwise refer to Error for the specific meaning.
Definition at line 80 of file TopsensPeople.hpp.
◆ Initialize() [2/2]
| Error Initialize |
( |
uint32_t |
width, |
|
|
uint32_t |
height, |
|
|
float |
fx, |
|
|
float |
fy, |
|
|
float |
cx, |
|
|
float |
cy |
|
) |
| |
|
inline |
Initializes people detector.
- Parameters
-
| width | Depth frame width. |
| height | Depth frame height. |
| fx | Horizontal focal length. |
| fy | Vertical focal length. |
| cx | Horizontal center coordinate. |
| cy | Vertical center coordinate. |
- Returns
- Error::Ok if the call succeeds, otherwise refer to Error for the specific meaning.
Definition at line 59 of file TopsensPeople.hpp.
◆ IsImageFlipped()
| bool IsImageFlipped |
( |
| ) |
const |
|
inline |
Checks whether the depth image is flipped.
- Returns
- True if the depth image is flipped, otherwise false.
Definition at line 139 of file TopsensPeople.hpp.
◆ IsMaskEnabled()
| bool IsMaskEnabled |
( |
| ) |
const |
|
inline |
Checks whether user mask is enabled.
- Returns
- True if user mask is enabled, otherwise false.
Definition at line 161 of file TopsensPeople.hpp.
◆ Preload() [1/2]
| static Error Preload |
( |
const std::initializer_list< uint32_t > & |
list | ) |
|
|
inlinestatic |
Preloads resource data to minimize sensor startup delay.
- Parameters
-
| list | Preloaded resource id list. |
- Returns
- Error::Ok if the call succeeds, otherwise refer to Error for the specific meaning.
Definition at line 30 of file TopsensPeople.hpp.
◆ Preload() [2/2]
| static Error Preload |
( |
const uint32_t * |
resources, |
|
|
uint32_t |
count |
|
) |
| |
|
inlinestatic |
Preloads resource data to minimize sensor startup delay.
- Parameters
-
| resources | Preloaded resource id array. |
| count | The count of id array. |
- Returns
- Error::Ok if the call succeeds, otherwise refer to Error for the specific meaning.
Definition at line 20 of file TopsensPeople.hpp.
◆ SetConfig()
◆ SetImageFlipped()
| Error SetImageFlipped |
( |
bool |
flipped | ) |
|
|
inline |
Sets whether the depth image is flipped.
- Parameters
-
| flipped | The depth image is flipped if true, otherwise not flipped. |
- Returns
- Error.Ok if the call succeeds, Error::InvalidOperation if the people detector has been initialized, otherwise refer to Error for the specific meaning.
Definition at line 129 of file TopsensPeople.hpp.
◆ SetMaskEnabled()
| Error SetMaskEnabled |
( |
bool |
enabled | ) |
|
|
inline |
Sets whether to enable user mask.
- Parameters
-
| enabled | Enabled if true, otherwise not enabled. |
- Returns
- Error.Ok if the call succeeds, Error::InvalidOperation if the people detector has been initialized, otherwise refer to Error for the specific meaning.
Definition at line 151 of file TopsensPeople.hpp.
◆ SetOrientation()
Sets depth image orientation.
- Parameters
-
| orientation | Depth image orientation. |
- Returns
- Error.Ok if the call succeeds, Error::InvalidOperation if the people detector has been initialized, otherwise refer to Error for the specific meaning.
Definition at line 107 of file TopsensPeople.hpp.
◆ Uninitialize()
The documentation for this class was generated from the following file:


--- layout: guide ---
| Cockpit Guide |
|---|
cockpit.js: Localizationcockpit.js: Localization — Localization and translations |
Cockpit provides a
gettext()
like
API for easy translation of strings.
The current locale language code. This is set based on the
cockpit.locale()
data loaded.
cockpit.locale(po)
Load locale information for a given po
data. The data should
be JSON data in the po2json
format. The data will be loaded globally. If po
data has already been
loaded, then this will extend that loaded data with additional strings. Any identical
translations strings will be replaced with the new strings. A null
argument
clears all the locale information previously loaded.
Various methods such as
cockpit.gettext()
make use
of the loaded data.
translated = cockpit.gettext([context], string) var _ = cockpit.gettext var C_ = cockpit.gettext translated = _("string") translated = C_("context", "string")
Lookup string
for translation in the loaded locale data. The translated string will
be returned, or string
will be returned if no such translated string is
present. The context
argument is an optional string used to qualify the
string.
This function can be assigned to a variable called _
(underscore) which
will make your code work with the typical _("string")
syntax.
var N_ = cockpit.noop var NC_ = cockpit.noop
A noop function suitable for assigning to N_
or NC_
so that
gettext scanners will be able to find translatable strings. More specifically this function
returns its last argument.
translated = cockpit.ngettext([context], string1, stringN, number)
Lookup a string appropriate for a pluralization form of the number
.
Various languages have complex pluralization forms that go far between the singular
and plural forms speakers of English are familiar with. If no such translated
string is found then either one of string1
or stringN
is
returned according to simple pluralization rules.
The context
argument is an optional string used to qualify the string.
cockpit.translate() cockpit.translate(element, ...) cockpit.translate(selection)
The document will be scanned for translate
tags and they will be translated according
to the strings in loaded locale data. One or more element
arguments may be specified.
These are DOM elements for specific parts of the document to be translated. If no element
is specified then the entire document is translated.
If an array or array-like object is passed as a selection
then all DOM elements
in the array will be treated as parts of the document to be translated.


If you plan to bring one, remember it is subject to the same traffic rules as automobiles: Bicycling is permitted on established public roads, parking areas, and designated routes. Bikes are prohibited on backcountry trails and boardwalks. Xanterra Parks and Resorts rent bicycles at the Old Faithful location in the park.
Use extreme caution when riding on park roads; roads are winding and narrow while shoulders are either narrow or nonexistent. Vehicle traffic is heavy most of the time. There are no bicycle paths along roadways.
We strongly recommend that safety gear, including helmet and high visibility clothing, be worn by all bicyclists. During April, May, and June, high snowbanks make travel more dangerous. Road elevations range from 5,300 to 8,860 feet (1615 to 2700 meters), and services and facilities are relatively far apart--typically 20 to 30 miles (37 to 56 kilometers).
Motorists frequently do not see bicyclists or fail to give them sufficient space on the road. Drivers sometimes pass on hill crests, blind curves, or in oncoming traffic. Vehicles, especially motor homes or those towing trailers, may have wide mirrors, posing an additional hazard. For more information about bicycling, stop at a visitor center.
Bicycling Routes:
Several gravel roads are open to BOTH bicycle and automotive traffic. The Old Gardiner Road and Blacktail Plateau Drive allow two-way bike traffic and one-way auto traffic. These roads are best suited for mountain bikes. Please do not block entrance gates when parking at these locations.
Biker / Hiker Campsites:
Camping for bicyclists is limited to the developed campgrounds located throughout the park. Campsites are available by reservation and on a first come, first served basis.
The distances separating campgrounds and the fact that the campgrounds typically fill early each day during the peak visitation season will pose logistical problems for the bicycle camper in Yellowstone. A limited number of campsites are reserved for hikers and bicyclists at all campgrounds with the exception of Slough Creek. Camping is not available at Old Faithful. If you are traveling with a group of bicyclists, call Xanterra prior to your arrival to check on group campsite availability; not all campgrounds can accommodate groups. If you have access to a vehicle, use it to find a campsite in your destination campground early each day.
Bicyclists camping without a vehicle can use designated hiker/biker sites in the campgrounds. Opening and closing dates vary considerably for each campground. Check the camping page to make sure that a campground is open if you are planning a spring or fall visit to Yellowstone.
Yellowstone has a designated backcountry campsite system, and a Backcountry Use Permit is required for all overnight stays: Each designated campsite has a maximum limit for the number of people and stock allowed per night. The maximum stay per campsite varies from 1 to 3 nights per trip. Campfires are permitted only in established fire pits. Wood fires are not allowed in some backcountry campsites. A food storage pole is provided at designated campsites so that food and attractants may be secured from bears.
Permits may be obtained only in person and no more than 48 hours in advance of your trip. Permits are available from backcountry offices located in most ranger stations or visitor centers. In order to obtain the best information on trail conditions, permits should be obtained from the ranger station or visitor center nearest to the area where your trip is to begin. The Backcountry Use Permit is valid only for the itinerary and dates specified. Backcountry travelers must have their permits in possession while in the backcountry.
Advance Reservations for Backcountry Campsites:
Although permits must be obtained in person no more than 48 hours in advance, backcountry campsites may be reserved in advance. Reservation applications for backcountry permits are accepted from January 1 to October 31 of each calendar year. Reservations received by March 31 will be processed in random order starting April 1. Reservations received April 1 and later will be processed after those received by March 31 are processed, and will be reserved in the order received.
Requests for reservations must be submitted by mail, fax, or in person. They cannot be made over the phone or by email. A confirmation notice, not a permit, will be emailed to you. This confirmation notice must then be converted to the actual permit not more than 48 hours in advance of the first camping date. Details are provided on the confirmation notice. The reservation fee is $25.00 regardless of the number of nights out or the number of people involved. The fee is not refundable.
Permits Made Less Than 48 Hours in Advance:
Because only a portion of the approximately 300 backcountry campsites are available for advance reservations, you may choose to wait until you arrive in the park to reserve your site(s) and obtain your permit.
Backcountry Permit Fees:
Visitors obtaining a backcountry permit with trip dates between Memorial Day and September 10 will be charged per person, per night permit fee. Backcountry permit fees apply for group members that are 9 years or older. Backcountry permit fees will be collected when you pick up your permit.
Backpackers/ Boaters: $3 per-person per-night. The group per night fee will be capped at $15 dollars per night.
Stock Parties (Horses/Mules/Llamas):
$5 per person per night. There is no cap on the group per night fee.
Annual Backcountry Pass: For individuals taking several or extended overnight trips in Yellowstone's backcountry there is an Annual Backcountry Pass available for $25 dollars.The Annual Backcountry pass is valid for one season (Memorial Day- September 10) and will exempt the individual from the per-person per night fee. You will need to present both the Annual Backcountry Pass and photo identification when using the Annual Backcountry Pass in order for the Backcountry permit fees to be waived. The Annual Backcountry Pass does not cover advanced reservations.
| Decade |
Park Visits |
Number of grizzly bear inflicted human injuries |
Injuries per one million visits |
| 1930-1939 |
3,232,417 |
6 |
1.9 |
| 1940-1949 |
5,524,563 |
11 |
2 |
| 1950-1959 |
13,553,771 |
6 |
0.4 |
| 1960-1969 |
19,520,600 |
36 |
1.8 |
| 1970-1979 |
22,397,176 |
15 |
0.7 |
| 1980-1989 |
23,449,930 |
12 |
0.4 |
| 1990-1999 |
30,126,032 |
9 |
0.3 |
| 2000-2009 |
29,677,184 |
12 |
0.4 |
| 2010-2014 |
17,183,756 |
4 |
0.2 |
Fishing in Yellowstone:
Yellowstone National Park is managed to protect cultural and natural resources and outstanding scenery, and to provide for visitor use. Fishing has been a major visitor activity for well over a century. Because of this history, fishing continues to be allowed and can complement, and in some cases even enhance, the park's primary purpose to preserve natural environments and native species.
Fish Species
- Arctic Grayling
- Snake River Finespotted Cutthroat Trout
- Westslope Cutthroat Trout
- Yellowstone Cutthroat Trout
- Longnose Dace
- Longnose Sucker
- Mottled Sculpin
- Mountain Sucker
- Mountain Whitefish
- Redside Shiner
- Speckled Dace
- Utah Chub
- Utah Sucker
- Brook Trout
- Brown Trout
- Lake Chub
- Lake Trout
- Rainbow Trout
- Cutthroat Trout x Rainbow Trout Hybrid
Fisheries Science in Yellowstone
Native cutthroat trout are the most ecologically important fish of the park and the most prized, and highly regarded by visiting anglers. Several factors, mostly related to exotic species introductions, are threatening the persistence of these fish. The Yellowstone Fisheries Program strives to use best available science in addressing these threats, with a focus on direct, aggressive intervention, and welcomed assistance by visiting anglers.
Hiking:
Hiking is a great way to see and enjoy the park. It allows a unique opportunity for non-motorized use of certain park roads. Hiking, bicycling, jogging, roller blades, roller skis, and similar means of non-motorized travel are permitted between the West Entrance and Mammoth Hot Springs as conditions allow from mid-March through mid-April. The opening day in March is weather dependant.


Physiotherapist
Senior Advisor
Pyschologist/Special Education Adviser
Psychologist Intensive Wraparound Services
Project Co-ordinator
Speech Langauge Therapists
Speech Language Therapist
SpeechLanguage Therapist
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