This YoloV5 based model is fit to detect people and different types of land vehicles,
and displaying their density on a fitted map, according to their coordinates and detected labels.
- Multiple Objects Detection
- Trained on 3,000 street view Images
- Exports Fitted and adjustable Maps
- Calculates a density score according to image detected labels
- in order to plot the map:
pip install mplleaflet
- to use YoloV5, see: Yolov5_requirements
identifies people and land vehicles in your images:
python src/yolov5/detect.py --source <path to images folder> --project <output path> --name <output folder name> --save-txt --conf 0.3
running this action will save your images with the anchor boxes around objects that the model found:
(if you don't want to save the labeled images, just add
--nosave to the command above) in addition, it will save the detected object labels for each image.
Plotting a fitted map:
display the density on a fitted map (requires a .csv file)
python src/steetdensityai.py --labels <labels path that were created after the images detection> --coordinates <path-to-csv/file.csv> --images <path to images folder> --img-per-cord 1 --output <output path>
- csv requires 2 columns to display the coordinates named: "longitude" and "latitude"
- the code asumes that the coordinates are sorted by the image's name.
- If you have multiple images per coordinate (for example if you have a 360 view, divided to 4 images), you can set the number of images per coordinate with :
--img-per-cord <integer of images per coordinate >
# detect objects: python src/yolov5/detect.py --source example/images --project example/images --name detected_images --save-txt --conf 0.4 # creates a label folder in example/images/detected_images named "labels" # saves the images with the newly found objects anchor, and each image labels #plot desnity map python src/steetdensityai.py --labels example/images/detected_images/labels --coordinates example/coordinates.csv --images example/images --img-per-cord 4 --output example/images # will save the map.html file to example/images