"Monexif" is a working title for a GUI data entry program which extracts exif data from site Monitoring images.
The primary function on Monexif is to allow recording of relationships between images collected over time at a site. Monexif extracts EXIF data from images to get the time and date of image capture, and provides an interface that allows the user to relate multiple images together in a group. Monexif also allows entry of descriptors for images - composition of groups present, activity, etc., see the spreadsheet columns section for more details.
Monexif is intended to be a functional minimal viable product (MVP), solving the specific problem of relating images into groups and allowing associated data entry. Part of its minimalistic design is the use of a single Excel spreadsheet (.xlsx) as a data-store. The use of the Tkinter interface, a simple GUI library bundled with Python, also emphasizes a light weight low dependency approach. Similarly, although unrelated to the code is this repository, Auto PY to EXE is used to package Monexif into a zero install executable. This results in a large executable that is somewhat slow to launch, but avoids spending time on setting up a build environment for a more customized software stack.
- Distributed .exe loads v. slowly.
- New file can only be created as the first action, not after an existing file has been loaded.
- Data file name must end in .xlsx, but not enforced.
- No way to un-add incorrectly added group other than editing .xlsx file directly.
- Having to resize the feedback panel every time.
- Distributed .exe is large.
In the order they occur from left to right:
image_time: The time the image was captured according to the trail camera. YYYY/MM/DD HH:MM:SS. Not unique when there's more than one group seen in a single image or two different trail cameras coincidentally capture images in the same second.
group_number: Very rarely two groups of resource users may be seen in the same
image. In these cases there will be multiple records (rows) in the spreadsheet
referring to the same image (added using the Add group button). The typical case is
that there is a single record for each image, and the group_number is 1. This is true
even for images with no relevant content, e.g. triggered by animals or wind. When the
data entry person adds a second record for the same image with the Add group button,
that record will have group_number = 2, and so on if there are additional groups.
adults_n: Number of adults seen in the image (in the group the record describes, typically only one group per image).
children_n: Number of children seen in the image / group.
pets_n: Number of pets seen in the image / group.
direction: Direction of travel.
activity: Activity in which the group is engaged.
other: When activity = "other", describe here.
camera_deploy: Text identifying the camera and the deployment period, e.g. "main-gate-2022-spring" as the camera's precise position may vary between deployments.
temperature_f: The temperature in degrees Fahrenheit as shown on the image. This information is not available in the image's metadata (exif-data) and must be entered manually.
group_id: Not to be confused with group_number, this unique value identifies the
group being described by the record, and will be shared between all records in which the
same group is seen. This is the field controlled by the "Related observations" Add
and Unrelate buttons.
entry_by: The name / initials of the person doing initial data entry. Each person should always use exactly the same name / initials, the drop down menu provides values previously used.
qa_by: The name / initials of the person doing quality assurance (QA). Each person should always use exactly the same name / initials, the drop down menu provides values previously used.
observation_status: The (workflow) status of the observation. This starts off as blank or "not classified" when a record is first created for an image, and can be advanced through various states such as "entry complete" and "QA complete".
image_name: The filename of the image without path, usually something like
YYYYMMDD_HHMMSS.jpg.
image_path: The path and name of the image within the project's top level image
folder, something like site3/north_camera/2021/20210804_114454.jpg.
image_path_full: The full path to the image the last time the data was saved. This
will include the location of the project's not level image folder, which can be changed
as needed. Something like
c:\User\Jo\projects\monitoring\images\site3\north_camera\2021\20210804_114454.jpg.
image_w: The width of the image in pixels as read when the observation is created.
image_h: The height of the image in pixels as read when the observation is created.
image_bytes: The size of the image in bytes as read when the observation is created.
observation_id: A unique ID for this observation (row).
image_hash: A unique fingerprint for the image. If the image_path field was lost
or the images were re-arranged in the folder so they no longer match the image_path
field, this fingerprint could be used to re-link the images and the observation records,
as long as the images have not been altered in any way (annotated, resized, etc.).
python -m venv venv_monexif
. venv_monexif/bin/activate
pip install -r requirements.txt
# see pre-commit
mkdir -p .git/hooks; cp pre-commit .git/hooks; chmod +x .git/hooks/pre-commit