{"id":19451787,"url":"https://github.com/oveys96/eit_seminar","last_synced_at":"2025-02-25T09:26:56.378Z","repository":{"id":184476796,"uuid":"669128815","full_name":"Oveys96/eit_seminar","owner":"Oveys96","description":"Comparison of different image reconstruction methods with different injection pattern","archived":false,"fork":false,"pushed_at":"2023-08-03T10:44:01.000Z","size":6438,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-07T23:53:48.490Z","etag":null,"topics":["eit","pyeit"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Oveys96.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-07-21T12:12:45.000Z","updated_at":"2023-12-04T14:39:15.000Z","dependencies_parsed_at":"2024-11-10T16:44:42.866Z","dependency_job_id":"cfdca79b-81da-43ba-8900-57b596b11d90","html_url":"https://github.com/Oveys96/eit_seminar","commit_stats":null,"previous_names":["oveys96/eit_project_seminar","oveys96/eit_seminar"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Oveys96%2Feit_seminar","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Oveys96%2Feit_seminar/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Oveys96%2Feit_seminar/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Oveys96%2Feit_seminar/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Oveys96","download_url":"https://codeload.github.com/Oveys96/eit_seminar/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240639279,"owners_count":19833448,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["eit","pyeit"],"created_at":"2024-11-10T16:43:03.626Z","updated_at":"2025-02-25T09:26:56.340Z","avatar_url":"https://github.com/Oveys96.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Comparison of numerical 2D reconstruction methods and the corresponding requirements and impact of parameters using the pyEIT package.\n\n\n## Install Requirements\n\nAll requirements are provided inside the `requirements.txt`.\n\n    pip3 install -r requirements.txt # Linux, macOS, Windows\n    pip install -r requirements.txt  # Windows\n## Run the examples\n\nFrom the example folder, pick one demo and run!\n\nYou will see the results of (2D) forward and inverse computing with different parameter in each demo.\n\n**Note:** the following images may be outdated due to that the parameters of a EIT algorithm may be changed in different versions of `pyEIT`.\n\n### (2D) forward and inverse computing\n\n\n\n### **Using** `codes/examples/one_obj_cent.ipynb`\n\n# ![1obj_center](https://github.com/Oveys96/eit_seminar/blob/main/codes/images/1obj_center.png)\n```\nREMARK:\n\nThree different image reconstruction methods are shown.\nThe number of electrodes in each row is equal.\nThe image reconstruction method in each colomn is same.\n\nResult:\n\nJacobian is the slowest and Backprojection is the fastest method.\nGREIT is the trade off between Jacobian and Backprojection for time and resolution.\n```\n\n\n### **Using** `codes/examples/one_obj_side.ipynb`\n\n# ![1obj_side](https://github.com/Oveys96/eit_seminar/blob/main/codes/images/1obj_side.png)\n```\nREMARK:\n\nThree different image reconstruction methods are shown.\nThe number of electrodes in each row is equal.\nThe image reconstruction method in each colomn is same.\n\nResult:\n\nJacobian is the slowest and Backprojection is the fastest method.\nGREIT is the trade off between Jacobian and Backprojection for time and resolution.\n```\n\n\n### **Using** `codes/examples/two_obj_sides.py`\n\n# ![2obj_sides](https://github.com/Oveys96/eit_seminar/blob/main/codes/images/2obj_sides.png)\n```\nREMARK:\n\nThree different image reconstruction methods are shown.\nThe number of electrodes in each row is equal.\nThe image reconstruction method in each colomn is same.\n\nResult:\n\nJacobian is the slowest and Backprojection is the fastest method.\nGREIT is the trade off between Jacobian and Backprojection for time and resolution.\nAll three methods are good with goal of the detection of the number of the separated objects.\n```\n\n\n### **Using** `codes/examples/twoD_obj_sides.py`\n\n# ![2Dobj_sides](https://github.com/Oveys96/eit_seminar/blob/main/codes/images/2Dobj_sides.png)\n```\nREMARK:\n\nThree different image reconstruction methods are shown.\nThe number of electrodes in each row is equal.\nThe image reconstruction method in each colomn is same.\nThe target is trying to detect two objects with same conductivity but different size.\n\nResult:\n\nJacobian is the slowest and Backprojection is the fastest method.\nGREIT is the trade off between Jacobian and Backprojection for time and resolution.\n```\n\n\n### **Using** `codes/examples/adjandopp.py`\n\n# ![adjandoppr](https://github.com/Oveys96/eit_seminar/blob/main/codes/images/adjandoppr.png)\n```\nREMARK:\n\nThree different image reconstruction methods are shown.\nThe number of electrodes in any case is equal.\nThe image reconstruction method in each colomn is same.\nThe target is trying to compare adjacent with opposite injection pattern.\n\nResult:\n\nOpposite injection pattern showes separated objects clearer.\nGREIT is the best and Backprojection is the worst.\n```\n\n\n### **Using** `codes/examples/geometry.py`\n\n# ![triangelandcircle](https://github.com/Oveys96/eit_seminar/blob/main/codes/images/triangelandcircle.png)\n```\nREMARK:\n\nThree different image reconstruction methods are shown.\nThe number of electrodes in any case is equal.\nThe image reconstruction method in each colomn is same.\nThe goal is trying to detect the object like triangle with sharp edges with adjacent and opposite injection pattern.\n\nResult:\n\nGREIT almost fails even in separating the objects.\nJacobian has the highest resolution.\nAdjacent injection pattern causes better results for objects with sharp edges.\nSimulating the edges is difficult task and almost all these methods fail in it. We should use other tools for post processing the image.\n```\n\n\n\n\n\n## Contact\n\nEmail: oveys.javanmardtilaki@uni-rostock.de\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Foveys96%2Feit_seminar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Foveys96%2Feit_seminar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Foveys96%2Feit_seminar/lists"}