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Object Tracking On Fish-eye Image

Installation

Basic usage

a. Undistort images

​ Here, we support two datasets, one is the oxford RobotCar dataset and the other is ROS bags.

​ RobotCar Dataset for Oxford RobotCar

​ tools.py for ros bags, for example:

python tools.py --bag 2019-11-13-17-16-19-highway-fov150-yaw.bag --calib_dir ~/calib/FOV150_red --output j7-8L4E-sensor

​ where --calib_dir is the directory where puts chessboard images.

b. do obstacle detection and save to csv file

​ Open source detection model is used here.

​ refer to https://github.com/xingyizhou/CenterNet

​ And the detection result should be saved as following csv format:

​ img,id,class,tlx,tly,brx,bry,score

​ some dump code.

defmain(opt):
os.environ['CUDA_VISIBLE_DEVICES'] =opt.gpus_stropt.debug=max(opt.debug, 0)
Detector=detector_factory[opt.task]
detector=Detector(opt)
ifos.path.isdir(opt.demo):
image_names= []
ls=os.listdir(opt.demo)
forfile_nameinsorted(ls):
ext=file_name[file_name.rfind('.') +1:].lower()
ifextinimage_ext:
image_names.append(os.path.join(opt.demo, file_name))
else:
image_names= [opt.demo]
withopen(output_file, "w") asf:
f.write("img,id,class,tlx,tly,brx,bry,score\n")
for (image_name) inimage_names:
print(image_name)
ret=detector.run(image_name)
detection=ret['results']
idx=0forkey, itemsindetection.items():
foriinrange(items.shape[0]):
line=image_name+","+str(idx) +","+str(key) +","+str(items[i, 0]) +","+str(items[i, 1]) +","+str(items[i, 2]) +","+str(items[i, 3]) +","+str(items[i, 4]) +"\n"f.write(line)
idx+=1
c. estimate 3-d position
python mono_dist.py --dir oxford/2014-06-24-14-15-17 --output oxford/2014-06-24-14-15-17/mono_left_dist
python mono_dist.py --dir /media/andy/jinwen-2TB/j7-8L4E-sensor/2019-11-13-17-16-19-highway-fov150-yaw --calib_path /media/andy/jinwen-2TB/j7-8L4E-sensor/calib --is_plusai true --fov 150

calib_path stores calibration files.

e. tracking object

TODO

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

2 stars

Watchers

1 watching

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0 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
Object Tracking On Fish-eye Image
Installation
Basic usage
a. Undistort images
​	Here, we support two datasets, one is the oxford RobotCar dataset and the other is ROS bags.
​	RobotCar Dataset for Oxford RobotCar
​	tools.py for ros bags, for example:
python tools.py --bag 2019-11-13-17-16-19-highway-fov150-yaw.bag --calib_dir ~/calib/FOV150_red --output j7-8L4E-sensor 
​ where --calib_dir is the directory where puts chessboard images.
b. do obstacle detection and save to csv file
​	Open source detection model is used here.
​	refer to https://github.com/xingyizhou/CenterNet
​	And the detection result should be saved as following csv format:
​	img,id,class,tlx,tly,brx,bry,score
​	some dump code.
defmain(opt):
os.environ['CUDA_VISIBLE_DEVICES'] =opt.gpus_stropt.debug=max(opt.debug, 0)
Detector=detector_factory[opt.task]
detector=Detector(opt)
ifos.path.isdir(opt.demo):
image_names= []
ls=os.listdir(opt.demo)
forfile_nameinsorted(ls):
ext=file_name[file_name.rfind('.') +1:].lower()
ifextinimage_ext:
image_names.append(os.path.join(opt.demo, file_name))
else:
image_names= [opt.demo]
withopen(output_file, "w") asf:
f.write("img,id,class,tlx,tly,brx,bry,score\n")
for (image_name) inimage_names:
print(image_name)
ret=detector.run(image_name)
detection=ret['results']
idx=0forkey, itemsindetection.items():
foriinrange(items.shape[0]):
line=image_name+","+str(idx) +","+str(key) +","+str(items[i, 0]) +","+str(items[i, 1]) +","+str(items[i, 2]) +","+str(items[i, 3]) +","+str(items[i, 4]) +"\n"f.write(line)
idx+=1
c. estimate 3-d position
python mono_dist.py --dir oxford/2014-06-24-14-15-17 --output oxford/2014-06-24-14-15-17/mono_left_dist
python mono_dist.py --dir /media/andy/jinwen-2TB/j7-8L4E-sensor/2019-11-13-17-16-19-highway-fov150-yaw --calib_path /media/andy/jinwen-2TB/j7-8L4E-sensor/calib --is_plusai true --fov 150 
calib_path stores calibration files.
e. tracking object
TODO
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Readme
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2 stars
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1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Object Tracking On Fish-eye Image
Installation
Basic usage
a. Undistort images
​	Here, we support two datasets, one is the oxford RobotCar dataset and the other is ROS bags.
​	RobotCar Dataset for Oxford RobotCar
​	tools.py for ros bags, for example:
python tools.py --bag 2019-11-13-17-16-19-highway-fov150-yaw.bag --calib_dir ~/calib/FOV150_red --output j7-8L4E-sensor 
​ where --calib_dir is the directory where puts chessboard images.
b. do obstacle detection and save to csv file
​	Open source detection model is used here.
​	refer to https://github.com/xingyizhou/CenterNet
​	And the detection result should be saved as following csv format:
​	img,id,class,tlx,tly,brx,bry,score
​	some dump code.
defmain(opt):
os.environ['CUDA_VISIBLE_DEVICES'] =opt.gpus_stropt.debug=max(opt.debug, 0)
Detector=detector_factory[opt.task]
detector=Detector(opt)
ifos.path.isdir(opt.demo):
image_names= []
ls=os.listdir(opt.demo)
forfile_nameinsorted(ls):
ext=file_name[file_name.rfind('.') +1:].lower()
ifextinimage_ext:
image_names.append(os.path.join(opt.demo, file_name))
else:
image_names= [opt.demo]
withopen(output_file, "w") asf:
f.write("img,id,class,tlx,tly,brx,bry,score\n")
for (image_name) inimage_names:
print(image_name)
ret=detector.run(image_name)
detection=ret['results']
idx=0forkey, itemsindetection.items():
foriinrange(items.shape[0]):
line=image_name+","+str(idx) +","+str(key) +","+str(items[i, 0]) +","+str(items[i, 1]) +","+str(items[i, 2]) +","+str(items[i, 3]) +","+str(items[i, 4]) +"\n"f.write(line)
idx+=1
c. estimate 3-d position
python mono_dist.py --dir oxford/2014-06-24-14-15-17 --output oxford/2014-06-24-14-15-17/mono_left_dist
python mono_dist.py --dir /media/andy/jinwen-2TB/j7-8L4E-sensor/2019-11-13-17-16-19-highway-fov150-yaw --calib_path /media/andy/jinwen-2TB/j7-8L4E-sensor/calib --is_plusai true --fov 150 
calib_path stores calibration files.
e. tracking object
TODO
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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requirements.txt
Repository files navigation
Object Tracking On Fish-eye Image
Installation
Basic usage
a. Undistort images
​	Here, we support two datasets, one is the oxford RobotCar dataset and the other is ROS bags.
​	RobotCar Dataset for Oxford RobotCar
​	tools.py for ros bags, for example:
python tools.py --bag 2019-11-13-17-16-19-highway-fov150-yaw.bag --calib_dir ~/calib/FOV150_red --output j7-8L4E-sensor 
​ where --calib_dir is the directory where puts chessboard images.
b. do obstacle detection and save to csv file
​	Open source detection model is used here.
​	refer to https://github.com/xingyizhou/CenterNet
​	And the detection result should be saved as following csv format:
​	img,id,class,tlx,tly,brx,bry,score
​	some dump code.
defmain(opt):
os.environ['CUDA_VISIBLE_DEVICES'] =opt.gpus_stropt.debug=max(opt.debug, 0)
Detector=detector_factory[opt.task]
detector=Detector(opt)
ifos.path.isdir(opt.demo):
image_names= []
ls=os.listdir(opt.demo)
forfile_nameinsorted(ls):
ext=file_name[file_name.rfind('.') +1:].lower()
ifextinimage_ext:
image_names.append(os.path.join(opt.demo, file_name))
else:
image_names= [opt.demo]
withopen(output_file, "w") asf:
f.write("img,id,class,tlx,tly,brx,bry,score\n")
for (image_name) inimage_names:
print(image_name)
ret=detector.run(image_name)
detection=ret['results']
idx=0forkey, itemsindetection.items():
foriinrange(items.shape[0]):
line=image_name+","+str(idx) +","+str(key) +","+str(items[i, 0]) +","+str(items[i, 1]) +","+str(items[i, 2]) +","+str(items[i, 3]) +","+str(items[i, 4]) +"\n"f.write(line)
idx+=1
c. estimate 3-d position
python mono_dist.py --dir oxford/2014-06-24-14-15-17 --output oxford/2014-06-24-14-15-17/mono_left_dist
python mono_dist.py --dir /media/andy/jinwen-2TB/j7-8L4E-sensor/2019-11-13-17-16-19-highway-fov150-yaw --calib_path /media/andy/jinwen-2TB/j7-8L4E-sensor/calib --is_plusai true --fov 150 
calib_path stores calibration files.
e. tracking object
TODO
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Repository files navigation
Object Tracking On Fish-eye Image
Installation
Basic usage
a. Undistort images
​	Here, we support two datasets, one is the oxford RobotCar dataset and the other is ROS bags.
​	RobotCar Dataset for Oxford RobotCar
​	tools.py for ros bags, for example:
python tools.py --bag 2019-11-13-17-16-19-highway-fov150-yaw.bag --calib_dir ~/calib/FOV150_red --output j7-8L4E-sensor 
​ where --calib_dir is the directory where puts chessboard images.
b. do obstacle detection and save to csv file
​	Open source detection model is used here.
​	refer to https://github.com/xingyizhou/CenterNet
​	And the detection result should be saved as following csv format:
​	img,id,class,tlx,tly,brx,bry,score
​	some dump code.
defmain(opt):
os.environ['CUDA_VISIBLE_DEVICES'] =opt.gpus_stropt.debug=max(opt.debug, 0)
Detector=detector_factory[opt.task]
detector=Detector(opt)
ifos.path.isdir(opt.demo):
image_names= []
ls=os.listdir(opt.demo)
forfile_nameinsorted(ls):
ext=file_name[file_name.rfind('.') +1:].lower()
ifextinimage_ext:
image_names.append(os.path.join(opt.demo, file_name))
else:
image_names= [opt.demo]
withopen(output_file, "w") asf:
f.write("img,id,class,tlx,tly,brx,bry,score\n")
for (image_name) inimage_names:
print(image_name)
ret=detector.run(image_name)
detection=ret['results']
idx=0forkey, itemsindetection.items():
foriinrange(items.shape[0]):
line=image_name+","+str(idx) +","+str(key) +","+str(items[i, 0]) +","+str(items[i, 1]) +","+str(items[i, 2]) +","+str(items[i, 3]) +","+str(items[i, 4]) +"\n"f.write(line)
idx+=1
c. estimate 3-d position
python mono_dist.py --dir oxford/2014-06-24-14-15-17 --output oxford/2014-06-24-14-15-17/mono_left_dist
python mono_dist.py --dir /media/andy/jinwen-2TB/j7-8L4E-sensor/2019-11-13-17-16-19-highway-fov150-yaw --calib_path /media/andy/jinwen-2TB/j7-8L4E-sensor/calib --is_plusai true --fov 150 
calib_path stores calibration files.
e. tracking object
TODO
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
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Used by
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Object Tracking On Fish-eye Image
Installation
Basic usage
a. Undistort images
​	Here, we support two datasets, one is the oxford RobotCar dataset and the other is ROS bags.
​	RobotCar Dataset for Oxford RobotCar
​	tools.py for ros bags, for example:
python tools.py --bag 2019-11-13-17-16-19-highway-fov150-yaw.bag --calib_dir ~/calib/FOV150_red --output j7-8L4E-sensor 
​ where --calib_dir is the directory where puts chessboard images.
b. do obstacle detection and save to csv file
​	Open source detection model is used here.
​	refer to https://github.com/xingyizhou/CenterNet
​	And the detection result should be saved as following csv format:
​	img,id,class,tlx,tly,brx,bry,score
​	some dump code.
defmain(opt):
os.environ['CUDA_VISIBLE_DEVICES'] =opt.gpus_stropt.debug=max(opt.debug, 0)
Detector=detector_factory[opt.task]
detector=Detector(opt)
ifos.path.isdir(opt.demo):
image_names= []
ls=os.listdir(opt.demo)
forfile_nameinsorted(ls):
ext=file_name[file_name.rfind('.') +1:].lower()
ifextinimage_ext:
image_names.append(os.path.join(opt.demo, file_name))
else:
image_names= [opt.demo]
withopen(output_file, "w") asf:
f.write("img,id,class,tlx,tly,brx,bry,score\n")
for (image_name) inimage_names:
print(image_name)
ret=detector.run(image_name)
detection=ret['results']
idx=0forkey, itemsindetection.items():
foriinrange(items.shape[0]):
line=image_name+","+str(idx) +","+str(key) +","+str(items[i, 0]) +","+str(items[i, 1]) +","+str(items[i, 2]) +","+str(items[i, 3]) +","+str(items[i, 4]) +"\n"f.write(line)
idx+=1
c. estimate 3-d position
python mono_dist.py --dir oxford/2014-06-24-14-15-17 --output oxford/2014-06-24-14-15-17/mono_left_dist
python mono_dist.py --dir /media/andy/jinwen-2TB/j7-8L4E-sensor/2019-11-13-17-16-19-highway-fov150-yaw --calib_path /media/andy/jinwen-2TB/j7-8L4E-sensor/calib --is_plusai true --fov 150 
calib_path stores calibration files.
e. tracking object
TODO
About
No description, website, or topics provided.
Resources
Readme
Activity
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injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Object Tracking On Fish-eye Image
Installation
Basic usage
a. Undistort images
​	Here, we support two datasets, one is the oxford RobotCar dataset and the other is ROS bags.
​	RobotCar Dataset for Oxford RobotCar
​	tools.py for ros bags, for example:
python tools.py --bag 2019-11-13-17-16-19-highway-fov150-yaw.bag --calib_dir ~/calib/FOV150_red --output j7-8L4E-sensor 
​ where --calib_dir is the directory where puts chessboard images.
b. do obstacle detection and save to csv file
​	Open source detection model is used here.
​	refer to https://github.com/xingyizhou/CenterNet
​	And the detection result should be saved as following csv format:
​	img,id,class,tlx,tly,brx,bry,score
​	some dump code.
defmain(opt):
os.environ['CUDA_VISIBLE_DEVICES'] =opt.gpus_stropt.debug=max(opt.debug, 0)
Detector=detector_factory[opt.task]
detector=Detector(opt)
ifos.path.isdir(opt.demo):
image_names= []
ls=os.listdir(opt.demo)
forfile_nameinsorted(ls):
ext=file_name[file_name.rfind('.') +1:].lower()
ifextinimage_ext:
image_names.append(os.path.join(opt.demo, file_name))
else:
image_names= [opt.demo]
withopen(output_file, "w") asf:
f.write("img,id,class,tlx,tly,brx,bry,score\n")
for (image_name) inimage_names:
print(image_name)
ret=detector.run(image_name)
detection=ret['results']
idx=0forkey, itemsindetection.items():
foriinrange(items.shape[0]):
line=image_name+","+str(idx) +","+str(key) +","+str(items[i, 0]) +","+str(items[i, 1]) +","+str(items[i, 2]) +","+str(items[i, 3]) +","+str(items[i, 4]) +"\n"f.write(line)
idx+=1
c. estimate 3-d position
python mono_dist.py --dir oxford/2014-06-24-14-15-17 --output oxford/2014-06-24-14-15-17/mono_left_dist
python mono_dist.py --dir /media/andy/jinwen-2TB/j7-8L4E-sensor/2019-11-13-17-16-19-highway-fov150-yaw --calib_path /media/andy/jinwen-2TB/j7-8L4E-sensor/calib --is_plusai true --fov 150 
calib_path stores calibration files.
e. tracking object
TODO
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}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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SETUP.md
requirements.txt
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Repository files navigation
Object Tracking On Fish-eye Image
Installation
Basic usage
a. Undistort images
​	Here, we support two datasets, one is the oxford RobotCar dataset and the other is ROS bags.
​	RobotCar Dataset for Oxford RobotCar
​	tools.py for ros bags, for example:
python tools.py --bag 2019-11-13-17-16-19-highway-fov150-yaw.bag --calib_dir ~/calib/FOV150_red --output j7-8L4E-sensor 
​ where --calib_dir is the directory where puts chessboard images.
b. do obstacle detection and save to csv file
​	Open source detection model is used here.
​	refer to https://github.com/xingyizhou/CenterNet
​	And the detection result should be saved as following csv format:
​	img,id,class,tlx,tly,brx,bry,score
​	some dump code.
defmain(opt):
os.environ['CUDA_VISIBLE_DEVICES'] =opt.gpus_stropt.debug=max(opt.debug, 0)
Detector=detector_factory[opt.task]
detector=Detector(opt)
ifos.path.isdir(opt.demo):
image_names= []
ls=os.listdir(opt.demo)
forfile_nameinsorted(ls):
ext=file_name[file_name.rfind('.') +1:].lower()
ifextinimage_ext:
image_names.append(os.path.join(opt.demo, file_name))
else:
image_names= [opt.demo]
withopen(output_file, "w") asf:
f.write("img,id,class,tlx,tly,brx,bry,score\n")
for (image_name) inimage_names:
print(image_name)
ret=detector.run(image_name)
detection=ret['results']
idx=0forkey, itemsindetection.items():
foriinrange(items.shape[0]):
line=image_name+","+str(idx) +","+str(key) +","+str(items[i, 0]) +","+str(items[i, 1]) +","+str(items[i, 2]) +","+str(items[i, 3]) +","+str(items[i, 4]) +"\n"f.write(line)
idx+=1
c. estimate 3-d position
python mono_dist.py --dir oxford/2014-06-24-14-15-17 --output oxford/2014-06-24-14-15-17/mono_left_dist
python mono_dist.py --dir /media/andy/jinwen-2TB/j7-8L4E-sensor/2019-11-13-17-16-19-highway-fov150-yaw --calib_path /media/andy/jinwen-2TB/j7-8L4E-sensor/calib --is_plusai true --fov 150 
calib_path stores calibration files.
e. tracking object
TODO
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
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Used by
Contributors
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