8 skills found
BIGBOSS-dedsec / OpenCV Unity To Build 3DHandsPython uses OpenCV image capture, Mediapipe to realize gesture and human movement detection and recognition; The recognition results are synchronized to Unity in real time to realize the recognition of the moving body structure of hand and character models in Unity
lxaw / PoseAngleEstimationUsing OpenPose to detect body angles.
imhighoncoffee / Body Detection HaarA repo for full body, upper body and lower body detection using HAAR Cascading in Python with OpenCV.
HlaingPhyoAung / SqlmapUsage: python sqlmap.py [options] Options: -h, --help Show basic help message and exit -hh Show advanced help message and exit --version Show program's version number and exit -v VERBOSE Verbosity level: 0-6 (default 1) Target: At least one of these options has to be provided to define the target(s) -d DIRECT Connection string for direct database connection -u URL, --url=URL Target URL (e.g. "http://www.site.com/vuln.php?id=1") -l LOGFILE Parse target(s) from Burp or WebScarab proxy log file -x SITEMAPURL Parse target(s) from remote sitemap(.xml) file -m BULKFILE Scan multiple targets given in a textual file -r REQUESTFILE Load HTTP request from a file -g GOOGLEDORK Process Google dork results as target URLs -c CONFIGFILE Load options from a configuration INI file Request: These options can be used to specify how to connect to the target URL --method=METHOD Force usage of given HTTP method (e.g. PUT) --data=DATA Data string to be sent through POST --param-del=PARA.. Character used for splitting parameter values --cookie=COOKIE HTTP Cookie header value --cookie-del=COO.. Character used for splitting cookie values --load-cookies=L.. File containing cookies in Netscape/wget format --drop-set-cookie Ignore Set-Cookie header from response --user-agent=AGENT HTTP User-Agent header value --random-agent Use randomly selected HTTP User-Agent header value --host=HOST HTTP Host header value --referer=REFERER HTTP Referer header value -H HEADER, --hea.. Extra header (e.g. "X-Forwarded-For: 127.0.0.1") --headers=HEADERS Extra headers (e.g. "Accept-Language: fr\nETag: 123") --auth-type=AUTH.. HTTP authentication type (Basic, Digest, NTLM or PKI) --auth-cred=AUTH.. HTTP authentication credentials (name:password) --auth-file=AUTH.. HTTP authentication PEM cert/private key file --ignore-401 Ignore HTTP Error 401 (Unauthorized) --proxy=PROXY Use a proxy to connect to the target URL --proxy-cred=PRO.. Proxy authentication credentials (name:password) --proxy-file=PRO.. Load proxy list from a file --ignore-proxy Ignore system default proxy settings --tor Use Tor anonymity network --tor-port=TORPORT Set Tor proxy port other than default --tor-type=TORTYPE Set Tor proxy type (HTTP (default), SOCKS4 or SOCKS5) --check-tor Check to see if Tor is used properly --delay=DELAY Delay in seconds between each HTTP request --timeout=TIMEOUT Seconds to wait before timeout connection (default 30) --retries=RETRIES Retries when the connection timeouts (default 3) --randomize=RPARAM Randomly change value for given parameter(s) --safe-url=SAFEURL URL address to visit frequently during testing --safe-post=SAFE.. POST data to send to a safe URL --safe-req=SAFER.. Load safe HTTP request from a file --safe-freq=SAFE.. Test requests between two visits to a given safe URL --skip-urlencode Skip URL encoding of payload data --csrf-token=CSR.. Parameter used to hold anti-CSRF token --csrf-url=CSRFURL URL address to visit to extract anti-CSRF token --force-ssl Force usage of SSL/HTTPS --hpp Use HTTP parameter pollution method --eval=EVALCODE Evaluate provided Python code before the request (e.g. "import hashlib;id2=hashlib.md5(id).hexdigest()") Optimization: These options can be used to optimize the performance of sqlmap -o Turn on all optimization switches --predict-output Predict common queries output --keep-alive Use persistent HTTP(s) connections --null-connection Retrieve page length without actual HTTP response body --threads=THREADS Max number of concurrent HTTP(s) requests (default 1) Injection: These options can be used to specify which parameters to test for, provide custom injection payloads and optional tampering scripts -p TESTPARAMETER Testable parameter(s) --skip=SKIP Skip testing for given parameter(s) --skip-static Skip testing parameters that not appear dynamic --dbms=DBMS Force back-end DBMS to this value --dbms-cred=DBMS.. DBMS authentication credentials (user:password) --os=OS Force back-end DBMS operating system to this value --invalid-bignum Use big numbers for invalidating values --invalid-logical Use logical operations for invalidating values --invalid-string Use random strings for invalidating values --no-cast Turn off payload casting mechanism --no-escape Turn off string escaping mechanism --prefix=PREFIX Injection payload prefix string --suffix=SUFFIX Injection payload suffix string --tamper=TAMPER Use given script(s) for tampering injection data Detection: These options can be used to customize the detection phase --level=LEVEL Level of tests to perform (1-5, default 1) --risk=RISK Risk of tests to perform (1-3, default 1) --string=STRING String to match when query is evaluated to True --not-string=NOT.. String to match when query is evaluated to False --regexp=REGEXP Regexp to match when query is evaluated to True --code=CODE HTTP code to match when query is evaluated to True --text-only Compare pages based only on the textual content --titles Compare pages based only on their titles Techniques: These options can be used to tweak testing of specific SQL injection techniques --technique=TECH SQL injection techniques to use (default "BEUSTQ") --time-sec=TIMESEC Seconds to delay the DBMS response (default 5) --union-cols=UCOLS Range of columns to test for UNION query SQL injection --union-char=UCHAR Character to use for bruteforcing number of columns --union-from=UFROM Table to use in FROM part of UNION query SQL injection --dns-domain=DNS.. Domain name used for DNS exfiltration attack --second-order=S.. Resulting page URL searched for second-order response Fingerprint: -f, --fingerprint Perform an extensive DBMS version fingerprint Enumeration: These options can be used to enumerate the back-end database management system information, structure and data contained in the tables. Moreover you can run your own SQL statements -a, --all Retrieve everything -b, --banner Retrieve DBMS banner --current-user Retrieve DBMS current user --current-db Retrieve DBMS current database --hostname Retrieve DBMS server hostname --is-dba Detect if the DBMS current user is DBA --users Enumerate DBMS users --passwords Enumerate DBMS users password hashes --privileges Enumerate DBMS users privileges --roles Enumerate DBMS users roles --dbs Enumerate DBMS databases --tables Enumerate DBMS database tables --columns Enumerate DBMS database table columns --schema Enumerate DBMS schema --count Retrieve number of entries for table(s) --dump Dump DBMS database table entries --dump-all Dump all DBMS databases tables entries --search Search column(s), table(s) and/or database name(s) --comments Retrieve DBMS comments -D DB DBMS database to enumerate -T TBL DBMS database table(s) to enumerate -C COL DBMS database table column(s) to enumerate -X EXCLUDECOL DBMS database table column(s) to not enumerate -U USER DBMS user to enumerate --exclude-sysdbs Exclude DBMS system databases when enumerating tables --pivot-column=P.. Pivot column name --where=DUMPWHERE Use WHERE condition while table dumping --start=LIMITSTART First query output entry to retrieve --stop=LIMITSTOP Last query output entry to retrieve --first=FIRSTCHAR First query output word character to retrieve --last=LASTCHAR Last query output word character to retrieve --sql-query=QUERY SQL statement to be executed --sql-shell Prompt for an interactive SQL shell --sql-file=SQLFILE Execute SQL statements from given file(s) Brute force: These options can be used to run brute force checks --common-tables Check existence of common tables --common-columns Check existence of common columns User-defined function injection: These options can be used to create custom user-defined functions --udf-inject Inject custom user-defined functions --shared-lib=SHLIB Local path of the shared library File system access: These options can be used to access the back-end database management system underlying file system --file-read=RFILE Read a file from the back-end DBMS file system --file-write=WFILE Write a local file on the back-end DBMS file system --file-dest=DFILE Back-end DBMS absolute filepath to write to Operating system access: These options can be used to access the back-end database management system underlying operating system --os-cmd=OSCMD Execute an operating system command --os-shell Prompt for an interactive operating system shell --os-pwn Prompt for an OOB shell, Meterpreter or VNC --os-smbrelay One click prompt for an OOB shell, Meterpreter or VNC --os-bof Stored procedure buffer overflow exploitation --priv-esc Database process user privilege escalation --msf-path=MSFPATH Local path where Metasploit Framework is installed --tmp-path=TMPPATH Remote absolute path of temporary files directory Windows registry access: These options can be used to access the back-end database management system Windows registry --reg-read Read a Windows registry key value --reg-add Write a Windows registry key value data --reg-del Delete a Windows registry key value --reg-key=REGKEY Windows registry key --reg-value=REGVAL Windows registry key value --reg-data=REGDATA Windows registry key value data --reg-type=REGTYPE Windows registry key value type General: These options can be used to set some general working parameters -s SESSIONFILE Load session from a stored (.sqlite) file -t TRAFFICFILE Log all HTTP traffic into a textual file --batch Never ask for user input, use the default behaviour --binary-fields=.. Result fields having binary values (e.g. "digest") --charset=CHARSET Force character encoding used for data retrieval --crawl=CRAWLDEPTH Crawl the website starting from the target URL --crawl-exclude=.. Regexp to exclude pages from crawling (e.g. "logout") --csv-del=CSVDEL Delimiting character used in CSV output (default ",") --dump-format=DU.. Format of dumped data (CSV (default), HTML or SQLITE) --eta Display for each output the estimated time of arrival --flush-session Flush session files for current target --forms Parse and test forms on target URL --fresh-queries Ignore query results stored in session file --hex Use DBMS hex function(s) for data retrieval --output-dir=OUT.. Custom output directory path --parse-errors Parse and display DBMS error messages from responses --save=SAVECONFIG Save options to a configuration INI file --scope=SCOPE Regexp to filter targets from provided proxy log --test-filter=TE.. Select tests by payloads and/or titles (e.g. ROW) --test-skip=TEST.. Skip tests by payloads and/or titles (e.g. BENCHMARK) --update Update sqlmap Miscellaneous: -z MNEMONICS Use short mnemonics (e.g. "flu,bat,ban,tec=EU") --alert=ALERT Run host OS command(s) when SQL injection is found --answers=ANSWERS Set question answers (e.g. "quit=N,follow=N") --beep Beep on question and/or when SQL injection is found --cleanup Clean up the DBMS from sqlmap specific UDF and tables --dependencies Check for missing (non-core) sqlmap dependencies --disable-coloring Disable console output coloring --gpage=GOOGLEPAGE Use Google dork results from specified page number --identify-waf Make a thorough testing for a WAF/IPS/IDS protection --skip-waf Skip heuristic detection of WAF/IPS/IDS protection --mobile Imitate smartphone through HTTP User-Agent header --offline Work in offline mode (only use session data) --page-rank Display page rank (PR) for Google dork results --purge-output Safely remove all content from output directory --smart Conduct thorough tests only if positive heuristic(s) --sqlmap-shell Prompt for an interactive sqlmap shell --wizard Simple wizard interface for beginner users
BernardNyarko / Smart Monitor An AI Powered IoT Monitoring System For Small Medium Scale PremisesWith recent advances in both Artificial Intelligence (AI) and Internet of Things (IoT) capabilities, it is more possible than ever to implement surveillance systems that can automatically identify people who might represent a potential security threat to the public in real-time. Imagine a surveillance camera system that can detect various on-body weapons, suspicious objects, and traffic. This system could transform surveillance cameras from passive sentries into active observers, which would help prevent a possible mass shooting in a school, stadium, or mall. In this project, we tried to realize such systems by implementing Smart-Monitor, an AI-powered threat detector for intelligent surveillance cameras. The developed system can be deployed locally on the surveillance cameras at the network edge. Deploying AI-enabled surveillance applications at the edge enables the initial analysis of the captured images on-site, reducing the communication overheads and enabling swift security actions. We developed a mobile app that users can detect suspicious objects in an image and video captured by several cameras at the network edge. Also, the model can generate a high-quality segmentation mask for each object instance in the photo, along with the confidence percentage. The camera side used a Raspberry Pi 4 device, Neural Compute Stick 2 (NCS 2), Logitech C920 webcam, motion sensors, buzzers, pushbuttons, LED lights, Python Face recognition, and TensorFlow Custom Object Detection. When the system detects a motion in the surrounding environment, the motion sensors send a signal to the Raspberry Pi device notifying it to start capturing images for such physical activity. Using Python’s face recognition and TensorFlow 2 custom object detection Smart-Monitor can recognize eight classes, including a baseball bat, bird, cat, dog, gun, hammer, knife, and human faces. Finally, we evaluated our system using various performance metrics such as classification time and accuracy, scalability, etc.
priyamittal15 / Implementation Of Different Deep Learning Algorithms For Fracture Detection Image ClassificationUsing-Deep-Learning-Techniques-perform-Fracture-Detection-Image-Processing Using Different Image Processing techniques Implementing Fracture Detection on X rays Images on 8000 + images of dataset Description About Project: Bones are the stiff organs that protect vital organs such as the brain, heart, lungs, and other internal organs in the human body. There are 206 bones in the human body, all of which has different shapes, sizes, and structures. The femur bones are the largest, and the auditory ossicles are the smallest. Humans suffer from bone fractures on a regular basis. Bone fractures can happen as a result of an accident or any other situation in which the bones are put under a lot of pressure. Oblique, complex, comminute, spiral, greenstick, and transverse bone fractures are among the many forms that can occur. X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and other types of medical imaging techniques are available to detect various types of disorders. So we design the architecture of it using Neural Networks different models, compare the accuracy, and get a result of which model works better for our dataset and which model delivers correct results on a specific related dataset with 10 classes. Basically our main motive is to check that which model works better on our dataset so in future reference we all get an idea that which model gives better type of accuracy for a respective dataset . Proposed Method for Project: we decided to make this project because we have seen a lot of times that report that are generated by computer produce error sometimes so we wanted to find out which model gives good accuracy and produce less error so we start to research over image processing nd those libraries which are used in image processing like Keras , Matplot lib , Image Generator , tensor flow and other libraries and used some of them and implement it on different image processing algorithm like as CNN , VGG-16 Model ,ResNet50 Model , InceptionV3 Model . and then find the best model which gives best accuracy for that we generate classification report using predefined libraries in python such as precision , recall ,r2score , mean square error etc by importing Sklearn. Methodology of Project: Phase 1: Requirement analysis: • Study concepts of Basic Python programming. • Study of Tensor flow, keras and Python API interface . • Study of basic algorithms of Image Processing and neural network And deep learning concepts. • Collect the dataset from different resources and describe it into Different classes(5 Fractured + 5 non fractured). Phase 2: Designing and development: The stages of design and development are further segmented. This step starts with data from the Requirement and Analysis phase, which will lead to the model construction phase, where a model will be created and an algorithm will be devised. After the algorithm design phase is completed, the focus will shift to algorithm analysis and implementation in this project. Phase 3: Coding Phase: Before real coding begins, the task is divided into modules/units and assigned to team members once the system design papers are received. Because code is developed during this phase, it is the developers' primary emphasis. The most time-consuming aspect of the project will be this. This project's implementation begins with the development of a program in the relevant programming language and the production of an error-free executable program. Phase 4: Testing Phase: When it comes to the testing phase, we may test our model based on the classification report it generates, which contains a variety of factors such as accuracy, f1score, precision, and recall, and we can also test our model based on its training and testing accuracy. Phase 5: Deployment Phase: One of our goals is to bring all of the previous steps together and put them into practice. Another goal is to deploy our model into a python-based interface application after comparing the classification reports and determining which model is best for our dataset.
amiroooamiran / Body DitectionAI Face Body and Hand Pose Detection with Opencv-Python and Mediapipe
OstrichAlgorithm / Play Subway Surfice Body DetectionIn this project I create a body detection game. this project is created in deep learning. And the language is used in this project is python. Thanks a lot python to providing this amazing facilities.
